From d380f939b5ab6a28bed6d1de3cf283e194255963 Mon Sep 17 00:00:00 2001
From: Leon Feng <523684+leon0707@users.noreply.github.com>
Date: Sat, 15 Jul 2023 23:31:59 -0400
Subject: Update shared.py

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index a0862055..564799bc 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -394,7 +394,7 @@ options_templates.update(options_section(('training', "Training"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
+    "sd_model_checkpoint": OptionInfo("", "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
-- 
cgit v1.2.3


From 35510f7529dc05437a82496187ef06b852be9ab1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 17 Jul 2023 10:06:02 +0300
Subject: add alias to lyco network read networks from LyCORIS dir if it exists
 add credits

---
 modules/extra_networks.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/extra_networks.py b/modules/extra_networks.py
index 41799b0a..6ae07e91 100644
--- a/modules/extra_networks.py
+++ b/modules/extra_networks.py
@@ -4,16 +4,22 @@ from collections import defaultdict
 from modules import errors
 
 extra_network_registry = {}
+extra_network_aliases = {}
 
 
 def initialize():
     extra_network_registry.clear()
+    extra_network_aliases.clear()
 
 
 def register_extra_network(extra_network):
     extra_network_registry[extra_network.name] = extra_network
 
 
+def register_extra_network_alias(extra_network, alias):
+    extra_network_aliases[alias] = extra_network
+
+
 def register_default_extra_networks():
     from modules.extra_networks_hypernet import ExtraNetworkHypernet
     register_extra_network(ExtraNetworkHypernet())
@@ -82,20 +88,26 @@ def activate(p, extra_network_data):
     """call activate for extra networks in extra_network_data in specified order, then call
     activate for all remaining registered networks with an empty argument list"""
 
+    activated = []
+
     for extra_network_name, extra_network_args in extra_network_data.items():
         extra_network = extra_network_registry.get(extra_network_name, None)
+
+        if extra_network is None:
+            extra_network = extra_network_aliases.get(extra_network_name, None)
+
         if extra_network is None:
             print(f"Skipping unknown extra network: {extra_network_name}")
             continue
 
         try:
             extra_network.activate(p, extra_network_args)
+            activated.append(extra_network)
         except Exception as e:
             errors.display(e, f"activating extra network {extra_network_name} with arguments {extra_network_args}")
 
     for extra_network_name, extra_network in extra_network_registry.items():
-        args = extra_network_data.get(extra_network_name, None)
-        if args is not None:
+        if extra_network in activated:
             continue
 
         try:
-- 
cgit v1.2.3


From 699108bfbb05c2a7d2ee4a2c7abcfaa0a244d8ea Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 17 Jul 2023 18:56:14 +0300
Subject: hide cards for networks of incompatible stable diffusion version in
 Lora extra networks interface

---
 modules/sd_models.py                       | 3 +++
 modules/ui_extra_networks.py               | 3 ++-
 modules/ui_extra_networks_user_metadata.py | 7 ++++++-
 3 files changed, 11 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 729f03d7..4d9382dd 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -290,6 +290,9 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
         state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
 
     model.is_sdxl = hasattr(model, 'conditioner')
+    model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
+    model.is_sd1 = not model.is_sdxl and not model.is_sd2
+
     if model.is_sdxl:
         sd_models_xl.extend_sdxl(model)
 
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 6c73998f..49612298 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -62,7 +62,8 @@ def get_single_card(page: str = "", tabname: str = "", name: str = ""):
     page = next(iter([x for x in extra_pages if x.name == page]), None)
 
     try:
-        item = page.create_item(name)
+        item = page.create_item(name, enable_filter=False)
+        page.items[name] = item
     except Exception as e:
         errors.display(e, "creating item for extra network")
         item = page.items.get(name)
diff --git a/modules/ui_extra_networks_user_metadata.py b/modules/ui_extra_networks_user_metadata.py
index 01ff4e4b..63d4b503 100644
--- a/modules/ui_extra_networks_user_metadata.py
+++ b/modules/ui_extra_networks_user_metadata.py
@@ -42,6 +42,9 @@ class UserMetadataEditor:
 
         return user_metadata
 
+    def create_extra_default_items_in_left_column(self):
+        pass
+
     def create_default_editor_elems(self):
         with gr.Row():
             with gr.Column(scale=2):
@@ -49,6 +52,8 @@ class UserMetadataEditor:
                 self.edit_description = gr.Textbox(label="Description", lines=4)
                 self.html_filedata = gr.HTML()
 
+                self.create_extra_default_items_in_left_column()
+
             with gr.Column(scale=1, min_width=0):
                 self.html_preview = gr.HTML()
 
@@ -111,7 +116,7 @@ class UserMetadataEditor:
 
         table = '<table class="file-metadata">' + "".join(f"<tr><th>{name}</th><td>{value}</td></tr>" for name, value in params) + '</table>'
 
-        return html.escape(name), user_metadata.get('description', ''), table, self.get_card_html(name), user_metadata.get('notes', ''),
+        return html.escape(name), user_metadata.get('description', ''), table, self.get_card_html(name), user_metadata.get('notes', '')
 
     def write_user_metadata(self, name, metadata):
         item = self.page.items.get(name, {})
-- 
cgit v1.2.3


From a99d5708e6d603e8f7cfd1b8c6595f8026219ba0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 17 Jul 2023 20:10:24 +0300
Subject: skip installing packages with pip if theyare already installed record
 time it took to launch

---
 modules/launch_utils.py | 46 +++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 45 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 434facbc..03552bc2 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -1,4 +1,5 @@
 # this scripts installs necessary requirements and launches main program in webui.py
+import re
 import subprocess
 import os
 import sys
@@ -9,6 +10,9 @@ from functools import lru_cache
 
 from modules import cmd_args, errors
 from modules.paths_internal import script_path, extensions_dir
+from modules import timer
+
+timer.startup_timer.record("start")
 
 args, _ = cmd_args.parser.parse_known_args()
 
@@ -226,6 +230,44 @@ def run_extensions_installers(settings_file):
         run_extension_installer(os.path.join(extensions_dir, dirname_extension))
 
 
+re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
+
+
+def requrements_met(requirements_file):
+    """
+    Does a simple parse of a requirements.txt file to determine if all rerqirements in it
+    are already installed. Returns True if so, False if not installed or parsing fails.
+    """
+
+    import importlib.metadata
+    import packaging.version
+
+    with open(requirements_file, "r", encoding="utf8") as file:
+        for line in file:
+            if line.strip() == "":
+                continue
+
+            m = re.match(re_requirement, line)
+            if m is None:
+                return False
+
+            package = m.group(1).strip()
+            version_required = (m.group(2) or "").strip()
+
+            if version_required == "":
+                continue
+
+            try:
+                version_installed = importlib.metadata.version(package)
+            except Exception:
+                return False
+
+            if packaging.version.parse(version_required) != packaging.version.parse(version_installed):
+                return False
+
+    return True
+
+
 def prepare_environment():
     torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
     torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
@@ -311,7 +353,9 @@ def prepare_environment():
 
     if not os.path.isfile(requirements_file):
         requirements_file = os.path.join(script_path, requirements_file)
-    run_pip(f"install -r \"{requirements_file}\"", "requirements")
+
+    if not requrements_met(requirements_file):
+        run_pip(f"install -r \"{requirements_file}\"", "requirements")
 
     run_extensions_installers(settings_file=args.ui_settings_file)
 
-- 
cgit v1.2.3


From 40a18d38a8fcb88d1c2947a2653b52cd2085536f Mon Sep 17 00:00:00 2001
From: lambertae <dengm@mit.edu>
Date: Tue, 18 Jul 2023 00:32:01 -0400
Subject: add restart sampler

---
 modules/sd_samplers_kdiffusion.py | 70 +++++++++++++++++++++++++++++++++++++--
 1 file changed, 68 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 71581b76..c63b677c 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -1,3 +1,5 @@
+# export PIP_CACHE_DIR=/scratch/dengm/cache
+# export XDG_CACHE_HOME=/scratch/dengm/cache
 from collections import deque
 import torch
 import inspect
@@ -30,12 +32,76 @@ samplers_k_diffusion = [
     ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
     ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
     ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
+    ('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}),
 ]
 
+
+@torch.no_grad()
+def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = {0.1: [10, 2, 2]}):
+    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
+    '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
+    
+    from tqdm.auto import trange, tqdm
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    step_id = 0
+    
+    from k_diffusion.sampling import to_d, append_zero
+
+    def heun_step(x, old_sigma, new_sigma):
+        nonlocal step_id
+        denoised = model(x, old_sigma * s_in, **extra_args)
+        d = to_d(x, old_sigma, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
+        dt = new_sigma - old_sigma
+        if new_sigma == 0:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Heun's method
+            x_2 = x + d * dt
+            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
+            d_2 = to_d(x_2, new_sigma, denoised_2)
+            d_prime = (d + d_2) / 2
+            x = x + d_prime * dt
+        step_id += 1
+        return x
+    # print(sigmas)
+    temp_list = dict()
+    for key, value in restart_list.items():
+        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
+    restart_list = temp_list
+        
+            
+    def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
+        ramp = torch.linspace(0, 1, n).to(device)
+        min_inv_rho = (sigma_min ** (1 / rho))
+        max_inv_rho = (sigma_max ** (1 / rho))
+        if isinstance(min_inv_rho, torch.Tensor):
+            min_inv_rho = min_inv_rho.to(device)
+        if isinstance(max_inv_rho, torch.Tensor):
+            max_inv_rho = max_inv_rho.to(device)
+        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+        return append_zero(sigmas).to(device)
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        x = heun_step(x, sigmas[i], sigmas[i+1])
+        if i + 1 in restart_list:
+            restart_steps, restart_times, restart_max = restart_list[i + 1]
+            min_idx = i + 1
+            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
+            sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end
+            for times in range(restart_times):
+                x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
+                for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):
+                    x = heun_step(x, old_sigma, new_sigma)
+    return x
+
 samplers_data_k_diffusion = [
     sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
     for label, funcname, aliases, options in samplers_k_diffusion
-    if hasattr(k_diffusion.sampling, funcname)
+    if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler')
 ]
 
 sampler_extra_params = {
@@ -245,7 +311,7 @@ class KDiffusionSampler:
 
         self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
         self.funcname = funcname
-        self.func = getattr(k_diffusion.sampling, self.funcname)
+        self.func = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler
         self.extra_params = sampler_extra_params.get(funcname, [])
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
         self.sampler_noises = None
-- 
cgit v1.2.3


From 15a94d6cf7fa075c09362e73c1239692d021c559 Mon Sep 17 00:00:00 2001
From: lambertae <dengm@mit.edu>
Date: Tue, 18 Jul 2023 00:39:26 -0400
Subject: remove useless header

---
 modules/sd_samplers_kdiffusion.py | 2 --
 1 file changed, 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index c63b677c..7888d864 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -1,5 +1,3 @@
-# export PIP_CACHE_DIR=/scratch/dengm/cache
-# export XDG_CACHE_HOME=/scratch/dengm/cache
 from collections import deque
 import torch
 import inspect
-- 
cgit v1.2.3


From f0e2098f1a533c88396536282c1d6cd7d847a51c Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 17 Jul 2023 23:39:38 -0400
Subject: Add support for `--upcast-sampling` with SD XL

---
 modules/sd_hijack_unet.py | 8 +++++++-
 modules/sd_models.py      | 2 +-
 2 files changed, 8 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
index ca1daf45..2101f1a0 100644
--- a/modules/sd_hijack_unet.py
+++ b/modules/sd_hijack_unet.py
@@ -39,7 +39,10 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
 
     if isinstance(cond, dict):
         for y in cond.keys():
-            cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+            if isinstance(cond[y], list):
+                cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+            else:
+                cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
 
     with devices.autocast():
         return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
@@ -77,3 +80,6 @@ first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devi
 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
+
+CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model, unet_needs_upcast)
+CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4d9382dd..5813b550 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -326,7 +326,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
 
         timer.record("apply half()")
 
-    devices.dtype_unet = model.model.diffusion_model.dtype
+    devices.dtype_unet = torch.float16 if model.is_sdxl and not shared.cmd_opts.no_half else model.model.diffusion_model.dtype
     devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
 
     model.first_stage_model.to(devices.dtype_vae)
-- 
cgit v1.2.3


From 37e048a7e2356f4caebfd976351112f03856f082 Mon Sep 17 00:00:00 2001
From: lambertae <dengm@mit.edu>
Date: Tue, 18 Jul 2023 00:55:02 -0400
Subject: fix floating error

---
 modules/sd_samplers_kdiffusion.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 7888d864..1bb25adf 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -89,11 +89,12 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             restart_steps, restart_times, restart_max = restart_list[i + 1]
             min_idx = i + 1
             max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
-            sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end
-            for times in range(restart_times):
-                x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
-                for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):
-                    x = heun_step(x, old_sigma, new_sigma)
+            if max_idx < min_idx:
+                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end
+                for times in range(restart_times):
+                    x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
+                    for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):
+                        x = heun_step(x, old_sigma, new_sigma)
     return x
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.3


From 7bb0fbed136c6a345b211e09102659fd89362576 Mon Sep 17 00:00:00 2001
From: lambertae <dengm@mit.edu>
Date: Tue, 18 Jul 2023 01:02:04 -0400
Subject: code styling

---
 modules/sd_samplers_kdiffusion.py | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 1bb25adf..db7013f2 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -35,17 +35,15 @@ samplers_k_diffusion = [
 
 
 @torch.no_grad()
-def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = {0.1: [10, 2, 2]}):
+def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.):
     """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
     '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
-    
-    from tqdm.auto import trange, tqdm
+    restart_list = {0.1: [10, 2, 2]}
+    from tqdm.auto import trange
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     step_id = 0
-    
     from k_diffusion.sampling import to_d, append_zero
-
     def heun_step(x, old_sigma, new_sigma):
         nonlocal step_id
         denoised = model(x, old_sigma * s_in, **extra_args)
@@ -70,8 +68,6 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
     for key, value in restart_list.items():
         temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
     restart_list = temp_list
-        
-            
     def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
         ramp = torch.linspace(0, 1, n).to(device)
         min_inv_rho = (sigma_min ** (1 / rho))
@@ -82,7 +78,6 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             max_inv_rho = max_inv_rho.to(device)
         sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return append_zero(sigmas).to(device)
-
     for i in trange(len(sigmas) - 1, disable=disable):
         x = heun_step(x, sigmas[i], sigmas[i+1])
         if i + 1 in restart_list:
@@ -91,7 +86,8 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
             if max_idx < min_idx:
                 sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end
-                for times in range(restart_times):
+                while restart_times > 0:
+                    restart_times -= 1
                     x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
                     for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):
                         x = heun_step(x, old_sigma, new_sigma)
-- 
cgit v1.2.3


From d6668347c8b85b11b696ac56777cc396e34ee1f9 Mon Sep 17 00:00:00 2001
From: Leon Feng <best.sum@gmail.com>
Date: Tue, 18 Jul 2023 04:19:58 -0400
Subject: remove duplicate

---
 modules/textual_inversion/logging.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/logging.py b/modules/textual_inversion/logging.py
index 734a4b6f..a822a7a5 100644
--- a/modules/textual_inversion/logging.py
+++ b/modules/textual_inversion/logging.py
@@ -2,7 +2,7 @@ import datetime
 import json
 import os
 
-saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "gradient_step", "latent_sampling_method"}
+saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "gradient_step", "latent_sampling_method"}
 saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
 saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
 saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
-- 
cgit v1.2.3


From 420cc8f68e6aca8a3a0f42ee0e626a6b03712763 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 18 Jul 2023 11:48:40 +0300
Subject: also make None a valid option for options API for #11854

---
 modules/api/models.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index b5683071..b55fa728 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,4 +1,6 @@
 import inspect
+import types
+
 from pydantic import BaseModel, Field, create_model
 from typing import Any, Optional
 from typing_extensions import Literal
@@ -207,11 +209,14 @@ class PreprocessResponse(BaseModel):
 fields = {}
 for key, metadata in opts.data_labels.items():
     value = opts.data.get(key)
+    if key == 'sd_model_checkpoint':
+        value = None
     optType = opts.typemap.get(type(metadata.default), type(value))
 
-    if (metadata is not None):
-        fields.update({key: (Optional[optType], Field(
-            default=metadata.default ,description=metadata.label))})
+    if optType == types.NoneType:
+        pass
+    elif metadata is not None:
+        fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))})
     else:
         fields.update({key: (Optional[optType], Field())})
 
-- 
cgit v1.2.3


From 3c570421d3a2eb24528b5f5bb615dcb0c7717e4a Mon Sep 17 00:00:00 2001
From: wfjsw <wfjsw@users.noreply.github.com>
Date: Tue, 18 Jul 2023 19:00:16 +0800
Subject: move start timer

---
 modules/api/models.py   | 2 +-
 modules/launch_utils.py | 3 ---
 2 files changed, 1 insertion(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index b55fa728..96cfe920 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -213,7 +213,7 @@ for key, metadata in opts.data_labels.items():
         value = None
     optType = opts.typemap.get(type(metadata.default), type(value))
 
-    if optType == types.NoneType:
+    if isinstance(optType, types.NoneType):
         pass
     elif metadata is not None:
         fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))})
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 03552bc2..ea995eda 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -10,9 +10,6 @@ from functools import lru_cache
 
 from modules import cmd_args, errors
 from modules.paths_internal import script_path, extensions_dir
-from modules import timer
-
-timer.startup_timer.record("start")
 
 args, _ = cmd_args.parser.parse_known_args()
 
-- 
cgit v1.2.3


From ed82f1c5f1677c85298f4d2c6c030a5551682c71 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 18 Jul 2023 15:55:23 +0300
Subject: lint

---
 modules/api/models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index b55fa728..96cfe920 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -213,7 +213,7 @@ for key, metadata in opts.data_labels.items():
         value = None
     optType = opts.typemap.get(type(metadata.default), type(value))
 
-    if optType == types.NoneType:
+    if isinstance(optType, types.NoneType):
         pass
     elif metadata is not None:
         fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))})
-- 
cgit v1.2.3


From 66c5f1bb1556a2d86d9f11aeb92f83d4a09832cc Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 18 Jul 2023 17:41:37 +0300
Subject: return sd_model_checkpoint to None

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index a256d090..6162938a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -409,7 +409,7 @@ options_templates.update(options_section(('training', "Training"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo("", "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
-- 
cgit v1.2.3


From b270ded268c92950a35a7a326da54496ef4151c8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 18 Jul 2023 18:10:04 +0300
Subject: fix the issue with /sdapi/v1/options failing (this time for sure!)
 fix automated tests downloading CLIP model

---
 modules/api/models.py | 6 ++----
 modules/cmd_args.py   | 1 +
 modules/sd_models.py  | 2 +-
 3 files changed, 4 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index 96cfe920..4cd20a92 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -209,11 +209,9 @@ class PreprocessResponse(BaseModel):
 fields = {}
 for key, metadata in opts.data_labels.items():
     value = opts.data.get(key)
-    if key == 'sd_model_checkpoint':
-        value = None
-    optType = opts.typemap.get(type(metadata.default), type(value))
+    optType = opts.typemap.get(type(metadata.default), type(metadata.default))
 
-    if isinstance(optType, types.NoneType):
+    if metadata.default is None:
         pass
     elif metadata is not None:
         fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))})
diff --git a/modules/cmd_args.py b/modules/cmd_args.py
index ae78f469..e401f641 100644
--- a/modules/cmd_args.py
+++ b/modules/cmd_args.py
@@ -15,6 +15,7 @@ parser.add_argument("--update-check", action='store_true', help="launch.py argum
 parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
 parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
 parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
+parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
 parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
 parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5813b550..fb31a793 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -494,7 +494,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
 
     sd_model = None
     try:
-        with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
+        with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip):
             sd_model = instantiate_from_config(sd_config.model)
     except Exception:
         pass
-- 
cgit v1.2.3


From 7f7db1700bda40ba3171a49b6a4ef38f868b7d0a Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 18 Jul 2023 18:16:23 +0300
Subject: linter fix

---
 modules/api/models.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index 4cd20a92..bf97b1a3 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,5 +1,4 @@
 import inspect
-import types
 
 from pydantic import BaseModel, Field, create_model
 from typing import Any, Optional
-- 
cgit v1.2.3


From 136c8859a49a35cbffe269aafc0bbdfca0b3561d Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 18 Jul 2023 20:11:30 +0300
Subject: add backwards compatibility --lyco-dir-backcompat option, use that
 for LyCORIS directory instead of hardcoded value prevent running preload.py
 for disabled extensions

---
 modules/script_loading.py | 5 +++--
 modules/shared.py         | 3 ++-
 2 files changed, 5 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/script_loading.py b/modules/script_loading.py
index 306a1f35..0d55f193 100644
--- a/modules/script_loading.py
+++ b/modules/script_loading.py
@@ -12,11 +12,12 @@ def load_module(path):
     return module
 
 
-def preload_extensions(extensions_dir, parser):
+def preload_extensions(extensions_dir, parser, extension_list=None):
     if not os.path.isdir(extensions_dir):
         return
 
-    for dirname in sorted(os.listdir(extensions_dir)):
+    extensions = extension_list if extension_list is not None else os.listdir(extensions_dir)
+    for dirname in sorted(extensions):
         preload_script = os.path.join(extensions_dir, dirname, "preload.py")
         if not os.path.isfile(preload_script):
             continue
diff --git a/modules/shared.py b/modules/shared.py
index 6162938a..1ce7b49e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -11,6 +11,7 @@ import gradio as gr
 import torch
 import tqdm
 
+import launch
 import modules.interrogate
 import modules.memmon
 import modules.styles
@@ -26,7 +27,7 @@ demo = None
 
 parser = cmd_args.parser
 
-script_loading.preload_extensions(extensions_dir, parser)
+script_loading.preload_extensions(extensions_dir, parser, extension_list=launch.list_extensions(launch.args.ui_settings_file))
 script_loading.preload_extensions(extensions_builtin_dir, parser)
 
 if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
-- 
cgit v1.2.3


From c8b55f29e2838e67bd9e394f5dbca4350ccbb68f Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Wed, 19 Jul 2023 08:27:19 +0900
Subject: missing p save_image before-highres-fix

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 6567b3cf..b89ca5c2 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1029,7 +1029,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 image = sd_samplers.sample_to_image(image, index, approximation=0)
 
             info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
-            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
+            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix")
 
         if latent_scale_mode is not None:
             for i in range(samples.shape[0]):
-- 
cgit v1.2.3


From cb7573489670cc7a042d24285e158b797c9558b2 Mon Sep 17 00:00:00 2001
From: yfzhou <zhougynui@sina.com>
Date: Wed, 19 Jul 2023 17:53:28 +0800
Subject: =?UTF-8?q?=E3=80=90bug=E3=80=91reload=20altclip=20model=20error?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

When using BertSeriesModelWithTransformation as the cond_stage_model, the undo_hijack should be performed using the FrozenXLMREmbedderWithCustomWords type; otherwise, it will result in a failed model reload.
---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 3b6f95ce..928233ab 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -203,7 +203,7 @@ class StableDiffusionModelHijack:
         ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
 
     def undo_hijack(self, m):
-        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+        if type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped
 
         elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
-- 
cgit v1.2.3


From 4334d25978ded517a76359e9e92b8101610cc35f Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 19 Jul 2023 15:49:31 +0300
Subject: bugfix: model name was added together with directory name to infotext
 and to [model_name] filename pattern

---
 modules/images.py     | 2 +-
 modules/processing.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index fb5d2e75..38aa933d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -363,7 +363,7 @@ class FilenameGenerator:
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
-        'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.model_name, replace_spaces=False),
+        'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.name_for_extra, replace_spaces=False),
         'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
         'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
         'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
diff --git a/modules/processing.py b/modules/processing.py
index b89ca5c2..e028bf9e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -587,7 +587,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
+        "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
-- 
cgit v1.2.3


From 23c947ab0374220c39ac54fc00afcb74e809dd95 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 19 Jul 2023 20:23:30 +0300
Subject: automatically switch to 32-bit float VAE if the generated picture has
 NaNs.

---
 modules/processing.py | 41 ++++++++++++++++++++++++++++++++++++-----
 modules/shared.py     |  1 +
 2 files changed, 37 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index e028bf9e..a74a5302 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -14,7 +14,7 @@ from skimage import exposure
 from typing import Any, Dict, List
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -538,6 +538,40 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     return x
 
 
+def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
+    samples = []
+
+    for i in range(batch.shape[0]):
+        sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+        if check_for_nans:
+            try:
+                devices.test_for_nans(sample, "vae")
+            except devices.NansException as e:
+                if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:
+                    raise e
+
+                errors.print_error_explanation(
+                    "A tensor with all NaNs was produced in VAE.\n"
+                    "Web UI will now convert VAE into 32-bit float and retry.\n"
+                    "To disable this behavior, disable the 'Automaticlly revert VAE to 32-bit floats' setting.\n"
+                    "To always start with 32-bit VAE, use --no-half-vae commandline flag."
+                )
+
+                devices.dtype_vae = torch.float32
+                model.first_stage_model.to(devices.dtype_vae)
+                batch = batch.to(devices.dtype_vae)
+
+                sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+        if target_device is not None:
+            sample = sample.to(target_device)
+
+        samples.append(sample)
+
+    return samples
+
+
 def decode_first_stage(model, x):
     x = model.decode_first_stage(x.to(devices.dtype_vae))
 
@@ -758,10 +792,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                 samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
 
-            x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
-            for x in x_samples_ddim:
-                devices.test_for_nans(x, "vae")
-
+            x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
             x_samples_ddim = torch.stack(x_samples_ddim).float()
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
diff --git a/modules/shared.py b/modules/shared.py
index 1ce7b49e..aa72c9c8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -427,6 +427,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
     "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
     "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
+    "auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
     "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
 }))
 
-- 
cgit v1.2.3


From ddbf4a73f5c0cfe63ca0988b8e642d3b977a3fa9 Mon Sep 17 00:00:00 2001
From: lambertae <dengm@mit.edu>
Date: Thu, 20 Jul 2023 02:24:18 -0400
Subject: restart-sampler with correct steps

---
 modules/sd_samplers_kdiffusion.py | 22 ++++++++++++++--------
 1 file changed, 14 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index db7013f2..ed5e6c79 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -38,20 +38,19 @@ samplers_k_diffusion = [
 def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.):
     """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
     '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
-    restart_list = {0.1: [10, 2, 2]}
     from tqdm.auto import trange
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     step_id = 0
     from k_diffusion.sampling import to_d, append_zero
-    def heun_step(x, old_sigma, new_sigma):
+    def heun_step(x, old_sigma, new_sigma, second_order = True):
         nonlocal step_id
         denoised = model(x, old_sigma * s_in, **extra_args)
         d = to_d(x, old_sigma, denoised)
         if callback is not None:
             callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
         dt = new_sigma - old_sigma
-        if new_sigma == 0:
+        if new_sigma == 0 or not second_order:
             # Euler method
             x = x + d * dt
         else:
@@ -63,11 +62,6 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             x = x + d_prime * dt
         step_id += 1
         return x
-    # print(sigmas)
-    temp_list = dict()
-    for key, value in restart_list.items():
-        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
-    restart_list = temp_list
     def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
         ramp = torch.linspace(0, 1, n).to(device)
         min_inv_rho = (sigma_min ** (1 / rho))
@@ -78,6 +72,18 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             max_inv_rho = max_inv_rho.to(device)
         sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return append_zero(sigmas).to(device)
+    steps = sigmas.shape[0] - 1
+    if steps >= 20:
+        restart_steps = 9
+        restart_times = 2 if steps >= 36 else 1
+        sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2], sigmas[0], device=sigmas.device)
+        restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
+    else:
+        restart_list = dict()
+    temp_list = dict()
+    for key, value in restart_list.items():
+        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
+    restart_list = temp_list
     for i in trange(len(sigmas) - 1, disable=disable):
         x = heun_step(x, sigmas[i], sigmas[i+1])
         if i + 1 in restart_list:
-- 
cgit v1.2.3


From 8218f6cd37040eb3ec5d9c68ad54249a44c39166 Mon Sep 17 00:00:00 2001
From: Janek Mann <janekm@gmail.com>
Date: Thu, 20 Jul 2023 16:22:52 +0100
Subject: Update sd_models_xl.py

Fix width/height not getting fed into the conditioning
---
 modules/sd_models_xl.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py
index 01320c7a..40559208 100644
--- a/modules/sd_models_xl.py
+++ b/modules/sd_models_xl.py
@@ -12,8 +12,8 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
     for embedder in self.conditioner.embedders:
         embedder.ucg_rate = 0.0
 
-    width = getattr(self, 'target_width', 1024)
-    height = getattr(self, 'target_height', 1024)
+    width = getattr(batch, 'width', 1024)
+    height = getattr(batch, 'height', 1024)
     is_negative_prompt = getattr(batch, 'is_negative_prompt', False)
     aesthetic_score = shared.opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else shared.opts.sdxl_refiner_high_aesthetic_score
 
-- 
cgit v1.2.3


From 2f57a559ac3381c1ef2516655c3a3d1088191c54 Mon Sep 17 00:00:00 2001
From: lambertae <dengm@mit.edu>
Date: Thu, 20 Jul 2023 20:34:41 -0400
Subject:  allow choise of restart_list & use karras from kdiffusion

---
 modules/sd_samplers_kdiffusion.py | 32 ++++++++++++--------------------
 1 file changed, 12 insertions(+), 20 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index ed5e6c79..c72d01c8 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -35,14 +35,15 @@ samplers_k_diffusion = [
 
 
 @torch.no_grad()
-def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.):
+def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None):
     """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
     '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
+    '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list'''
     from tqdm.auto import trange
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     step_id = 0
-    from k_diffusion.sampling import to_d, append_zero
+    from k_diffusion.sampling import to_d, append_zero, get_sigmas_karras
     def heun_step(x, old_sigma, new_sigma, second_order = True):
         nonlocal step_id
         denoised = model(x, old_sigma * s_in, **extra_args)
@@ -62,24 +63,15 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             x = x + d_prime * dt
         step_id += 1
         return x
-    def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
-        ramp = torch.linspace(0, 1, n).to(device)
-        min_inv_rho = (sigma_min ** (1 / rho))
-        max_inv_rho = (sigma_max ** (1 / rho))
-        if isinstance(min_inv_rho, torch.Tensor):
-            min_inv_rho = min_inv_rho.to(device)
-        if isinstance(max_inv_rho, torch.Tensor):
-            max_inv_rho = max_inv_rho.to(device)
-        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
-        return append_zero(sigmas).to(device)
     steps = sigmas.shape[0] - 1
-    if steps >= 20:
-        restart_steps = 9
-        restart_times = 2 if steps >= 36 else 1
-        sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2], sigmas[0], device=sigmas.device)
-        restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
-    else:
-        restart_list = dict()
+    if restart_list is None:
+        if steps >= 20:
+            restart_steps = 9
+            restart_times = 2 if steps >= 36 else 1
+            sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
+            restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
+        else:
+            restart_list = dict()
     temp_list = dict()
     for key, value in restart_list.items():
         temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
@@ -91,7 +83,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             min_idx = i + 1
             max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
             if max_idx < min_idx:
-                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx], sigmas[max_idx], device=sigmas.device)[:-1] # remove the zero at the end
+                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] # remove the zero at the end
                 while restart_times > 0:
                     restart_times -= 1
                     x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
-- 
cgit v1.2.3


From 128d59c9ccfbc9c7fccd6f1b2fe58bbbb18459f9 Mon Sep 17 00:00:00 2001
From: lambertae <dengm@mit.edu>
Date: Thu, 20 Jul 2023 20:36:40 -0400
Subject: fix ruff

---
 modules/sd_samplers_kdiffusion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index c72d01c8..21b347ed 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -43,7 +43,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     step_id = 0
-    from k_diffusion.sampling import to_d, append_zero, get_sigmas_karras
+    from k_diffusion.sampling import to_d, get_sigmas_karras
     def heun_step(x, old_sigma, new_sigma, second_order = True):
         nonlocal step_id
         denoised = model(x, old_sigma * s_in, **extra_args)
-- 
cgit v1.2.3


From f87389029839a27464a18846815339e81787b882 Mon Sep 17 00:00:00 2001
From: lambertae <dengm@mit.edu>
Date: Thu, 20 Jul 2023 21:27:43 -0400
Subject: new restart scheme

---
 modules/sd_samplers_kdiffusion.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 21b347ed..ed60670c 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -67,7 +67,10 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
     if restart_list is None:
         if steps >= 20:
             restart_steps = 9
-            restart_times = 2 if steps >= 36 else 1
+            restart_times = 1
+            if steps >= 36:
+                restart_steps = steps // 4
+                restart_times = 2
             sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
             restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
         else:
-- 
cgit v1.2.3


From 118529a6dc9481499ebcd589a9b42f98b58bca8a Mon Sep 17 00:00:00 2001
From: Jabasukuriputo Wang <wfjsw@users.noreply.github.com>
Date: Fri, 21 Jul 2023 21:49:33 +0800
Subject: typo fix

---
 modules/launch_utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 03552bc2..8e11bf42 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -233,7 +233,7 @@ def run_extensions_installers(settings_file):
 re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
 
 
-def requrements_met(requirements_file):
+def requirements_met(requirements_file):
     """
     Does a simple parse of a requirements.txt file to determine if all rerqirements in it
     are already installed. Returns True if so, False if not installed or parsing fails.
@@ -293,7 +293,7 @@ def prepare_environment():
     try:
         # the existance of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
         os.remove(os.path.join(script_path, "tmp", "restart"))
-        os.environ.setdefault('SD_WEBUI_RESTARTING ', '1')
+        os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
     except OSError:
         pass
 
@@ -354,7 +354,7 @@ def prepare_environment():
     if not os.path.isfile(requirements_file):
         requirements_file = os.path.join(script_path, requirements_file)
 
-    if not requrements_met(requirements_file):
+    if not requirements_met(requirements_file):
         run_pip(f"install -r \"{requirements_file}\"", "requirements")
 
     run_extensions_installers(settings_file=args.ui_settings_file)
-- 
cgit v1.2.3


From bc91f15ed32ac29c7bdf9e5abbbf02f18d79a1ab Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Fri, 21 Jul 2023 22:56:41 +0900
Subject: typo fix

---
 modules/scripts.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 7d9dd59f..f34240a0 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -119,7 +119,7 @@ class Script:
 
     def after_extra_networks_activate(self, p, *args, **kwargs):
         """
-        Calledafter extra networks activation, before conds calculation
+        Called after extra networks activation, before conds calculation
         allow modification of the network after extra networks activation been applied
         won't be call if p.disable_extra_networks
 
-- 
cgit v1.2.3


From 16eddc622e6d091f51d22269742afddc9b6d0f4b Mon Sep 17 00:00:00 2001
From: Jabasukuriputo Wang <wfjsw@users.noreply.github.com>
Date: Fri, 21 Jul 2023 22:00:03 +0800
Subject: prepend the pythonpath instead of overriding it

---
 modules/launch_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 609a181e..231ebc5f 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -190,7 +190,7 @@ def run_extension_installer(extension_dir):
 
     try:
         env = os.environ.copy()
-        env['PYTHONPATH'] = os.path.abspath(".")
+        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env['PYTHONPATH']}"
 
         print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
     except Exception as e:
-- 
cgit v1.2.3


From 1fe2dcaa2a5c7f6f4864a94ffb65f8eedd8a0129 Mon Sep 17 00:00:00 2001
From: AnyISalIn <anyisalin@gmail.com>
Date: Sat, 22 Jul 2023 10:00:27 +0800
Subject: [bug] If txt2img/img2img raises an exception, finally call
 state.end()

Signed-off-by: AnyISalIn <anyisalin@gmail.com>
---
 modules/api/api.py | 36 ++++++++++++++++++++----------------
 1 file changed, 20 insertions(+), 16 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 2a4cd8a2..9d73083f 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -333,14 +333,16 @@ class Api:
                 p.outpath_grids = opts.outdir_txt2img_grids
                 p.outpath_samples = opts.outdir_txt2img_samples
 
-                shared.state.begin(job="scripts_txt2img")
-                if selectable_scripts is not None:
-                    p.script_args = script_args
-                    processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
-                else:
-                    p.script_args = tuple(script_args) # Need to pass args as tuple here
-                    processed = process_images(p)
-                shared.state.end()
+                try:
+                    shared.state.begin(job="scripts_txt2img")
+                    if selectable_scripts is not None:
+                        p.script_args = script_args
+                        processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
+                    else:
+                        p.script_args = tuple(script_args) # Need to pass args as tuple here
+                        processed = process_images(p)
+                finally:
+                    shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
@@ -390,14 +392,16 @@ class Api:
                 p.outpath_grids = opts.outdir_img2img_grids
                 p.outpath_samples = opts.outdir_img2img_samples
 
-                shared.state.begin(job="scripts_img2img")
-                if selectable_scripts is not None:
-                    p.script_args = script_args
-                    processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
-                else:
-                    p.script_args = tuple(script_args) # Need to pass args as tuple here
-                    processed = process_images(p)
-                shared.state.end()
+                try:
+                    shared.state.begin(job="scripts_img2img")
+                    if selectable_scripts is not None:
+                        p.script_args = script_args
+                        processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
+                    else:
+                        p.script_args = tuple(script_args) # Need to pass args as tuple here
+                        processed = process_images(p)
+                finally:
+                    shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
-- 
cgit v1.2.3


From 90eb731ff1d73fdc5872ff9682d5c88c9737ba38 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 22 Jul 2023 12:21:05 +0300
Subject: start timer early anyway

---
 modules/launch_utils.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index aaa671ab..18b444d4 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -10,6 +10,7 @@ from functools import lru_cache
 
 from modules import cmd_args, errors
 from modules.paths_internal import script_path, extensions_dir
+from modules import timer  # noqa:F401
 
 args, _ = cmd_args.parser.parse_known_args()
 
-- 
cgit v1.2.3


From 2a7e34fe79c82ac264919be8965b8d51afa32ac1 Mon Sep 17 00:00:00 2001
From: Jabasukuriputo Wang <wfjsw@users.noreply.github.com>
Date: Sat, 22 Jul 2023 18:09:00 +0800
Subject: fix
 https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11921#issuecomment-1646547908

---
 modules/launch_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 18b444d4..0c863e4a 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -194,7 +194,7 @@ def run_extension_installer(extension_dir):
 
     try:
         env = os.environ.copy()
-        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env['PYTHONPATH']}"
+        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH')}"
 
         print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
     except Exception as e:
-- 
cgit v1.2.3


From 3c26734d603560f57f7a145eed6cbdd50d20379c Mon Sep 17 00:00:00 2001
From: Jabasukuriputo Wang <wfjsw@users.noreply.github.com>
Date: Sat, 22 Jul 2023 18:33:59 +0800
Subject: nop

---
 modules/launch_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 0c863e4a..c61edb36 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -194,7 +194,7 @@ def run_extension_installer(extension_dir):
 
     try:
         env = os.environ.copy()
-        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH')}"
+        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH', '')}"
 
         print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
     except Exception as e:
-- 
cgit v1.2.3


From c76a30af41e50932847230631d26bfa9635ebd62 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 22 Jul 2023 13:49:29 +0300
Subject: more info for startup timings

---
 modules/launch_utils.py | 26 +++++++++++++++++++++++---
 1 file changed, 23 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 18b444d4..0178e1b0 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -10,7 +10,7 @@ from functools import lru_cache
 
 from modules import cmd_args, errors
 from modules.paths_internal import script_path, extensions_dir
-from modules import timer  # noqa:F401
+from modules.timer import startup_timer
 
 args, _ = cmd_args.parser.parse_known_args()
 
@@ -224,8 +224,10 @@ def run_extensions_installers(settings_file):
     if not os.path.isdir(extensions_dir):
         return
 
-    for dirname_extension in list_extensions(settings_file):
-        run_extension_installer(os.path.join(extensions_dir, dirname_extension))
+    with startup_timer.subcategory("run extensions installers"):
+        for dirname_extension in list_extensions(settings_file):
+            run_extension_installer(os.path.join(extensions_dir, dirname_extension))
+            startup_timer.record(dirname_extension)
 
 
 re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
@@ -298,8 +300,11 @@ def prepare_environment():
     if not args.skip_python_version_check:
         check_python_version()
 
+    startup_timer.record("checks")
+
     commit = commit_hash()
     tag = git_tag()
+    startup_timer.record("git version info")
 
     print(f"Python {sys.version}")
     print(f"Version: {tag}")
@@ -307,21 +312,27 @@ def prepare_environment():
 
     if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
+        startup_timer.record("install torch")
 
     if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
         raise RuntimeError(
             'Torch is not able to use GPU; '
             'add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'
         )
+    startup_timer.record("torch GPU test")
+
 
     if not is_installed("gfpgan"):
         run_pip(f"install {gfpgan_package}", "gfpgan")
+        startup_timer.record("install gfpgan")
 
     if not is_installed("clip"):
         run_pip(f"install {clip_package}", "clip")
+        startup_timer.record("install clip")
 
     if not is_installed("open_clip"):
         run_pip(f"install {openclip_package}", "open_clip")
+        startup_timer.record("install open_clip")
 
     if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
         if platform.system() == "Windows":
@@ -335,8 +346,11 @@ def prepare_environment():
         elif platform.system() == "Linux":
             run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
 
+        startup_timer.record("install xformers")
+
     if not is_installed("ngrok") and args.ngrok:
         run_pip("install ngrok", "ngrok")
+        startup_timer.record("install ngrok")
 
     os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
 
@@ -346,22 +360,28 @@ def prepare_environment():
     git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
     git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
 
+    startup_timer.record("clone repositores")
+
     if not is_installed("lpips"):
         run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
+        startup_timer.record("install CodeFormer requirements")
 
     if not os.path.isfile(requirements_file):
         requirements_file = os.path.join(script_path, requirements_file)
 
     if not requirements_met(requirements_file):
         run_pip(f"install -r \"{requirements_file}\"", "requirements")
+        startup_timer.record("install requirements")
 
     run_extensions_installers(settings_file=args.ui_settings_file)
 
     if args.update_check:
         version_check(commit)
+        startup_timer.record("check version")
 
     if args.update_all_extensions:
         git_pull_recursive(extensions_dir)
+        startup_timer.record("update extensions")
 
     if "--exit" in sys.argv:
         print("Exiting because of --exit argument")
-- 
cgit v1.2.3


From 0674fabd0d82c69c02ff362762815c4536687b95 Mon Sep 17 00:00:00 2001
From: ljleb <set>
Date: Sat, 22 Jul 2023 07:10:20 -0400
Subject: fix AND linebreaks

---
 modules/prompt_parser.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 0069d8b0..c514e63b 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -153,7 +153,7 @@ def get_learned_conditioning(model, prompts, steps):
 
 
 re_AND = re.compile(r"\bAND\b")
-re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
+re_weight = re.compile(r"^(\s*.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
 
 def get_multicond_prompt_list(prompts):
     res_indexes = []
-- 
cgit v1.2.3


From 88a3e1d3062401d7f5775cf362975de6327fded2 Mon Sep 17 00:00:00 2001
From: ljleb <set>
Date: Sat, 22 Jul 2023 07:40:30 -0400
Subject: fix AND linebreaks

---
 modules/prompt_parser.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index c514e63b..607b1ad3 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -153,7 +153,7 @@ def get_learned_conditioning(model, prompts, steps):
 
 
 re_AND = re.compile(r"\bAND\b")
-re_weight = re.compile(r"^(\s*.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
+re_weight = re.compile(r"^((?:\s|.)*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
 
 def get_multicond_prompt_list(prompts):
     res_indexes = []
-- 
cgit v1.2.3


From a8d4213317c6970aa3ca8cbeeaacb07b936b591c Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 22 Jul 2023 17:08:45 +0300
Subject: add --log-startup option to print detailed startup progress

---
 modules/cmd_args.py     |  1 +
 modules/launch_utils.py |  7 +++++--
 modules/timer.py        | 23 +++++++++++++++++++----
 3 files changed, 25 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/cmd_args.py b/modules/cmd_args.py
index e401f641..dd5fadc4 100644
--- a/modules/cmd_args.py
+++ b/modules/cmd_args.py
@@ -13,6 +13,7 @@ parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py
 parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
 parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup")
 parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
+parser.add_argument("--log-startup", action='store_true', help="launch.py argument: print a detailed log of what's happening at startup")
 parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
 parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
 parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index c9e4344b..f77b577a 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -226,8 +226,11 @@ def run_extensions_installers(settings_file):
 
     with startup_timer.subcategory("run extensions installers"):
         for dirname_extension in list_extensions(settings_file):
-            run_extension_installer(os.path.join(extensions_dir, dirname_extension))
-            startup_timer.record(dirname_extension)
+            path = os.path.join(extensions_dir, dirname_extension)
+
+            if os.path.isdir(path):
+                run_extension_installer(path)
+                startup_timer.record(dirname_extension)
 
 
 re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
diff --git a/modules/timer.py b/modules/timer.py
index da99e49f..1d38595c 100644
--- a/modules/timer.py
+++ b/modules/timer.py
@@ -1,4 +1,5 @@
 import time
+import argparse
 
 
 class TimerSubcategory:
@@ -11,20 +12,27 @@ class TimerSubcategory:
     def __enter__(self):
         self.start = time.time()
         self.timer.base_category = self.original_base_category + self.category + "/"
+        self.timer.subcategory_level += 1
+
+        if self.timer.print_log:
+            print(f"{'  ' * self.timer.subcategory_level}{self.category}:")
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         elapsed_for_subcategroy = time.time() - self.start
         self.timer.base_category = self.original_base_category
         self.timer.add_time_to_record(self.original_base_category + self.category, elapsed_for_subcategroy)
-        self.timer.record(self.category)
+        self.timer.subcategory_level -= 1
+        self.timer.record(self.category, disable_log=True)
 
 
 class Timer:
-    def __init__(self):
+    def __init__(self, print_log=False):
         self.start = time.time()
         self.records = {}
         self.total = 0
         self.base_category = ''
+        self.print_log = print_log
+        self.subcategory_level = 0
 
     def elapsed(self):
         end = time.time()
@@ -38,13 +46,16 @@ class Timer:
 
         self.records[category] += amount
 
-    def record(self, category, extra_time=0):
+    def record(self, category, extra_time=0, disable_log=False):
         e = self.elapsed()
 
         self.add_time_to_record(self.base_category + category, e + extra_time)
 
         self.total += e + extra_time
 
+        if self.print_log and not disable_log:
+            print(f"{'  ' * self.subcategory_level}{category}: done in {e + extra_time:.3f}s")
+
     def subcategory(self, name):
         self.elapsed()
 
@@ -71,6 +82,10 @@ class Timer:
         self.__init__()
 
 
-startup_timer = Timer()
+parser = argparse.ArgumentParser(add_help=False)
+parser.add_argument("--log-startup", action='store_true', help="print a detailed log of what's happening at startup")
+args = parser.parse_known_args()[0]
+
+startup_timer = Timer(print_log=args.log_startup)
 
 startup_record = None
-- 
cgit v1.2.3


From 2c11e9009ea18bab4ce2963d44db0c6fd3227370 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 24 Jul 2023 11:57:59 +0300
Subject: repair --medvram for SD2.x too after SDXL update

---
 modules/lowvram.py             | 7 ++++---
 modules/sd_hijack_open_clip.py | 2 +-
 2 files changed, 5 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/lowvram.py b/modules/lowvram.py
index 6bbc11eb..3f830664 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -90,8 +90,12 @@ def setup_for_low_vram(sd_model, use_medvram):
         sd_model.conditioner.register_forward_pre_hook(send_me_to_gpu)
     elif is_sd2:
         sd_model.cond_stage_model.model.register_forward_pre_hook(send_me_to_gpu)
+        sd_model.cond_stage_model.model.token_embedding.register_forward_pre_hook(send_me_to_gpu)
+        parents[sd_model.cond_stage_model.model] = sd_model.cond_stage_model
+        parents[sd_model.cond_stage_model.model.token_embedding] = sd_model.cond_stage_model
     else:
         sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
+        parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
     sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
     sd_model.first_stage_model.encode = first_stage_model_encode_wrap
@@ -101,9 +105,6 @@ def setup_for_low_vram(sd_model, use_medvram):
     if sd_model.embedder:
         sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
 
-    if hasattr(sd_model, 'cond_stage_model'):
-        parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
-
     if use_medvram:
         sd_model.model.register_forward_pre_hook(send_me_to_gpu)
     else:
diff --git a/modules/sd_hijack_open_clip.py b/modules/sd_hijack_open_clip.py
index bb0b96c7..25c5e983 100644
--- a/modules/sd_hijack_open_clip.py
+++ b/modules/sd_hijack_open_clip.py
@@ -32,7 +32,7 @@ class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWit
     def encode_embedding_init_text(self, init_text, nvpt):
         ids = tokenizer.encode(init_text)
         ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
-        embedded = self.wrapped.model.token_embedding.wrapped(ids.to(self.wrapped.model.token_embedding.wrapped.weight.device)).squeeze(0)
+        embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
 
         return embedded
 
-- 
cgit v1.2.3


From 1cbfafafd2dc68c4985da2ae88d7e30f7074fed9 Mon Sep 17 00:00:00 2001
From: AnyISalIn <anyisalin@gmail.com>
Date: Mon, 24 Jul 2023 19:45:08 +0800
Subject: feat: add refresh vae api

Signed-off-by: AnyISalIn <anyisalin@gmail.com>
---
 modules/api/api.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 9d73083f..09166df2 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -15,7 +15,7 @@ from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -197,6 +197,7 @@ class Api:
         self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
         self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
         self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
+        self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
         self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
         self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
         self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
@@ -608,6 +609,10 @@ class Api:
         with self.queue_lock:
             shared.refresh_checkpoints()
 
+    def refresh_vae(self):
+        with self.queue_lock:
+            shared_items.refresh_vae_list()
+
     def create_embedding(self, args: dict):
         try:
             shared.state.begin(job="create_embedding")
-- 
cgit v1.2.3


From ca45ff1ae6fdd5c2dcd754fde95dd29f49bd414b Mon Sep 17 00:00:00 2001
From: ljleb <set>
Date: Mon, 24 Jul 2023 13:52:24 -0400
Subject: add postprocess_batch_list callback

---
 modules/processing.py | 24 +++++++++++++++++++++++-
 modules/scripts.py    | 32 ++++++++++++++++++++++++++++++++
 2 files changed, 55 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index a74a5302..c16404f4 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -717,7 +717,25 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
-        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
+        all_prompts = p.all_prompts[:]
+        all_seeds = p.all_seeds[:]
+        all_subseeds = p.all_subseeds[:]
+
+        # apply changes to generation data
+        all_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.prompts
+        all_seeds[n * p.batch_size:(n + 1) * p.batch_size] = p.seeds
+        all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] = p.subseeds
+
+        # update p.all_negative_prompts in case extensions changed the size of the batch
+        # create_infotext below uses it
+        old_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+        p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.negative_prompts
+
+        try:
+            return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
+        finally:
+            # restore p.all_negative_prompts in case extensions changed the size of the batch
+            p.all_negative_prompts[n * p.batch_size:n * p.batch_size + len(p.negative_prompts)] = old_negative_prompts
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
@@ -806,6 +824,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
+                postprocess_batch_list_args = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
+                p.scripts.postprocess_batch_list(p, postprocess_batch_list_args, batch_number=n)
+                x_samples_ddim = postprocess_batch_list_args.images
+
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
 
diff --git a/modules/scripts.py b/modules/scripts.py
index f34240a0..5b4edcac 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -16,6 +16,11 @@ class PostprocessImageArgs:
         self.image = image
 
 
+class PostprocessBatchListArgs:
+    def __init__(self, images):
+        self.images = images
+
+
 class Script:
     name = None
     """script's internal name derived from title"""
@@ -156,6 +161,25 @@ class Script:
 
         pass
 
+    def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, *args, **kwargs):
+        """
+        Same as postprocess_batch(), but receives batch images as a list of 3D tensors instead of a 4D tensor.
+        This is useful when you want to update the entire batch instead of individual images.
+
+        You can modify the postprocessing object (pp) to update the images in the batch, remove images, add images, etc.
+        If the number of images is different from the batch size when returning,
+        then the script has the responsibility to also update the following attributes in the processing object (p):
+          - p.prompts
+          - p.negative_prompts
+          - p.seeds
+          - p.subseeds
+
+        **kwargs will have same items as process_batch, and also:
+          - batch_number - index of current batch, from 0 to number of batches-1
+        """
+
+        pass
+
     def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
         """
         Called for every image after it has been generated.
@@ -536,6 +560,14 @@ class ScriptRunner:
             except Exception:
                 errors.report(f"Error running postprocess_batch: {script.filename}", exc_info=True)
 
+    def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, **kwargs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess_batch_list(p, pp, *script_args, **kwargs)
+            except Exception:
+                errors.report(f"Error running postprocess_batch_list: {script.filename}", exc_info=True)
+
     def postprocess_image(self, p, pp: PostprocessImageArgs):
         for script in self.alwayson_scripts:
             try:
-- 
cgit v1.2.3


From 0a89cd1a584b1584a0609c0ba27fb35c434b0b68 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 24 Jul 2023 22:08:08 +0300
Subject: Use less RAM when creating models

---
 modules/cmd_args.py                  |   1 +
 modules/sd_disable_initialization.py | 106 +++++++++++++++++++++++++++++++++--
 modules/sd_models.py                 |  16 ++++--
 3 files changed, 112 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/cmd_args.py b/modules/cmd_args.py
index dd5fadc4..cb4ec5f7 100644
--- a/modules/cmd_args.py
+++ b/modules/cmd_args.py
@@ -67,6 +67,7 @@ parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="pre
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
 parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
 parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py
index 9fc89dc6..695c5736 100644
--- a/modules/sd_disable_initialization.py
+++ b/modules/sd_disable_initialization.py
@@ -3,8 +3,31 @@ import open_clip
 import torch
 import transformers.utils.hub
 
+from modules import shared
 
-class DisableInitialization:
+
+class ReplaceHelper:
+    def __init__(self):
+        self.replaced = []
+
+    def replace(self, obj, field, func):
+        original = getattr(obj, field, None)
+        if original is None:
+            return None
+
+        self.replaced.append((obj, field, original))
+        setattr(obj, field, func)
+
+        return original
+
+    def restore(self):
+        for obj, field, original in self.replaced:
+            setattr(obj, field, original)
+
+        self.replaced.clear()
+
+
+class DisableInitialization(ReplaceHelper):
     """
     When an object of this class enters a `with` block, it starts:
     - preventing torch's layer initialization functions from working
@@ -21,7 +44,7 @@ class DisableInitialization:
     """
 
     def __init__(self, disable_clip=True):
-        self.replaced = []
+        super().__init__()
         self.disable_clip = disable_clip
 
     def replace(self, obj, field, func):
@@ -86,8 +109,81 @@ class DisableInitialization:
             self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
 
     def __exit__(self, exc_type, exc_val, exc_tb):
-        for obj, field, original in self.replaced:
-            setattr(obj, field, original)
+        self.restore()
 
-        self.replaced.clear()
 
+class InitializeOnMeta(ReplaceHelper):
+    """
+    Context manager that causes all parameters for linear/conv2d/mha layers to be allocated on meta device,
+    which results in those parameters having no values and taking no memory. model.to() will be broken and
+    will need to be repaired by using LoadStateDictOnMeta below when loading params from state dict.
+
+    Usage:
+    ```
+    with sd_disable_initialization.InitializeOnMeta():
+        sd_model = instantiate_from_config(sd_config.model)
+    ```
+    """
+
+    def __enter__(self):
+        if shared.cmd_opts.disable_model_loading_ram_optimization:
+            return
+
+        def set_device(x):
+            x["device"] = "meta"
+            return x
+
+        linear_init = self.replace(torch.nn.Linear, '__init__', lambda *args, **kwargs: linear_init(*args, **set_device(kwargs)))
+        conv2d_init = self.replace(torch.nn.Conv2d, '__init__', lambda *args, **kwargs: conv2d_init(*args, **set_device(kwargs)))
+        mha_init = self.replace(torch.nn.MultiheadAttention, '__init__', lambda *args, **kwargs: mha_init(*args, **set_device(kwargs)))
+        self.replace(torch.nn.Module, 'to', lambda *args, **kwargs: None)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.restore()
+
+
+class LoadStateDictOnMeta(ReplaceHelper):
+    """
+    Context manager that allows to read parameters from state_dict into a model that has some of its parameters in the meta device.
+    As those parameters are read from state_dict, they will be deleted from it, so by the end state_dict will be mostly empty, to save memory.
+    Meant to be used together with InitializeOnMeta above.
+
+    Usage:
+    ```
+    with sd_disable_initialization.LoadStateDictOnMeta(state_dict):
+        model.load_state_dict(state_dict, strict=False)
+    ```
+    """
+
+    def __init__(self, state_dict, device):
+        super().__init__()
+        self.state_dict = state_dict
+        self.device = device
+
+    def __enter__(self):
+        if shared.cmd_opts.disable_model_loading_ram_optimization:
+            return
+
+        sd = self.state_dict
+        device = self.device
+
+        def load_from_state_dict(original, self, state_dict, prefix, *args, **kwargs):
+            params = [(name, param) for name, param in self._parameters.items() if param is not None and param.is_meta]
+
+            for name, param in params:
+                if param.is_meta:
+                    self._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device), requires_grad=param.requires_grad)
+
+            original(self, state_dict, prefix, *args, **kwargs)
+
+            for name, _ in params:
+                key = prefix + name
+                if key in sd:
+                    del sd[key]
+
+        linear_load_from_state_dict = self.replace(torch.nn.Linear, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(linear_load_from_state_dict, *args, **kwargs))
+        conv2d_load_from_state_dict = self.replace(torch.nn.Conv2d, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(conv2d_load_from_state_dict, *args, **kwargs))
+        mha_load_from_state_dict = self.replace(torch.nn.MultiheadAttention, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(mha_load_from_state_dict, *args, **kwargs))
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.restore()
diff --git a/modules/sd_models.py b/modules/sd_models.py
index fb31a793..acb1e817 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -460,7 +460,6 @@ def get_empty_cond(sd_model):
         return sd_model.cond_stage_model([""])
 
 
-
 def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
@@ -495,19 +494,24 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     sd_model = None
     try:
         with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip):
-            sd_model = instantiate_from_config(sd_config.model)
-    except Exception:
-        pass
+            with sd_disable_initialization.InitializeOnMeta():
+                sd_model = instantiate_from_config(sd_config.model)
+
+    except Exception as e:
+        errors.display(e, "creating model quickly", full_traceback=True)
 
     if sd_model is None:
         print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
-        sd_model = instantiate_from_config(sd_config.model)
+
+        with sd_disable_initialization.InitializeOnMeta():
+            sd_model = instantiate_from_config(sd_config.model)
 
     sd_model.used_config = checkpoint_config
 
     timer.record("create model")
 
-    load_model_weights(sd_model, checkpoint_info, state_dict, timer)
+    with sd_disable_initialization.LoadStateDictOnMeta(state_dict, devices.cpu):
+        load_model_weights(sd_model, checkpoint_info, state_dict, timer)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
-- 
cgit v1.2.3


From 6b68b590321fcac2ad6d71c5aee1ac02687328d7 Mon Sep 17 00:00:00 2001
From: ljleb <set>
Date: Mon, 24 Jul 2023 15:38:52 -0400
Subject: use local vars

---
 modules/processing.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index c16404f4..7043477f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -722,20 +722,20 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         all_subseeds = p.all_subseeds[:]
 
         # apply changes to generation data
-        all_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.prompts
-        all_seeds[n * p.batch_size:(n + 1) * p.batch_size] = p.seeds
-        all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] = p.subseeds
+        all_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.prompts
+        all_seeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.seeds
+        all_subseeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.subseeds
 
         # update p.all_negative_prompts in case extensions changed the size of the batch
         # create_infotext below uses it
-        old_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-        p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.negative_prompts
+        old_negative_prompts = p.all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size]
+        p.all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
 
         try:
             return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
         finally:
             # restore p.all_negative_prompts in case extensions changed the size of the batch
-            p.all_negative_prompts[n * p.batch_size:n * p.batch_size + len(p.negative_prompts)] = old_negative_prompts
+            p.all_negative_prompts[iteration * p.batch_size:iteration * p.batch_size + len(p.negative_prompts)] = old_negative_prompts
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
-- 
cgit v1.2.3


From 5b06607476d1ef2c9d16fe8b21c786b2ca13b95c Mon Sep 17 00:00:00 2001
From: ljleb <set>
Date: Mon, 24 Jul 2023 15:43:06 -0400
Subject: simplify

---
 modules/processing.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 7043477f..6dc178e1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -718,24 +718,26 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
         all_prompts = p.all_prompts[:]
+        all_negative_prompts = p.all_negative_prompts[:]
         all_seeds = p.all_seeds[:]
         all_subseeds = p.all_subseeds[:]
 
         # apply changes to generation data
         all_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.prompts
+        all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
         all_seeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.seeds
         all_subseeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.subseeds
 
         # update p.all_negative_prompts in case extensions changed the size of the batch
         # create_infotext below uses it
-        old_negative_prompts = p.all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size]
-        p.all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
+        old_negative_prompts = p.all_negative_prompts
+        p.all_negative_prompts = all_negative_prompts
 
         try:
             return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
         finally:
             # restore p.all_negative_prompts in case extensions changed the size of the batch
-            p.all_negative_prompts[iteration * p.batch_size:iteration * p.batch_size + len(p.negative_prompts)] = old_negative_prompts
+            p.all_negative_prompts = old_negative_prompts
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
-- 
cgit v1.2.3


From fee593a07f5784bd0bc169535e7017edf22448b4 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Tue, 25 Jul 2023 20:01:10 +0900
Subject: catch exception for non git extensions

---
 modules/extensions.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/extensions.py b/modules/extensions.py
index c561159a..09d1e550 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -56,9 +56,11 @@ class Extension:
                 self.do_read_info_from_repo()
 
                 return self.to_dict()
-
-        d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
-        self.from_dict(d)
+        try:
+            d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
+            self.from_dict(d)
+        except FileNotFoundError:
+            pass
         self.status = 'unknown'
 
     def do_read_info_from_repo(self):
-- 
cgit v1.2.3


From b1a16a298c7f71e8e7d6254f16d4143f77b5c9a5 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Tue, 25 Jul 2023 20:36:39 +0900
Subject: api only subpath (rootpath)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-Authored-By: 陈杰 <pythias@gmail.com>
---
 modules/api/api.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 9d73083f..606db179 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -724,9 +724,9 @@ class Api:
             cuda = {'error': f'{err}'}
         return models.MemoryResponse(ram=ram, cuda=cuda)
 
-    def launch(self, server_name, port):
+    def launch(self, server_name, port, root_path):
         self.app.include_router(self.router)
-        uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive)
+        uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)
 
     def kill_webui(self):
         restart.stop_program()
-- 
cgit v1.2.3


From c5934fb6e3007795efcf9b71be6df1581f61e4cb Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 19 Jul 2023 16:26:12 +0300
Subject: Merge pull request #11878 from Bourne-M/patch-1
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

【bug】reload altclip model error
---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index f5615967..c8fdd4f1 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -243,7 +243,7 @@ class StableDiffusionModelHijack:
         ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
 
     def undo_hijack(self, m):
-        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+        if type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped
 
         elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
-- 
cgit v1.2.3


From 51206edb620b098f286cc33fab748ab90c5b20d4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Fri, 21 Jul 2023 18:00:03 +0300
Subject: Merge pull request #11921 from wfjsw/prepend-pythonpath

prepend the pythonpath instead of overriding it
---
 modules/launch_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 03552bc2..93838745 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -196,7 +196,7 @@ def run_extension_installer(extension_dir):
 
     try:
         env = os.environ.copy()
-        env['PYTHONPATH'] = os.path.abspath(".")
+        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env['PYTHONPATH']}"
 
         print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
     except Exception as e:
-- 
cgit v1.2.3


From 636ff513b0b5939f15456b82abd2e99951f6e3c1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Fri, 21 Jul 2023 18:01:20 +0300
Subject: Merge pull request #11920 from wfjsw/typo-fix-1

typo fix
---
 modules/launch_utils.py | 6 +++---
 modules/scripts.py      | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 93838745..93c8c8ca 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -233,7 +233,7 @@ def run_extensions_installers(settings_file):
 re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
 
 
-def requrements_met(requirements_file):
+def requirements_met(requirements_file):
     """
     Does a simple parse of a requirements.txt file to determine if all rerqirements in it
     are already installed. Returns True if so, False if not installed or parsing fails.
@@ -293,7 +293,7 @@ def prepare_environment():
     try:
         # the existance of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
         os.remove(os.path.join(script_path, "tmp", "restart"))
-        os.environ.setdefault('SD_WEBUI_RESTARTING ', '1')
+        os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
     except OSError:
         pass
 
@@ -354,7 +354,7 @@ def prepare_environment():
     if not os.path.isfile(requirements_file):
         requirements_file = os.path.join(script_path, requirements_file)
 
-    if not requrements_met(requirements_file):
+    if not requirements_met(requirements_file):
         run_pip(f"install -r \"{requirements_file}\"", "requirements")
 
     run_extensions_installers(settings_file=args.ui_settings_file)
diff --git a/modules/scripts.py b/modules/scripts.py
index 7d9dd59f..f34240a0 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -119,7 +119,7 @@ class Script:
 
     def after_extra_networks_activate(self, p, *args, **kwargs):
         """
-        Calledafter extra networks activation, before conds calculation
+        Called after extra networks activation, before conds calculation
         allow modification of the network after extra networks activation been applied
         won't be call if p.disable_extra_networks
 
-- 
cgit v1.2.3


From 704628b903fc7d54ad7cb9b0f0dd4296a5913c2d Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 22 Jul 2023 07:03:21 +0300
Subject: Merge pull request #11923 from AnyISalIn/dev

[bug] If txt2img/img2img raises an exception, finally call state.end()
---
 modules/api/api.py | 36 ++++++++++++++++++++----------------
 1 file changed, 20 insertions(+), 16 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 2a4cd8a2..9d73083f 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -333,14 +333,16 @@ class Api:
                 p.outpath_grids = opts.outdir_txt2img_grids
                 p.outpath_samples = opts.outdir_txt2img_samples
 
-                shared.state.begin(job="scripts_txt2img")
-                if selectable_scripts is not None:
-                    p.script_args = script_args
-                    processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
-                else:
-                    p.script_args = tuple(script_args) # Need to pass args as tuple here
-                    processed = process_images(p)
-                shared.state.end()
+                try:
+                    shared.state.begin(job="scripts_txt2img")
+                    if selectable_scripts is not None:
+                        p.script_args = script_args
+                        processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
+                    else:
+                        p.script_args = tuple(script_args) # Need to pass args as tuple here
+                        processed = process_images(p)
+                finally:
+                    shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
@@ -390,14 +392,16 @@ class Api:
                 p.outpath_grids = opts.outdir_img2img_grids
                 p.outpath_samples = opts.outdir_img2img_samples
 
-                shared.state.begin(job="scripts_img2img")
-                if selectable_scripts is not None:
-                    p.script_args = script_args
-                    processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
-                else:
-                    p.script_args = tuple(script_args) # Need to pass args as tuple here
-                    processed = process_images(p)
-                shared.state.end()
+                try:
+                    shared.state.begin(job="scripts_img2img")
+                    if selectable_scripts is not None:
+                        p.script_args = script_args
+                        processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
+                    else:
+                        p.script_args = tuple(script_args) # Need to pass args as tuple here
+                        processed = process_images(p)
+                finally:
+                    shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
-- 
cgit v1.2.3


From cad87bf4e3e0b0a759afa94e933527c3123d59bc Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 22 Jul 2023 16:36:40 +0300
Subject: Merge pull request #11927 from ljleb/fix-AND

Fix composable diffusion weight parsing
---
 modules/prompt_parser.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index b29d079d..203ae1ac 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -178,7 +178,7 @@ def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):
 
 
 re_AND = re.compile(r"\bAND\b")
-re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
+re_weight = re.compile(r"^((?:\s|.)*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
 
 
 def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
-- 
cgit v1.2.3


From 1f59be5188c0989db3bc8900f2c11b6706c07d70 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 22 Jul 2023 16:37:03 +0300
Subject: Merge pull request #11926 from wfjsw/fix-env-get-1

fix 11291#issuecomment-1646547908
---
 modules/launch_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 93c8c8ca..e1c9cfbe 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -196,7 +196,7 @@ def run_extension_installer(extension_dir):
 
     try:
         env = os.environ.copy()
-        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env['PYTHONPATH']}"
+        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH', '')}"
 
         print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
     except Exception as e:
-- 
cgit v1.2.3


From c251e8db8d71e649e4350f13aad1a76ed98d35c3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 24 Jul 2023 23:18:16 +0300
Subject: Merge pull request #11957 from ljleb/pp-batch-list

Add postprocess_batch_list script callback
---
 modules/processing.py | 26 +++++++++++++++++++++++++-
 modules/scripts.py    | 32 ++++++++++++++++++++++++++++++++
 2 files changed, 57 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index a74a5302..6dc178e1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -717,7 +717,27 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
-        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
+        all_prompts = p.all_prompts[:]
+        all_negative_prompts = p.all_negative_prompts[:]
+        all_seeds = p.all_seeds[:]
+        all_subseeds = p.all_subseeds[:]
+
+        # apply changes to generation data
+        all_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.prompts
+        all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
+        all_seeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.seeds
+        all_subseeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.subseeds
+
+        # update p.all_negative_prompts in case extensions changed the size of the batch
+        # create_infotext below uses it
+        old_negative_prompts = p.all_negative_prompts
+        p.all_negative_prompts = all_negative_prompts
+
+        try:
+            return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
+        finally:
+            # restore p.all_negative_prompts in case extensions changed the size of the batch
+            p.all_negative_prompts = old_negative_prompts
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
@@ -806,6 +826,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
+                postprocess_batch_list_args = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
+                p.scripts.postprocess_batch_list(p, postprocess_batch_list_args, batch_number=n)
+                x_samples_ddim = postprocess_batch_list_args.images
+
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
 
diff --git a/modules/scripts.py b/modules/scripts.py
index f34240a0..5b4edcac 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -16,6 +16,11 @@ class PostprocessImageArgs:
         self.image = image
 
 
+class PostprocessBatchListArgs:
+    def __init__(self, images):
+        self.images = images
+
+
 class Script:
     name = None
     """script's internal name derived from title"""
@@ -156,6 +161,25 @@ class Script:
 
         pass
 
+    def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, *args, **kwargs):
+        """
+        Same as postprocess_batch(), but receives batch images as a list of 3D tensors instead of a 4D tensor.
+        This is useful when you want to update the entire batch instead of individual images.
+
+        You can modify the postprocessing object (pp) to update the images in the batch, remove images, add images, etc.
+        If the number of images is different from the batch size when returning,
+        then the script has the responsibility to also update the following attributes in the processing object (p):
+          - p.prompts
+          - p.negative_prompts
+          - p.seeds
+          - p.subseeds
+
+        **kwargs will have same items as process_batch, and also:
+          - batch_number - index of current batch, from 0 to number of batches-1
+        """
+
+        pass
+
     def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
         """
         Called for every image after it has been generated.
@@ -536,6 +560,14 @@ class ScriptRunner:
             except Exception:
                 errors.report(f"Error running postprocess_batch: {script.filename}", exc_info=True)
 
+    def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, **kwargs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess_batch_list(p, pp, *script_args, **kwargs)
+            except Exception:
+                errors.report(f"Error running postprocess_batch_list: {script.filename}", exc_info=True)
+
     def postprocess_image(self, p, pp: PostprocessImageArgs):
         for script in self.alwayson_scripts:
             try:
-- 
cgit v1.2.3


From ce0aab36432c655c8cb48ccc2caf23d10d77cad1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 25 Jul 2023 15:19:10 +0300
Subject: Merge pull request #11984 from
 AUTOMATIC1111/api-only-subpath-(root_path)

api only subpath (rootpath)
---
 modules/api/api.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 9d73083f..606db179 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -724,9 +724,9 @@ class Api:
             cuda = {'error': f'{err}'}
         return models.MemoryResponse(ram=ram, cuda=cuda)
 
-    def launch(self, server_name, port):
+    def launch(self, server_name, port, root_path):
         self.app.include_router(self.router)
-        uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive)
+        uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)
 
     def kill_webui(self):
         restart.stop_program()
-- 
cgit v1.2.3


From 11f996a096e43092da0c7b8cf5b9d7a19de57828 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 25 Jul 2023 15:23:35 +0300
Subject: Merge pull request #11979 from
 AUTOMATIC1111/catch-exception-for-non-git-extensions

catch exception for non git extensions
---
 modules/extensions.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/extensions.py b/modules/extensions.py
index c561159a..09d1e550 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -56,9 +56,11 @@ class Extension:
                 self.do_read_info_from_repo()
 
                 return self.to_dict()
-
-        d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
-        self.from_dict(d)
+        try:
+            d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
+            self.from_dict(d)
+        except FileNotFoundError:
+            pass
         self.status = 'unknown'
 
     def do_read_info_from_repo(self):
-- 
cgit v1.2.3


From 8de6d3ff77e841a5fd9d5f1b16bdd22737c8d657 Mon Sep 17 00:00:00 2001
From: lambertae <dengm@mit.edu>
Date: Tue, 25 Jul 2023 22:35:43 -0400
Subject: fix progress bar & torchHijack

---
 modules/sd_samplers_kdiffusion.py | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index ed60670c..7a2427b5 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -79,19 +79,26 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
     for key, value in restart_list.items():
         temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
     restart_list = temp_list
-    for i in trange(len(sigmas) - 1, disable=disable):
-        x = heun_step(x, sigmas[i], sigmas[i+1])
+    step_list = []
+    for i in range(len(sigmas) - 1):
+        step_list.append((sigmas[i], sigmas[i + 1]))
         if i + 1 in restart_list:
             restart_steps, restart_times, restart_max = restart_list[i + 1]
             min_idx = i + 1
             max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
             if max_idx < min_idx:
-                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] # remove the zero at the end
+                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
                 while restart_times > 0:
                     restart_times -= 1
-                    x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
-                    for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):
-                        x = heun_step(x, old_sigma, new_sigma)
+                    step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
+    last_sigma = None
+    for i in trange(len(step_list), disable=disable):
+        if last_sigma is None:
+            last_sigma = step_list[i][0]
+        elif last_sigma < step_list[i][0]:
+            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5
+        x = heun_step(x, step_list[i][0], step_list[i][1])
+        last_sigma = step_list[i][1]
     return x
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.3


From b73c405013f63afa82d6358f9ce544931e5e9bc6 Mon Sep 17 00:00:00 2001
From: Littleor <me@littleor.cn>
Date: Wed, 26 Jul 2023 11:02:34 +0800
Subject: fix: error rendering name and description in extra network ui

---
 modules/ui_extra_networks.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 49612298..48537bc1 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -253,8 +253,8 @@ class ExtraNetworksPage:
             "prompt": item.get("prompt", None),
             "tabname": quote_js(tabname),
             "local_preview": quote_js(item["local_preview"]),
-            "name": item["name"],
-            "description": (item.get("description") or "" if shared.opts.extra_networks_card_show_desc else ""),
+            "name": html.escape(item["name"]),
+            "description": html.escape(item.get("description") or "" if shared.opts.extra_networks_card_show_desc else ""),
             "card_clicked": onclick,
             "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {quote_js(tabname)}, {quote_js(item["local_preview"])})""") + '"',
             "search_term": item.get("search_term", ""),
-- 
cgit v1.2.3


From ae36e0899fe912cd701fc4bae5c9d0ce9a5b3e41 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 06:36:06 +0300
Subject: alternative solution for infotext issue

---
 modules/processing.py | 62 ++++++++++++++++++++++-----------------------------
 modules/scripts.py    |  6 ++++-
 2 files changed, 32 insertions(+), 36 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 6dc178e1..146e409a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -600,8 +600,12 @@ def program_version():
     return res
 
 
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False):
-    index = position_in_batch + iteration * p.batch_size
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):
+    if index is None:
+        index = position_in_batch + iteration * p.batch_size
+
+    if all_negative_prompts is None:
+        all_negative_prompts = p.all_negative_prompts
 
     clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
     enable_hr = getattr(p, 'enable_hr', False)
@@ -642,7 +646,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
     prompt_text = p.prompt if use_main_prompt else all_prompts[index]
-    negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
+    negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""
 
     return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
 
@@ -716,29 +720,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     else:
         p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
-    def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
-        all_prompts = p.all_prompts[:]
-        all_negative_prompts = p.all_negative_prompts[:]
-        all_seeds = p.all_seeds[:]
-        all_subseeds = p.all_subseeds[:]
-
-        # apply changes to generation data
-        all_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.prompts
-        all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
-        all_seeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.seeds
-        all_subseeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.subseeds
-
-        # update p.all_negative_prompts in case extensions changed the size of the batch
-        # create_infotext below uses it
-        old_negative_prompts = p.all_negative_prompts
-        p.all_negative_prompts = all_negative_prompts
-
-        try:
-            return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
-        finally:
-            # restore p.all_negative_prompts in case extensions changed the size of the batch
-            p.all_negative_prompts = old_negative_prompts
-
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
@@ -826,9 +807,20 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
-                postprocess_batch_list_args = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
-                p.scripts.postprocess_batch_list(p, postprocess_batch_list_args, batch_number=n)
-                x_samples_ddim = postprocess_batch_list_args.images
+            batch_params = scripts.PostprocessBatchListArgs(
+                list(x_samples_ddim),
+                p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size],
+                p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size],
+                p.seeds,
+                p.subseeds,
+            )
+
+            if p.scripts is not None:
+                p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
+                x_samples_ddim = batch_params.images
+
+            def infotext(index=0, use_main_prompt=False):
+                return create_infotext(p, batch_params.prompts, batch_params.seeds, batch_params.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=batch_params.negative_prompts)
 
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
@@ -838,7 +830,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
                 if p.restore_faces:
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
-                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
+                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restoration")
 
                     devices.torch_gc()
 
@@ -855,15 +847,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
                         image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
-                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
                 image = apply_overlay(image, p.paste_to, i, p.overlay_images)
 
                 if opts.samples_save and not p.do_not_save_samples:
-                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p)
+                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
 
-                text = infotext(n, i)
+                text = infotext(i)
                 infotexts.append(text)
                 if opts.enable_pnginfo:
                     image.info["parameters"] = text
@@ -874,10 +866,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
 
                     if opts.save_mask:
-                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
 
                     if opts.save_mask_composite:
-                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
 
                     if opts.return_mask:
                         output_images.append(image_mask)
diff --git a/modules/scripts.py b/modules/scripts.py
index 5b4edcac..1049740d 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -17,8 +17,12 @@ class PostprocessImageArgs:
 
 
 class PostprocessBatchListArgs:
-    def __init__(self, images):
+    def __init__(self, images, prompts, negative_prompts, seeds, subseeds):
         self.images = images
+        self.prompts = prompts
+        self.negative_prompts = negative_prompts
+        self.seeds = seeds
+        self.subseeds = subseeds
 
 
 class Script:
-- 
cgit v1.2.3


From 13e371af73645934bd57178a6ced1e9480cc4300 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 06:37:13 +0300
Subject: doc update

---
 modules/scripts.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 1049740d..4317cbb6 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -172,11 +172,11 @@ class Script:
 
         You can modify the postprocessing object (pp) to update the images in the batch, remove images, add images, etc.
         If the number of images is different from the batch size when returning,
-        then the script has the responsibility to also update the following attributes in the processing object (p):
-          - p.prompts
-          - p.negative_prompts
-          - p.seeds
-          - p.subseeds
+        then the script has the responsibility to also update the following attributes in the processing object (pp):
+          - pp.prompts
+          - pp.negative_prompts
+          - pp.seeds
+          - pp.subseeds
 
         **kwargs will have same items as process_batch, and also:
           - batch_number - index of current batch, from 0 to number of batches-1
-- 
cgit v1.2.3


From 7c22bbd3ad5a149e0cf29df887405188fb2d0471 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 07:04:07 +0300
Subject: attempt 2

---
 modules/processing.py | 28 ++++++++++++++++------------
 1 file changed, 16 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 146e409a..e9108f11 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -621,12 +621,12 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Sampler": p.sampler_name,
         "CFG scale": p.cfg_scale,
         "Image CFG scale": getattr(p, 'image_cfg_scale', None),
-        "Seed": all_seeds[index],
+        "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
-        "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
+        "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
@@ -807,20 +807,24 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
-            batch_params = scripts.PostprocessBatchListArgs(
-                list(x_samples_ddim),
-                p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size],
-                p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size],
-                p.seeds,
-                p.subseeds,
-            )
+                batch_params = scripts.PostprocessBatchListArgs(
+                    list(x_samples_ddim),
+                    p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size],
+                    p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size],
+                    p.seeds,
+                    p.subseeds,
+                )
 
-            if p.scripts is not None:
                 p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
+
                 x_samples_ddim = batch_params.images
+                p.prompts = batch_params.prompts
+                p.negative_prompts = batch_params.negative_prompts
+                p.seeds = batch_params.seeds
+                p.subseeds = batch_params.subseeds
 
             def infotext(index=0, use_main_prompt=False):
-                return create_infotext(p, batch_params.prompts, batch_params.seeds, batch_params.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=batch_params.negative_prompts)
+                return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
 
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
@@ -910,7 +914,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         p,
         images_list=output_images,
         seed=p.all_seeds[0],
-        info=infotext(),
+        info=infotexts[0],
         comments="".join(f"{comment}\n" for comment in comments),
         subseed=p.all_subseeds[0],
         index_of_first_image=index_of_first_image,
-- 
cgit v1.2.3


From b8a903efbe76824da48747bec637db2470d1b25d Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Wed, 26 Jul 2023 13:43:38 +0900
Subject: fix check for updates status always "unknown"

---
 modules/extensions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extensions.py b/modules/extensions.py
index 09d1e550..3ad5ed53 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -61,7 +61,7 @@ class Extension:
             self.from_dict(d)
         except FileNotFoundError:
             pass
-        self.status = 'unknown'
+        self.status = 'unknown' if self.status == '' else self.status
 
     def do_read_info_from_repo(self):
         repo = None
-- 
cgit v1.2.3


From 835a7dbf0e73c4cdf945b588d319a6c36652cbe5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 07:49:57 +0300
Subject: simplify PostprocessBatchListArgs

---
 modules/processing.py | 15 +++------------
 modules/scripts.py    | 16 ++++++----------
 2 files changed, 9 insertions(+), 22 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index e9108f11..b0992ee1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -807,21 +807,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
-                batch_params = scripts.PostprocessBatchListArgs(
-                    list(x_samples_ddim),
-                    p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size],
-                    p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size],
-                    p.seeds,
-                    p.subseeds,
-                )
+                p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+                p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
 
+                batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
                 p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
-
                 x_samples_ddim = batch_params.images
-                p.prompts = batch_params.prompts
-                p.negative_prompts = batch_params.negative_prompts
-                p.seeds = batch_params.seeds
-                p.subseeds = batch_params.subseeds
 
             def infotext(index=0, use_main_prompt=False):
                 return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
diff --git a/modules/scripts.py b/modules/scripts.py
index 4317cbb6..5b4edcac 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -17,12 +17,8 @@ class PostprocessImageArgs:
 
 
 class PostprocessBatchListArgs:
-    def __init__(self, images, prompts, negative_prompts, seeds, subseeds):
+    def __init__(self, images):
         self.images = images
-        self.prompts = prompts
-        self.negative_prompts = negative_prompts
-        self.seeds = seeds
-        self.subseeds = subseeds
 
 
 class Script:
@@ -172,11 +168,11 @@ class Script:
 
         You can modify the postprocessing object (pp) to update the images in the batch, remove images, add images, etc.
         If the number of images is different from the batch size when returning,
-        then the script has the responsibility to also update the following attributes in the processing object (pp):
-          - pp.prompts
-          - pp.negative_prompts
-          - pp.seeds
-          - pp.subseeds
+        then the script has the responsibility to also update the following attributes in the processing object (p):
+          - p.prompts
+          - p.negative_prompts
+          - p.seeds
+          - p.subseeds
 
         **kwargs will have same items as process_batch, and also:
           - batch_number - index of current batch, from 0 to number of batches-1
-- 
cgit v1.2.3


From e16eb3d0cb5a7c7a6b73afc5b6ae11831b480931 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 07:45:48 +0300
Subject: Merge pull request #12024 from
 AUTOMATIC1111/fix-check-for-updates-status-always-unknown-

fix check for updates status always "unknown"
---
 modules/extensions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extensions.py b/modules/extensions.py
index 09d1e550..3ad5ed53 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -61,7 +61,7 @@ class Extension:
             self.from_dict(d)
         except FileNotFoundError:
             pass
-        self.status = 'unknown'
+        self.status = 'unknown' if self.status == '' else self.status
 
     def do_read_info_from_repo(self):
         repo = None
-- 
cgit v1.2.3


From 5360ae2cc57a093a8e26d2152635a05e0329d8fe Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 08:07:07 +0300
Subject: Merge pull request #12023 from AUTOMATIC1111/create_infotext_fix

Create infotext fix
---
 modules/processing.py | 63 ++++++++++++++++++++-------------------------------
 1 file changed, 25 insertions(+), 38 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 6dc178e1..b0992ee1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -600,8 +600,12 @@ def program_version():
     return res
 
 
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False):
-    index = position_in_batch + iteration * p.batch_size
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):
+    if index is None:
+        index = position_in_batch + iteration * p.batch_size
+
+    if all_negative_prompts is None:
+        all_negative_prompts = p.all_negative_prompts
 
     clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
     enable_hr = getattr(p, 'enable_hr', False)
@@ -617,12 +621,12 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Sampler": p.sampler_name,
         "CFG scale": p.cfg_scale,
         "Image CFG scale": getattr(p, 'image_cfg_scale', None),
-        "Seed": all_seeds[index],
+        "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
-        "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
+        "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
@@ -642,7 +646,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
     prompt_text = p.prompt if use_main_prompt else all_prompts[index]
-    negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
+    negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""
 
     return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
 
@@ -716,29 +720,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     else:
         p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
-    def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
-        all_prompts = p.all_prompts[:]
-        all_negative_prompts = p.all_negative_prompts[:]
-        all_seeds = p.all_seeds[:]
-        all_subseeds = p.all_subseeds[:]
-
-        # apply changes to generation data
-        all_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.prompts
-        all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
-        all_seeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.seeds
-        all_subseeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.subseeds
-
-        # update p.all_negative_prompts in case extensions changed the size of the batch
-        # create_infotext below uses it
-        old_negative_prompts = p.all_negative_prompts
-        p.all_negative_prompts = all_negative_prompts
-
-        try:
-            return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
-        finally:
-            # restore p.all_negative_prompts in case extensions changed the size of the batch
-            p.all_negative_prompts = old_negative_prompts
-
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
@@ -826,9 +807,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
-                postprocess_batch_list_args = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
-                p.scripts.postprocess_batch_list(p, postprocess_batch_list_args, batch_number=n)
-                x_samples_ddim = postprocess_batch_list_args.images
+                p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+                p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+
+                batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
+                p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
+                x_samples_ddim = batch_params.images
+
+            def infotext(index=0, use_main_prompt=False):
+                return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
 
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
@@ -838,7 +825,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
                 if p.restore_faces:
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
-                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
+                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restoration")
 
                     devices.torch_gc()
 
@@ -855,15 +842,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
                         image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
-                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
                 image = apply_overlay(image, p.paste_to, i, p.overlay_images)
 
                 if opts.samples_save and not p.do_not_save_samples:
-                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p)
+                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
 
-                text = infotext(n, i)
+                text = infotext(i)
                 infotexts.append(text)
                 if opts.enable_pnginfo:
                     image.info["parameters"] = text
@@ -874,10 +861,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
 
                     if opts.save_mask:
-                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
 
                     if opts.save_mask_composite:
-                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
 
                     if opts.return_mask:
                         output_images.append(image_mask)
@@ -918,7 +905,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         p,
         images_list=output_images,
         seed=p.all_seeds[0],
-        info=infotext(),
+        info=infotexts[0],
         comments="".join(f"{comment}\n" for comment in comments),
         subseed=p.all_subseeds[0],
         index_of_first_image=index_of_first_image,
-- 
cgit v1.2.3


From 6305632493406ff078a2a3fd22c06859fee260d4 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Wed, 26 Jul 2023 16:47:12 +0900
Subject: use "Any" type when type is None

---
 modules/api/models.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index bf97b1a3..800c9b93 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -208,11 +208,9 @@ class PreprocessResponse(BaseModel):
 fields = {}
 for key, metadata in opts.data_labels.items():
     value = opts.data.get(key)
-    optType = opts.typemap.get(type(metadata.default), type(metadata.default))
+    optType = opts.typemap.get(type(metadata.default), type(metadata.default)) if metadata.default else Any
 
-    if metadata.default is None:
-        pass
-    elif metadata is not None:
+    if metadata is not None:
         fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))})
     else:
         fields.update({key: (Optional[optType], Field())})
-- 
cgit v1.2.3


From 6b877c35da12029106062bfa8dfefb6a565f13ff Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 11:52:42 +0300
Subject: Merge pull request #12032 from
 AUTOMATIC1111/fix-api-get-options-sd_model_checkpoint

api /sdapi/v1/options use "Any" type when default type is None
---
 modules/api/models.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index bf97b1a3..800c9b93 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -208,11 +208,9 @@ class PreprocessResponse(BaseModel):
 fields = {}
 for key, metadata in opts.data_labels.items():
     value = opts.data.get(key)
-    optType = opts.typemap.get(type(metadata.default), type(metadata.default))
+    optType = opts.typemap.get(type(metadata.default), type(metadata.default)) if metadata.default else Any
 
-    if metadata.default is None:
-        pass
-    elif metadata is not None:
+    if metadata is not None:
         fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))})
     else:
         fields.update({key: (Optional[optType], Field())})
-- 
cgit v1.2.3


From 187323a606bc0e4913240e5f20c51c9789234654 Mon Sep 17 00:00:00 2001
From: Littleor <me@littleor.cn>
Date: Wed, 26 Jul 2023 17:23:57 +0800
Subject: fix: extra network ui description allow HTML tags

---
 modules/ui_extra_networks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 48537bc1..f2752f10 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -254,7 +254,7 @@ class ExtraNetworksPage:
             "tabname": quote_js(tabname),
             "local_preview": quote_js(item["local_preview"]),
             "name": html.escape(item["name"]),
-            "description": html.escape(item.get("description") or "" if shared.opts.extra_networks_card_show_desc else ""),
+            "description": (item.get("description") or "" if shared.opts.extra_networks_card_show_desc else ""),
             "card_clicked": onclick,
             "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {quote_js(tabname)}, {quote_js(item["local_preview"])})""") + '"',
             "search_term": item.get("search_term", ""),
-- 
cgit v1.2.3


From 8284ebd94c4d4f7e7141ebb47af206faab66ffed Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 13:03:52 +0300
Subject: fix autograd which i broke for no good reason when implementing SDXL

---
 modules/sd_hijack_clip.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 5443e609..990533fe 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -273,9 +273,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
         original_mean = z.mean()
-        z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
+        z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
         new_mean = z.mean()
-        z *= (original_mean / new_mean)
+        z = z * (original_mean / new_mean)
 
         return z
 
-- 
cgit v1.2.3


From 5c8f91b22975701af22d24f947af82e7d23264d5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 13:03:52 +0300
Subject: fix autograd which i broke for no good reason when implementing SDXL

---
 modules/sd_hijack_clip.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 5443e609..990533fe 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -273,9 +273,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
         original_mean = z.mean()
-        z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
+        z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
         new_mean = z.mean()
-        z *= (original_mean / new_mean)
+        z = z * (original_mean / new_mean)
 
         return z
 
-- 
cgit v1.2.3


From 89e6dfff717e2c2e57009fdabb0ed2b4f57b98a2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 15:07:56 +0300
Subject: repair SDXL

---
 modules/sd_hijack_clip.py | 5 +++++
 1 file changed, 5 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 990533fe..16a5500e 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -270,6 +270,8 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
 
         z = self.encode_with_transformers(tokens)
 
+        pooled = getattr(z, 'pooled', None)
+
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
         original_mean = z.mean()
@@ -277,6 +279,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         new_mean = z.mean()
         z = z * (original_mean / new_mean)
 
+        if pooled is not None:
+            z.pooled = pooled
+
         return z
 
 
-- 
cgit v1.2.3


From 055461ae41436e0fcbdf9f5c6b82c9441c6b5b5f Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 15:07:56 +0300
Subject: repair SDXL

---
 modules/sd_hijack_clip.py | 5 +++++
 1 file changed, 5 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 990533fe..16a5500e 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -270,6 +270,8 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
 
         z = self.encode_with_transformers(tokens)
 
+        pooled = getattr(z, 'pooled', None)
+
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
         original_mean = z.mean()
@@ -277,6 +279,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         new_mean = z.mean()
         z = z * (original_mean / new_mean)
 
+        if pooled is not None:
+            z.pooled = pooled
+
         return z
 
 
-- 
cgit v1.2.3


From 8220cf37da8130980b036535bc2fba6b89417158 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 26 Jul 2023 15:17:37 +0300
Subject: Merge pull request #12020 from Littleor/dev

Fix the error in rendering the name and description in the extra network UI.
---
 modules/ui_extra_networks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 49612298..f2752f10 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -253,7 +253,7 @@ class ExtraNetworksPage:
             "prompt": item.get("prompt", None),
             "tabname": quote_js(tabname),
             "local_preview": quote_js(item["local_preview"]),
-            "name": item["name"],
+            "name": html.escape(item["name"]),
             "description": (item.get("description") or "" if shared.opts.extra_networks_card_show_desc else ""),
             "card_clicked": onclick,
             "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {quote_js(tabname)}, {quote_js(item["local_preview"])})""") + '"',
-- 
cgit v1.2.3


From 9cbf3461f7ecb7aa556a4683912f171be9254480 Mon Sep 17 00:00:00 2001
From: DiabolicDiabetic <41561319+DiabolicDiabetic@users.noreply.github.com>
Date: Thu, 27 Jul 2023 20:15:50 -0500
Subject: IMG2IMG TIF batch fix img2img.py

IMG2IMG batch tab wouldn't process tif images
---
 modules/img2img.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index a811e7a4..132cd100 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -20,7 +20,7 @@ import modules.scripts
 def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
     processing.fix_seed(p)
 
-    images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp")))
+    images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
 
     is_inpaint_batch = False
     if inpaint_mask_dir:
-- 
cgit v1.2.3


From 6cc5a886ae9ff30148c43a95904accd191922afc Mon Sep 17 00:00:00 2001
From: caoxipeng <caoxipeng@kuaishou.com>
Date: Fri, 28 Jul 2023 11:40:10 +0800
Subject: Add total_tqdm clear in the end of txt2img & img2img api.

---
 modules/api/api.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 606db179..909ea976 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -343,6 +343,7 @@ class Api:
                         processed = process_images(p)
                 finally:
                     shared.state.end()
+                    shared.total_tqdm.clear()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
@@ -402,6 +403,7 @@ class Api:
                         processed = process_images(p)
                 finally:
                     shared.state.end()
+                    shared.total_tqdm.clear()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
-- 
cgit v1.2.3


From 3ac950248df33d85b0a1ef82f7fa29ca36024649 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 29 Jul 2023 08:06:03 +0300
Subject: Split history: mv modules/sd_samplers_kdiffusion.py
 modules/sd_samplers_extra.py

---
 modules/sd_samplers_extra.py      | 545 ++++++++++++++++++++++++++++++++++++++
 modules/sd_samplers_kdiffusion.py | 545 --------------------------------------
 2 files changed, 545 insertions(+), 545 deletions(-)
 create mode 100644 modules/sd_samplers_extra.py
 delete mode 100644 modules/sd_samplers_kdiffusion.py

(limited to 'modules')

diff --git a/modules/sd_samplers_extra.py b/modules/sd_samplers_extra.py
new file mode 100644
index 00000000..a54673eb
--- /dev/null
+++ b/modules/sd_samplers_extra.py
@@ -0,0 +1,545 @@
+from collections import deque
+import torch
+import inspect
+import k_diffusion.sampling
+from modules import prompt_parser, devices, sd_samplers_common
+
+from modules.shared import opts, state
+import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
+from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
+from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
+
+samplers_k_diffusion = [
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
+    ('Euler', 'sample_euler', ['k_euler'], {}),
+    ('LMS', 'sample_lms', ['k_lms'], {}),
+    ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
+    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
+    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
+    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
+    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
+    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
+    ('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}),
+]
+
+
+@torch.no_grad()
+def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None):
+    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
+    '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
+    '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list'''
+    from tqdm.auto import trange
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    step_id = 0
+    from k_diffusion.sampling import to_d, get_sigmas_karras
+    def heun_step(x, old_sigma, new_sigma, second_order = True):
+        nonlocal step_id
+        denoised = model(x, old_sigma * s_in, **extra_args)
+        d = to_d(x, old_sigma, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
+        dt = new_sigma - old_sigma
+        if new_sigma == 0 or not second_order:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Heun's method
+            x_2 = x + d * dt
+            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
+            d_2 = to_d(x_2, new_sigma, denoised_2)
+            d_prime = (d + d_2) / 2
+            x = x + d_prime * dt
+        step_id += 1
+        return x
+    steps = sigmas.shape[0] - 1
+    if restart_list is None:
+        if steps >= 20:
+            restart_steps = 9
+            restart_times = 1
+            if steps >= 36:
+                restart_steps = steps // 4
+                restart_times = 2
+            sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
+            restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
+        else:
+            restart_list = dict()
+    temp_list = dict()
+    for key, value in restart_list.items():
+        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
+    restart_list = temp_list
+    step_list = []
+    for i in range(len(sigmas) - 1):
+        step_list.append((sigmas[i], sigmas[i + 1]))
+        if i + 1 in restart_list:
+            restart_steps, restart_times, restart_max = restart_list[i + 1]
+            min_idx = i + 1
+            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
+            if max_idx < min_idx:
+                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
+                while restart_times > 0:
+                    restart_times -= 1
+                    step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
+    last_sigma = None
+    for i in trange(len(step_list), disable=disable):
+        if last_sigma is None:
+            last_sigma = step_list[i][0]
+        elif last_sigma < step_list[i][0]:
+            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5
+        x = heun_step(x, step_list[i][0], step_list[i][1])
+        last_sigma = step_list[i][1]
+    return x
+
+samplers_data_k_diffusion = [
+    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+    for label, funcname, aliases, options in samplers_k_diffusion
+    if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler')
+]
+
+sampler_extra_params = {
+    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+}
+
+k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
+k_diffusion_scheduler = {
+    'Automatic': None,
+    'karras': k_diffusion.sampling.get_sigmas_karras,
+    'exponential': k_diffusion.sampling.get_sigmas_exponential,
+    'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
+}
+
+
+def catenate_conds(conds):
+    if not isinstance(conds[0], dict):
+        return torch.cat(conds)
+
+    return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
+
+
+def subscript_cond(cond, a, b):
+    if not isinstance(cond, dict):
+        return cond[a:b]
+
+    return {key: vec[a:b] for key, vec in cond.items()}
+
+
+def pad_cond(tensor, repeats, empty):
+    if not isinstance(tensor, dict):
+        return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
+
+    tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
+    return tensor
+
+
+class CFGDenoiser(torch.nn.Module):
+    """
+    Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
+    that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
+    instead of one. Originally, the second prompt is just an empty string, but we use non-empty
+    negative prompt.
+    """
+
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+        self.step = 0
+        self.image_cfg_scale = None
+        self.padded_cond_uncond = False
+
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+        return denoised
+
+    def combine_denoised_for_edit_model(self, x_out, cond_scale):
+        out_cond, out_img_cond, out_uncond = x_out.chunk(3)
+        denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
+
+        return denoised
+
+    def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
+        if state.interrupted or state.skipped:
+            raise sd_samplers_common.InterruptedException
+
+        # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
+        # so is_edit_model is set to False to support AND composition.
+        is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
+
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+
+        assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+
+        batch_size = len(conds_list)
+        repeats = [len(conds_list[i]) for i in range(batch_size)]
+
+        if shared.sd_model.model.conditioning_key == "crossattn-adm":
+            image_uncond = torch.zeros_like(image_cond)
+            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
+        else:
+            image_uncond = image_cond
+            if isinstance(uncond, dict):
+                make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
+            else:
+                make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
+
+        if not is_edit_model:
+            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
+        else:
+            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
+            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
+
+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
+        cfg_denoiser_callback(denoiser_params)
+        x_in = denoiser_params.x
+        image_cond_in = denoiser_params.image_cond
+        sigma_in = denoiser_params.sigma
+        tensor = denoiser_params.text_cond
+        uncond = denoiser_params.text_uncond
+        skip_uncond = False
+
+        # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
+        if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+            skip_uncond = True
+            x_in = x_in[:-batch_size]
+            sigma_in = sigma_in[:-batch_size]
+
+        self.padded_cond_uncond = False
+        if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
+            empty = shared.sd_model.cond_stage_model_empty_prompt
+            num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
+
+            if num_repeats < 0:
+                tensor = pad_cond(tensor, -num_repeats, empty)
+                self.padded_cond_uncond = True
+            elif num_repeats > 0:
+                uncond = pad_cond(uncond, num_repeats, empty)
+                self.padded_cond_uncond = True
+
+        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+            if is_edit_model:
+                cond_in = catenate_conds([tensor, uncond, uncond])
+            elif skip_uncond:
+                cond_in = tensor
+            else:
+                cond_in = catenate_conds([tensor, uncond])
+
+            if shared.batch_cond_uncond:
+                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
+            else:
+                x_out = torch.zeros_like(x_in)
+                for batch_offset in range(0, x_out.shape[0], batch_size):
+                    a = batch_offset
+                    b = a + batch_size
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
+        else:
+            x_out = torch.zeros_like(x_in)
+            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
+            for batch_offset in range(0, tensor.shape[0], batch_size):
+                a = batch_offset
+                b = min(a + batch_size, tensor.shape[0])
+
+                if not is_edit_model:
+                    c_crossattn = subscript_cond(tensor, a, b)
+                else:
+                    c_crossattn = torch.cat([tensor[a:b]], uncond)
+
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
+
+            if not skip_uncond:
+                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
+
+        denoised_image_indexes = [x[0][0] for x in conds_list]
+        if skip_uncond:
+            fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
+            x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
+
+        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
+        cfg_denoised_callback(denoised_params)
+
+        devices.test_for_nans(x_out, "unet")
+
+        if opts.live_preview_content == "Prompt":
+            sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
+        elif opts.live_preview_content == "Negative prompt":
+            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
+
+        if is_edit_model:
+            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
+        elif skip_uncond:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
+        else:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+
+        if self.mask is not None:
+            denoised = self.init_latent * self.mask + self.nmask * denoised
+
+        after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
+        cfg_after_cfg_callback(after_cfg_callback_params)
+        denoised = after_cfg_callback_params.x
+
+        self.step += 1
+        return denoised
+
+
+class TorchHijack:
+    def __init__(self, sampler_noises):
+        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
+        # implementation.
+        self.sampler_noises = deque(sampler_noises)
+
+    def __getattr__(self, item):
+        if item == 'randn_like':
+            return self.randn_like
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
+
+    def randn_like(self, x):
+        if self.sampler_noises:
+            noise = self.sampler_noises.popleft()
+            if noise.shape == x.shape:
+                return noise
+
+        if opts.randn_source == "CPU" or x.device.type == 'mps':
+            return torch.randn_like(x, device=devices.cpu).to(x.device)
+        else:
+            return torch.randn_like(x)
+
+
+class KDiffusionSampler:
+    def __init__(self, funcname, sd_model):
+        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+
+        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
+        self.funcname = funcname
+        self.func = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler
+        self.extra_params = sampler_extra_params.get(funcname, [])
+        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
+        self.sampler_noises = None
+        self.stop_at = None
+        self.eta = None
+        self.config = None  # set by the function calling the constructor
+        self.last_latent = None
+        self.s_min_uncond = None
+
+        self.conditioning_key = sd_model.model.conditioning_key
+
+    def callback_state(self, d):
+        step = d['i']
+        latent = d["denoised"]
+        if opts.live_preview_content == "Combined":
+            sd_samplers_common.store_latent(latent)
+        self.last_latent = latent
+
+        if self.stop_at is not None and step > self.stop_at:
+            raise sd_samplers_common.InterruptedException
+
+        state.sampling_step = step
+        shared.total_tqdm.update()
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except RecursionError:
+            print(
+                'Encountered RecursionError during sampling, returning last latent. '
+                'rho >5 with a polyexponential scheduler may cause this error. '
+                'You should try to use a smaller rho value instead.'
+            )
+            return self.last_latent
+        except sd_samplers_common.InterruptedException:
+            return self.last_latent
+
+    def number_of_needed_noises(self, p):
+        return p.steps
+
+    def initialize(self, p):
+        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
+        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
+        self.model_wrap_cfg.step = 0
+        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
+        self.eta = p.eta if p.eta is not None else opts.eta_ancestral
+        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
+
+        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
+
+        extra_params_kwargs = {}
+        for param_name in self.extra_params:
+            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+                extra_params_kwargs[param_name] = getattr(p, param_name)
+
+        if 'eta' in inspect.signature(self.func).parameters:
+            if self.eta != 1.0:
+                p.extra_generation_params["Eta"] = self.eta
+
+            extra_params_kwargs['eta'] = self.eta
+
+        return extra_params_kwargs
+
+    def get_sigmas(self, p, steps):
+        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
+        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
+            discard_next_to_last_sigma = True
+            p.extra_generation_params["Discard penultimate sigma"] = True
+
+        steps += 1 if discard_next_to_last_sigma else 0
+
+        if p.sampler_noise_scheduler_override:
+            sigmas = p.sampler_noise_scheduler_override(steps)
+        elif opts.k_sched_type != "Automatic":
+            m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
+            sigmas_kwargs = {
+                'sigma_min': sigma_min,
+                'sigma_max': sigma_max,
+            }
+
+            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+            p.extra_generation_params["Schedule type"] = opts.k_sched_type
+
+            if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
+                sigmas_kwargs['sigma_min'] = opts.sigma_min
+                p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
+            if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
+                sigmas_kwargs['sigma_max'] = opts.sigma_max
+                p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
+
+            default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
+
+            if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
+                sigmas_kwargs['rho'] = opts.rho
+                p.extra_generation_params["Schedule rho"] = opts.rho
+
+            sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+        else:
+            sigmas = self.model_wrap.get_sigmas(steps)
+
+        if discard_next_to_last_sigma:
+            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
+        return sigmas
+
+    def create_noise_sampler(self, x, sigmas, p):
+        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
+        if shared.opts.no_dpmpp_sde_batch_determinism:
+            return None
+
+        from k_diffusion.sampling import BrownianTreeNoiseSampler
+        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
+        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
+
+        sigmas = self.get_sigmas(p, steps)
+
+        sigma_sched = sigmas[steps - t_enc - 1:]
+        xi = x + noise * sigma_sched[0]
+
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'sigma_min' in parameters:
+            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
+            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
+        if 'sigma_max' in parameters:
+            extra_params_kwargs['sigma_max'] = sigma_sched[0]
+        if 'n' in parameters:
+            extra_params_kwargs['n'] = len(sigma_sched) - 1
+        if 'sigma_sched' in parameters:
+            extra_params_kwargs['sigma_sched'] = sigma_sched
+        if 'sigmas' in parameters:
+            extra_params_kwargs['sigmas'] = sigma_sched
+
+        if self.config.options.get('brownian_noise', False):
+            noise_sampler = self.create_noise_sampler(x, sigmas, p)
+            extra_params_kwargs['noise_sampler'] = noise_sampler
+
+        self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
+        extra_args = {
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
+        }
+
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
+        return samples
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps = steps or p.steps
+
+        sigmas = self.get_sigmas(p, steps)
+
+        x = x * sigmas[0]
+
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'sigma_min' in parameters:
+            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
+            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
+            if 'n' in parameters:
+                extra_params_kwargs['n'] = steps
+        else:
+            extra_params_kwargs['sigmas'] = sigmas
+
+        if self.config.options.get('brownian_noise', False):
+            noise_sampler = self.create_noise_sampler(x, sigmas, p)
+            extra_params_kwargs['noise_sampler'] = noise_sampler
+
+        self.last_latent = x
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
+        return samples
+
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
deleted file mode 100644
index a54673eb..00000000
--- a/modules/sd_samplers_kdiffusion.py
+++ /dev/null
@@ -1,545 +0,0 @@
-from collections import deque
-import torch
-import inspect
-import k_diffusion.sampling
-from modules import prompt_parser, devices, sd_samplers_common
-
-from modules.shared import opts, state
-import modules.shared as shared
-from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
-from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
-from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
-
-samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
-    ('Euler', 'sample_euler', ['k_euler'], {}),
-    ('LMS', 'sample_lms', ['k_lms'], {}),
-    ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
-    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
-    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
-    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
-    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
-    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
-    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
-    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
-    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
-    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
-    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
-    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
-    ('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}),
-]
-
-
-@torch.no_grad()
-def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None):
-    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
-    '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
-    '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list'''
-    from tqdm.auto import trange
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    step_id = 0
-    from k_diffusion.sampling import to_d, get_sigmas_karras
-    def heun_step(x, old_sigma, new_sigma, second_order = True):
-        nonlocal step_id
-        denoised = model(x, old_sigma * s_in, **extra_args)
-        d = to_d(x, old_sigma, denoised)
-        if callback is not None:
-            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
-        dt = new_sigma - old_sigma
-        if new_sigma == 0 or not second_order:
-            # Euler method
-            x = x + d * dt
-        else:
-            # Heun's method
-            x_2 = x + d * dt
-            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
-            d_2 = to_d(x_2, new_sigma, denoised_2)
-            d_prime = (d + d_2) / 2
-            x = x + d_prime * dt
-        step_id += 1
-        return x
-    steps = sigmas.shape[0] - 1
-    if restart_list is None:
-        if steps >= 20:
-            restart_steps = 9
-            restart_times = 1
-            if steps >= 36:
-                restart_steps = steps // 4
-                restart_times = 2
-            sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
-            restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
-        else:
-            restart_list = dict()
-    temp_list = dict()
-    for key, value in restart_list.items():
-        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
-    restart_list = temp_list
-    step_list = []
-    for i in range(len(sigmas) - 1):
-        step_list.append((sigmas[i], sigmas[i + 1]))
-        if i + 1 in restart_list:
-            restart_steps, restart_times, restart_max = restart_list[i + 1]
-            min_idx = i + 1
-            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
-            if max_idx < min_idx:
-                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
-                while restart_times > 0:
-                    restart_times -= 1
-                    step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
-    last_sigma = None
-    for i in trange(len(step_list), disable=disable):
-        if last_sigma is None:
-            last_sigma = step_list[i][0]
-        elif last_sigma < step_list[i][0]:
-            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5
-        x = heun_step(x, step_list[i][0], step_list[i][1])
-        last_sigma = step_list[i][1]
-    return x
-
-samplers_data_k_diffusion = [
-    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
-    for label, funcname, aliases, options in samplers_k_diffusion
-    if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler')
-]
-
-sampler_extra_params = {
-    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-}
-
-k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
-k_diffusion_scheduler = {
-    'Automatic': None,
-    'karras': k_diffusion.sampling.get_sigmas_karras,
-    'exponential': k_diffusion.sampling.get_sigmas_exponential,
-    'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
-}
-
-
-def catenate_conds(conds):
-    if not isinstance(conds[0], dict):
-        return torch.cat(conds)
-
-    return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
-
-
-def subscript_cond(cond, a, b):
-    if not isinstance(cond, dict):
-        return cond[a:b]
-
-    return {key: vec[a:b] for key, vec in cond.items()}
-
-
-def pad_cond(tensor, repeats, empty):
-    if not isinstance(tensor, dict):
-        return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
-
-    tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
-    return tensor
-
-
-class CFGDenoiser(torch.nn.Module):
-    """
-    Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
-    that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
-    instead of one. Originally, the second prompt is just an empty string, but we use non-empty
-    negative prompt.
-    """
-
-    def __init__(self, model):
-        super().__init__()
-        self.inner_model = model
-        self.mask = None
-        self.nmask = None
-        self.init_latent = None
-        self.step = 0
-        self.image_cfg_scale = None
-        self.padded_cond_uncond = False
-
-    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
-        denoised_uncond = x_out[-uncond.shape[0]:]
-        denoised = torch.clone(denoised_uncond)
-
-        for i, conds in enumerate(conds_list):
-            for cond_index, weight in conds:
-                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
-
-        return denoised
-
-    def combine_denoised_for_edit_model(self, x_out, cond_scale):
-        out_cond, out_img_cond, out_uncond = x_out.chunk(3)
-        denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
-
-        return denoised
-
-    def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
-        if state.interrupted or state.skipped:
-            raise sd_samplers_common.InterruptedException
-
-        # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
-        # so is_edit_model is set to False to support AND composition.
-        is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
-
-        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
-
-        assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
-
-        batch_size = len(conds_list)
-        repeats = [len(conds_list[i]) for i in range(batch_size)]
-
-        if shared.sd_model.model.conditioning_key == "crossattn-adm":
-            image_uncond = torch.zeros_like(image_cond)
-            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
-        else:
-            image_uncond = image_cond
-            if isinstance(uncond, dict):
-                make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
-            else:
-                make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
-
-        if not is_edit_model:
-            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
-            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
-        else:
-            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
-            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
-
-        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
-        cfg_denoiser_callback(denoiser_params)
-        x_in = denoiser_params.x
-        image_cond_in = denoiser_params.image_cond
-        sigma_in = denoiser_params.sigma
-        tensor = denoiser_params.text_cond
-        uncond = denoiser_params.text_uncond
-        skip_uncond = False
-
-        # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
-        if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
-            skip_uncond = True
-            x_in = x_in[:-batch_size]
-            sigma_in = sigma_in[:-batch_size]
-
-        self.padded_cond_uncond = False
-        if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
-            empty = shared.sd_model.cond_stage_model_empty_prompt
-            num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
-
-            if num_repeats < 0:
-                tensor = pad_cond(tensor, -num_repeats, empty)
-                self.padded_cond_uncond = True
-            elif num_repeats > 0:
-                uncond = pad_cond(uncond, num_repeats, empty)
-                self.padded_cond_uncond = True
-
-        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
-            if is_edit_model:
-                cond_in = catenate_conds([tensor, uncond, uncond])
-            elif skip_uncond:
-                cond_in = tensor
-            else:
-                cond_in = catenate_conds([tensor, uncond])
-
-            if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
-            else:
-                x_out = torch.zeros_like(x_in)
-                for batch_offset in range(0, x_out.shape[0], batch_size):
-                    a = batch_offset
-                    b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
-        else:
-            x_out = torch.zeros_like(x_in)
-            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
-            for batch_offset in range(0, tensor.shape[0], batch_size):
-                a = batch_offset
-                b = min(a + batch_size, tensor.shape[0])
-
-                if not is_edit_model:
-                    c_crossattn = subscript_cond(tensor, a, b)
-                else:
-                    c_crossattn = torch.cat([tensor[a:b]], uncond)
-
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
-
-            if not skip_uncond:
-                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
-
-        denoised_image_indexes = [x[0][0] for x in conds_list]
-        if skip_uncond:
-            fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
-            x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
-
-        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
-        cfg_denoised_callback(denoised_params)
-
-        devices.test_for_nans(x_out, "unet")
-
-        if opts.live_preview_content == "Prompt":
-            sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
-        elif opts.live_preview_content == "Negative prompt":
-            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
-
-        if is_edit_model:
-            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
-        elif skip_uncond:
-            denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
-        else:
-            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-
-        if self.mask is not None:
-            denoised = self.init_latent * self.mask + self.nmask * denoised
-
-        after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
-        cfg_after_cfg_callback(after_cfg_callback_params)
-        denoised = after_cfg_callback_params.x
-
-        self.step += 1
-        return denoised
-
-
-class TorchHijack:
-    def __init__(self, sampler_noises):
-        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
-        # implementation.
-        self.sampler_noises = deque(sampler_noises)
-
-    def __getattr__(self, item):
-        if item == 'randn_like':
-            return self.randn_like
-
-        if hasattr(torch, item):
-            return getattr(torch, item)
-
-        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
-
-    def randn_like(self, x):
-        if self.sampler_noises:
-            noise = self.sampler_noises.popleft()
-            if noise.shape == x.shape:
-                return noise
-
-        if opts.randn_source == "CPU" or x.device.type == 'mps':
-            return torch.randn_like(x, device=devices.cpu).to(x.device)
-        else:
-            return torch.randn_like(x)
-
-
-class KDiffusionSampler:
-    def __init__(self, funcname, sd_model):
-        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-
-        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
-        self.funcname = funcname
-        self.func = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler
-        self.extra_params = sampler_extra_params.get(funcname, [])
-        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
-        self.sampler_noises = None
-        self.stop_at = None
-        self.eta = None
-        self.config = None  # set by the function calling the constructor
-        self.last_latent = None
-        self.s_min_uncond = None
-
-        self.conditioning_key = sd_model.model.conditioning_key
-
-    def callback_state(self, d):
-        step = d['i']
-        latent = d["denoised"]
-        if opts.live_preview_content == "Combined":
-            sd_samplers_common.store_latent(latent)
-        self.last_latent = latent
-
-        if self.stop_at is not None and step > self.stop_at:
-            raise sd_samplers_common.InterruptedException
-
-        state.sampling_step = step
-        shared.total_tqdm.update()
-
-    def launch_sampling(self, steps, func):
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except RecursionError:
-            print(
-                'Encountered RecursionError during sampling, returning last latent. '
-                'rho >5 with a polyexponential scheduler may cause this error. '
-                'You should try to use a smaller rho value instead.'
-            )
-            return self.last_latent
-        except sd_samplers_common.InterruptedException:
-            return self.last_latent
-
-    def number_of_needed_noises(self, p):
-        return p.steps
-
-    def initialize(self, p):
-        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
-        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
-        self.model_wrap_cfg.step = 0
-        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
-        self.eta = p.eta if p.eta is not None else opts.eta_ancestral
-        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
-
-        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
-
-        extra_params_kwargs = {}
-        for param_name in self.extra_params:
-            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
-                extra_params_kwargs[param_name] = getattr(p, param_name)
-
-        if 'eta' in inspect.signature(self.func).parameters:
-            if self.eta != 1.0:
-                p.extra_generation_params["Eta"] = self.eta
-
-            extra_params_kwargs['eta'] = self.eta
-
-        return extra_params_kwargs
-
-    def get_sigmas(self, p, steps):
-        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
-        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
-            discard_next_to_last_sigma = True
-            p.extra_generation_params["Discard penultimate sigma"] = True
-
-        steps += 1 if discard_next_to_last_sigma else 0
-
-        if p.sampler_noise_scheduler_override:
-            sigmas = p.sampler_noise_scheduler_override(steps)
-        elif opts.k_sched_type != "Automatic":
-            m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
-            sigmas_kwargs = {
-                'sigma_min': sigma_min,
-                'sigma_max': sigma_max,
-            }
-
-            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
-            p.extra_generation_params["Schedule type"] = opts.k_sched_type
-
-            if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
-                sigmas_kwargs['sigma_min'] = opts.sigma_min
-                p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
-            if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
-                sigmas_kwargs['sigma_max'] = opts.sigma_max
-                p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
-
-            default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
-
-            if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
-                sigmas_kwargs['rho'] = opts.rho
-                p.extra_generation_params["Schedule rho"] = opts.rho
-
-            sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
-        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
-        else:
-            sigmas = self.model_wrap.get_sigmas(steps)
-
-        if discard_next_to_last_sigma:
-            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
-        return sigmas
-
-    def create_noise_sampler(self, x, sigmas, p):
-        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
-        if shared.opts.no_dpmpp_sde_batch_determinism:
-            return None
-
-        from k_diffusion.sampling import BrownianTreeNoiseSampler
-        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
-        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
-
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
-
-        sigmas = self.get_sigmas(p, steps)
-
-        sigma_sched = sigmas[steps - t_enc - 1:]
-        xi = x + noise * sigma_sched[0]
-
-        extra_params_kwargs = self.initialize(p)
-        parameters = inspect.signature(self.func).parameters
-
-        if 'sigma_min' in parameters:
-            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
-            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
-        if 'sigma_max' in parameters:
-            extra_params_kwargs['sigma_max'] = sigma_sched[0]
-        if 'n' in parameters:
-            extra_params_kwargs['n'] = len(sigma_sched) - 1
-        if 'sigma_sched' in parameters:
-            extra_params_kwargs['sigma_sched'] = sigma_sched
-        if 'sigmas' in parameters:
-            extra_params_kwargs['sigmas'] = sigma_sched
-
-        if self.config.options.get('brownian_noise', False):
-            noise_sampler = self.create_noise_sampler(x, sigmas, p)
-            extra_params_kwargs['noise_sampler'] = noise_sampler
-
-        self.model_wrap_cfg.init_latent = x
-        self.last_latent = x
-        extra_args = {
-            'cond': conditioning,
-            'image_cond': image_conditioning,
-            'uncond': unconditional_conditioning,
-            'cond_scale': p.cfg_scale,
-            's_min_uncond': self.s_min_uncond
-        }
-
-        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        if self.model_wrap_cfg.padded_cond_uncond:
-            p.extra_generation_params["Pad conds"] = True
-
-        return samples
-
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps = steps or p.steps
-
-        sigmas = self.get_sigmas(p, steps)
-
-        x = x * sigmas[0]
-
-        extra_params_kwargs = self.initialize(p)
-        parameters = inspect.signature(self.func).parameters
-
-        if 'sigma_min' in parameters:
-            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
-            if 'n' in parameters:
-                extra_params_kwargs['n'] = steps
-        else:
-            extra_params_kwargs['sigmas'] = sigmas
-
-        if self.config.options.get('brownian_noise', False):
-            noise_sampler = self.create_noise_sampler(x, sigmas, p)
-            extra_params_kwargs['noise_sampler'] = noise_sampler
-
-        self.last_latent = x
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
-            'cond': conditioning,
-            'image_cond': image_conditioning,
-            'uncond': unconditional_conditioning,
-            'cond_scale': p.cfg_scale,
-            's_min_uncond': self.s_min_uncond
-        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        if self.model_wrap_cfg.padded_cond_uncond:
-            p.extra_generation_params["Pad conds"] = True
-
-        return samples
-
-- 
cgit v1.2.3


From 11dc92dc0ae8fe477539583e6a743e57a7cd69ad Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 29 Jul 2023 08:06:04 +0300
Subject: Split history: mv temp modules/sd_samplers_kdiffusion.py

---
 modules/sd_samplers_kdiffusion.py | 545 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 545 insertions(+)
 create mode 100644 modules/sd_samplers_kdiffusion.py

(limited to 'modules')

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
new file mode 100644
index 00000000..a54673eb
--- /dev/null
+++ b/modules/sd_samplers_kdiffusion.py
@@ -0,0 +1,545 @@
+from collections import deque
+import torch
+import inspect
+import k_diffusion.sampling
+from modules import prompt_parser, devices, sd_samplers_common
+
+from modules.shared import opts, state
+import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
+from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
+from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
+
+samplers_k_diffusion = [
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
+    ('Euler', 'sample_euler', ['k_euler'], {}),
+    ('LMS', 'sample_lms', ['k_lms'], {}),
+    ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
+    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
+    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
+    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
+    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
+    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
+    ('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}),
+]
+
+
+@torch.no_grad()
+def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None):
+    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
+    '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
+    '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list'''
+    from tqdm.auto import trange
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    step_id = 0
+    from k_diffusion.sampling import to_d, get_sigmas_karras
+    def heun_step(x, old_sigma, new_sigma, second_order = True):
+        nonlocal step_id
+        denoised = model(x, old_sigma * s_in, **extra_args)
+        d = to_d(x, old_sigma, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
+        dt = new_sigma - old_sigma
+        if new_sigma == 0 or not second_order:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Heun's method
+            x_2 = x + d * dt
+            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
+            d_2 = to_d(x_2, new_sigma, denoised_2)
+            d_prime = (d + d_2) / 2
+            x = x + d_prime * dt
+        step_id += 1
+        return x
+    steps = sigmas.shape[0] - 1
+    if restart_list is None:
+        if steps >= 20:
+            restart_steps = 9
+            restart_times = 1
+            if steps >= 36:
+                restart_steps = steps // 4
+                restart_times = 2
+            sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
+            restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
+        else:
+            restart_list = dict()
+    temp_list = dict()
+    for key, value in restart_list.items():
+        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
+    restart_list = temp_list
+    step_list = []
+    for i in range(len(sigmas) - 1):
+        step_list.append((sigmas[i], sigmas[i + 1]))
+        if i + 1 in restart_list:
+            restart_steps, restart_times, restart_max = restart_list[i + 1]
+            min_idx = i + 1
+            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
+            if max_idx < min_idx:
+                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
+                while restart_times > 0:
+                    restart_times -= 1
+                    step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
+    last_sigma = None
+    for i in trange(len(step_list), disable=disable):
+        if last_sigma is None:
+            last_sigma = step_list[i][0]
+        elif last_sigma < step_list[i][0]:
+            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5
+        x = heun_step(x, step_list[i][0], step_list[i][1])
+        last_sigma = step_list[i][1]
+    return x
+
+samplers_data_k_diffusion = [
+    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+    for label, funcname, aliases, options in samplers_k_diffusion
+    if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler')
+]
+
+sampler_extra_params = {
+    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+}
+
+k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
+k_diffusion_scheduler = {
+    'Automatic': None,
+    'karras': k_diffusion.sampling.get_sigmas_karras,
+    'exponential': k_diffusion.sampling.get_sigmas_exponential,
+    'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
+}
+
+
+def catenate_conds(conds):
+    if not isinstance(conds[0], dict):
+        return torch.cat(conds)
+
+    return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
+
+
+def subscript_cond(cond, a, b):
+    if not isinstance(cond, dict):
+        return cond[a:b]
+
+    return {key: vec[a:b] for key, vec in cond.items()}
+
+
+def pad_cond(tensor, repeats, empty):
+    if not isinstance(tensor, dict):
+        return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
+
+    tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
+    return tensor
+
+
+class CFGDenoiser(torch.nn.Module):
+    """
+    Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
+    that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
+    instead of one. Originally, the second prompt is just an empty string, but we use non-empty
+    negative prompt.
+    """
+
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+        self.step = 0
+        self.image_cfg_scale = None
+        self.padded_cond_uncond = False
+
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+        return denoised
+
+    def combine_denoised_for_edit_model(self, x_out, cond_scale):
+        out_cond, out_img_cond, out_uncond = x_out.chunk(3)
+        denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
+
+        return denoised
+
+    def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
+        if state.interrupted or state.skipped:
+            raise sd_samplers_common.InterruptedException
+
+        # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
+        # so is_edit_model is set to False to support AND composition.
+        is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
+
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+
+        assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+
+        batch_size = len(conds_list)
+        repeats = [len(conds_list[i]) for i in range(batch_size)]
+
+        if shared.sd_model.model.conditioning_key == "crossattn-adm":
+            image_uncond = torch.zeros_like(image_cond)
+            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
+        else:
+            image_uncond = image_cond
+            if isinstance(uncond, dict):
+                make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
+            else:
+                make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
+
+        if not is_edit_model:
+            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
+        else:
+            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
+            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
+
+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
+        cfg_denoiser_callback(denoiser_params)
+        x_in = denoiser_params.x
+        image_cond_in = denoiser_params.image_cond
+        sigma_in = denoiser_params.sigma
+        tensor = denoiser_params.text_cond
+        uncond = denoiser_params.text_uncond
+        skip_uncond = False
+
+        # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
+        if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+            skip_uncond = True
+            x_in = x_in[:-batch_size]
+            sigma_in = sigma_in[:-batch_size]
+
+        self.padded_cond_uncond = False
+        if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
+            empty = shared.sd_model.cond_stage_model_empty_prompt
+            num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
+
+            if num_repeats < 0:
+                tensor = pad_cond(tensor, -num_repeats, empty)
+                self.padded_cond_uncond = True
+            elif num_repeats > 0:
+                uncond = pad_cond(uncond, num_repeats, empty)
+                self.padded_cond_uncond = True
+
+        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+            if is_edit_model:
+                cond_in = catenate_conds([tensor, uncond, uncond])
+            elif skip_uncond:
+                cond_in = tensor
+            else:
+                cond_in = catenate_conds([tensor, uncond])
+
+            if shared.batch_cond_uncond:
+                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
+            else:
+                x_out = torch.zeros_like(x_in)
+                for batch_offset in range(0, x_out.shape[0], batch_size):
+                    a = batch_offset
+                    b = a + batch_size
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
+        else:
+            x_out = torch.zeros_like(x_in)
+            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
+            for batch_offset in range(0, tensor.shape[0], batch_size):
+                a = batch_offset
+                b = min(a + batch_size, tensor.shape[0])
+
+                if not is_edit_model:
+                    c_crossattn = subscript_cond(tensor, a, b)
+                else:
+                    c_crossattn = torch.cat([tensor[a:b]], uncond)
+
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
+
+            if not skip_uncond:
+                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
+
+        denoised_image_indexes = [x[0][0] for x in conds_list]
+        if skip_uncond:
+            fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
+            x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
+
+        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
+        cfg_denoised_callback(denoised_params)
+
+        devices.test_for_nans(x_out, "unet")
+
+        if opts.live_preview_content == "Prompt":
+            sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
+        elif opts.live_preview_content == "Negative prompt":
+            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
+
+        if is_edit_model:
+            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
+        elif skip_uncond:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
+        else:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+
+        if self.mask is not None:
+            denoised = self.init_latent * self.mask + self.nmask * denoised
+
+        after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
+        cfg_after_cfg_callback(after_cfg_callback_params)
+        denoised = after_cfg_callback_params.x
+
+        self.step += 1
+        return denoised
+
+
+class TorchHijack:
+    def __init__(self, sampler_noises):
+        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
+        # implementation.
+        self.sampler_noises = deque(sampler_noises)
+
+    def __getattr__(self, item):
+        if item == 'randn_like':
+            return self.randn_like
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
+
+    def randn_like(self, x):
+        if self.sampler_noises:
+            noise = self.sampler_noises.popleft()
+            if noise.shape == x.shape:
+                return noise
+
+        if opts.randn_source == "CPU" or x.device.type == 'mps':
+            return torch.randn_like(x, device=devices.cpu).to(x.device)
+        else:
+            return torch.randn_like(x)
+
+
+class KDiffusionSampler:
+    def __init__(self, funcname, sd_model):
+        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+
+        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
+        self.funcname = funcname
+        self.func = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler
+        self.extra_params = sampler_extra_params.get(funcname, [])
+        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
+        self.sampler_noises = None
+        self.stop_at = None
+        self.eta = None
+        self.config = None  # set by the function calling the constructor
+        self.last_latent = None
+        self.s_min_uncond = None
+
+        self.conditioning_key = sd_model.model.conditioning_key
+
+    def callback_state(self, d):
+        step = d['i']
+        latent = d["denoised"]
+        if opts.live_preview_content == "Combined":
+            sd_samplers_common.store_latent(latent)
+        self.last_latent = latent
+
+        if self.stop_at is not None and step > self.stop_at:
+            raise sd_samplers_common.InterruptedException
+
+        state.sampling_step = step
+        shared.total_tqdm.update()
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except RecursionError:
+            print(
+                'Encountered RecursionError during sampling, returning last latent. '
+                'rho >5 with a polyexponential scheduler may cause this error. '
+                'You should try to use a smaller rho value instead.'
+            )
+            return self.last_latent
+        except sd_samplers_common.InterruptedException:
+            return self.last_latent
+
+    def number_of_needed_noises(self, p):
+        return p.steps
+
+    def initialize(self, p):
+        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
+        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
+        self.model_wrap_cfg.step = 0
+        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
+        self.eta = p.eta if p.eta is not None else opts.eta_ancestral
+        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
+
+        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
+
+        extra_params_kwargs = {}
+        for param_name in self.extra_params:
+            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+                extra_params_kwargs[param_name] = getattr(p, param_name)
+
+        if 'eta' in inspect.signature(self.func).parameters:
+            if self.eta != 1.0:
+                p.extra_generation_params["Eta"] = self.eta
+
+            extra_params_kwargs['eta'] = self.eta
+
+        return extra_params_kwargs
+
+    def get_sigmas(self, p, steps):
+        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
+        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
+            discard_next_to_last_sigma = True
+            p.extra_generation_params["Discard penultimate sigma"] = True
+
+        steps += 1 if discard_next_to_last_sigma else 0
+
+        if p.sampler_noise_scheduler_override:
+            sigmas = p.sampler_noise_scheduler_override(steps)
+        elif opts.k_sched_type != "Automatic":
+            m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
+            sigmas_kwargs = {
+                'sigma_min': sigma_min,
+                'sigma_max': sigma_max,
+            }
+
+            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+            p.extra_generation_params["Schedule type"] = opts.k_sched_type
+
+            if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
+                sigmas_kwargs['sigma_min'] = opts.sigma_min
+                p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
+            if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
+                sigmas_kwargs['sigma_max'] = opts.sigma_max
+                p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
+
+            default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
+
+            if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
+                sigmas_kwargs['rho'] = opts.rho
+                p.extra_generation_params["Schedule rho"] = opts.rho
+
+            sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+        else:
+            sigmas = self.model_wrap.get_sigmas(steps)
+
+        if discard_next_to_last_sigma:
+            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
+        return sigmas
+
+    def create_noise_sampler(self, x, sigmas, p):
+        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
+        if shared.opts.no_dpmpp_sde_batch_determinism:
+            return None
+
+        from k_diffusion.sampling import BrownianTreeNoiseSampler
+        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
+        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
+
+        sigmas = self.get_sigmas(p, steps)
+
+        sigma_sched = sigmas[steps - t_enc - 1:]
+        xi = x + noise * sigma_sched[0]
+
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'sigma_min' in parameters:
+            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
+            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
+        if 'sigma_max' in parameters:
+            extra_params_kwargs['sigma_max'] = sigma_sched[0]
+        if 'n' in parameters:
+            extra_params_kwargs['n'] = len(sigma_sched) - 1
+        if 'sigma_sched' in parameters:
+            extra_params_kwargs['sigma_sched'] = sigma_sched
+        if 'sigmas' in parameters:
+            extra_params_kwargs['sigmas'] = sigma_sched
+
+        if self.config.options.get('brownian_noise', False):
+            noise_sampler = self.create_noise_sampler(x, sigmas, p)
+            extra_params_kwargs['noise_sampler'] = noise_sampler
+
+        self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
+        extra_args = {
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
+        }
+
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
+        return samples
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps = steps or p.steps
+
+        sigmas = self.get_sigmas(p, steps)
+
+        x = x * sigmas[0]
+
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'sigma_min' in parameters:
+            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
+            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
+            if 'n' in parameters:
+                extra_params_kwargs['n'] = steps
+        else:
+            extra_params_kwargs['sigmas'] = sigmas
+
+        if self.config.options.get('brownian_noise', False):
+            noise_sampler = self.create_noise_sampler(x, sigmas, p)
+            extra_params_kwargs['noise_sampler'] = noise_sampler
+
+        self.last_latent = x
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+            'cond': conditioning,
+            'image_cond': image_conditioning,
+            'uncond': unconditional_conditioning,
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
+        return samples
+
-- 
cgit v1.2.3


From aefe1325df60a925b3a75a2cb58bf74e8ca86df4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 29 Jul 2023 08:11:59 +0300
Subject: split the new sampler into a different file

---
 modules/sd_samplers_extra.py      | 475 --------------------------------------
 modules/sd_samplers_kdiffusion.py |  75 +-----
 2 files changed, 4 insertions(+), 546 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers_extra.py b/modules/sd_samplers_extra.py
index a54673eb..a1b5dab3 100644
--- a/modules/sd_samplers_extra.py
+++ b/modules/sd_samplers_extra.py
@@ -1,38 +1,5 @@
-from collections import deque
 import torch
-import inspect
 import k_diffusion.sampling
-from modules import prompt_parser, devices, sd_samplers_common
-
-from modules.shared import opts, state
-import modules.shared as shared
-from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
-from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
-from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
-
-samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
-    ('Euler', 'sample_euler', ['k_euler'], {}),
-    ('LMS', 'sample_lms', ['k_lms'], {}),
-    ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
-    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
-    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
-    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
-    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
-    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
-    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
-    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
-    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
-    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
-    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
-    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
-    ('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}),
-]
-
 
 @torch.no_grad()
 def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None):
@@ -101,445 +68,3 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
         last_sigma = step_list[i][1]
     return x
 
-samplers_data_k_diffusion = [
-    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
-    for label, funcname, aliases, options in samplers_k_diffusion
-    if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler')
-]
-
-sampler_extra_params = {
-    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-}
-
-k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
-k_diffusion_scheduler = {
-    'Automatic': None,
-    'karras': k_diffusion.sampling.get_sigmas_karras,
-    'exponential': k_diffusion.sampling.get_sigmas_exponential,
-    'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
-}
-
-
-def catenate_conds(conds):
-    if not isinstance(conds[0], dict):
-        return torch.cat(conds)
-
-    return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
-
-
-def subscript_cond(cond, a, b):
-    if not isinstance(cond, dict):
-        return cond[a:b]
-
-    return {key: vec[a:b] for key, vec in cond.items()}
-
-
-def pad_cond(tensor, repeats, empty):
-    if not isinstance(tensor, dict):
-        return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
-
-    tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
-    return tensor
-
-
-class CFGDenoiser(torch.nn.Module):
-    """
-    Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
-    that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
-    instead of one. Originally, the second prompt is just an empty string, but we use non-empty
-    negative prompt.
-    """
-
-    def __init__(self, model):
-        super().__init__()
-        self.inner_model = model
-        self.mask = None
-        self.nmask = None
-        self.init_latent = None
-        self.step = 0
-        self.image_cfg_scale = None
-        self.padded_cond_uncond = False
-
-    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
-        denoised_uncond = x_out[-uncond.shape[0]:]
-        denoised = torch.clone(denoised_uncond)
-
-        for i, conds in enumerate(conds_list):
-            for cond_index, weight in conds:
-                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
-
-        return denoised
-
-    def combine_denoised_for_edit_model(self, x_out, cond_scale):
-        out_cond, out_img_cond, out_uncond = x_out.chunk(3)
-        denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
-
-        return denoised
-
-    def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
-        if state.interrupted or state.skipped:
-            raise sd_samplers_common.InterruptedException
-
-        # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
-        # so is_edit_model is set to False to support AND composition.
-        is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
-
-        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
-
-        assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
-
-        batch_size = len(conds_list)
-        repeats = [len(conds_list[i]) for i in range(batch_size)]
-
-        if shared.sd_model.model.conditioning_key == "crossattn-adm":
-            image_uncond = torch.zeros_like(image_cond)
-            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
-        else:
-            image_uncond = image_cond
-            if isinstance(uncond, dict):
-                make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
-            else:
-                make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
-
-        if not is_edit_model:
-            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
-            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
-        else:
-            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
-            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
-
-        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
-        cfg_denoiser_callback(denoiser_params)
-        x_in = denoiser_params.x
-        image_cond_in = denoiser_params.image_cond
-        sigma_in = denoiser_params.sigma
-        tensor = denoiser_params.text_cond
-        uncond = denoiser_params.text_uncond
-        skip_uncond = False
-
-        # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
-        if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
-            skip_uncond = True
-            x_in = x_in[:-batch_size]
-            sigma_in = sigma_in[:-batch_size]
-
-        self.padded_cond_uncond = False
-        if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
-            empty = shared.sd_model.cond_stage_model_empty_prompt
-            num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
-
-            if num_repeats < 0:
-                tensor = pad_cond(tensor, -num_repeats, empty)
-                self.padded_cond_uncond = True
-            elif num_repeats > 0:
-                uncond = pad_cond(uncond, num_repeats, empty)
-                self.padded_cond_uncond = True
-
-        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
-            if is_edit_model:
-                cond_in = catenate_conds([tensor, uncond, uncond])
-            elif skip_uncond:
-                cond_in = tensor
-            else:
-                cond_in = catenate_conds([tensor, uncond])
-
-            if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
-            else:
-                x_out = torch.zeros_like(x_in)
-                for batch_offset in range(0, x_out.shape[0], batch_size):
-                    a = batch_offset
-                    b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
-        else:
-            x_out = torch.zeros_like(x_in)
-            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
-            for batch_offset in range(0, tensor.shape[0], batch_size):
-                a = batch_offset
-                b = min(a + batch_size, tensor.shape[0])
-
-                if not is_edit_model:
-                    c_crossattn = subscript_cond(tensor, a, b)
-                else:
-                    c_crossattn = torch.cat([tensor[a:b]], uncond)
-
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
-
-            if not skip_uncond:
-                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
-
-        denoised_image_indexes = [x[0][0] for x in conds_list]
-        if skip_uncond:
-            fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
-            x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
-
-        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
-        cfg_denoised_callback(denoised_params)
-
-        devices.test_for_nans(x_out, "unet")
-
-        if opts.live_preview_content == "Prompt":
-            sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
-        elif opts.live_preview_content == "Negative prompt":
-            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
-
-        if is_edit_model:
-            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
-        elif skip_uncond:
-            denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
-        else:
-            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-
-        if self.mask is not None:
-            denoised = self.init_latent * self.mask + self.nmask * denoised
-
-        after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
-        cfg_after_cfg_callback(after_cfg_callback_params)
-        denoised = after_cfg_callback_params.x
-
-        self.step += 1
-        return denoised
-
-
-class TorchHijack:
-    def __init__(self, sampler_noises):
-        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
-        # implementation.
-        self.sampler_noises = deque(sampler_noises)
-
-    def __getattr__(self, item):
-        if item == 'randn_like':
-            return self.randn_like
-
-        if hasattr(torch, item):
-            return getattr(torch, item)
-
-        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
-
-    def randn_like(self, x):
-        if self.sampler_noises:
-            noise = self.sampler_noises.popleft()
-            if noise.shape == x.shape:
-                return noise
-
-        if opts.randn_source == "CPU" or x.device.type == 'mps':
-            return torch.randn_like(x, device=devices.cpu).to(x.device)
-        else:
-            return torch.randn_like(x)
-
-
-class KDiffusionSampler:
-    def __init__(self, funcname, sd_model):
-        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-
-        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
-        self.funcname = funcname
-        self.func = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler
-        self.extra_params = sampler_extra_params.get(funcname, [])
-        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
-        self.sampler_noises = None
-        self.stop_at = None
-        self.eta = None
-        self.config = None  # set by the function calling the constructor
-        self.last_latent = None
-        self.s_min_uncond = None
-
-        self.conditioning_key = sd_model.model.conditioning_key
-
-    def callback_state(self, d):
-        step = d['i']
-        latent = d["denoised"]
-        if opts.live_preview_content == "Combined":
-            sd_samplers_common.store_latent(latent)
-        self.last_latent = latent
-
-        if self.stop_at is not None and step > self.stop_at:
-            raise sd_samplers_common.InterruptedException
-
-        state.sampling_step = step
-        shared.total_tqdm.update()
-
-    def launch_sampling(self, steps, func):
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except RecursionError:
-            print(
-                'Encountered RecursionError during sampling, returning last latent. '
-                'rho >5 with a polyexponential scheduler may cause this error. '
-                'You should try to use a smaller rho value instead.'
-            )
-            return self.last_latent
-        except sd_samplers_common.InterruptedException:
-            return self.last_latent
-
-    def number_of_needed_noises(self, p):
-        return p.steps
-
-    def initialize(self, p):
-        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
-        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
-        self.model_wrap_cfg.step = 0
-        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
-        self.eta = p.eta if p.eta is not None else opts.eta_ancestral
-        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
-
-        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
-
-        extra_params_kwargs = {}
-        for param_name in self.extra_params:
-            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
-                extra_params_kwargs[param_name] = getattr(p, param_name)
-
-        if 'eta' in inspect.signature(self.func).parameters:
-            if self.eta != 1.0:
-                p.extra_generation_params["Eta"] = self.eta
-
-            extra_params_kwargs['eta'] = self.eta
-
-        return extra_params_kwargs
-
-    def get_sigmas(self, p, steps):
-        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
-        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
-            discard_next_to_last_sigma = True
-            p.extra_generation_params["Discard penultimate sigma"] = True
-
-        steps += 1 if discard_next_to_last_sigma else 0
-
-        if p.sampler_noise_scheduler_override:
-            sigmas = p.sampler_noise_scheduler_override(steps)
-        elif opts.k_sched_type != "Automatic":
-            m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
-            sigmas_kwargs = {
-                'sigma_min': sigma_min,
-                'sigma_max': sigma_max,
-            }
-
-            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
-            p.extra_generation_params["Schedule type"] = opts.k_sched_type
-
-            if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
-                sigmas_kwargs['sigma_min'] = opts.sigma_min
-                p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
-            if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
-                sigmas_kwargs['sigma_max'] = opts.sigma_max
-                p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
-
-            default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
-
-            if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
-                sigmas_kwargs['rho'] = opts.rho
-                p.extra_generation_params["Schedule rho"] = opts.rho
-
-            sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
-        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
-        else:
-            sigmas = self.model_wrap.get_sigmas(steps)
-
-        if discard_next_to_last_sigma:
-            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
-        return sigmas
-
-    def create_noise_sampler(self, x, sigmas, p):
-        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
-        if shared.opts.no_dpmpp_sde_batch_determinism:
-            return None
-
-        from k_diffusion.sampling import BrownianTreeNoiseSampler
-        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
-        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
-
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
-
-        sigmas = self.get_sigmas(p, steps)
-
-        sigma_sched = sigmas[steps - t_enc - 1:]
-        xi = x + noise * sigma_sched[0]
-
-        extra_params_kwargs = self.initialize(p)
-        parameters = inspect.signature(self.func).parameters
-
-        if 'sigma_min' in parameters:
-            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
-            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
-        if 'sigma_max' in parameters:
-            extra_params_kwargs['sigma_max'] = sigma_sched[0]
-        if 'n' in parameters:
-            extra_params_kwargs['n'] = len(sigma_sched) - 1
-        if 'sigma_sched' in parameters:
-            extra_params_kwargs['sigma_sched'] = sigma_sched
-        if 'sigmas' in parameters:
-            extra_params_kwargs['sigmas'] = sigma_sched
-
-        if self.config.options.get('brownian_noise', False):
-            noise_sampler = self.create_noise_sampler(x, sigmas, p)
-            extra_params_kwargs['noise_sampler'] = noise_sampler
-
-        self.model_wrap_cfg.init_latent = x
-        self.last_latent = x
-        extra_args = {
-            'cond': conditioning,
-            'image_cond': image_conditioning,
-            'uncond': unconditional_conditioning,
-            'cond_scale': p.cfg_scale,
-            's_min_uncond': self.s_min_uncond
-        }
-
-        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        if self.model_wrap_cfg.padded_cond_uncond:
-            p.extra_generation_params["Pad conds"] = True
-
-        return samples
-
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps = steps or p.steps
-
-        sigmas = self.get_sigmas(p, steps)
-
-        x = x * sigmas[0]
-
-        extra_params_kwargs = self.initialize(p)
-        parameters = inspect.signature(self.func).parameters
-
-        if 'sigma_min' in parameters:
-            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
-            if 'n' in parameters:
-                extra_params_kwargs['n'] = steps
-        else:
-            extra_params_kwargs['sigmas'] = sigmas
-
-        if self.config.options.get('brownian_noise', False):
-            noise_sampler = self.create_noise_sampler(x, sigmas, p)
-            extra_params_kwargs['noise_sampler'] = noise_sampler
-
-        self.last_latent = x
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
-            'cond': conditioning,
-            'image_cond': image_conditioning,
-            'uncond': unconditional_conditioning,
-            'cond_scale': p.cfg_scale,
-            's_min_uncond': self.s_min_uncond
-        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        if self.model_wrap_cfg.padded_cond_uncond:
-            p.extra_generation_params["Pad conds"] = True
-
-        return samples
-
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index a54673eb..e0da3425 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -2,7 +2,7 @@ from collections import deque
 import torch
 import inspect
 import k_diffusion.sampling
-from modules import prompt_parser, devices, sd_samplers_common
+from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra
 
 from modules.shared import opts, state
 import modules.shared as shared
@@ -30,81 +30,14 @@ samplers_k_diffusion = [
     ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
     ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
     ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
-    ('Restart (new)', 'restart_sampler', ['restart'], {'scheduler': 'karras', "second_order": True}),
+    ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras'}),
 ]
 
 
-@torch.no_grad()
-def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None):
-    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
-    '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
-    '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list'''
-    from tqdm.auto import trange
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    step_id = 0
-    from k_diffusion.sampling import to_d, get_sigmas_karras
-    def heun_step(x, old_sigma, new_sigma, second_order = True):
-        nonlocal step_id
-        denoised = model(x, old_sigma * s_in, **extra_args)
-        d = to_d(x, old_sigma, denoised)
-        if callback is not None:
-            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
-        dt = new_sigma - old_sigma
-        if new_sigma == 0 or not second_order:
-            # Euler method
-            x = x + d * dt
-        else:
-            # Heun's method
-            x_2 = x + d * dt
-            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
-            d_2 = to_d(x_2, new_sigma, denoised_2)
-            d_prime = (d + d_2) / 2
-            x = x + d_prime * dt
-        step_id += 1
-        return x
-    steps = sigmas.shape[0] - 1
-    if restart_list is None:
-        if steps >= 20:
-            restart_steps = 9
-            restart_times = 1
-            if steps >= 36:
-                restart_steps = steps // 4
-                restart_times = 2
-            sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
-            restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
-        else:
-            restart_list = dict()
-    temp_list = dict()
-    for key, value in restart_list.items():
-        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
-    restart_list = temp_list
-    step_list = []
-    for i in range(len(sigmas) - 1):
-        step_list.append((sigmas[i], sigmas[i + 1]))
-        if i + 1 in restart_list:
-            restart_steps, restart_times, restart_max = restart_list[i + 1]
-            min_idx = i + 1
-            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
-            if max_idx < min_idx:
-                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
-                while restart_times > 0:
-                    restart_times -= 1
-                    step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
-    last_sigma = None
-    for i in trange(len(step_list), disable=disable):
-        if last_sigma is None:
-            last_sigma = step_list[i][0]
-        elif last_sigma < step_list[i][0]:
-            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5
-        x = heun_step(x, step_list[i][0], step_list[i][1])
-        last_sigma = step_list[i][1]
-    return x
-
 samplers_data_k_diffusion = [
     sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
     for label, funcname, aliases, options in samplers_k_diffusion
-    if (hasattr(k_diffusion.sampling, funcname) or funcname == 'restart_sampler')
+    if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
 ]
 
 sampler_extra_params = {
@@ -339,7 +272,7 @@ class KDiffusionSampler:
 
         self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
         self.funcname = funcname
-        self.func = getattr(k_diffusion.sampling, self.funcname) if funcname != "restart_sampler" else restart_sampler
+        self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
         self.extra_params = sampler_extra_params.get(funcname, [])
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
         self.sampler_noises = None
-- 
cgit v1.2.3


From 79d6e9cd325353b5c6a02f8374494f781760d211 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 29 Jul 2023 08:38:00 +0300
Subject: some stylistic changes for the sampler code

---
 modules/sd_samplers_extra.py | 40 ++++++++++++++++++++++------------------
 1 file changed, 22 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers_extra.py b/modules/sd_samplers_extra.py
index a1b5dab3..1b981ca8 100644
--- a/modules/sd_samplers_extra.py
+++ b/modules/sd_samplers_extra.py
@@ -1,17 +1,20 @@
 import torch
+import tqdm
 import k_diffusion.sampling
 
+
 @torch.no_grad()
-def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None):
-    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
-    '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
-    '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list'''
-    from tqdm.auto import trange
+def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None):
+    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)
+    Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}
+    If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list
+    """
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     step_id = 0
     from k_diffusion.sampling import to_d, get_sigmas_karras
-    def heun_step(x, old_sigma, new_sigma, second_order = True):
+
+    def heun_step(x, old_sigma, new_sigma, second_order=True):
         nonlocal step_id
         denoised = model(x, old_sigma * s_in, **extra_args)
         d = to_d(x, old_sigma, denoised)
@@ -30,6 +33,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             x = x + d_prime * dt
         step_id += 1
         return x
+
     steps = sigmas.shape[0] - 1
     if restart_list is None:
         if steps >= 20:
@@ -41,11 +45,10 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
             restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
         else:
-            restart_list = dict()
-    temp_list = dict()
-    for key, value in restart_list.items():
-        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
-    restart_list = temp_list
+            restart_list = {}
+
+    restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}
+
     step_list = []
     for i in range(len(sigmas) - 1):
         step_list.append((sigmas[i], sigmas[i + 1]))
@@ -58,13 +61,14 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
                 while restart_times > 0:
                     restart_times -= 1
                     step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
+
     last_sigma = None
-    for i in trange(len(step_list), disable=disable):
+    for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):
         if last_sigma is None:
-            last_sigma = step_list[i][0]
-        elif last_sigma < step_list[i][0]:
-            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (step_list[i][0] ** 2 - last_sigma ** 2) ** 0.5
-        x = heun_step(x, step_list[i][0], step_list[i][1])
-        last_sigma = step_list[i][1]
-    return x
+            last_sigma = old_sigma
+        elif last_sigma < old_sigma:
+            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5
+        x = heun_step(x, old_sigma, new_sigma)
+        last_sigma = new_sigma
 
+    return x
-- 
cgit v1.2.3


From 6f0abbb71a3f29d6df63fed82d5d5e196ca0d4de Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 29 Jul 2023 15:15:06 +0300
Subject: textual inversion support for SDXL

---
 modules/sd_hijack.py                           |  8 +++++---
 modules/sd_hijack_clip.py                      |  2 +-
 modules/sd_models_xl.py                        |  9 +++++++++
 modules/textual_inversion/textual_inversion.py | 19 ++++++++++++++-----
 4 files changed, 29 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index c8fdd4f1..cfa5f0eb 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -197,7 +197,7 @@ class StableDiffusionModelHijack:
                     conditioner.embedders[i] = sd_hijack_clip.FrozenCLIPEmbedderForSDXLWithCustomWords(embedder, self)
                     text_cond_models.append(conditioner.embedders[i])
                 if typename == 'FrozenOpenCLIPEmbedder2':
-                    embedder.model.token_embedding = EmbeddingsWithFixes(embedder.model.token_embedding, self)
+                    embedder.model.token_embedding = EmbeddingsWithFixes(embedder.model.token_embedding, self, textual_inversion_key='clip_g')
                     conditioner.embedders[i] = sd_hijack_open_clip.FrozenOpenCLIPEmbedder2WithCustomWords(embedder, self)
                     text_cond_models.append(conditioner.embedders[i])
 
@@ -292,10 +292,11 @@ class StableDiffusionModelHijack:
 
 
 class EmbeddingsWithFixes(torch.nn.Module):
-    def __init__(self, wrapped, embeddings):
+    def __init__(self, wrapped, embeddings, textual_inversion_key='clip_l'):
         super().__init__()
         self.wrapped = wrapped
         self.embeddings = embeddings
+        self.textual_inversion_key = textual_inversion_key
 
     def forward(self, input_ids):
         batch_fixes = self.embeddings.fixes
@@ -309,7 +310,8 @@ class EmbeddingsWithFixes(torch.nn.Module):
         vecs = []
         for fixes, tensor in zip(batch_fixes, inputs_embeds):
             for offset, embedding in fixes:
-                emb = devices.cond_cast_unet(embedding.vec)
+                vec = embedding.vec[self.textual_inversion_key] if isinstance(embedding.vec, dict) else embedding.vec
+                emb = devices.cond_cast_unet(vec)
                 emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
                 tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
 
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 16a5500e..2f9d569b 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -161,7 +161,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                     position += 1
                     continue
 
-                emb_len = int(embedding.vec.shape[0])
+                emb_len = int(embedding.vectors)
                 if len(chunk.tokens) + emb_len > self.chunk_length:
                     next_chunk()
 
diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py
index 40559208..bc219508 100644
--- a/modules/sd_models_xl.py
+++ b/modules/sd_models_xl.py
@@ -56,6 +56,14 @@ def encode_embedding_init_text(self: sgm.modules.GeneralConditioner, init_text,
     return torch.cat(res, dim=1)
 
 
+def tokenize(self: sgm.modules.GeneralConditioner, texts):
+    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'tokenize')]:
+        return embedder.tokenize(texts)
+
+    raise AssertionError('no tokenizer available')
+
+
+
 def process_texts(self, texts):
     for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'process_texts')]:
         return embedder.process_texts(texts)
@@ -68,6 +76,7 @@ def get_target_prompt_token_count(self, token_count):
 
 # those additions to GeneralConditioner make it possible to use it as model.cond_stage_model from SD1.5 in exist
 sgm.modules.GeneralConditioner.encode_embedding_init_text = encode_embedding_init_text
+sgm.modules.GeneralConditioner.tokenize = tokenize
 sgm.modules.GeneralConditioner.process_texts = process_texts
 sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt_token_count
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 6166c76f..4713bc2d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -181,29 +181,38 @@ class EmbeddingDatabase:
         else:
             return
 
+
         # textual inversion embeddings
         if 'string_to_param' in data:
             param_dict = data['string_to_param']
             param_dict = getattr(param_dict, '_parameters', param_dict)  # fix for torch 1.12.1 loading saved file from torch 1.11
             assert len(param_dict) == 1, 'embedding file has multiple terms in it'
             emb = next(iter(param_dict.items()))[1]
-        # diffuser concepts
-        elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
+            vec = emb.detach().to(devices.device, dtype=torch.float32)
+            shape = vec.shape[-1]
+            vectors = vec.shape[0]
+        elif type(data) == dict and 'clip_g' in data and 'clip_l' in data:  # SDXL embedding
+            vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
+            shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
+            vectors = data['clip_g'].shape[0]
+        elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
             assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
 
             emb = next(iter(data.values()))
             if len(emb.shape) == 1:
                 emb = emb.unsqueeze(0)
+            vec = emb.detach().to(devices.device, dtype=torch.float32)
+            shape = vec.shape[-1]
+            vectors = vec.shape[0]
         else:
             raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
 
-        vec = emb.detach().to(devices.device, dtype=torch.float32)
         embedding = Embedding(vec, name)
         embedding.step = data.get('step', None)
         embedding.sd_checkpoint = data.get('sd_checkpoint', None)
         embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
-        embedding.vectors = vec.shape[0]
-        embedding.shape = vec.shape[-1]
+        embedding.vectors = vectors
+        embedding.shape = shape
         embedding.filename = path
         embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')
 
-- 
cgit v1.2.3


From b95a41ad724c8993aec4e752bd6d0c9c150be963 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sun, 30 Jul 2023 00:01:53 +0900
Subject: rework img2img batch image save

---
 modules/img2img.py | 27 +++++++--------------------
 1 file changed, 7 insertions(+), 20 deletions(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index 132cd100..fc8a81eb 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -32,11 +32,6 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
 
     print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
 
-    save_normally = output_dir == ''
-
-    p.do_not_save_grid = True
-    p.do_not_save_samples = not save_normally
-
     state.job_count = len(images) * p.n_iter
 
     # extract "default" params to use in case getting png info fails
@@ -111,21 +106,13 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
 
         proc = modules.scripts.scripts_img2img.run(p, *args)
         if proc is None:
-            proc = process_images(p)
-
-        for n, processed_image in enumerate(proc.images):
-            filename = image_path.stem
-            infotext = proc.infotext(p, n)
-            relpath = os.path.dirname(os.path.relpath(image, input_dir))
-
-            if n > 0:
-                filename += f"-{n}"
-
-            if not save_normally:
-                os.makedirs(os.path.join(output_dir, relpath), exist_ok=True)
-                if processed_image.mode == 'RGBA':
-                    processed_image = processed_image.convert("RGB")
-                save_image(processed_image, os.path.join(output_dir, relpath), None, extension=opts.samples_format, info=infotext, forced_filename=filename, save_to_dirs=False)
+            p.outpath_samples = output_dir
+            p.override_settings['save_to_dirs'] = False
+            if p.n_iter > 1 or p.batch_size > 1:
+                p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
+            else:
+                p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
+            process_images(p)
 
 
 def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
-- 
cgit v1.2.3


From 9857537053a13a8963b70b67757201a3e1f941f1 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sun, 30 Jul 2023 00:06:25 +0900
Subject: lint

---
 modules/img2img.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index fc8a81eb..fee6f8a5 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -10,7 +10,6 @@ from modules import sd_samplers, images as imgutil
 from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
-from modules.images import save_image
 import modules.shared as shared
 import modules.processing as processing
 from modules.ui import plaintext_to_html
-- 
cgit v1.2.3


From 53ccdefc015d7a05e03b603d6c7356574c240026 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sun, 30 Jul 2023 00:34:04 +0900
Subject: don't override default if output_dir is blank

---
 modules/img2img.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index fee6f8a5..d41f34d0 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -105,12 +105,13 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
 
         proc = modules.scripts.scripts_img2img.run(p, *args)
         if proc is None:
-            p.outpath_samples = output_dir
-            p.override_settings['save_to_dirs'] = False
-            if p.n_iter > 1 or p.batch_size > 1:
-                p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
-            else:
-                p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
+            if output_dir:
+                p.outpath_samples = output_dir
+                p.override_settings['save_to_dirs'] = False
+                if p.n_iter > 1 or p.batch_size > 1:
+                    p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
+                else:
+                    p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
             process_images(p)
 
 
-- 
cgit v1.2.3


From fb44838176f50a07ff787d8004f5ad514b427ae8 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sun, 30 Jul 2023 14:47:24 +0900
Subject: strip output_dir

---
 modules/img2img.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index d41f34d0..68e415ef 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -17,6 +17,7 @@ import modules.scripts
 
 
 def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
+    output_dir = output_dir.strip()
     processing.fix_seed(p)
 
     images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
-- 
cgit v1.2.3


From 8a40e30d08d4bd2fc9b3b8b5d697abfda7d31bc0 Mon Sep 17 00:00:00 2001
From: Robert Barron <rubberbaron@robustspread.com>
Date: Sun, 30 Jul 2023 01:22:00 -0700
Subject: add support for whitespace after the number in constructions like
 [foo:bar: 0.5 ] and (foo : 0.5 )

---
 modules/prompt_parser.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 203ae1ac..8169a459 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -19,7 +19,7 @@ prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
 !emphasized: "(" prompt ")"
         | "(" prompt ":" prompt ")"
         | "[" prompt "]"
-scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
+scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER [WHITESPACE] "]"
 alternate: "[" prompt ("|" prompt)+ "]"
 WHITESPACE: /\s+/
 plain: /([^\\\[\]():|]|\\.)+/
@@ -60,11 +60,11 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
 
         class CollectSteps(lark.Visitor):
             def scheduled(self, tree):
-                tree.children[-1] = float(tree.children[-1])
-                if tree.children[-1] < 1:
-                    tree.children[-1] *= steps
-                tree.children[-1] = min(steps, int(tree.children[-1]))
-                res.append(tree.children[-1])
+                tree.children[-2] = float(tree.children[-2])
+                if tree.children[-2] < 1:
+                    tree.children[-2] *= steps
+                tree.children[-2] = min(steps, int(tree.children[-2]))
+                res.append(tree.children[-2])
 
             def alternate(self, tree):
                 res.extend(range(1, steps+1))
@@ -75,7 +75,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     def at_step(step, tree):
         class AtStep(lark.Transformer):
             def scheduled(self, args):
-                before, after, _, when = args
+                before, after, _, when, _ = args
                 yield before or () if step <= when else after
             def alternate(self, args):
                 yield next(args[(step - 1)%len(args)])
@@ -333,7 +333,7 @@ re_attention = re.compile(r"""
 \\|
 \(|
 \[|
-:([+-]?[.\d]+)\)|
+:\s*([+-]?[.\d]+)\s*\)|
 \)|
 ]|
 [^\\()\[\]:]+|
-- 
cgit v1.2.3


From c10633f93a646b06f62bf1b24adba52f539dd6b6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 31 Jul 2023 22:01:53 +0300
Subject: fix memory leak when generation fails

---
 modules/call_queue.py | 4 +++-
 modules/errors.py     | 3 ++-
 modules/sysinfo.py    | 6 +++++-
 3 files changed, 10 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/call_queue.py b/modules/call_queue.py
index 61aa240f..f2eb17d6 100644
--- a/modules/call_queue.py
+++ b/modules/call_queue.py
@@ -3,7 +3,7 @@ import html
 import threading
 import time
 
-from modules import shared, progress, errors
+from modules import shared, progress, errors, devices
 
 queue_lock = threading.Lock()
 
@@ -75,6 +75,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
             error_message = f'{type(e).__name__}: {e}'
             res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
 
+        devices.torch_gc()
+
         shared.state.skipped = False
         shared.state.interrupted = False
         shared.state.job_count = 0
diff --git a/modules/errors.py b/modules/errors.py
index 5271a9fe..dffabe45 100644
--- a/modules/errors.py
+++ b/modules/errors.py
@@ -14,7 +14,8 @@ def record_exception():
     if exception_records and exception_records[-1] == e:
         return
 
-    exception_records.append((e, tb))
+    from modules import sysinfo
+    exception_records.append(sysinfo.format_exception(e, tb))
 
     if len(exception_records) > 5:
         exception_records.pop(0)
diff --git a/modules/sysinfo.py b/modules/sysinfo.py
index 5f15ac4f..cf24c6dd 100644
--- a/modules/sysinfo.py
+++ b/modules/sysinfo.py
@@ -109,11 +109,15 @@ def format_traceback(tb):
     return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
 
 
+def format_exception(e, tb):
+    return {"exception": str(e), "traceback": format_traceback(tb)}
+
+
 def get_exceptions():
     try:
         from modules import errors
 
-        return [{"exception": str(e), "traceback": format_traceback(tb)} for e, tb in reversed(errors.exception_records)]
+        return list(reversed(errors.exception_records))
     except Exception as e:
         return str(e)
 
-- 
cgit v1.2.3


From 4b43480fe8b65a3bd24dc9bc03a7e910c9b0314f Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 1 Aug 2023 07:08:11 +0300
Subject: show metadata for SD checkpoints in the extra networks UI

---
 modules/sd_models.py                     | 26 ++++++++++++++++----------
 modules/ui_extra_networks_checkpoints.py |  1 +
 2 files changed, 17 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index acb1e817..1af7fd78 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -14,7 +14,7 @@ import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache
 from modules.sd_hijack_inpainting import do_inpainting_hijack
 from modules.timer import Timer
 import tomesd
@@ -33,6 +33,8 @@ class CheckpointInfo:
         self.filename = filename
         abspath = os.path.abspath(filename)
 
+        self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
+
         if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
             name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
         elif abspath.startswith(model_path):
@@ -43,6 +45,19 @@ class CheckpointInfo:
         if name.startswith("\\") or name.startswith("/"):
             name = name[1:]
 
+        def read_metadata():
+            metadata = read_metadata_from_safetensors(filename)
+            self.modelspec_thumbnail = metadata.pop('modelspec.thumbnail', None)
+
+            return metadata
+
+        self.metadata = {}
+        if self.is_safetensors:
+            try:
+                self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name, filename, read_metadata)
+            except Exception as e:
+                errors.display(e, f"reading metadata for {filename}")
+
         self.name = name
         self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
         self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
@@ -55,15 +70,6 @@ class CheckpointInfo:
 
         self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
 
-        self.metadata = {}
-
-        _, ext = os.path.splitext(self.filename)
-        if ext.lower() == ".safetensors":
-            try:
-                self.metadata = read_metadata_from_safetensors(filename)
-            except Exception as e:
-                errors.display(e, f"reading checkpoint metadata: {filename}")
-
     def register(self):
         checkpoints_list[self.title] = self
         for id in self.ids:
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py
index 76780cfd..2bb0a222 100644
--- a/modules/ui_extra_networks_checkpoints.py
+++ b/modules/ui_extra_networks_checkpoints.py
@@ -23,6 +23,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
             "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
             "onclick": '"' + html.escape(f"""return selectCheckpoint({quote_js(name)})""") + '"',
             "local_preview": f"{path}.{shared.opts.samples_format}",
+            "metadata": checkpoint.metadata,
             "sort_keys": {'default': index, **self.get_sort_keys(checkpoint.filename)},
         }
 
-- 
cgit v1.2.3


From 2860c3be3e29d7f0a7d8e1ada6594078aee3aebb Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 1 Aug 2023 07:10:42 +0300
Subject: add filename to to the table in user metadata editor

---
 modules/ui_extra_networks_user_metadata.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/ui_extra_networks_user_metadata.py b/modules/ui_extra_networks_user_metadata.py
index 63d4b503..1cb9eb6f 100644
--- a/modules/ui_extra_networks_user_metadata.py
+++ b/modules/ui_extra_networks_user_metadata.py
@@ -96,6 +96,7 @@ class UserMetadataEditor:
 
             stats = os.stat(filename)
             params = [
+                ('Filename: ', os.path.basename(filename)),
                 ('File size: ', sysinfo.pretty_bytes(stats.st_size)),
                 ('Modified: ', datetime.datetime.fromtimestamp(stats.st_mtime).strftime('%Y-%m-%d %H:%M')),
             ]
-- 
cgit v1.2.3


From c6b826d796d10e1428506dc988a940a184f7d09b Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 1 Aug 2023 07:15:15 +0300
Subject: Split history: mv modules/ui.py modules/ui_checkpoint_merger.py

---
 modules/ui.py                   | 1621 ---------------------------------------
 modules/ui_checkpoint_merger.py | 1621 +++++++++++++++++++++++++++++++++++++++
 2 files changed, 1621 insertions(+), 1621 deletions(-)
 delete mode 100644 modules/ui.py
 create mode 100644 modules/ui_checkpoint_merger.py

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
deleted file mode 100644
index 07ecee7b..00000000
--- a/modules/ui.py
+++ /dev/null
@@ -1,1621 +0,0 @@
-import datetime
-import json
-import mimetypes
-import os
-import sys
-from functools import reduce
-import warnings
-
-import gradio as gr
-import gradio.utils
-import numpy as np
-from PIL import Image, PngImagePlugin  # noqa: F401
-from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
-
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
-from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
-from modules.paths import script_path
-from modules.ui_common import create_refresh_button
-from modules.ui_gradio_extensions import reload_javascript
-
-
-from modules.shared import opts, cmd_opts
-
-import modules.codeformer_model
-import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.scripts
-import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
-from modules import prompt_parser
-from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
-from modules.textual_inversion import textual_inversion
-import modules.hypernetworks.ui
-from modules.generation_parameters_copypaste import image_from_url_text
-import modules.extras
-
-create_setting_component = ui_settings.create_setting_component
-
-warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
-
-# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
-mimetypes.init()
-mimetypes.add_type('application/javascript', '.js')
-
-if not cmd_opts.share and not cmd_opts.listen:
-    # fix gradio phoning home
-    gradio.utils.version_check = lambda: None
-    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
-
-if cmd_opts.ngrok is not None:
-    import modules.ngrok as ngrok
-    print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(
-        cmd_opts.ngrok,
-        cmd_opts.port if cmd_opts.port is not None else 7860,
-        cmd_opts.ngrok_options
-        )
-
-
-def gr_show(visible=True):
-    return {"visible": visible, "__type__": "update"}
-
-
-sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
-sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-
-# Using constants for these since the variation selector isn't visible.
-# Important that they exactly match script.js for tooltip to work.
-random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
-reuse_symbol = '\u267b\ufe0f'  # ♻️
-paste_symbol = '\u2199\ufe0f'  # ↙
-refresh_symbol = '\U0001f504'  # 🔄
-save_style_symbol = '\U0001f4be'  # 💾
-apply_style_symbol = '\U0001f4cb'  # 📋
-clear_prompt_symbol = '\U0001f5d1\ufe0f'  # 🗑️
-extra_networks_symbol = '\U0001F3B4'  # 🎴
-switch_values_symbol = '\U000021C5' # ⇅
-restore_progress_symbol = '\U0001F300' # 🌀
-detect_image_size_symbol = '\U0001F4D0'  # 📐
-up_down_symbol = '\u2195\ufe0f' # ↕️
-
-
-plaintext_to_html = ui_common.plaintext_to_html
-
-
-def send_gradio_gallery_to_image(x):
-    if len(x) == 0:
-        return None
-    return image_from_url_text(x[0])
-
-
-def add_style(name: str, prompt: str, negative_prompt: str):
-    if name is None:
-        return [gr_show() for x in range(4)]
-
-    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
-    shared.prompt_styles.styles[style.name] = style
-    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
-    # reserialize all styles every time we save them
-    shared.prompt_styles.save_styles(shared.styles_filename)
-
-    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
-
-
-def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
-    from modules import processing, devices
-
-    if not enable:
-        return ""
-
-    p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
-
-    with devices.autocast():
-        p.init([""], [0], [0])
-
-    return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
-
-
-def resize_from_to_html(width, height, scale_by):
-    target_width = int(width * scale_by)
-    target_height = int(height * scale_by)
-
-    if not target_width or not target_height:
-        return "no image selected"
-
-    return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
-
-
-def apply_styles(prompt, prompt_neg, styles):
-    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
-    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
-
-    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
-
-
-def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
-    if mode in {0, 1, 3, 4}:
-        return [interrogation_function(ii_singles[mode]), None]
-    elif mode == 2:
-        return [interrogation_function(ii_singles[mode]["image"]), None]
-    elif mode == 5:
-        assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
-        images = shared.listfiles(ii_input_dir)
-        print(f"Will process {len(images)} images.")
-        if ii_output_dir != "":
-            os.makedirs(ii_output_dir, exist_ok=True)
-        else:
-            ii_output_dir = ii_input_dir
-
-        for image in images:
-            img = Image.open(image)
-            filename = os.path.basename(image)
-            left, _ = os.path.splitext(filename)
-            print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
-
-        return [gr.update(), None]
-
-
-def interrogate(image):
-    prompt = shared.interrogator.interrogate(image.convert("RGB"))
-    return gr.update() if prompt is None else prompt
-
-
-def interrogate_deepbooru(image):
-    prompt = deepbooru.model.tag(image)
-    return gr.update() if prompt is None else prompt
-
-
-def create_seed_inputs(target_interface):
-    with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
-        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
-        seed.style(container=False)
-        random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
-        reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
-
-        seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)
-
-    # Components to show/hide based on the 'Extra' checkbox
-    seed_extras = []
-
-    with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
-        seed_extras.append(seed_extra_row_1)
-        subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
-        subseed.style(container=False)
-        random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
-        reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
-        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
-
-    with FormRow(visible=False) as seed_extra_row_2:
-        seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")
-
-    random_seed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_seed')}", show_progress=False, inputs=[], outputs=[])
-    random_subseed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_subseed')}", show_progress=False, inputs=[], outputs=[])
-
-    def change_visibility(show):
-        return {comp: gr_show(show) for comp in seed_extras}
-
-    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
-
-    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
-
-
-
-def connect_clear_prompt(button):
-    """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
-    button.click(
-        _js="clear_prompt",
-        fn=None,
-        inputs=[],
-        outputs=[],
-    )
-
-
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
-    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
-        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
-        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
-    def copy_seed(gen_info_string: str, index):
-        res = -1
-
-        try:
-            gen_info = json.loads(gen_info_string)
-            index -= gen_info.get('index_of_first_image', 0)
-
-            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
-                all_subseeds = gen_info.get('all_subseeds', [-1])
-                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
-            else:
-                all_seeds = gen_info.get('all_seeds', [-1])
-                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
-        except json.decoder.JSONDecodeError:
-            if gen_info_string:
-                errors.report(f"Error parsing JSON generation info: {gen_info_string}")
-
-        return [res, gr_show(False)]
-
-    reuse_seed.click(
-        fn=copy_seed,
-        _js="(x, y) => [x, selected_gallery_index()]",
-        show_progress=False,
-        inputs=[generation_info, dummy_component],
-        outputs=[seed, dummy_component]
-    )
-
-
-def update_token_counter(text, steps):
-    try:
-        text, _ = extra_networks.parse_prompt(text)
-
-        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
-        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
-
-    except Exception:
-        # a parsing error can happen here during typing, and we don't want to bother the user with
-        # messages related to it in console
-        prompt_schedules = [[[steps, text]]]
-
-    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
-    prompts = [prompt_text for step, prompt_text in flat_prompts]
-    token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
-    return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
-
-
-def create_toprow(is_img2img):
-    id_part = "img2img" if is_img2img else "txt2img"
-
-    with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
-        with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
-
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
-
-        button_interrogate = None
-        button_deepbooru = None
-        if is_img2img:
-            with gr.Column(scale=1, elem_classes="interrogate-col"):
-                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
-        with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
-            with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
-                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
-                skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
-                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
-                skip.click(
-                    fn=lambda: shared.state.skip(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                interrupt.click(
-                    fn=lambda: shared.state.interrupt(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-            with gr.Row(elem_id=f"{id_part}_tools"):
-                paste = ToolButton(value=paste_symbol, elem_id="paste")
-                clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
-                extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
-                prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
-                save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
-                restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
-
-                token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
-                token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-                negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
-                negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
-
-                clear_prompt_button.click(
-                    fn=lambda *x: x,
-                    _js="confirm_clear_prompt",
-                    inputs=[prompt, negative_prompt],
-                    outputs=[prompt, negative_prompt],
-                )
-
-            with gr.Row(elem_id=f"{id_part}_styles_row"):
-                prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
-                create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")
-
-    return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button
-
-
-def setup_progressbar(*args, **kwargs):
-    pass
-
-
-def apply_setting(key, value):
-    if value is None:
-        return gr.update()
-
-    if shared.cmd_opts.freeze_settings:
-        return gr.update()
-
-    # dont allow model to be swapped when model hash exists in prompt
-    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
-        return gr.update()
-
-    if key == "sd_model_checkpoint":
-        ckpt_info = sd_models.get_closet_checkpoint_match(value)
-
-        if ckpt_info is not None:
-            value = ckpt_info.title
-        else:
-            return gr.update()
-
-    comp_args = opts.data_labels[key].component_args
-    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
-        return
-
-    valtype = type(opts.data_labels[key].default)
-    oldval = opts.data.get(key, None)
-    opts.data[key] = valtype(value) if valtype != type(None) else value
-    if oldval != value and opts.data_labels[key].onchange is not None:
-        opts.data_labels[key].onchange()
-
-    opts.save(shared.config_filename)
-    return getattr(opts, key)
-
-
-def create_output_panel(tabname, outdir):
-    return ui_common.create_output_panel(tabname, outdir)
-
-
-def create_sampler_and_steps_selection(choices, tabname):
-    if opts.samplers_in_dropdown:
-        with FormRow(elem_id=f"sampler_selection_{tabname}"):
-            sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
-            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
-    else:
-        with FormGroup(elem_id=f"sampler_selection_{tabname}"):
-            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
-            sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
-
-    return steps, sampler_index
-
-
-def ordered_ui_categories():
-    user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
-
-    for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
-        yield category
-
-
-def create_override_settings_dropdown(tabname, row):
-    dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
-
-    dropdown.change(
-        fn=lambda x: gr.Dropdown.update(visible=bool(x)),
-        inputs=[dropdown],
-        outputs=[dropdown],
-    )
-
-    return dropdown
-
-
-def create_ui():
-    import modules.img2img
-    import modules.txt2img
-
-    reload_javascript()
-
-    parameters_copypaste.reset()
-
-    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
-    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
-
-    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=False)
-
-        dummy_component = gr.Label(visible=False)
-        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
-
-        with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
-            from modules import ui_extra_networks
-            extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='compact', elem_id="txt2img_settings"):
-                modules.scripts.scripts_txt2img.prepare_ui()
-
-                for category in ordered_ui_categories():
-                    if category == "sampler":
-                        steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
-
-                    elif category == "dimensions":
-                        with FormRow():
-                            with gr.Column(elem_id="txt2img_column_size", scale=4):
-                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
-                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
-
-                            with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
-
-                            if opts.dimensions_and_batch_together:
-                                with gr.Column(elem_id="txt2img_column_batch"):
-                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
-                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
-
-                    elif category == "cfg":
-                        cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
-
-                    elif category == "seed":
-                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
-
-                    elif category == "checkboxes":
-                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
-                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
-                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
-                            enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
-                            hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
-
-                    elif category == "hires_fix":
-                        with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
-                            with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
-                                hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
-                                hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
-                                denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
-
-                            with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
-                                hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
-                                hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
-                                hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
-
-                            with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
-                                hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")
-
-                            with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
-                                with gr.Column(scale=80):
-                                    with gr.Row():
-                                        hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
-                                with gr.Column(scale=80):
-                                    with gr.Row():
-                                        hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
-
-                    elif category == "batch":
-                        if not opts.dimensions_and_batch_together:
-                            with FormRow(elem_id="txt2img_column_batch"):
-                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
-                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
-
-                    elif category == "override_settings":
-                        with FormRow(elem_id="txt2img_override_settings_row") as row:
-                            override_settings = create_override_settings_dropdown('txt2img', row)
-
-                    elif category == "scripts":
-                        with FormGroup(elem_id="txt2img_script_container"):
-                            custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
-
-                    else:
-                        modules.scripts.scripts_txt2img.setup_ui_for_section(category)
-
-            hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
-
-            for component in hr_resolution_preview_inputs:
-                event = component.release if isinstance(component, gr.Slider) else component.change
-
-                event(
-                    fn=calc_resolution_hires,
-                    inputs=hr_resolution_preview_inputs,
-                    outputs=[hr_final_resolution],
-                    show_progress=False,
-                )
-                event(
-                    None,
-                    _js="onCalcResolutionHires",
-                    inputs=hr_resolution_preview_inputs,
-                    outputs=[],
-                    show_progress=False,
-                )
-
-            txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            txt2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
-                _js="submit",
-                inputs=[
-                    dummy_component,
-                    txt2img_prompt,
-                    txt2img_negative_prompt,
-                    txt2img_prompt_styles,
-                    steps,
-                    sampler_index,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    enable_hr,
-                    denoising_strength,
-                    hr_scale,
-                    hr_upscaler,
-                    hr_second_pass_steps,
-                    hr_resize_x,
-                    hr_resize_y,
-                    hr_sampler_index,
-                    hr_prompt,
-                    hr_negative_prompt,
-                    override_settings,
-
-                ] + custom_inputs,
-
-                outputs=[
-                    txt2img_gallery,
-                    generation_info,
-                    html_info,
-                    html_log,
-                ],
-                show_progress=False,
-            )
-
-            txt2img_prompt.submit(**txt2img_args)
-            submit.click(**txt2img_args)
-
-            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
-
-            restore_progress_button.click(
-                fn=progress.restore_progress,
-                _js="restoreProgressTxt2img",
-                inputs=[dummy_component],
-                outputs=[
-                    txt2img_gallery,
-                    generation_info,
-                    html_info,
-                    html_log,
-                ],
-                show_progress=False,
-            )
-
-            txt_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    txt_prompt_img
-                ],
-                outputs=[
-                    txt2img_prompt,
-                    txt_prompt_img
-                ],
-                show_progress=False,
-            )
-
-            enable_hr.change(
-                fn=lambda x: gr_show(x),
-                inputs=[enable_hr],
-                outputs=[hr_options],
-                show_progress = False,
-            )
-
-            txt2img_paste_fields = [
-                (txt2img_prompt, "Prompt"),
-                (txt2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
-                (denoising_strength, "Denoising strength"),
-                (enable_hr, lambda d: "Denoising strength" in d),
-                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (hr_scale, "Hires upscale"),
-                (hr_upscaler, "Hires upscaler"),
-                (hr_second_pass_steps, "Hires steps"),
-                (hr_resize_x, "Hires resize-1"),
-                (hr_resize_y, "Hires resize-2"),
-                (hr_sampler_index, "Hires sampler"),
-                (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
-                (hr_prompt, "Hires prompt"),
-                (hr_negative_prompt, "Hires negative prompt"),
-                (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
-                *modules.scripts.scripts_txt2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
-            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
-            ))
-
-            txt2img_preview_params = [
-                txt2img_prompt,
-                txt2img_negative_prompt,
-                steps,
-                sampler_index,
-                cfg_scale,
-                seed,
-                width,
-                height,
-            ]
-
-            token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
-            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
-
-            ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
-
-    modules.scripts.scripts_current = modules.scripts.scripts_img2img
-    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
-
-    with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=True)
-
-        img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
-
-        with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks:
-            from modules import ui_extra_networks
-            extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'img2img')
-
-        with FormRow().style(equal_height=False):
-            with gr.Column(variant='compact', elem_id="img2img_settings"):
-                copy_image_buttons = []
-                copy_image_destinations = {}
-
-                def add_copy_image_controls(tab_name, elem):
-                    with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
-                        gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
-
-                        for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
-                            if name == tab_name:
-                                gr.Button(title, interactive=False)
-                                copy_image_destinations[name] = elem
-                                continue
-
-                            button = gr.Button(title)
-                            copy_image_buttons.append((button, name, elem))
-
-                with gr.Tabs(elem_id="mode_img2img"):
-                    img2img_selected_tab = gr.State(0)
-
-                    with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=opts.img2img_editor_height)
-                        add_copy_image_controls('img2img', init_img)
-
-                    with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
-                        sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
-                        add_copy_image_controls('sketch', sketch)
-
-                    with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
-                        add_copy_image_controls('inpaint', init_img_with_mask)
-
-                    with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
-                        inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
-                        inpaint_color_sketch_orig = gr.State(None)
-                        add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
-
-                        def update_orig(image, state):
-                            if image is not None:
-                                same_size = state is not None and state.size == image.size
-                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
-                                edited = same_size and has_exact_match
-                                return image if not edited or state is None else state
-
-                        inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
-
-                    with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
-                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
-                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask")
-
-                    with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
-                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(
-                            "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
-                            "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
-                            f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
-                            f"{hidden}</p>"
-                        )
-                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
-                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
-                        img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
-                        with gr.Accordion("PNG info", open=False):
-                            img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
-                            img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
-                            img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
-
-                    img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
-
-                    for i, tab in enumerate(img2img_tabs):
-                        tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
-
-                def copy_image(img):
-                    if isinstance(img, dict) and 'image' in img:
-                        return img['image']
-
-                    return img
-
-                for button, name, elem in copy_image_buttons:
-                    button.click(
-                        fn=copy_image,
-                        inputs=[elem],
-                        outputs=[copy_image_destinations[name]],
-                    )
-                    button.click(
-                        fn=lambda: None,
-                        _js=f"switch_to_{name.replace(' ', '_')}",
-                        inputs=[],
-                        outputs=[],
-                    )
-
-                with FormRow():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
-
-                modules.scripts.scripts_img2img.prepare_ui()
-
-                for category in ordered_ui_categories():
-                    if category == "sampler":
-                        steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
-
-                    elif category == "dimensions":
-                        with FormRow():
-                            with gr.Column(elem_id="img2img_column_size", scale=4):
-                                selected_scale_tab = gr.State(value=0)
-
-                                with gr.Tabs():
-                                    with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
-                                        with FormRow():
-                                            with gr.Column(elem_id="img2img_column_size", scale=4):
-                                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
-                                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
-                                            with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
-                                                detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
-
-                                    with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
-                                        scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
-
-                                        with FormRow():
-                                            scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
-                                            gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
-                                            button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
-
-                                    on_change_args = dict(
-                                        fn=resize_from_to_html,
-                                        _js="currentImg2imgSourceResolution",
-                                        inputs=[dummy_component, dummy_component, scale_by],
-                                        outputs=scale_by_html,
-                                        show_progress=False,
-                                    )
-
-                                    scale_by.release(**on_change_args)
-                                    button_update_resize_to.click(**on_change_args)
-
-                                    # the code below is meant to update the resolution label after the image in the image selection UI has changed.
-                                    # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
-                                    # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
-                                    for component in [init_img, sketch]:
-                                        component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
-
-                            tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
-                            tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
-
-                            if opts.dimensions_and_batch_together:
-                                with gr.Column(elem_id="img2img_column_batch"):
-                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
-                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
-                    elif category == "cfg":
-                        with FormGroup():
-                            with FormRow():
-                                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
-                                image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
-                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
-
-                    elif category == "seed":
-                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
-
-                    elif category == "checkboxes":
-                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
-                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
-                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
-
-                    elif category == "batch":
-                        if not opts.dimensions_and_batch_together:
-                            with FormRow(elem_id="img2img_column_batch"):
-                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
-                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
-                    elif category == "override_settings":
-                        with FormRow(elem_id="img2img_override_settings_row") as row:
-                            override_settings = create_override_settings_dropdown('img2img', row)
-
-                    elif category == "scripts":
-                        with FormGroup(elem_id="img2img_script_container"):
-                            custom_inputs = modules.scripts.scripts_img2img.setup_ui()
-
-                    elif category == "inpaint":
-                        with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
-                            with FormRow():
-                                mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
-                                mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
-
-                            with FormRow():
-                                inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
-
-                            with FormRow():
-                                inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
-
-                            with FormRow():
-                                with gr.Column():
-                                    inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
-
-                                with gr.Column(scale=4):
-                                    inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
-
-                            def select_img2img_tab(tab):
-                                return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
-
-                            for i, elem in enumerate(img2img_tabs):
-                                elem.select(
-                                    fn=lambda tab=i: select_img2img_tab(tab),
-                                    inputs=[],
-                                    outputs=[inpaint_controls, mask_alpha],
-                                )
-                    else:
-                        modules.scripts.scripts_img2img.setup_ui_for_section(category)
-
-            img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            img2img_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    img2img_prompt_img
-                ],
-                outputs=[
-                    img2img_prompt,
-                    img2img_prompt_img
-                ],
-                show_progress=False,
-            )
-
-            img2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
-                _js="submit_img2img",
-                inputs=[
-                    dummy_component,
-                    dummy_component,
-                    img2img_prompt,
-                    img2img_negative_prompt,
-                    img2img_prompt_styles,
-                    init_img,
-                    sketch,
-                    init_img_with_mask,
-                    inpaint_color_sketch,
-                    inpaint_color_sketch_orig,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                    steps,
-                    sampler_index,
-                    mask_blur,
-                    mask_alpha,
-                    inpainting_fill,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    image_cfg_scale,
-                    denoising_strength,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    selected_scale_tab,
-                    height,
-                    width,
-                    scale_by,
-                    resize_mode,
-                    inpaint_full_res,
-                    inpaint_full_res_padding,
-                    inpainting_mask_invert,
-                    img2img_batch_input_dir,
-                    img2img_batch_output_dir,
-                    img2img_batch_inpaint_mask_dir,
-                    override_settings,
-                    img2img_batch_use_png_info,
-                    img2img_batch_png_info_props,
-                    img2img_batch_png_info_dir,
-                ] + custom_inputs,
-                outputs=[
-                    img2img_gallery,
-                    generation_info,
-                    html_info,
-                    html_log,
-                ],
-                show_progress=False,
-            )
-
-            interrogate_args = dict(
-                _js="get_img2img_tab_index",
-                inputs=[
-                    dummy_component,
-                    img2img_batch_input_dir,
-                    img2img_batch_output_dir,
-                    init_img,
-                    sketch,
-                    init_img_with_mask,
-                    inpaint_color_sketch,
-                    init_img_inpaint,
-                ],
-                outputs=[img2img_prompt, dummy_component],
-            )
-
-            img2img_prompt.submit(**img2img_args)
-            submit.click(**img2img_args)
-
-            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
-
-            detect_image_size_btn.click(
-                fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
-                _js="currentImg2imgSourceResolution",
-                inputs=[dummy_component, dummy_component, dummy_component],
-                outputs=[width, height],
-                show_progress=False,
-            )
-
-            restore_progress_button.click(
-                fn=progress.restore_progress,
-                _js="restoreProgressImg2img",
-                inputs=[dummy_component],
-                outputs=[
-                    img2img_gallery,
-                    generation_info,
-                    html_info,
-                    html_log,
-                ],
-                show_progress=False,
-            )
-
-            img2img_interrogate.click(
-                fn=lambda *args: process_interrogate(interrogate, *args),
-                **interrogate_args,
-            )
-
-            img2img_deepbooru.click(
-                fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
-                **interrogate_args,
-            )
-
-            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
-            style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
-            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-
-            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
-                button.click(
-                    fn=add_style,
-                    _js="ask_for_style_name",
-                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
-                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
-                    inputs=[dummy_component, prompt, negative_prompt],
-                    outputs=[txt2img_prompt_styles, img2img_prompt_styles],
-                )
-
-            for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
-                button.click(
-                    fn=apply_styles,
-                    _js=js_func,
-                    inputs=[prompt, negative_prompt, styles],
-                    outputs=[prompt, negative_prompt, styles],
-                )
-
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
-
-            ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
-
-            img2img_paste_fields = [
-                (img2img_prompt, "Prompt"),
-                (img2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (image_cfg_scale, "Image CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
-                (denoising_strength, "Denoising strength"),
-                (mask_blur, "Mask blur"),
-                *modules.scripts.scripts_img2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
-            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
-            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
-            ))
-
-    modules.scripts.scripts_current = None
-
-    with gr.Blocks(analytics_enabled=False) as extras_interface:
-        ui_postprocessing.create_ui()
-
-    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
-
-            with gr.Column(variant='panel'):
-                html = gr.HTML()
-                generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
-                html2 = gr.HTML()
-                with gr.Row():
-                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-
-                for tabname, button in buttons.items():
-                    parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                        paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
-                    ))
-
-        image.change(
-            fn=wrap_gradio_call(modules.extras.run_pnginfo),
-            inputs=[image],
-            outputs=[html, generation_info, html2],
-        )
-
-    def update_interp_description(value):
-        interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
-        interp_descriptions = {
-            "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
-            "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
-            "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
-        }
-        return interp_descriptions[value]
-
-    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='compact'):
-                interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
-
-                with FormRow(elem_id="modelmerger_models"):
-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
-                    create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
-
-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
-                    create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
-
-                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
-                    create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
-
-                custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
-                interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
-                interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
-
-                with FormRow():
-                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
-                    save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
-                    save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
-
-                with FormRow():
-                    with gr.Column():
-                        config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
-
-                    with gr.Column():
-                        with FormRow():
-                            bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
-                            create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
-
-                with FormRow():
-                    discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
-
-                with gr.Row():
-                    modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
-
-            with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
-                with gr.Group(elem_id="modelmerger_results_panel"):
-                    modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
-
-    with gr.Blocks(analytics_enabled=False) as train_interface:
-        with gr.Row().style(equal_height=False):
-            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
-
-        with gr.Row(variant="compact").style(equal_height=False):
-            with gr.Tabs(elem_id="train_tabs"):
-
-                with gr.Tab(label="Create embedding", id="create_embedding"):
-                    new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
-                    initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
-                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
-                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
-
-                with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
-                    new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
-                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
-                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
-                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
-                    new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
-                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
-
-                with gr.Tab(label="Preprocess images", id="preprocess_images"):
-                    process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
-                    process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
-                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
-
-                    with gr.Row():
-                        process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
-                        process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
-                        process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
-                        process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
-                        process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
-                        process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
-
-                    with gr.Row(visible=False) as process_split_extra_row:
-                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
-                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
-
-                    with gr.Row(visible=False) as process_focal_crop_row:
-                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
-                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
-                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
-                        process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
-
-                    with gr.Column(visible=False) as process_multicrop_col:
-                        gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
-                        with gr.Row():
-                            process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
-                            process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
-                        with gr.Row():
-                            process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
-                            process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
-                        with gr.Row():
-                            process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
-                            process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            with gr.Row():
-                                interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
-                            run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
-
-                    process_split.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_split],
-                        outputs=[process_split_extra_row],
-                    )
-
-                    process_focal_crop.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_focal_crop],
-                        outputs=[process_focal_crop_row],
-                    )
-
-                    process_multicrop.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_multicrop],
-                        outputs=[process_multicrop_col],
-                    )
-
-                def get_textual_inversion_template_names():
-                    return sorted(textual_inversion.textual_inversion_templates)
-
-                with gr.Tab(label="Train", id="train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
-                    with FormRow():
-                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-
-                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
-                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
-
-                    with FormRow():
-                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
-                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
-
-                    with FormRow():
-                        clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
-                        clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
-
-                    with FormRow():
-                        batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
-                        gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
-
-                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
-                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
-
-                    with FormRow():
-                        template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
-                        create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
-
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
-                    varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
-                    steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
-
-                    with FormRow():
-                        create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
-                        save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
-
-                    use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
-
-                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
-                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
-
-                    shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
-                    tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
-
-                    latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
-
-                    with gr.Row():
-                        train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
-                        interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
-                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
-
-                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
-
-                script_callbacks.ui_train_tabs_callback(params)
-
-            with gr.Column(elem_id='ti_gallery_container'):
-                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
-                gr.HTML(elem_id="ti_progress", value="")
-                ti_outcome = gr.HTML(elem_id="ti_error", value="")
-
-        create_embedding.click(
-            fn=modules.textual_inversion.ui.create_embedding,
-            inputs=[
-                new_embedding_name,
-                initialization_text,
-                nvpt,
-                overwrite_old_embedding,
-            ],
-            outputs=[
-                train_embedding_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        create_hypernetwork.click(
-            fn=modules.hypernetworks.ui.create_hypernetwork,
-            inputs=[
-                new_hypernetwork_name,
-                new_hypernetwork_sizes,
-                overwrite_old_hypernetwork,
-                new_hypernetwork_layer_structure,
-                new_hypernetwork_activation_func,
-                new_hypernetwork_initialization_option,
-                new_hypernetwork_add_layer_norm,
-                new_hypernetwork_use_dropout,
-                new_hypernetwork_dropout_structure
-            ],
-            outputs=[
-                train_hypernetwork_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        run_preprocess.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                dummy_component,
-                process_src,
-                process_dst,
-                process_width,
-                process_height,
-                preprocess_txt_action,
-                process_keep_original_size,
-                process_flip,
-                process_split,
-                process_caption,
-                process_caption_deepbooru,
-                process_split_threshold,
-                process_overlap_ratio,
-                process_focal_crop,
-                process_focal_crop_face_weight,
-                process_focal_crop_entropy_weight,
-                process_focal_crop_edges_weight,
-                process_focal_crop_debug,
-                process_multicrop,
-                process_multicrop_mindim,
-                process_multicrop_maxdim,
-                process_multicrop_minarea,
-                process_multicrop_maxarea,
-                process_multicrop_objective,
-                process_multicrop_threshold,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ],
-        )
-
-        train_embedding.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                dummy_component,
-                train_embedding_name,
-                embedding_learn_rate,
-                batch_size,
-                gradient_step,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                varsize,
-                steps,
-                clip_grad_mode,
-                clip_grad_value,
-                shuffle_tags,
-                tag_drop_out,
-                latent_sampling_method,
-                use_weight,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                save_image_with_stored_embedding,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        train_hypernetwork.click(
-            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                dummy_component,
-                train_hypernetwork_name,
-                hypernetwork_learn_rate,
-                batch_size,
-                gradient_step,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                varsize,
-                steps,
-                clip_grad_mode,
-                clip_grad_value,
-                shuffle_tags,
-                tag_drop_out,
-                latent_sampling_method,
-                use_weight,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        interrupt_training.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-        interrupt_preprocessing.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-    loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
-
-    settings = ui_settings.UiSettings()
-    settings.create_ui(loadsave, dummy_component)
-
-    interfaces = [
-        (txt2img_interface, "txt2img", "txt2img"),
-        (img2img_interface, "img2img", "img2img"),
-        (extras_interface, "Extras", "extras"),
-        (pnginfo_interface, "PNG Info", "pnginfo"),
-        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
-        (train_interface, "Train", "train"),
-    ]
-
-    interfaces += script_callbacks.ui_tabs_callback()
-    interfaces += [(settings.interface, "Settings", "settings")]
-
-    extensions_interface = ui_extensions.create_ui()
-    interfaces += [(extensions_interface, "Extensions", "extensions")]
-
-    shared.tab_names = []
-    for _interface, label, _ifid in interfaces:
-        shared.tab_names.append(label)
-
-    with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
-        settings.add_quicksettings()
-
-        parameters_copypaste.connect_paste_params_buttons()
-
-        with gr.Tabs(elem_id="tabs") as tabs:
-            tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
-            sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999))
-
-            for interface, label, ifid in sorted_interfaces:
-                if label in shared.opts.hidden_tabs:
-                    continue
-                with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
-                    interface.render()
-
-            for interface, _label, ifid in interfaces:
-                if ifid in ["extensions", "settings"]:
-                    continue
-
-                loadsave.add_block(interface, ifid)
-
-            loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
-
-            loadsave.setup_ui()
-
-        if os.path.exists(os.path.join(script_path, "notification.mp3")):
-            gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
-
-        footer = shared.html("footer.html")
-        footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
-        gr.HTML(footer, elem_id="footer")
-
-        settings.add_functionality(demo)
-
-        update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
-        settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
-        demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
-
-        def modelmerger(*args):
-            try:
-                results = modules.extras.run_modelmerger(*args)
-            except Exception as e:
-                errors.report("Error loading/saving model file", exc_info=True)
-                modules.sd_models.list_models()  # to remove the potentially missing models from the list
-                return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
-            return results
-
-        modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
-        modelmerger_merge.click(
-            fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
-            _js='modelmerger',
-            inputs=[
-                dummy_component,
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                interp_method,
-                interp_amount,
-                save_as_half,
-                custom_name,
-                checkpoint_format,
-                config_source,
-                bake_in_vae,
-                discard_weights,
-                save_metadata,
-            ],
-            outputs=[
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                settings.component_dict['sd_model_checkpoint'],
-                modelmerger_result,
-            ]
-        )
-
-    loadsave.dump_defaults()
-    demo.ui_loadsave = loadsave
-
-    # Required as a workaround for change() event not triggering when loading values from ui-config.json
-    interp_description.value = update_interp_description(interp_method.value)
-
-    return demo
-
-
-def versions_html():
-    import torch
-    import launch
-
-    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
-    commit = launch.commit_hash()
-    tag = launch.git_tag()
-
-    if shared.xformers_available:
-        import xformers
-        xformers_version = xformers.__version__
-    else:
-        xformers_version = "N/A"
-
-    return f"""
-version: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{tag}</a>
-&#x2000;•&#x2000;
-python: <span title="{sys.version}">{python_version}</span>
-&#x2000;•&#x2000;
-torch: {getattr(torch, '__long_version__',torch.__version__)}
-&#x2000;•&#x2000;
-xformers: {xformers_version}
-&#x2000;•&#x2000;
-gradio: {gr.__version__}
-&#x2000;•&#x2000;
-checkpoint: <a id="sd_checkpoint_hash">N/A</a>
-"""
-
-
-def setup_ui_api(app):
-    from pydantic import BaseModel, Field
-    from typing import List
-
-    class QuicksettingsHint(BaseModel):
-        name: str = Field(title="Name of the quicksettings field")
-        label: str = Field(title="Label of the quicksettings field")
-
-    def quicksettings_hint():
-        return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
-
-    app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
-
-    app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
-
-    app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
-
-    def download_sysinfo(attachment=False):
-        from fastapi.responses import PlainTextResponse
-
-        text = sysinfo.get()
-        filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
-
-        return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
-
-    app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
-    app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
-
diff --git a/modules/ui_checkpoint_merger.py b/modules/ui_checkpoint_merger.py
new file mode 100644
index 00000000..07ecee7b
--- /dev/null
+++ b/modules/ui_checkpoint_merger.py
@@ -0,0 +1,1621 @@
+import datetime
+import json
+import mimetypes
+import os
+import sys
+from functools import reduce
+import warnings
+
+import gradio as gr
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin  # noqa: F401
+from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
+
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
+from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
+from modules.paths import script_path
+from modules.ui_common import create_refresh_button
+from modules.ui_gradio_extensions import reload_javascript
+
+
+from modules.shared import opts, cmd_opts
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.scripts
+import modules.shared as shared
+import modules.styles
+import modules.textual_inversion.ui
+from modules import prompt_parser
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
+from modules.textual_inversion import textual_inversion
+import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
+import modules.extras
+
+create_setting_component = ui_settings.create_setting_component
+
+warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
+
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
+mimetypes.init()
+mimetypes.add_type('application/javascript', '.js')
+
+if not cmd_opts.share and not cmd_opts.listen:
+    # fix gradio phoning home
+    gradio.utils.version_check = lambda: None
+    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
+
+if cmd_opts.ngrok is not None:
+    import modules.ngrok as ngrok
+    print('ngrok authtoken detected, trying to connect...')
+    ngrok.connect(
+        cmd_opts.ngrok,
+        cmd_opts.port if cmd_opts.port is not None else 7860,
+        cmd_opts.ngrok_options
+        )
+
+
+def gr_show(visible=True):
+    return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+# Using constants for these since the variation selector isn't visible.
+# Important that they exactly match script.js for tooltip to work.
+random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
+reuse_symbol = '\u267b\ufe0f'  # ♻️
+paste_symbol = '\u2199\ufe0f'  # ↙
+refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+apply_style_symbol = '\U0001f4cb'  # 📋
+clear_prompt_symbol = '\U0001f5d1\ufe0f'  # 🗑️
+extra_networks_symbol = '\U0001F3B4'  # 🎴
+switch_values_symbol = '\U000021C5' # ⇅
+restore_progress_symbol = '\U0001F300' # 🌀
+detect_image_size_symbol = '\U0001F4D0'  # 📐
+up_down_symbol = '\u2195\ufe0f' # ↕️
+
+
+plaintext_to_html = ui_common.plaintext_to_html
+
+
+def send_gradio_gallery_to_image(x):
+    if len(x) == 0:
+        return None
+    return image_from_url_text(x[0])
+
+
+def add_style(name: str, prompt: str, negative_prompt: str):
+    if name is None:
+        return [gr_show() for x in range(4)]
+
+    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
+    shared.prompt_styles.styles[style.name] = style
+    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
+    # reserialize all styles every time we save them
+    shared.prompt_styles.save_styles(shared.styles_filename)
+
+    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
+
+
+def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
+    from modules import processing, devices
+
+    if not enable:
+        return ""
+
+    p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
+
+    with devices.autocast():
+        p.init([""], [0], [0])
+
+    return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
+
+
+def resize_from_to_html(width, height, scale_by):
+    target_width = int(width * scale_by)
+    target_height = int(height * scale_by)
+
+    if not target_width or not target_height:
+        return "no image selected"
+
+    return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
+
+
+def apply_styles(prompt, prompt_neg, styles):
+    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
+    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
+
+    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
+
+
+def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
+    if mode in {0, 1, 3, 4}:
+        return [interrogation_function(ii_singles[mode]), None]
+    elif mode == 2:
+        return [interrogation_function(ii_singles[mode]["image"]), None]
+    elif mode == 5:
+        assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
+        images = shared.listfiles(ii_input_dir)
+        print(f"Will process {len(images)} images.")
+        if ii_output_dir != "":
+            os.makedirs(ii_output_dir, exist_ok=True)
+        else:
+            ii_output_dir = ii_input_dir
+
+        for image in images:
+            img = Image.open(image)
+            filename = os.path.basename(image)
+            left, _ = os.path.splitext(filename)
+            print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
+
+        return [gr.update(), None]
+
+
+def interrogate(image):
+    prompt = shared.interrogator.interrogate(image.convert("RGB"))
+    return gr.update() if prompt is None else prompt
+
+
+def interrogate_deepbooru(image):
+    prompt = deepbooru.model.tag(image)
+    return gr.update() if prompt is None else prompt
+
+
+def create_seed_inputs(target_interface):
+    with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
+        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
+        seed.style(container=False)
+        random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
+        reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
+
+        seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)
+
+    # Components to show/hide based on the 'Extra' checkbox
+    seed_extras = []
+
+    with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
+        seed_extras.append(seed_extra_row_1)
+        subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
+        subseed.style(container=False)
+        random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
+        reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
+
+    with FormRow(visible=False) as seed_extra_row_2:
+        seed_extras.append(seed_extra_row_2)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")
+
+    random_seed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_seed')}", show_progress=False, inputs=[], outputs=[])
+    random_subseed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_subseed')}", show_progress=False, inputs=[], outputs=[])
+
+    def change_visibility(show):
+        return {comp: gr_show(show) for comp in seed_extras}
+
+    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
+
+    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
+
+
+
+def connect_clear_prompt(button):
+    """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
+    button.click(
+        _js="clear_prompt",
+        fn=None,
+        inputs=[],
+        outputs=[],
+    )
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
+    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+    def copy_seed(gen_info_string: str, index):
+        res = -1
+
+        try:
+            gen_info = json.loads(gen_info_string)
+            index -= gen_info.get('index_of_first_image', 0)
+
+            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+                all_subseeds = gen_info.get('all_subseeds', [-1])
+                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+            else:
+                all_seeds = gen_info.get('all_seeds', [-1])
+                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+        except json.decoder.JSONDecodeError:
+            if gen_info_string:
+                errors.report(f"Error parsing JSON generation info: {gen_info_string}")
+
+        return [res, gr_show(False)]
+
+    reuse_seed.click(
+        fn=copy_seed,
+        _js="(x, y) => [x, selected_gallery_index()]",
+        show_progress=False,
+        inputs=[generation_info, dummy_component],
+        outputs=[seed, dummy_component]
+    )
+
+
+def update_token_counter(text, steps):
+    try:
+        text, _ = extra_networks.parse_prompt(text)
+
+        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+
+    except Exception:
+        # a parsing error can happen here during typing, and we don't want to bother the user with
+        # messages related to it in console
+        prompt_schedules = [[[steps, text]]]
+
+    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+    prompts = [prompt_text for step, prompt_text in flat_prompts]
+    token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
+    return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
+
+
+def create_toprow(is_img2img):
+    id_part = "img2img" if is_img2img else "txt2img"
+
+    with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
+        with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+
+        button_interrogate = None
+        button_deepbooru = None
+        if is_img2img:
+            with gr.Column(scale=1, elem_classes="interrogate-col"):
+                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+        with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
+            with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
+                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+
+                skip.click(
+                    fn=lambda: shared.state.skip(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                interrupt.click(
+                    fn=lambda: shared.state.interrupt(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+            with gr.Row(elem_id=f"{id_part}_tools"):
+                paste = ToolButton(value=paste_symbol, elem_id="paste")
+                clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
+                extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
+                prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
+                save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
+                restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
+
+                token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
+                token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+                negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
+                negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
+
+                clear_prompt_button.click(
+                    fn=lambda *x: x,
+                    _js="confirm_clear_prompt",
+                    inputs=[prompt, negative_prompt],
+                    outputs=[prompt, negative_prompt],
+                )
+
+            with gr.Row(elem_id=f"{id_part}_styles_row"):
+                prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
+                create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")
+
+    return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button
+
+
+def setup_progressbar(*args, **kwargs):
+    pass
+
+
+def apply_setting(key, value):
+    if value is None:
+        return gr.update()
+
+    if shared.cmd_opts.freeze_settings:
+        return gr.update()
+
+    # dont allow model to be swapped when model hash exists in prompt
+    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+        return gr.update()
+
+    if key == "sd_model_checkpoint":
+        ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+        if ckpt_info is not None:
+            value = ckpt_info.title
+        else:
+            return gr.update()
+
+    comp_args = opts.data_labels[key].component_args
+    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+        return
+
+    valtype = type(opts.data_labels[key].default)
+    oldval = opts.data.get(key, None)
+    opts.data[key] = valtype(value) if valtype != type(None) else value
+    if oldval != value and opts.data_labels[key].onchange is not None:
+        opts.data_labels[key].onchange()
+
+    opts.save(shared.config_filename)
+    return getattr(opts, key)
+
+
+def create_output_panel(tabname, outdir):
+    return ui_common.create_output_panel(tabname, outdir)
+
+
+def create_sampler_and_steps_selection(choices, tabname):
+    if opts.samplers_in_dropdown:
+        with FormRow(elem_id=f"sampler_selection_{tabname}"):
+            sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
+    else:
+        with FormGroup(elem_id=f"sampler_selection_{tabname}"):
+            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
+            sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+
+    return steps, sampler_index
+
+
+def ordered_ui_categories():
+    user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
+
+    for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
+        yield category
+
+
+def create_override_settings_dropdown(tabname, row):
+    dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
+
+    dropdown.change(
+        fn=lambda x: gr.Dropdown.update(visible=bool(x)),
+        inputs=[dropdown],
+        outputs=[dropdown],
+    )
+
+    return dropdown
+
+
+def create_ui():
+    import modules.img2img
+    import modules.txt2img
+
+    reload_javascript()
+
+    parameters_copypaste.reset()
+
+    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
+    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
+    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+        txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=False)
+
+        dummy_component = gr.Label(visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
+
+        with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
+            from modules import ui_extra_networks
+            extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='compact', elem_id="txt2img_settings"):
+                modules.scripts.scripts_txt2img.prepare_ui()
+
+                for category in ordered_ui_categories():
+                    if category == "sampler":
+                        steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
+
+                    elif category == "dimensions":
+                        with FormRow():
+                            with gr.Column(elem_id="txt2img_column_size", scale=4):
+                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+
+                            with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
+
+                            if opts.dimensions_and_batch_together:
+                                with gr.Column(elem_id="txt2img_column_batch"):
+                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+                    elif category == "cfg":
+                        cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
+
+                    elif category == "seed":
+                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
+
+                    elif category == "checkboxes":
+                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
+                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
+                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
+                            enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
+                            hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
+
+                    elif category == "hires_fix":
+                        with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
+                            with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
+                                hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+                                hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
+                                denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
+
+                            with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
+                                hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
+                                hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
+                                hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
+
+                            with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
+                                hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")
+
+                            with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
+                                with gr.Column(scale=80):
+                                    with gr.Row():
+                                        hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
+                                with gr.Column(scale=80):
+                                    with gr.Row():
+                                        hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+
+                    elif category == "batch":
+                        if not opts.dimensions_and_batch_together:
+                            with FormRow(elem_id="txt2img_column_batch"):
+                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+                    elif category == "override_settings":
+                        with FormRow(elem_id="txt2img_override_settings_row") as row:
+                            override_settings = create_override_settings_dropdown('txt2img', row)
+
+                    elif category == "scripts":
+                        with FormGroup(elem_id="txt2img_script_container"):
+                            custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+
+                    else:
+                        modules.scripts.scripts_txt2img.setup_ui_for_section(category)
+
+            hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
+
+            for component in hr_resolution_preview_inputs:
+                event = component.release if isinstance(component, gr.Slider) else component.change
+
+                event(
+                    fn=calc_resolution_hires,
+                    inputs=hr_resolution_preview_inputs,
+                    outputs=[hr_final_resolution],
+                    show_progress=False,
+                )
+                event(
+                    None,
+                    _js="onCalcResolutionHires",
+                    inputs=hr_resolution_preview_inputs,
+                    outputs=[],
+                    show_progress=False,
+                )
+
+            txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            txt2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
+                _js="submit",
+                inputs=[
+                    dummy_component,
+                    txt2img_prompt,
+                    txt2img_negative_prompt,
+                    txt2img_prompt_styles,
+                    steps,
+                    sampler_index,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    height,
+                    width,
+                    enable_hr,
+                    denoising_strength,
+                    hr_scale,
+                    hr_upscaler,
+                    hr_second_pass_steps,
+                    hr_resize_x,
+                    hr_resize_y,
+                    hr_sampler_index,
+                    hr_prompt,
+                    hr_negative_prompt,
+                    override_settings,
+
+                ] + custom_inputs,
+
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            txt2img_prompt.submit(**txt2img_args)
+            submit.click(**txt2img_args)
+
+            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
+
+            restore_progress_button.click(
+                fn=progress.restore_progress,
+                _js="restoreProgressTxt2img",
+                inputs=[dummy_component],
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            txt_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    txt_prompt_img
+                ],
+                outputs=[
+                    txt2img_prompt,
+                    txt_prompt_img
+                ],
+                show_progress=False,
+            )
+
+            enable_hr.change(
+                fn=lambda x: gr_show(x),
+                inputs=[enable_hr],
+                outputs=[hr_options],
+                show_progress = False,
+            )
+
+            txt2img_paste_fields = [
+                (txt2img_prompt, "Prompt"),
+                (txt2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+                (denoising_strength, "Denoising strength"),
+                (enable_hr, lambda d: "Denoising strength" in d),
+                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+                (hr_scale, "Hires upscale"),
+                (hr_upscaler, "Hires upscaler"),
+                (hr_second_pass_steps, "Hires steps"),
+                (hr_resize_x, "Hires resize-1"),
+                (hr_resize_y, "Hires resize-2"),
+                (hr_sampler_index, "Hires sampler"),
+                (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
+                (hr_prompt, "Hires prompt"),
+                (hr_negative_prompt, "Hires negative prompt"),
+                (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
+                *modules.scripts.scripts_txt2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
+            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
+            ))
+
+            txt2img_preview_params = [
+                txt2img_prompt,
+                txt2img_negative_prompt,
+                steps,
+                sampler_index,
+                cfg_scale,
+                seed,
+                width,
+                height,
+            ]
+
+            token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
+            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
+
+            ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
+
+    modules.scripts.scripts_current = modules.scripts.scripts_img2img
+    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
+    with gr.Blocks(analytics_enabled=False) as img2img_interface:
+        img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=True)
+
+        img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
+
+        with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks:
+            from modules import ui_extra_networks
+            extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'img2img')
+
+        with FormRow().style(equal_height=False):
+            with gr.Column(variant='compact', elem_id="img2img_settings"):
+                copy_image_buttons = []
+                copy_image_destinations = {}
+
+                def add_copy_image_controls(tab_name, elem):
+                    with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
+                        gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
+
+                        for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
+                            if name == tab_name:
+                                gr.Button(title, interactive=False)
+                                copy_image_destinations[name] = elem
+                                continue
+
+                            button = gr.Button(title)
+                            copy_image_buttons.append((button, name, elem))
+
+                with gr.Tabs(elem_id="mode_img2img"):
+                    img2img_selected_tab = gr.State(0)
+
+                    with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        add_copy_image_controls('img2img', init_img)
+
+                    with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
+                        sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        add_copy_image_controls('sketch', sketch)
+
+                    with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        add_copy_image_controls('inpaint', init_img_with_mask)
+
+                    with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
+                        inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        inpaint_color_sketch_orig = gr.State(None)
+                        add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
+
+                        def update_orig(image, state):
+                            if image is not None:
+                                same_size = state is not None and state.size == image.size
+                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+                                edited = same_size and has_exact_match
+                                return image if not edited or state is None else state
+
+                        inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
+
+                    with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
+                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
+                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask")
+
+                    with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
+                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+                        gr.HTML(
+                            "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
+                            "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
+                            f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
+                            f"{hidden}</p>"
+                        )
+                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
+                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
+                        img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+                        with gr.Accordion("PNG info", open=False):
+                            img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
+                            img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
+                            img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
+
+                    img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
+
+                    for i, tab in enumerate(img2img_tabs):
+                        tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
+
+                def copy_image(img):
+                    if isinstance(img, dict) and 'image' in img:
+                        return img['image']
+
+                    return img
+
+                for button, name, elem in copy_image_buttons:
+                    button.click(
+                        fn=copy_image,
+                        inputs=[elem],
+                        outputs=[copy_image_destinations[name]],
+                    )
+                    button.click(
+                        fn=lambda: None,
+                        _js=f"switch_to_{name.replace(' ', '_')}",
+                        inputs=[],
+                        outputs=[],
+                    )
+
+                with FormRow():
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
+
+                modules.scripts.scripts_img2img.prepare_ui()
+
+                for category in ordered_ui_categories():
+                    if category == "sampler":
+                        steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
+
+                    elif category == "dimensions":
+                        with FormRow():
+                            with gr.Column(elem_id="img2img_column_size", scale=4):
+                                selected_scale_tab = gr.State(value=0)
+
+                                with gr.Tabs():
+                                    with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
+                                        with FormRow():
+                                            with gr.Column(elem_id="img2img_column_size", scale=4):
+                                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+                                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+                                            with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+                                                detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
+
+                                    with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
+                                        scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
+
+                                        with FormRow():
+                                            scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
+                                            gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
+                                            button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
+
+                                    on_change_args = dict(
+                                        fn=resize_from_to_html,
+                                        _js="currentImg2imgSourceResolution",
+                                        inputs=[dummy_component, dummy_component, scale_by],
+                                        outputs=scale_by_html,
+                                        show_progress=False,
+                                    )
+
+                                    scale_by.release(**on_change_args)
+                                    button_update_resize_to.click(**on_change_args)
+
+                                    # the code below is meant to update the resolution label after the image in the image selection UI has changed.
+                                    # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
+                                    # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
+                                    for component in [init_img, sketch]:
+                                        component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
+
+                            tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
+                            tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
+
+                            if opts.dimensions_and_batch_together:
+                                with gr.Column(elem_id="img2img_column_batch"):
+                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+                    elif category == "cfg":
+                        with FormGroup():
+                            with FormRow():
+                                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+                                image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
+                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
+
+                    elif category == "seed":
+                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
+
+                    elif category == "checkboxes":
+                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
+                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
+                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
+
+                    elif category == "batch":
+                        if not opts.dimensions_and_batch_together:
+                            with FormRow(elem_id="img2img_column_batch"):
+                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+                    elif category == "override_settings":
+                        with FormRow(elem_id="img2img_override_settings_row") as row:
+                            override_settings = create_override_settings_dropdown('img2img', row)
+
+                    elif category == "scripts":
+                        with FormGroup(elem_id="img2img_script_container"):
+                            custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+
+                    elif category == "inpaint":
+                        with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
+                            with FormRow():
+                                mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+                                mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
+
+                            with FormRow():
+                                inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
+
+                            with FormRow():
+                                inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
+
+                            with FormRow():
+                                with gr.Column():
+                                    inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
+
+                                with gr.Column(scale=4):
+                                    inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+
+                            def select_img2img_tab(tab):
+                                return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
+
+                            for i, elem in enumerate(img2img_tabs):
+                                elem.select(
+                                    fn=lambda tab=i: select_img2img_tab(tab),
+                                    inputs=[],
+                                    outputs=[inpaint_controls, mask_alpha],
+                                )
+                    else:
+                        modules.scripts.scripts_img2img.setup_ui_for_section(category)
+
+            img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            img2img_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    img2img_prompt_img
+                ],
+                outputs=[
+                    img2img_prompt,
+                    img2img_prompt_img
+                ],
+                show_progress=False,
+            )
+
+            img2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
+                _js="submit_img2img",
+                inputs=[
+                    dummy_component,
+                    dummy_component,
+                    img2img_prompt,
+                    img2img_negative_prompt,
+                    img2img_prompt_styles,
+                    init_img,
+                    sketch,
+                    init_img_with_mask,
+                    inpaint_color_sketch,
+                    inpaint_color_sketch_orig,
+                    init_img_inpaint,
+                    init_mask_inpaint,
+                    steps,
+                    sampler_index,
+                    mask_blur,
+                    mask_alpha,
+                    inpainting_fill,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    image_cfg_scale,
+                    denoising_strength,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    selected_scale_tab,
+                    height,
+                    width,
+                    scale_by,
+                    resize_mode,
+                    inpaint_full_res,
+                    inpaint_full_res_padding,
+                    inpainting_mask_invert,
+                    img2img_batch_input_dir,
+                    img2img_batch_output_dir,
+                    img2img_batch_inpaint_mask_dir,
+                    override_settings,
+                    img2img_batch_use_png_info,
+                    img2img_batch_png_info_props,
+                    img2img_batch_png_info_dir,
+                ] + custom_inputs,
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            interrogate_args = dict(
+                _js="get_img2img_tab_index",
+                inputs=[
+                    dummy_component,
+                    img2img_batch_input_dir,
+                    img2img_batch_output_dir,
+                    init_img,
+                    sketch,
+                    init_img_with_mask,
+                    inpaint_color_sketch,
+                    init_img_inpaint,
+                ],
+                outputs=[img2img_prompt, dummy_component],
+            )
+
+            img2img_prompt.submit(**img2img_args)
+            submit.click(**img2img_args)
+
+            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
+
+            detect_image_size_btn.click(
+                fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
+                _js="currentImg2imgSourceResolution",
+                inputs=[dummy_component, dummy_component, dummy_component],
+                outputs=[width, height],
+                show_progress=False,
+            )
+
+            restore_progress_button.click(
+                fn=progress.restore_progress,
+                _js="restoreProgressImg2img",
+                inputs=[dummy_component],
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            img2img_interrogate.click(
+                fn=lambda *args: process_interrogate(interrogate, *args),
+                **interrogate_args,
+            )
+
+            img2img_deepbooru.click(
+                fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
+                **interrogate_args,
+            )
+
+            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
+            style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
+            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
+
+            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
+                button.click(
+                    fn=add_style,
+                    _js="ask_for_style_name",
+                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
+                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
+                    inputs=[dummy_component, prompt, negative_prompt],
+                    outputs=[txt2img_prompt_styles, img2img_prompt_styles],
+                )
+
+            for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
+                button.click(
+                    fn=apply_styles,
+                    _js=js_func,
+                    inputs=[prompt, negative_prompt, styles],
+                    outputs=[prompt, negative_prompt, styles],
+                )
+
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
+
+            ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
+
+            img2img_paste_fields = [
+                (img2img_prompt, "Prompt"),
+                (img2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (image_cfg_scale, "Image CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+                (denoising_strength, "Denoising strength"),
+                (mask_blur, "Mask blur"),
+                *modules.scripts.scripts_img2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
+            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
+            ))
+
+    modules.scripts.scripts_current = None
+
+    with gr.Blocks(analytics_enabled=False) as extras_interface:
+        ui_postprocessing.create_ui()
+
+    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
+
+            with gr.Column(variant='panel'):
+                html = gr.HTML()
+                generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
+                html2 = gr.HTML()
+                with gr.Row():
+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+
+                for tabname, button in buttons.items():
+                    parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                        paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
+                    ))
+
+        image.change(
+            fn=wrap_gradio_call(modules.extras.run_pnginfo),
+            inputs=[image],
+            outputs=[html, generation_info, html2],
+        )
+
+    def update_interp_description(value):
+        interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
+        interp_descriptions = {
+            "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
+            "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
+            "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
+        }
+        return interp_descriptions[value]
+
+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='compact'):
+                interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
+
+                with FormRow(elem_id="modelmerger_models"):
+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                    create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
+
+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                    create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
+
+                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+                    create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
+
+                custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
+                interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
+                interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
+
+                with FormRow():
+                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+                    save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
+                    save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
+
+                with FormRow():
+                    with gr.Column():
+                        config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
+
+                    with gr.Column():
+                        with FormRow():
+                            bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
+                            create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
+
+                with FormRow():
+                    discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
+
+                with gr.Row():
+                    modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
+
+            with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
+                with gr.Group(elem_id="modelmerger_results_panel"):
+                    modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
+
+    with gr.Blocks(analytics_enabled=False) as train_interface:
+        with gr.Row().style(equal_height=False):
+            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
+        with gr.Row(variant="compact").style(equal_height=False):
+            with gr.Tabs(elem_id="train_tabs"):
+
+                with gr.Tab(label="Create embedding", id="create_embedding"):
+                    new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
+                    initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
+
+                with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
+                    new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
+                    new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
+
+                with gr.Tab(label="Preprocess images", id="preprocess_images"):
+                    process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
+                    process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
+
+                    with gr.Row():
+                        process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
+                        process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
+                        process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
+                        process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
+                        process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
+
+                    with gr.Row(visible=False) as process_split_extra_row:
+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
+
+                    with gr.Row(visible=False) as process_focal_crop_row:
+                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
+
+                    with gr.Column(visible=False) as process_multicrop_col:
+                        gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
+                        with gr.Row():
+                            process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
+                            process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
+                        with gr.Row():
+                            process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
+                            process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
+                        with gr.Row():
+                            process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
+                            process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            with gr.Row():
+                                interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
+                            run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
+
+                    process_split.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_split],
+                        outputs=[process_split_extra_row],
+                    )
+
+                    process_focal_crop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_focal_crop],
+                        outputs=[process_focal_crop_row],
+                    )
+
+                    process_multicrop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_multicrop],
+                        outputs=[process_multicrop_col],
+                    )
+
+                def get_textual_inversion_template_names():
+                    return sorted(textual_inversion.textual_inversion_templates)
+
+                with gr.Tab(label="Train", id="train"):
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+                    with FormRow():
+                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
+
+                    with FormRow():
+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
+
+                    with FormRow():
+                        clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
+                        clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
+
+                    with FormRow():
+                        batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
+                        gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
+
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
+
+                    with FormRow():
+                        template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
+                        create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
+
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
+                    varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
+                    steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
+
+                    with FormRow():
+                        create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
+                        save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
+
+                    use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
+
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
+
+                    shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
+                    tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
+
+                    latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
+
+                    with gr.Row():
+                        train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
+                        interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
+
+                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+                script_callbacks.ui_train_tabs_callback(params)
+
+            with gr.Column(elem_id='ti_gallery_container'):
+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
+                gr.HTML(elem_id="ti_progress", value="")
+                ti_outcome = gr.HTML(elem_id="ti_error", value="")
+
+        create_embedding.click(
+            fn=modules.textual_inversion.ui.create_embedding,
+            inputs=[
+                new_embedding_name,
+                initialization_text,
+                nvpt,
+                overwrite_old_embedding,
+            ],
+            outputs=[
+                train_embedding_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        create_hypernetwork.click(
+            fn=modules.hypernetworks.ui.create_hypernetwork,
+            inputs=[
+                new_hypernetwork_name,
+                new_hypernetwork_sizes,
+                overwrite_old_hypernetwork,
+                new_hypernetwork_layer_structure,
+                new_hypernetwork_activation_func,
+                new_hypernetwork_initialization_option,
+                new_hypernetwork_add_layer_norm,
+                new_hypernetwork_use_dropout,
+                new_hypernetwork_dropout_structure
+            ],
+            outputs=[
+                train_hypernetwork_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        run_preprocess.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                dummy_component,
+                process_src,
+                process_dst,
+                process_width,
+                process_height,
+                preprocess_txt_action,
+                process_keep_original_size,
+                process_flip,
+                process_split,
+                process_caption,
+                process_caption_deepbooru,
+                process_split_threshold,
+                process_overlap_ratio,
+                process_focal_crop,
+                process_focal_crop_face_weight,
+                process_focal_crop_entropy_weight,
+                process_focal_crop_edges_weight,
+                process_focal_crop_debug,
+                process_multicrop,
+                process_multicrop_mindim,
+                process_multicrop_maxdim,
+                process_multicrop_minarea,
+                process_multicrop_maxarea,
+                process_multicrop_objective,
+                process_multicrop_threshold,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ],
+        )
+
+        train_embedding.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                dummy_component,
+                train_embedding_name,
+                embedding_learn_rate,
+                batch_size,
+                gradient_step,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                varsize,
+                steps,
+                clip_grad_mode,
+                clip_grad_value,
+                shuffle_tags,
+                tag_drop_out,
+                latent_sampling_method,
+                use_weight,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                save_image_with_stored_embedding,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        train_hypernetwork.click(
+            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                dummy_component,
+                train_hypernetwork_name,
+                hypernetwork_learn_rate,
+                batch_size,
+                gradient_step,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                varsize,
+                steps,
+                clip_grad_mode,
+                clip_grad_value,
+                shuffle_tags,
+                tag_drop_out,
+                latent_sampling_method,
+                use_weight,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        interrupt_training.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+        interrupt_preprocessing.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+    loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
+
+    settings = ui_settings.UiSettings()
+    settings.create_ui(loadsave, dummy_component)
+
+    interfaces = [
+        (txt2img_interface, "txt2img", "txt2img"),
+        (img2img_interface, "img2img", "img2img"),
+        (extras_interface, "Extras", "extras"),
+        (pnginfo_interface, "PNG Info", "pnginfo"),
+        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (train_interface, "Train", "train"),
+    ]
+
+    interfaces += script_callbacks.ui_tabs_callback()
+    interfaces += [(settings.interface, "Settings", "settings")]
+
+    extensions_interface = ui_extensions.create_ui()
+    interfaces += [(extensions_interface, "Extensions", "extensions")]
+
+    shared.tab_names = []
+    for _interface, label, _ifid in interfaces:
+        shared.tab_names.append(label)
+
+    with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
+        settings.add_quicksettings()
+
+        parameters_copypaste.connect_paste_params_buttons()
+
+        with gr.Tabs(elem_id="tabs") as tabs:
+            tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
+            sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999))
+
+            for interface, label, ifid in sorted_interfaces:
+                if label in shared.opts.hidden_tabs:
+                    continue
+                with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
+                    interface.render()
+
+            for interface, _label, ifid in interfaces:
+                if ifid in ["extensions", "settings"]:
+                    continue
+
+                loadsave.add_block(interface, ifid)
+
+            loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
+
+            loadsave.setup_ui()
+
+        if os.path.exists(os.path.join(script_path, "notification.mp3")):
+            gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+        footer = shared.html("footer.html")
+        footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
+        gr.HTML(footer, elem_id="footer")
+
+        settings.add_functionality(demo)
+
+        update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
+        settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+        demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+
+        def modelmerger(*args):
+            try:
+                results = modules.extras.run_modelmerger(*args)
+            except Exception as e:
+                errors.report("Error loading/saving model file", exc_info=True)
+                modules.sd_models.list_models()  # to remove the potentially missing models from the list
+                return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
+            return results
+
+        modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
+        modelmerger_merge.click(
+            fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
+            _js='modelmerger',
+            inputs=[
+                dummy_component,
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                interp_method,
+                interp_amount,
+                save_as_half,
+                custom_name,
+                checkpoint_format,
+                config_source,
+                bake_in_vae,
+                discard_weights,
+                save_metadata,
+            ],
+            outputs=[
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                settings.component_dict['sd_model_checkpoint'],
+                modelmerger_result,
+            ]
+        )
+
+    loadsave.dump_defaults()
+    demo.ui_loadsave = loadsave
+
+    # Required as a workaround for change() event not triggering when loading values from ui-config.json
+    interp_description.value = update_interp_description(interp_method.value)
+
+    return demo
+
+
+def versions_html():
+    import torch
+    import launch
+
+    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
+    commit = launch.commit_hash()
+    tag = launch.git_tag()
+
+    if shared.xformers_available:
+        import xformers
+        xformers_version = xformers.__version__
+    else:
+        xformers_version = "N/A"
+
+    return f"""
+version: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{tag}</a>
+&#x2000;•&#x2000;
+python: <span title="{sys.version}">{python_version}</span>
+&#x2000;•&#x2000;
+torch: {getattr(torch, '__long_version__',torch.__version__)}
+&#x2000;•&#x2000;
+xformers: {xformers_version}
+&#x2000;•&#x2000;
+gradio: {gr.__version__}
+&#x2000;•&#x2000;
+checkpoint: <a id="sd_checkpoint_hash">N/A</a>
+"""
+
+
+def setup_ui_api(app):
+    from pydantic import BaseModel, Field
+    from typing import List
+
+    class QuicksettingsHint(BaseModel):
+        name: str = Field(title="Name of the quicksettings field")
+        label: str = Field(title="Label of the quicksettings field")
+
+    def quicksettings_hint():
+        return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
+
+    app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
+
+    app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
+
+    app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
+
+    def download_sysinfo(attachment=False):
+        from fastapi.responses import PlainTextResponse
+
+        text = sysinfo.get()
+        filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
+
+        return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
+
+    app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
+    app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
+
-- 
cgit v1.2.3


From 00429544905aaea124fee29b87e3a3b9469c64b0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 1 Aug 2023 07:15:16 +0300
Subject: Split history: mv temp modules/ui.py

---
 modules/ui.py | 1621 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1621 insertions(+)
 create mode 100644 modules/ui.py

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
new file mode 100644
index 00000000..07ecee7b
--- /dev/null
+++ b/modules/ui.py
@@ -0,0 +1,1621 @@
+import datetime
+import json
+import mimetypes
+import os
+import sys
+from functools import reduce
+import warnings
+
+import gradio as gr
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin  # noqa: F401
+from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
+
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
+from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
+from modules.paths import script_path
+from modules.ui_common import create_refresh_button
+from modules.ui_gradio_extensions import reload_javascript
+
+
+from modules.shared import opts, cmd_opts
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.scripts
+import modules.shared as shared
+import modules.styles
+import modules.textual_inversion.ui
+from modules import prompt_parser
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
+from modules.textual_inversion import textual_inversion
+import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
+import modules.extras
+
+create_setting_component = ui_settings.create_setting_component
+
+warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
+
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
+mimetypes.init()
+mimetypes.add_type('application/javascript', '.js')
+
+if not cmd_opts.share and not cmd_opts.listen:
+    # fix gradio phoning home
+    gradio.utils.version_check = lambda: None
+    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
+
+if cmd_opts.ngrok is not None:
+    import modules.ngrok as ngrok
+    print('ngrok authtoken detected, trying to connect...')
+    ngrok.connect(
+        cmd_opts.ngrok,
+        cmd_opts.port if cmd_opts.port is not None else 7860,
+        cmd_opts.ngrok_options
+        )
+
+
+def gr_show(visible=True):
+    return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+# Using constants for these since the variation selector isn't visible.
+# Important that they exactly match script.js for tooltip to work.
+random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
+reuse_symbol = '\u267b\ufe0f'  # ♻️
+paste_symbol = '\u2199\ufe0f'  # ↙
+refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+apply_style_symbol = '\U0001f4cb'  # 📋
+clear_prompt_symbol = '\U0001f5d1\ufe0f'  # 🗑️
+extra_networks_symbol = '\U0001F3B4'  # 🎴
+switch_values_symbol = '\U000021C5' # ⇅
+restore_progress_symbol = '\U0001F300' # 🌀
+detect_image_size_symbol = '\U0001F4D0'  # 📐
+up_down_symbol = '\u2195\ufe0f' # ↕️
+
+
+plaintext_to_html = ui_common.plaintext_to_html
+
+
+def send_gradio_gallery_to_image(x):
+    if len(x) == 0:
+        return None
+    return image_from_url_text(x[0])
+
+
+def add_style(name: str, prompt: str, negative_prompt: str):
+    if name is None:
+        return [gr_show() for x in range(4)]
+
+    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
+    shared.prompt_styles.styles[style.name] = style
+    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
+    # reserialize all styles every time we save them
+    shared.prompt_styles.save_styles(shared.styles_filename)
+
+    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
+
+
+def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
+    from modules import processing, devices
+
+    if not enable:
+        return ""
+
+    p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
+
+    with devices.autocast():
+        p.init([""], [0], [0])
+
+    return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
+
+
+def resize_from_to_html(width, height, scale_by):
+    target_width = int(width * scale_by)
+    target_height = int(height * scale_by)
+
+    if not target_width or not target_height:
+        return "no image selected"
+
+    return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
+
+
+def apply_styles(prompt, prompt_neg, styles):
+    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
+    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
+
+    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
+
+
+def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
+    if mode in {0, 1, 3, 4}:
+        return [interrogation_function(ii_singles[mode]), None]
+    elif mode == 2:
+        return [interrogation_function(ii_singles[mode]["image"]), None]
+    elif mode == 5:
+        assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
+        images = shared.listfiles(ii_input_dir)
+        print(f"Will process {len(images)} images.")
+        if ii_output_dir != "":
+            os.makedirs(ii_output_dir, exist_ok=True)
+        else:
+            ii_output_dir = ii_input_dir
+
+        for image in images:
+            img = Image.open(image)
+            filename = os.path.basename(image)
+            left, _ = os.path.splitext(filename)
+            print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
+
+        return [gr.update(), None]
+
+
+def interrogate(image):
+    prompt = shared.interrogator.interrogate(image.convert("RGB"))
+    return gr.update() if prompt is None else prompt
+
+
+def interrogate_deepbooru(image):
+    prompt = deepbooru.model.tag(image)
+    return gr.update() if prompt is None else prompt
+
+
+def create_seed_inputs(target_interface):
+    with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
+        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
+        seed.style(container=False)
+        random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
+        reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
+
+        seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)
+
+    # Components to show/hide based on the 'Extra' checkbox
+    seed_extras = []
+
+    with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
+        seed_extras.append(seed_extra_row_1)
+        subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
+        subseed.style(container=False)
+        random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
+        reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
+
+    with FormRow(visible=False) as seed_extra_row_2:
+        seed_extras.append(seed_extra_row_2)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")
+
+    random_seed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_seed')}", show_progress=False, inputs=[], outputs=[])
+    random_subseed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_subseed')}", show_progress=False, inputs=[], outputs=[])
+
+    def change_visibility(show):
+        return {comp: gr_show(show) for comp in seed_extras}
+
+    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
+
+    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
+
+
+
+def connect_clear_prompt(button):
+    """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
+    button.click(
+        _js="clear_prompt",
+        fn=None,
+        inputs=[],
+        outputs=[],
+    )
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
+    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+    def copy_seed(gen_info_string: str, index):
+        res = -1
+
+        try:
+            gen_info = json.loads(gen_info_string)
+            index -= gen_info.get('index_of_first_image', 0)
+
+            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+                all_subseeds = gen_info.get('all_subseeds', [-1])
+                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+            else:
+                all_seeds = gen_info.get('all_seeds', [-1])
+                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+        except json.decoder.JSONDecodeError:
+            if gen_info_string:
+                errors.report(f"Error parsing JSON generation info: {gen_info_string}")
+
+        return [res, gr_show(False)]
+
+    reuse_seed.click(
+        fn=copy_seed,
+        _js="(x, y) => [x, selected_gallery_index()]",
+        show_progress=False,
+        inputs=[generation_info, dummy_component],
+        outputs=[seed, dummy_component]
+    )
+
+
+def update_token_counter(text, steps):
+    try:
+        text, _ = extra_networks.parse_prompt(text)
+
+        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+
+    except Exception:
+        # a parsing error can happen here during typing, and we don't want to bother the user with
+        # messages related to it in console
+        prompt_schedules = [[[steps, text]]]
+
+    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+    prompts = [prompt_text for step, prompt_text in flat_prompts]
+    token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
+    return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
+
+
+def create_toprow(is_img2img):
+    id_part = "img2img" if is_img2img else "txt2img"
+
+    with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
+        with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+
+        button_interrogate = None
+        button_deepbooru = None
+        if is_img2img:
+            with gr.Column(scale=1, elem_classes="interrogate-col"):
+                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+        with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
+            with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
+                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+
+                skip.click(
+                    fn=lambda: shared.state.skip(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                interrupt.click(
+                    fn=lambda: shared.state.interrupt(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+            with gr.Row(elem_id=f"{id_part}_tools"):
+                paste = ToolButton(value=paste_symbol, elem_id="paste")
+                clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
+                extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
+                prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
+                save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
+                restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
+
+                token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
+                token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+                negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
+                negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
+
+                clear_prompt_button.click(
+                    fn=lambda *x: x,
+                    _js="confirm_clear_prompt",
+                    inputs=[prompt, negative_prompt],
+                    outputs=[prompt, negative_prompt],
+                )
+
+            with gr.Row(elem_id=f"{id_part}_styles_row"):
+                prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
+                create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")
+
+    return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button
+
+
+def setup_progressbar(*args, **kwargs):
+    pass
+
+
+def apply_setting(key, value):
+    if value is None:
+        return gr.update()
+
+    if shared.cmd_opts.freeze_settings:
+        return gr.update()
+
+    # dont allow model to be swapped when model hash exists in prompt
+    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+        return gr.update()
+
+    if key == "sd_model_checkpoint":
+        ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+        if ckpt_info is not None:
+            value = ckpt_info.title
+        else:
+            return gr.update()
+
+    comp_args = opts.data_labels[key].component_args
+    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+        return
+
+    valtype = type(opts.data_labels[key].default)
+    oldval = opts.data.get(key, None)
+    opts.data[key] = valtype(value) if valtype != type(None) else value
+    if oldval != value and opts.data_labels[key].onchange is not None:
+        opts.data_labels[key].onchange()
+
+    opts.save(shared.config_filename)
+    return getattr(opts, key)
+
+
+def create_output_panel(tabname, outdir):
+    return ui_common.create_output_panel(tabname, outdir)
+
+
+def create_sampler_and_steps_selection(choices, tabname):
+    if opts.samplers_in_dropdown:
+        with FormRow(elem_id=f"sampler_selection_{tabname}"):
+            sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
+    else:
+        with FormGroup(elem_id=f"sampler_selection_{tabname}"):
+            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
+            sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+
+    return steps, sampler_index
+
+
+def ordered_ui_categories():
+    user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
+
+    for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
+        yield category
+
+
+def create_override_settings_dropdown(tabname, row):
+    dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
+
+    dropdown.change(
+        fn=lambda x: gr.Dropdown.update(visible=bool(x)),
+        inputs=[dropdown],
+        outputs=[dropdown],
+    )
+
+    return dropdown
+
+
+def create_ui():
+    import modules.img2img
+    import modules.txt2img
+
+    reload_javascript()
+
+    parameters_copypaste.reset()
+
+    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
+    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
+    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+        txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=False)
+
+        dummy_component = gr.Label(visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
+
+        with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
+            from modules import ui_extra_networks
+            extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='compact', elem_id="txt2img_settings"):
+                modules.scripts.scripts_txt2img.prepare_ui()
+
+                for category in ordered_ui_categories():
+                    if category == "sampler":
+                        steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
+
+                    elif category == "dimensions":
+                        with FormRow():
+                            with gr.Column(elem_id="txt2img_column_size", scale=4):
+                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+
+                            with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
+
+                            if opts.dimensions_and_batch_together:
+                                with gr.Column(elem_id="txt2img_column_batch"):
+                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+                    elif category == "cfg":
+                        cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
+
+                    elif category == "seed":
+                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
+
+                    elif category == "checkboxes":
+                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
+                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
+                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
+                            enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
+                            hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
+
+                    elif category == "hires_fix":
+                        with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
+                            with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
+                                hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+                                hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
+                                denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
+
+                            with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
+                                hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
+                                hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
+                                hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
+
+                            with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
+                                hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")
+
+                            with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
+                                with gr.Column(scale=80):
+                                    with gr.Row():
+                                        hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
+                                with gr.Column(scale=80):
+                                    with gr.Row():
+                                        hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+
+                    elif category == "batch":
+                        if not opts.dimensions_and_batch_together:
+                            with FormRow(elem_id="txt2img_column_batch"):
+                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+                    elif category == "override_settings":
+                        with FormRow(elem_id="txt2img_override_settings_row") as row:
+                            override_settings = create_override_settings_dropdown('txt2img', row)
+
+                    elif category == "scripts":
+                        with FormGroup(elem_id="txt2img_script_container"):
+                            custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+
+                    else:
+                        modules.scripts.scripts_txt2img.setup_ui_for_section(category)
+
+            hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
+
+            for component in hr_resolution_preview_inputs:
+                event = component.release if isinstance(component, gr.Slider) else component.change
+
+                event(
+                    fn=calc_resolution_hires,
+                    inputs=hr_resolution_preview_inputs,
+                    outputs=[hr_final_resolution],
+                    show_progress=False,
+                )
+                event(
+                    None,
+                    _js="onCalcResolutionHires",
+                    inputs=hr_resolution_preview_inputs,
+                    outputs=[],
+                    show_progress=False,
+                )
+
+            txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            txt2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
+                _js="submit",
+                inputs=[
+                    dummy_component,
+                    txt2img_prompt,
+                    txt2img_negative_prompt,
+                    txt2img_prompt_styles,
+                    steps,
+                    sampler_index,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    height,
+                    width,
+                    enable_hr,
+                    denoising_strength,
+                    hr_scale,
+                    hr_upscaler,
+                    hr_second_pass_steps,
+                    hr_resize_x,
+                    hr_resize_y,
+                    hr_sampler_index,
+                    hr_prompt,
+                    hr_negative_prompt,
+                    override_settings,
+
+                ] + custom_inputs,
+
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            txt2img_prompt.submit(**txt2img_args)
+            submit.click(**txt2img_args)
+
+            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
+
+            restore_progress_button.click(
+                fn=progress.restore_progress,
+                _js="restoreProgressTxt2img",
+                inputs=[dummy_component],
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            txt_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    txt_prompt_img
+                ],
+                outputs=[
+                    txt2img_prompt,
+                    txt_prompt_img
+                ],
+                show_progress=False,
+            )
+
+            enable_hr.change(
+                fn=lambda x: gr_show(x),
+                inputs=[enable_hr],
+                outputs=[hr_options],
+                show_progress = False,
+            )
+
+            txt2img_paste_fields = [
+                (txt2img_prompt, "Prompt"),
+                (txt2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+                (denoising_strength, "Denoising strength"),
+                (enable_hr, lambda d: "Denoising strength" in d),
+                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+                (hr_scale, "Hires upscale"),
+                (hr_upscaler, "Hires upscaler"),
+                (hr_second_pass_steps, "Hires steps"),
+                (hr_resize_x, "Hires resize-1"),
+                (hr_resize_y, "Hires resize-2"),
+                (hr_sampler_index, "Hires sampler"),
+                (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
+                (hr_prompt, "Hires prompt"),
+                (hr_negative_prompt, "Hires negative prompt"),
+                (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
+                *modules.scripts.scripts_txt2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
+            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
+            ))
+
+            txt2img_preview_params = [
+                txt2img_prompt,
+                txt2img_negative_prompt,
+                steps,
+                sampler_index,
+                cfg_scale,
+                seed,
+                width,
+                height,
+            ]
+
+            token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
+            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
+
+            ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
+
+    modules.scripts.scripts_current = modules.scripts.scripts_img2img
+    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
+    with gr.Blocks(analytics_enabled=False) as img2img_interface:
+        img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=True)
+
+        img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
+
+        with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks:
+            from modules import ui_extra_networks
+            extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'img2img')
+
+        with FormRow().style(equal_height=False):
+            with gr.Column(variant='compact', elem_id="img2img_settings"):
+                copy_image_buttons = []
+                copy_image_destinations = {}
+
+                def add_copy_image_controls(tab_name, elem):
+                    with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
+                        gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
+
+                        for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
+                            if name == tab_name:
+                                gr.Button(title, interactive=False)
+                                copy_image_destinations[name] = elem
+                                continue
+
+                            button = gr.Button(title)
+                            copy_image_buttons.append((button, name, elem))
+
+                with gr.Tabs(elem_id="mode_img2img"):
+                    img2img_selected_tab = gr.State(0)
+
+                    with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        add_copy_image_controls('img2img', init_img)
+
+                    with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
+                        sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        add_copy_image_controls('sketch', sketch)
+
+                    with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        add_copy_image_controls('inpaint', init_img_with_mask)
+
+                    with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
+                        inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        inpaint_color_sketch_orig = gr.State(None)
+                        add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
+
+                        def update_orig(image, state):
+                            if image is not None:
+                                same_size = state is not None and state.size == image.size
+                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+                                edited = same_size and has_exact_match
+                                return image if not edited or state is None else state
+
+                        inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
+
+                    with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
+                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
+                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask")
+
+                    with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
+                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+                        gr.HTML(
+                            "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
+                            "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
+                            f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
+                            f"{hidden}</p>"
+                        )
+                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
+                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
+                        img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+                        with gr.Accordion("PNG info", open=False):
+                            img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
+                            img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
+                            img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
+
+                    img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
+
+                    for i, tab in enumerate(img2img_tabs):
+                        tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
+
+                def copy_image(img):
+                    if isinstance(img, dict) and 'image' in img:
+                        return img['image']
+
+                    return img
+
+                for button, name, elem in copy_image_buttons:
+                    button.click(
+                        fn=copy_image,
+                        inputs=[elem],
+                        outputs=[copy_image_destinations[name]],
+                    )
+                    button.click(
+                        fn=lambda: None,
+                        _js=f"switch_to_{name.replace(' ', '_')}",
+                        inputs=[],
+                        outputs=[],
+                    )
+
+                with FormRow():
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
+
+                modules.scripts.scripts_img2img.prepare_ui()
+
+                for category in ordered_ui_categories():
+                    if category == "sampler":
+                        steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
+
+                    elif category == "dimensions":
+                        with FormRow():
+                            with gr.Column(elem_id="img2img_column_size", scale=4):
+                                selected_scale_tab = gr.State(value=0)
+
+                                with gr.Tabs():
+                                    with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
+                                        with FormRow():
+                                            with gr.Column(elem_id="img2img_column_size", scale=4):
+                                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+                                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+                                            with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+                                                detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
+
+                                    with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
+                                        scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
+
+                                        with FormRow():
+                                            scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
+                                            gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
+                                            button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
+
+                                    on_change_args = dict(
+                                        fn=resize_from_to_html,
+                                        _js="currentImg2imgSourceResolution",
+                                        inputs=[dummy_component, dummy_component, scale_by],
+                                        outputs=scale_by_html,
+                                        show_progress=False,
+                                    )
+
+                                    scale_by.release(**on_change_args)
+                                    button_update_resize_to.click(**on_change_args)
+
+                                    # the code below is meant to update the resolution label after the image in the image selection UI has changed.
+                                    # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
+                                    # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
+                                    for component in [init_img, sketch]:
+                                        component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
+
+                            tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
+                            tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
+
+                            if opts.dimensions_and_batch_together:
+                                with gr.Column(elem_id="img2img_column_batch"):
+                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+                    elif category == "cfg":
+                        with FormGroup():
+                            with FormRow():
+                                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+                                image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
+                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
+
+                    elif category == "seed":
+                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
+
+                    elif category == "checkboxes":
+                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
+                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
+                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
+
+                    elif category == "batch":
+                        if not opts.dimensions_and_batch_together:
+                            with FormRow(elem_id="img2img_column_batch"):
+                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+                    elif category == "override_settings":
+                        with FormRow(elem_id="img2img_override_settings_row") as row:
+                            override_settings = create_override_settings_dropdown('img2img', row)
+
+                    elif category == "scripts":
+                        with FormGroup(elem_id="img2img_script_container"):
+                            custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+
+                    elif category == "inpaint":
+                        with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
+                            with FormRow():
+                                mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+                                mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
+
+                            with FormRow():
+                                inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
+
+                            with FormRow():
+                                inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
+
+                            with FormRow():
+                                with gr.Column():
+                                    inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
+
+                                with gr.Column(scale=4):
+                                    inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+
+                            def select_img2img_tab(tab):
+                                return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
+
+                            for i, elem in enumerate(img2img_tabs):
+                                elem.select(
+                                    fn=lambda tab=i: select_img2img_tab(tab),
+                                    inputs=[],
+                                    outputs=[inpaint_controls, mask_alpha],
+                                )
+                    else:
+                        modules.scripts.scripts_img2img.setup_ui_for_section(category)
+
+            img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            img2img_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    img2img_prompt_img
+                ],
+                outputs=[
+                    img2img_prompt,
+                    img2img_prompt_img
+                ],
+                show_progress=False,
+            )
+
+            img2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
+                _js="submit_img2img",
+                inputs=[
+                    dummy_component,
+                    dummy_component,
+                    img2img_prompt,
+                    img2img_negative_prompt,
+                    img2img_prompt_styles,
+                    init_img,
+                    sketch,
+                    init_img_with_mask,
+                    inpaint_color_sketch,
+                    inpaint_color_sketch_orig,
+                    init_img_inpaint,
+                    init_mask_inpaint,
+                    steps,
+                    sampler_index,
+                    mask_blur,
+                    mask_alpha,
+                    inpainting_fill,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    image_cfg_scale,
+                    denoising_strength,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    selected_scale_tab,
+                    height,
+                    width,
+                    scale_by,
+                    resize_mode,
+                    inpaint_full_res,
+                    inpaint_full_res_padding,
+                    inpainting_mask_invert,
+                    img2img_batch_input_dir,
+                    img2img_batch_output_dir,
+                    img2img_batch_inpaint_mask_dir,
+                    override_settings,
+                    img2img_batch_use_png_info,
+                    img2img_batch_png_info_props,
+                    img2img_batch_png_info_dir,
+                ] + custom_inputs,
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            interrogate_args = dict(
+                _js="get_img2img_tab_index",
+                inputs=[
+                    dummy_component,
+                    img2img_batch_input_dir,
+                    img2img_batch_output_dir,
+                    init_img,
+                    sketch,
+                    init_img_with_mask,
+                    inpaint_color_sketch,
+                    init_img_inpaint,
+                ],
+                outputs=[img2img_prompt, dummy_component],
+            )
+
+            img2img_prompt.submit(**img2img_args)
+            submit.click(**img2img_args)
+
+            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
+
+            detect_image_size_btn.click(
+                fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
+                _js="currentImg2imgSourceResolution",
+                inputs=[dummy_component, dummy_component, dummy_component],
+                outputs=[width, height],
+                show_progress=False,
+            )
+
+            restore_progress_button.click(
+                fn=progress.restore_progress,
+                _js="restoreProgressImg2img",
+                inputs=[dummy_component],
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
+            img2img_interrogate.click(
+                fn=lambda *args: process_interrogate(interrogate, *args),
+                **interrogate_args,
+            )
+
+            img2img_deepbooru.click(
+                fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
+                **interrogate_args,
+            )
+
+            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
+            style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
+            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
+
+            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
+                button.click(
+                    fn=add_style,
+                    _js="ask_for_style_name",
+                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
+                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
+                    inputs=[dummy_component, prompt, negative_prompt],
+                    outputs=[txt2img_prompt_styles, img2img_prompt_styles],
+                )
+
+            for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
+                button.click(
+                    fn=apply_styles,
+                    _js=js_func,
+                    inputs=[prompt, negative_prompt, styles],
+                    outputs=[prompt, negative_prompt, styles],
+                )
+
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
+
+            ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
+
+            img2img_paste_fields = [
+                (img2img_prompt, "Prompt"),
+                (img2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (image_cfg_scale, "Image CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+                (denoising_strength, "Denoising strength"),
+                (mask_blur, "Mask blur"),
+                *modules.scripts.scripts_img2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
+            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
+            ))
+
+    modules.scripts.scripts_current = None
+
+    with gr.Blocks(analytics_enabled=False) as extras_interface:
+        ui_postprocessing.create_ui()
+
+    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
+
+            with gr.Column(variant='panel'):
+                html = gr.HTML()
+                generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
+                html2 = gr.HTML()
+                with gr.Row():
+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+
+                for tabname, button in buttons.items():
+                    parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                        paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
+                    ))
+
+        image.change(
+            fn=wrap_gradio_call(modules.extras.run_pnginfo),
+            inputs=[image],
+            outputs=[html, generation_info, html2],
+        )
+
+    def update_interp_description(value):
+        interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
+        interp_descriptions = {
+            "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
+            "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
+            "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
+        }
+        return interp_descriptions[value]
+
+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='compact'):
+                interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
+
+                with FormRow(elem_id="modelmerger_models"):
+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                    create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
+
+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                    create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
+
+                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+                    create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
+
+                custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
+                interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
+                interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
+
+                with FormRow():
+                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+                    save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
+                    save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
+
+                with FormRow():
+                    with gr.Column():
+                        config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
+
+                    with gr.Column():
+                        with FormRow():
+                            bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
+                            create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
+
+                with FormRow():
+                    discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
+
+                with gr.Row():
+                    modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
+
+            with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
+                with gr.Group(elem_id="modelmerger_results_panel"):
+                    modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
+
+    with gr.Blocks(analytics_enabled=False) as train_interface:
+        with gr.Row().style(equal_height=False):
+            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
+        with gr.Row(variant="compact").style(equal_height=False):
+            with gr.Tabs(elem_id="train_tabs"):
+
+                with gr.Tab(label="Create embedding", id="create_embedding"):
+                    new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
+                    initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
+
+                with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
+                    new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
+                    new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
+
+                with gr.Tab(label="Preprocess images", id="preprocess_images"):
+                    process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
+                    process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
+
+                    with gr.Row():
+                        process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
+                        process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
+                        process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
+                        process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
+                        process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
+
+                    with gr.Row(visible=False) as process_split_extra_row:
+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
+
+                    with gr.Row(visible=False) as process_focal_crop_row:
+                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
+
+                    with gr.Column(visible=False) as process_multicrop_col:
+                        gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
+                        with gr.Row():
+                            process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
+                            process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
+                        with gr.Row():
+                            process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
+                            process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
+                        with gr.Row():
+                            process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
+                            process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            with gr.Row():
+                                interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
+                            run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
+
+                    process_split.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_split],
+                        outputs=[process_split_extra_row],
+                    )
+
+                    process_focal_crop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_focal_crop],
+                        outputs=[process_focal_crop_row],
+                    )
+
+                    process_multicrop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_multicrop],
+                        outputs=[process_multicrop_col],
+                    )
+
+                def get_textual_inversion_template_names():
+                    return sorted(textual_inversion.textual_inversion_templates)
+
+                with gr.Tab(label="Train", id="train"):
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+                    with FormRow():
+                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
+
+                    with FormRow():
+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
+
+                    with FormRow():
+                        clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
+                        clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
+
+                    with FormRow():
+                        batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
+                        gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
+
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
+
+                    with FormRow():
+                        template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
+                        create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
+
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
+                    varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
+                    steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
+
+                    with FormRow():
+                        create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
+                        save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
+
+                    use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
+
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
+
+                    shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
+                    tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
+
+                    latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
+
+                    with gr.Row():
+                        train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
+                        interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
+
+                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+                script_callbacks.ui_train_tabs_callback(params)
+
+            with gr.Column(elem_id='ti_gallery_container'):
+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
+                gr.HTML(elem_id="ti_progress", value="")
+                ti_outcome = gr.HTML(elem_id="ti_error", value="")
+
+        create_embedding.click(
+            fn=modules.textual_inversion.ui.create_embedding,
+            inputs=[
+                new_embedding_name,
+                initialization_text,
+                nvpt,
+                overwrite_old_embedding,
+            ],
+            outputs=[
+                train_embedding_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        create_hypernetwork.click(
+            fn=modules.hypernetworks.ui.create_hypernetwork,
+            inputs=[
+                new_hypernetwork_name,
+                new_hypernetwork_sizes,
+                overwrite_old_hypernetwork,
+                new_hypernetwork_layer_structure,
+                new_hypernetwork_activation_func,
+                new_hypernetwork_initialization_option,
+                new_hypernetwork_add_layer_norm,
+                new_hypernetwork_use_dropout,
+                new_hypernetwork_dropout_structure
+            ],
+            outputs=[
+                train_hypernetwork_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        run_preprocess.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                dummy_component,
+                process_src,
+                process_dst,
+                process_width,
+                process_height,
+                preprocess_txt_action,
+                process_keep_original_size,
+                process_flip,
+                process_split,
+                process_caption,
+                process_caption_deepbooru,
+                process_split_threshold,
+                process_overlap_ratio,
+                process_focal_crop,
+                process_focal_crop_face_weight,
+                process_focal_crop_entropy_weight,
+                process_focal_crop_edges_weight,
+                process_focal_crop_debug,
+                process_multicrop,
+                process_multicrop_mindim,
+                process_multicrop_maxdim,
+                process_multicrop_minarea,
+                process_multicrop_maxarea,
+                process_multicrop_objective,
+                process_multicrop_threshold,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ],
+        )
+
+        train_embedding.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                dummy_component,
+                train_embedding_name,
+                embedding_learn_rate,
+                batch_size,
+                gradient_step,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                varsize,
+                steps,
+                clip_grad_mode,
+                clip_grad_value,
+                shuffle_tags,
+                tag_drop_out,
+                latent_sampling_method,
+                use_weight,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                save_image_with_stored_embedding,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        train_hypernetwork.click(
+            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                dummy_component,
+                train_hypernetwork_name,
+                hypernetwork_learn_rate,
+                batch_size,
+                gradient_step,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                varsize,
+                steps,
+                clip_grad_mode,
+                clip_grad_value,
+                shuffle_tags,
+                tag_drop_out,
+                latent_sampling_method,
+                use_weight,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        interrupt_training.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+        interrupt_preprocessing.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+    loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
+
+    settings = ui_settings.UiSettings()
+    settings.create_ui(loadsave, dummy_component)
+
+    interfaces = [
+        (txt2img_interface, "txt2img", "txt2img"),
+        (img2img_interface, "img2img", "img2img"),
+        (extras_interface, "Extras", "extras"),
+        (pnginfo_interface, "PNG Info", "pnginfo"),
+        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (train_interface, "Train", "train"),
+    ]
+
+    interfaces += script_callbacks.ui_tabs_callback()
+    interfaces += [(settings.interface, "Settings", "settings")]
+
+    extensions_interface = ui_extensions.create_ui()
+    interfaces += [(extensions_interface, "Extensions", "extensions")]
+
+    shared.tab_names = []
+    for _interface, label, _ifid in interfaces:
+        shared.tab_names.append(label)
+
+    with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
+        settings.add_quicksettings()
+
+        parameters_copypaste.connect_paste_params_buttons()
+
+        with gr.Tabs(elem_id="tabs") as tabs:
+            tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
+            sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999))
+
+            for interface, label, ifid in sorted_interfaces:
+                if label in shared.opts.hidden_tabs:
+                    continue
+                with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
+                    interface.render()
+
+            for interface, _label, ifid in interfaces:
+                if ifid in ["extensions", "settings"]:
+                    continue
+
+                loadsave.add_block(interface, ifid)
+
+            loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
+
+            loadsave.setup_ui()
+
+        if os.path.exists(os.path.join(script_path, "notification.mp3")):
+            gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+        footer = shared.html("footer.html")
+        footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
+        gr.HTML(footer, elem_id="footer")
+
+        settings.add_functionality(demo)
+
+        update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
+        settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+        demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+
+        def modelmerger(*args):
+            try:
+                results = modules.extras.run_modelmerger(*args)
+            except Exception as e:
+                errors.report("Error loading/saving model file", exc_info=True)
+                modules.sd_models.list_models()  # to remove the potentially missing models from the list
+                return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
+            return results
+
+        modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
+        modelmerger_merge.click(
+            fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
+            _js='modelmerger',
+            inputs=[
+                dummy_component,
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                interp_method,
+                interp_amount,
+                save_as_half,
+                custom_name,
+                checkpoint_format,
+                config_source,
+                bake_in_vae,
+                discard_weights,
+                save_metadata,
+            ],
+            outputs=[
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                settings.component_dict['sd_model_checkpoint'],
+                modelmerger_result,
+            ]
+        )
+
+    loadsave.dump_defaults()
+    demo.ui_loadsave = loadsave
+
+    # Required as a workaround for change() event not triggering when loading values from ui-config.json
+    interp_description.value = update_interp_description(interp_method.value)
+
+    return demo
+
+
+def versions_html():
+    import torch
+    import launch
+
+    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
+    commit = launch.commit_hash()
+    tag = launch.git_tag()
+
+    if shared.xformers_available:
+        import xformers
+        xformers_version = xformers.__version__
+    else:
+        xformers_version = "N/A"
+
+    return f"""
+version: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{tag}</a>
+&#x2000;•&#x2000;
+python: <span title="{sys.version}">{python_version}</span>
+&#x2000;•&#x2000;
+torch: {getattr(torch, '__long_version__',torch.__version__)}
+&#x2000;•&#x2000;
+xformers: {xformers_version}
+&#x2000;•&#x2000;
+gradio: {gr.__version__}
+&#x2000;•&#x2000;
+checkpoint: <a id="sd_checkpoint_hash">N/A</a>
+"""
+
+
+def setup_ui_api(app):
+    from pydantic import BaseModel, Field
+    from typing import List
+
+    class QuicksettingsHint(BaseModel):
+        name: str = Field(title="Name of the quicksettings field")
+        label: str = Field(title="Label of the quicksettings field")
+
+    def quicksettings_hint():
+        return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
+
+    app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
+
+    app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
+
+    app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
+
+    def download_sysinfo(attachment=False):
+        from fastapi.responses import PlainTextResponse
+
+        text = sysinfo.get()
+        filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
+
+        return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
+
+    app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
+    app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
+
-- 
cgit v1.2.3


From 6d3a0c950626e887f20bfc9946b84f9685303bab Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 1 Aug 2023 07:43:43 +0300
Subject: move checkpoint merger UI to its own file

---
 modules/ui.py                   |   97 +--
 modules/ui_checkpoint_merger.py | 1657 ++-------------------------------------
 2 files changed, 76 insertions(+), 1678 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 07ecee7b..ac2787eb 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -12,7 +12,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin  # noqa: F401
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger
 from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
 from modules.paths import script_path
 from modules.ui_common import create_refresh_button
@@ -1083,58 +1083,7 @@ def create_ui():
             outputs=[html, generation_info, html2],
         )
 
-    def update_interp_description(value):
-        interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
-        interp_descriptions = {
-            "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
-            "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
-            "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
-        }
-        return interp_descriptions[value]
-
-    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='compact'):
-                interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
-
-                with FormRow(elem_id="modelmerger_models"):
-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
-                    create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
-
-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
-                    create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
-
-                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
-                    create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
-
-                custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
-                interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
-                interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
-
-                with FormRow():
-                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
-                    save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
-                    save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
-
-                with FormRow():
-                    with gr.Column():
-                        config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
-
-                    with gr.Column():
-                        with FormRow():
-                            bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
-                            create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
-
-                with FormRow():
-                    discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
-
-                with gr.Row():
-                    modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
-
-            with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
-                with gr.Group(elem_id="modelmerger_results_panel"):
-                    modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
+    modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger()
 
     with gr.Blocks(analytics_enabled=False) as train_interface:
         with gr.Row().style(equal_height=False):
@@ -1464,7 +1413,7 @@ def create_ui():
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (modelmerger_ui.blocks, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "train"),
     ]
 
@@ -1516,49 +1465,11 @@ def create_ui():
         settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
         demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
 
-        def modelmerger(*args):
-            try:
-                results = modules.extras.run_modelmerger(*args)
-            except Exception as e:
-                errors.report("Error loading/saving model file", exc_info=True)
-                modules.sd_models.list_models()  # to remove the potentially missing models from the list
-                return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
-            return results
-
-        modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
-        modelmerger_merge.click(
-            fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
-            _js='modelmerger',
-            inputs=[
-                dummy_component,
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                interp_method,
-                interp_amount,
-                save_as_half,
-                custom_name,
-                checkpoint_format,
-                config_source,
-                bake_in_vae,
-                discard_weights,
-                save_metadata,
-            ],
-            outputs=[
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                settings.component_dict['sd_model_checkpoint'],
-                modelmerger_result,
-            ]
-        )
+        modelmerger_ui.setup_ui(dummy_component=dummy_component, sd_model_checkpoint_component=settings.component_dict['sd_model_checkpoint'])
 
     loadsave.dump_defaults()
     demo.ui_loadsave = loadsave
 
-    # Required as a workaround for change() event not triggering when loading values from ui-config.json
-    interp_description.value = update_interp_description(interp_method.value)
-
     return demo
 
 
diff --git a/modules/ui_checkpoint_merger.py b/modules/ui_checkpoint_merger.py
index 07ecee7b..8e72258a 100644
--- a/modules/ui_checkpoint_merger.py
+++ b/modules/ui_checkpoint_merger.py
@@ -1,1621 +1,108 @@
-import datetime
-import json
-import mimetypes
-import os
-import sys
-from functools import reduce
-import warnings
 
 import gradio as gr
-import gradio.utils
-import numpy as np
-from PIL import Image, PngImagePlugin  # noqa: F401
-from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
-from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
-from modules.paths import script_path
+from modules import sd_models, sd_vae, errors, extras, call_queue
+from modules.ui_components import FormRow
 from modules.ui_common import create_refresh_button
-from modules.ui_gradio_extensions import reload_javascript
 
 
-from modules.shared import opts, cmd_opts
+def update_interp_description(value):
+    interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
+    interp_descriptions = {
+        "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
+        "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
+        "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
+    }
+    return interp_descriptions[value]
 
-import modules.codeformer_model
-import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.scripts
-import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
-from modules import prompt_parser
-from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
-from modules.textual_inversion import textual_inversion
-import modules.hypernetworks.ui
-from modules.generation_parameters_copypaste import image_from_url_text
-import modules.extras
 
-create_setting_component = ui_settings.create_setting_component
-
-warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
-
-# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
-mimetypes.init()
-mimetypes.add_type('application/javascript', '.js')
-
-if not cmd_opts.share and not cmd_opts.listen:
-    # fix gradio phoning home
-    gradio.utils.version_check = lambda: None
-    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
-
-if cmd_opts.ngrok is not None:
-    import modules.ngrok as ngrok
-    print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(
-        cmd_opts.ngrok,
-        cmd_opts.port if cmd_opts.port is not None else 7860,
-        cmd_opts.ngrok_options
-        )
-
-
-def gr_show(visible=True):
-    return {"visible": visible, "__type__": "update"}
-
-
-sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
-sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-
-# Using constants for these since the variation selector isn't visible.
-# Important that they exactly match script.js for tooltip to work.
-random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
-reuse_symbol = '\u267b\ufe0f'  # ♻️
-paste_symbol = '\u2199\ufe0f'  # ↙
-refresh_symbol = '\U0001f504'  # 🔄
-save_style_symbol = '\U0001f4be'  # 💾
-apply_style_symbol = '\U0001f4cb'  # 📋
-clear_prompt_symbol = '\U0001f5d1\ufe0f'  # 🗑️
-extra_networks_symbol = '\U0001F3B4'  # 🎴
-switch_values_symbol = '\U000021C5' # ⇅
-restore_progress_symbol = '\U0001F300' # 🌀
-detect_image_size_symbol = '\U0001F4D0'  # 📐
-up_down_symbol = '\u2195\ufe0f' # ↕️
-
-
-plaintext_to_html = ui_common.plaintext_to_html
-
-
-def send_gradio_gallery_to_image(x):
-    if len(x) == 0:
-        return None
-    return image_from_url_text(x[0])
-
-
-def add_style(name: str, prompt: str, negative_prompt: str):
-    if name is None:
-        return [gr_show() for x in range(4)]
-
-    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
-    shared.prompt_styles.styles[style.name] = style
-    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
-    # reserialize all styles every time we save them
-    shared.prompt_styles.save_styles(shared.styles_filename)
-
-    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
-
-
-def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
-    from modules import processing, devices
-
-    if not enable:
-        return ""
-
-    p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
-
-    with devices.autocast():
-        p.init([""], [0], [0])
-
-    return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
-
-
-def resize_from_to_html(width, height, scale_by):
-    target_width = int(width * scale_by)
-    target_height = int(height * scale_by)
-
-    if not target_width or not target_height:
-        return "no image selected"
-
-    return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
-
-
-def apply_styles(prompt, prompt_neg, styles):
-    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
-    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
-
-    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
-
-
-def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
-    if mode in {0, 1, 3, 4}:
-        return [interrogation_function(ii_singles[mode]), None]
-    elif mode == 2:
-        return [interrogation_function(ii_singles[mode]["image"]), None]
-    elif mode == 5:
-        assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
-        images = shared.listfiles(ii_input_dir)
-        print(f"Will process {len(images)} images.")
-        if ii_output_dir != "":
-            os.makedirs(ii_output_dir, exist_ok=True)
-        else:
-            ii_output_dir = ii_input_dir
-
-        for image in images:
-            img = Image.open(image)
-            filename = os.path.basename(image)
-            left, _ = os.path.splitext(filename)
-            print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
-
-        return [gr.update(), None]
-
-
-def interrogate(image):
-    prompt = shared.interrogator.interrogate(image.convert("RGB"))
-    return gr.update() if prompt is None else prompt
-
-
-def interrogate_deepbooru(image):
-    prompt = deepbooru.model.tag(image)
-    return gr.update() if prompt is None else prompt
-
-
-def create_seed_inputs(target_interface):
-    with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
-        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
-        seed.style(container=False)
-        random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
-        reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
-
-        seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)
-
-    # Components to show/hide based on the 'Extra' checkbox
-    seed_extras = []
-
-    with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
-        seed_extras.append(seed_extra_row_1)
-        subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
-        subseed.style(container=False)
-        random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
-        reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
-        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
-
-    with FormRow(visible=False) as seed_extra_row_2:
-        seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")
-
-    random_seed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_seed')}", show_progress=False, inputs=[], outputs=[])
-    random_subseed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_subseed')}", show_progress=False, inputs=[], outputs=[])
-
-    def change_visibility(show):
-        return {comp: gr_show(show) for comp in seed_extras}
-
-    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
-
-    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
-
-
-
-def connect_clear_prompt(button):
-    """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
-    button.click(
-        _js="clear_prompt",
-        fn=None,
-        inputs=[],
-        outputs=[],
-    )
-
-
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
-    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
-        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
-        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
-    def copy_seed(gen_info_string: str, index):
-        res = -1
-
-        try:
-            gen_info = json.loads(gen_info_string)
-            index -= gen_info.get('index_of_first_image', 0)
-
-            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
-                all_subseeds = gen_info.get('all_subseeds', [-1])
-                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
-            else:
-                all_seeds = gen_info.get('all_seeds', [-1])
-                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
-        except json.decoder.JSONDecodeError:
-            if gen_info_string:
-                errors.report(f"Error parsing JSON generation info: {gen_info_string}")
-
-        return [res, gr_show(False)]
-
-    reuse_seed.click(
-        fn=copy_seed,
-        _js="(x, y) => [x, selected_gallery_index()]",
-        show_progress=False,
-        inputs=[generation_info, dummy_component],
-        outputs=[seed, dummy_component]
-    )
-
-
-def update_token_counter(text, steps):
+def modelmerger(*args):
     try:
-        text, _ = extra_networks.parse_prompt(text)
-
-        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
-        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
-
-    except Exception:
-        # a parsing error can happen here during typing, and we don't want to bother the user with
-        # messages related to it in console
-        prompt_schedules = [[[steps, text]]]
-
-    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
-    prompts = [prompt_text for step, prompt_text in flat_prompts]
-    token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
-    return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
-
-
-def create_toprow(is_img2img):
-    id_part = "img2img" if is_img2img else "txt2img"
-
-    with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
-        with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
-
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
-
-        button_interrogate = None
-        button_deepbooru = None
-        if is_img2img:
-            with gr.Column(scale=1, elem_classes="interrogate-col"):
-                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
-        with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
-            with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
-                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
-                skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
-                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
-                skip.click(
-                    fn=lambda: shared.state.skip(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                interrupt.click(
-                    fn=lambda: shared.state.interrupt(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-            with gr.Row(elem_id=f"{id_part}_tools"):
-                paste = ToolButton(value=paste_symbol, elem_id="paste")
-                clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
-                extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
-                prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
-                save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
-                restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
-
-                token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
-                token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-                negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
-                negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
-
-                clear_prompt_button.click(
-                    fn=lambda *x: x,
-                    _js="confirm_clear_prompt",
-                    inputs=[prompt, negative_prompt],
-                    outputs=[prompt, negative_prompt],
-                )
-
-            with gr.Row(elem_id=f"{id_part}_styles_row"):
-                prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
-                create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")
-
-    return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button
-
-
-def setup_progressbar(*args, **kwargs):
-    pass
-
-
-def apply_setting(key, value):
-    if value is None:
-        return gr.update()
-
-    if shared.cmd_opts.freeze_settings:
-        return gr.update()
-
-    # dont allow model to be swapped when model hash exists in prompt
-    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
-        return gr.update()
-
-    if key == "sd_model_checkpoint":
-        ckpt_info = sd_models.get_closet_checkpoint_match(value)
-
-        if ckpt_info is not None:
-            value = ckpt_info.title
-        else:
-            return gr.update()
-
-    comp_args = opts.data_labels[key].component_args
-    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
-        return
-
-    valtype = type(opts.data_labels[key].default)
-    oldval = opts.data.get(key, None)
-    opts.data[key] = valtype(value) if valtype != type(None) else value
-    if oldval != value and opts.data_labels[key].onchange is not None:
-        opts.data_labels[key].onchange()
-
-    opts.save(shared.config_filename)
-    return getattr(opts, key)
-
-
-def create_output_panel(tabname, outdir):
-    return ui_common.create_output_panel(tabname, outdir)
-
-
-def create_sampler_and_steps_selection(choices, tabname):
-    if opts.samplers_in_dropdown:
-        with FormRow(elem_id=f"sampler_selection_{tabname}"):
-            sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
-            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
-    else:
-        with FormGroup(elem_id=f"sampler_selection_{tabname}"):
-            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
-            sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
-
-    return steps, sampler_index
-
-
-def ordered_ui_categories():
-    user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
-
-    for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
-        yield category
-
-
-def create_override_settings_dropdown(tabname, row):
-    dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
-
-    dropdown.change(
-        fn=lambda x: gr.Dropdown.update(visible=bool(x)),
-        inputs=[dropdown],
-        outputs=[dropdown],
-    )
-
-    return dropdown
-
-
-def create_ui():
-    import modules.img2img
-    import modules.txt2img
-
-    reload_javascript()
-
-    parameters_copypaste.reset()
-
-    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
-    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
-
-    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=False)
-
-        dummy_component = gr.Label(visible=False)
-        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
-
-        with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
-            from modules import ui_extra_networks
-            extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='compact', elem_id="txt2img_settings"):
-                modules.scripts.scripts_txt2img.prepare_ui()
-
-                for category in ordered_ui_categories():
-                    if category == "sampler":
-                        steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
-
-                    elif category == "dimensions":
-                        with FormRow():
-                            with gr.Column(elem_id="txt2img_column_size", scale=4):
-                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
-                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
-
-                            with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
-
-                            if opts.dimensions_and_batch_together:
-                                with gr.Column(elem_id="txt2img_column_batch"):
-                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
-                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
-
-                    elif category == "cfg":
-                        cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
-
-                    elif category == "seed":
-                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
-
-                    elif category == "checkboxes":
-                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
-                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
-                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
-                            enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
-                            hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
-
-                    elif category == "hires_fix":
-                        with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
-                            with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
-                                hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
-                                hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
-                                denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
-
-                            with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
-                                hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
-                                hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
-                                hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
-
-                            with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
-                                hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")
-
-                            with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
-                                with gr.Column(scale=80):
-                                    with gr.Row():
-                                        hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
-                                with gr.Column(scale=80):
-                                    with gr.Row():
-                                        hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
-
-                    elif category == "batch":
-                        if not opts.dimensions_and_batch_together:
-                            with FormRow(elem_id="txt2img_column_batch"):
-                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
-                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
-
-                    elif category == "override_settings":
-                        with FormRow(elem_id="txt2img_override_settings_row") as row:
-                            override_settings = create_override_settings_dropdown('txt2img', row)
-
-                    elif category == "scripts":
-                        with FormGroup(elem_id="txt2img_script_container"):
-                            custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
-
-                    else:
-                        modules.scripts.scripts_txt2img.setup_ui_for_section(category)
-
-            hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
-
-            for component in hr_resolution_preview_inputs:
-                event = component.release if isinstance(component, gr.Slider) else component.change
-
-                event(
-                    fn=calc_resolution_hires,
-                    inputs=hr_resolution_preview_inputs,
-                    outputs=[hr_final_resolution],
-                    show_progress=False,
-                )
-                event(
-                    None,
-                    _js="onCalcResolutionHires",
-                    inputs=hr_resolution_preview_inputs,
-                    outputs=[],
-                    show_progress=False,
-                )
-
-            txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            txt2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
-                _js="submit",
-                inputs=[
-                    dummy_component,
-                    txt2img_prompt,
-                    txt2img_negative_prompt,
-                    txt2img_prompt_styles,
-                    steps,
-                    sampler_index,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    enable_hr,
-                    denoising_strength,
-                    hr_scale,
-                    hr_upscaler,
-                    hr_second_pass_steps,
-                    hr_resize_x,
-                    hr_resize_y,
-                    hr_sampler_index,
-                    hr_prompt,
-                    hr_negative_prompt,
-                    override_settings,
-
-                ] + custom_inputs,
-
-                outputs=[
-                    txt2img_gallery,
-                    generation_info,
-                    html_info,
-                    html_log,
-                ],
-                show_progress=False,
-            )
-
-            txt2img_prompt.submit(**txt2img_args)
-            submit.click(**txt2img_args)
-
-            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
-
-            restore_progress_button.click(
-                fn=progress.restore_progress,
-                _js="restoreProgressTxt2img",
-                inputs=[dummy_component],
-                outputs=[
-                    txt2img_gallery,
-                    generation_info,
-                    html_info,
-                    html_log,
-                ],
-                show_progress=False,
-            )
-
-            txt_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    txt_prompt_img
-                ],
-                outputs=[
-                    txt2img_prompt,
-                    txt_prompt_img
-                ],
-                show_progress=False,
-            )
-
-            enable_hr.change(
-                fn=lambda x: gr_show(x),
-                inputs=[enable_hr],
-                outputs=[hr_options],
-                show_progress = False,
-            )
-
-            txt2img_paste_fields = [
-                (txt2img_prompt, "Prompt"),
-                (txt2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
-                (denoising_strength, "Denoising strength"),
-                (enable_hr, lambda d: "Denoising strength" in d),
-                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (hr_scale, "Hires upscale"),
-                (hr_upscaler, "Hires upscaler"),
-                (hr_second_pass_steps, "Hires steps"),
-                (hr_resize_x, "Hires resize-1"),
-                (hr_resize_y, "Hires resize-2"),
-                (hr_sampler_index, "Hires sampler"),
-                (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
-                (hr_prompt, "Hires prompt"),
-                (hr_negative_prompt, "Hires negative prompt"),
-                (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
-                *modules.scripts.scripts_txt2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
-            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
-            ))
-
-            txt2img_preview_params = [
-                txt2img_prompt,
-                txt2img_negative_prompt,
-                steps,
-                sampler_index,
-                cfg_scale,
-                seed,
-                width,
-                height,
-            ]
-
-            token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
-            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
-
-            ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
-
-    modules.scripts.scripts_current = modules.scripts.scripts_img2img
-    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
-
-    with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=True)
-
-        img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
-
-        with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks:
-            from modules import ui_extra_networks
-            extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'img2img')
-
-        with FormRow().style(equal_height=False):
-            with gr.Column(variant='compact', elem_id="img2img_settings"):
-                copy_image_buttons = []
-                copy_image_destinations = {}
-
-                def add_copy_image_controls(tab_name, elem):
-                    with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
-                        gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
-
-                        for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
-                            if name == tab_name:
-                                gr.Button(title, interactive=False)
-                                copy_image_destinations[name] = elem
-                                continue
-
-                            button = gr.Button(title)
-                            copy_image_buttons.append((button, name, elem))
-
-                with gr.Tabs(elem_id="mode_img2img"):
-                    img2img_selected_tab = gr.State(0)
-
-                    with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=opts.img2img_editor_height)
-                        add_copy_image_controls('img2img', init_img)
-
-                    with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
-                        sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
-                        add_copy_image_controls('sketch', sketch)
-
-                    with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
-                        add_copy_image_controls('inpaint', init_img_with_mask)
-
-                    with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
-                        inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
-                        inpaint_color_sketch_orig = gr.State(None)
-                        add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
-
-                        def update_orig(image, state):
-                            if image is not None:
-                                same_size = state is not None and state.size == image.size
-                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
-                                edited = same_size and has_exact_match
-                                return image if not edited or state is None else state
-
-                        inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
-
-                    with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
-                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
-                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask")
-
-                    with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
-                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(
-                            "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
-                            "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
-                            f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
-                            f"{hidden}</p>"
-                        )
-                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
-                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
-                        img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
-                        with gr.Accordion("PNG info", open=False):
-                            img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
-                            img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
-                            img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
-
-                    img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
-
-                    for i, tab in enumerate(img2img_tabs):
-                        tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
-
-                def copy_image(img):
-                    if isinstance(img, dict) and 'image' in img:
-                        return img['image']
-
-                    return img
-
-                for button, name, elem in copy_image_buttons:
-                    button.click(
-                        fn=copy_image,
-                        inputs=[elem],
-                        outputs=[copy_image_destinations[name]],
-                    )
-                    button.click(
-                        fn=lambda: None,
-                        _js=f"switch_to_{name.replace(' ', '_')}",
-                        inputs=[],
-                        outputs=[],
-                    )
-
-                with FormRow():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
-
-                modules.scripts.scripts_img2img.prepare_ui()
-
-                for category in ordered_ui_categories():
-                    if category == "sampler":
-                        steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
-
-                    elif category == "dimensions":
-                        with FormRow():
-                            with gr.Column(elem_id="img2img_column_size", scale=4):
-                                selected_scale_tab = gr.State(value=0)
-
-                                with gr.Tabs():
-                                    with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
-                                        with FormRow():
-                                            with gr.Column(elem_id="img2img_column_size", scale=4):
-                                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
-                                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
-                                            with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
-                                                detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
+        results = extras.run_modelmerger(*args)
+    except Exception as e:
+        errors.report("Error loading/saving model file", exc_info=True)
+        sd_models.list_models()  # to remove the potentially missing models from the list
+        return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
+    return results
 
-                                    with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
-                                        scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
 
-                                        with FormRow():
-                                            scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
-                                            gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
-                                            button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
+class UiCheckpointMerger:
+    def __init__(self):
+        with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+            with gr.Row().style(equal_height=False):
+                with gr.Column(variant='compact'):
+                    self.interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
 
-                                    on_change_args = dict(
-                                        fn=resize_from_to_html,
-                                        _js="currentImg2imgSourceResolution",
-                                        inputs=[dummy_component, dummy_component, scale_by],
-                                        outputs=scale_by_html,
-                                        show_progress=False,
-                                    )
+                    with FormRow(elem_id="modelmerger_models"):
+                        self.primary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                        create_refresh_button(self.primary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
 
-                                    scale_by.release(**on_change_args)
-                                    button_update_resize_to.click(**on_change_args)
+                        self.secondary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                        create_refresh_button(self.secondary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
 
-                                    # the code below is meant to update the resolution label after the image in the image selection UI has changed.
-                                    # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
-                                    # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
-                                    for component in [init_img, sketch]:
-                                        component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
+                        self.tertiary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+                        create_refresh_button(self.tertiary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
 
-                            tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
-                            tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
-
-                            if opts.dimensions_and_batch_together:
-                                with gr.Column(elem_id="img2img_column_batch"):
-                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
-                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
-                    elif category == "cfg":
-                        with FormGroup():
-                            with FormRow():
-                                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
-                                image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
-                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
-
-                    elif category == "seed":
-                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
-
-                    elif category == "checkboxes":
-                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
-                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
-                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
-
-                    elif category == "batch":
-                        if not opts.dimensions_and_batch_together:
-                            with FormRow(elem_id="img2img_column_batch"):
-                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
-                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
-                    elif category == "override_settings":
-                        with FormRow(elem_id="img2img_override_settings_row") as row:
-                            override_settings = create_override_settings_dropdown('img2img', row)
-
-                    elif category == "scripts":
-                        with FormGroup(elem_id="img2img_script_container"):
-                            custom_inputs = modules.scripts.scripts_img2img.setup_ui()
-
-                    elif category == "inpaint":
-                        with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
-                            with FormRow():
-                                mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
-                                mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
-
-                            with FormRow():
-                                inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
-
-                            with FormRow():
-                                inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
-
-                            with FormRow():
-                                with gr.Column():
-                                    inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
-
-                                with gr.Column(scale=4):
-                                    inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
-
-                            def select_img2img_tab(tab):
-                                return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
-
-                            for i, elem in enumerate(img2img_tabs):
-                                elem.select(
-                                    fn=lambda tab=i: select_img2img_tab(tab),
-                                    inputs=[],
-                                    outputs=[inpaint_controls, mask_alpha],
-                                )
-                    else:
-                        modules.scripts.scripts_img2img.setup_ui_for_section(category)
-
-            img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            img2img_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    img2img_prompt_img
-                ],
-                outputs=[
-                    img2img_prompt,
-                    img2img_prompt_img
-                ],
-                show_progress=False,
-            )
-
-            img2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
-                _js="submit_img2img",
-                inputs=[
-                    dummy_component,
-                    dummy_component,
-                    img2img_prompt,
-                    img2img_negative_prompt,
-                    img2img_prompt_styles,
-                    init_img,
-                    sketch,
-                    init_img_with_mask,
-                    inpaint_color_sketch,
-                    inpaint_color_sketch_orig,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                    steps,
-                    sampler_index,
-                    mask_blur,
-                    mask_alpha,
-                    inpainting_fill,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    image_cfg_scale,
-                    denoising_strength,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    selected_scale_tab,
-                    height,
-                    width,
-                    scale_by,
-                    resize_mode,
-                    inpaint_full_res,
-                    inpaint_full_res_padding,
-                    inpainting_mask_invert,
-                    img2img_batch_input_dir,
-                    img2img_batch_output_dir,
-                    img2img_batch_inpaint_mask_dir,
-                    override_settings,
-                    img2img_batch_use_png_info,
-                    img2img_batch_png_info_props,
-                    img2img_batch_png_info_dir,
-                ] + custom_inputs,
-                outputs=[
-                    img2img_gallery,
-                    generation_info,
-                    html_info,
-                    html_log,
-                ],
-                show_progress=False,
-            )
-
-            interrogate_args = dict(
-                _js="get_img2img_tab_index",
-                inputs=[
-                    dummy_component,
-                    img2img_batch_input_dir,
-                    img2img_batch_output_dir,
-                    init_img,
-                    sketch,
-                    init_img_with_mask,
-                    inpaint_color_sketch,
-                    init_img_inpaint,
-                ],
-                outputs=[img2img_prompt, dummy_component],
-            )
-
-            img2img_prompt.submit(**img2img_args)
-            submit.click(**img2img_args)
-
-            res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
-
-            detect_image_size_btn.click(
-                fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
-                _js="currentImg2imgSourceResolution",
-                inputs=[dummy_component, dummy_component, dummy_component],
-                outputs=[width, height],
-                show_progress=False,
-            )
-
-            restore_progress_button.click(
-                fn=progress.restore_progress,
-                _js="restoreProgressImg2img",
-                inputs=[dummy_component],
-                outputs=[
-                    img2img_gallery,
-                    generation_info,
-                    html_info,
-                    html_log,
-                ],
-                show_progress=False,
-            )
-
-            img2img_interrogate.click(
-                fn=lambda *args: process_interrogate(interrogate, *args),
-                **interrogate_args,
-            )
-
-            img2img_deepbooru.click(
-                fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
-                **interrogate_args,
-            )
-
-            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
-            style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
-            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-
-            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
-                button.click(
-                    fn=add_style,
-                    _js="ask_for_style_name",
-                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
-                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
-                    inputs=[dummy_component, prompt, negative_prompt],
-                    outputs=[txt2img_prompt_styles, img2img_prompt_styles],
-                )
-
-            for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
-                button.click(
-                    fn=apply_styles,
-                    _js=js_func,
-                    inputs=[prompt, negative_prompt, styles],
-                    outputs=[prompt, negative_prompt, styles],
-                )
-
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
-
-            ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
-
-            img2img_paste_fields = [
-                (img2img_prompt, "Prompt"),
-                (img2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (image_cfg_scale, "Image CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
-                (denoising_strength, "Denoising strength"),
-                (mask_blur, "Mask blur"),
-                *modules.scripts.scripts_img2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
-            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
-            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
-            ))
-
-    modules.scripts.scripts_current = None
-
-    with gr.Blocks(analytics_enabled=False) as extras_interface:
-        ui_postprocessing.create_ui()
-
-    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
-
-            with gr.Column(variant='panel'):
-                html = gr.HTML()
-                generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
-                html2 = gr.HTML()
-                with gr.Row():
-                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-
-                for tabname, button in buttons.items():
-                    parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                        paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
-                    ))
-
-        image.change(
-            fn=wrap_gradio_call(modules.extras.run_pnginfo),
-            inputs=[image],
-            outputs=[html, generation_info, html2],
-        )
-
-    def update_interp_description(value):
-        interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
-        interp_descriptions = {
-            "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
-            "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
-            "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
-        }
-        return interp_descriptions[value]
-
-    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='compact'):
-                interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
-
-                with FormRow(elem_id="modelmerger_models"):
-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
-                    create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
-
-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
-                    create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
-
-                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
-                    create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
-
-                custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
-                interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
-                interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
-
-                with FormRow():
-                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
-                    save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
-                    save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
-
-                with FormRow():
-                    with gr.Column():
-                        config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
-
-                    with gr.Column():
-                        with FormRow():
-                            bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
-                            create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
-
-                with FormRow():
-                    discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
-
-                with gr.Row():
-                    modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
-
-            with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
-                with gr.Group(elem_id="modelmerger_results_panel"):
-                    modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
-
-    with gr.Blocks(analytics_enabled=False) as train_interface:
-        with gr.Row().style(equal_height=False):
-            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
-
-        with gr.Row(variant="compact").style(equal_height=False):
-            with gr.Tabs(elem_id="train_tabs"):
-
-                with gr.Tab(label="Create embedding", id="create_embedding"):
-                    new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
-                    initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
-                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
-                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
-
-                with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
-                    new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
-                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
-                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
-                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
-                    new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
-                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
-
-                with gr.Tab(label="Preprocess images", id="preprocess_images"):
-                    process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
-                    process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
-                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
-
-                    with gr.Row():
-                        process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
-                        process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
-                        process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
-                        process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
-                        process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
-                        process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
-
-                    with gr.Row(visible=False) as process_split_extra_row:
-                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
-                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
-
-                    with gr.Row(visible=False) as process_focal_crop_row:
-                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
-                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
-                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
-                        process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
-
-                    with gr.Column(visible=False) as process_multicrop_col:
-                        gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
-                        with gr.Row():
-                            process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
-                            process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
-                        with gr.Row():
-                            process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
-                            process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
-                        with gr.Row():
-                            process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
-                            process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            with gr.Row():
-                                interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
-                            run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
-
-                    process_split.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_split],
-                        outputs=[process_split_extra_row],
-                    )
-
-                    process_focal_crop.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_focal_crop],
-                        outputs=[process_focal_crop_row],
-                    )
-
-                    process_multicrop.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_multicrop],
-                        outputs=[process_multicrop_col],
-                    )
-
-                def get_textual_inversion_template_names():
-                    return sorted(textual_inversion.textual_inversion_templates)
-
-                with gr.Tab(label="Train", id="train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
-                    with FormRow():
-                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-
-                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
-                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
-
-                    with FormRow():
-                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
-                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
-
-                    with FormRow():
-                        clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
-                        clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
+                    self.custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
+                    self.interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
+                    self.interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
+                    self.interp_method.change(fn=update_interp_description, inputs=[self.interp_method], outputs=[self.interp_description])
 
                     with FormRow():
-                        batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
-                        gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
-
-                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
-                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
+                        self.checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+                        self.save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
+                        self.save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
 
                     with FormRow():
-                        template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
-                        create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
+                        with gr.Column():
+                            self.config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
 
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
-                    varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
-                    steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
+                        with gr.Column():
+                            with FormRow():
+                                self.bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
+                                create_refresh_button(self.bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
 
                     with FormRow():
-                        create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
-                        save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
-
-                    use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
-
-                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
-                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
-
-                    shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
-                    tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
-
-                    latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
+                        self.discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
 
                     with gr.Row():
-                        train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
-                        interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
-                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
-
-                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
-
-                script_callbacks.ui_train_tabs_callback(params)
-
-            with gr.Column(elem_id='ti_gallery_container'):
-                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
-                gr.HTML(elem_id="ti_progress", value="")
-                ti_outcome = gr.HTML(elem_id="ti_error", value="")
-
-        create_embedding.click(
-            fn=modules.textual_inversion.ui.create_embedding,
-            inputs=[
-                new_embedding_name,
-                initialization_text,
-                nvpt,
-                overwrite_old_embedding,
-            ],
-            outputs=[
-                train_embedding_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        create_hypernetwork.click(
-            fn=modules.hypernetworks.ui.create_hypernetwork,
-            inputs=[
-                new_hypernetwork_name,
-                new_hypernetwork_sizes,
-                overwrite_old_hypernetwork,
-                new_hypernetwork_layer_structure,
-                new_hypernetwork_activation_func,
-                new_hypernetwork_initialization_option,
-                new_hypernetwork_add_layer_norm,
-                new_hypernetwork_use_dropout,
-                new_hypernetwork_dropout_structure
-            ],
-            outputs=[
-                train_hypernetwork_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        run_preprocess.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                dummy_component,
-                process_src,
-                process_dst,
-                process_width,
-                process_height,
-                preprocess_txt_action,
-                process_keep_original_size,
-                process_flip,
-                process_split,
-                process_caption,
-                process_caption_deepbooru,
-                process_split_threshold,
-                process_overlap_ratio,
-                process_focal_crop,
-                process_focal_crop_face_weight,
-                process_focal_crop_entropy_weight,
-                process_focal_crop_edges_weight,
-                process_focal_crop_debug,
-                process_multicrop,
-                process_multicrop_mindim,
-                process_multicrop_maxdim,
-                process_multicrop_minarea,
-                process_multicrop_maxarea,
-                process_multicrop_objective,
-                process_multicrop_threshold,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ],
-        )
-
-        train_embedding.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                dummy_component,
-                train_embedding_name,
-                embedding_learn_rate,
-                batch_size,
-                gradient_step,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                varsize,
-                steps,
-                clip_grad_mode,
-                clip_grad_value,
-                shuffle_tags,
-                tag_drop_out,
-                latent_sampling_method,
-                use_weight,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                save_image_with_stored_embedding,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        train_hypernetwork.click(
-            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                dummy_component,
-                train_hypernetwork_name,
-                hypernetwork_learn_rate,
-                batch_size,
-                gradient_step,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                varsize,
-                steps,
-                clip_grad_mode,
-                clip_grad_value,
-                shuffle_tags,
-                tag_drop_out,
-                latent_sampling_method,
-                use_weight,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        interrupt_training.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-        interrupt_preprocessing.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-    loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
-
-    settings = ui_settings.UiSettings()
-    settings.create_ui(loadsave, dummy_component)
-
-    interfaces = [
-        (txt2img_interface, "txt2img", "txt2img"),
-        (img2img_interface, "img2img", "img2img"),
-        (extras_interface, "Extras", "extras"),
-        (pnginfo_interface, "PNG Info", "pnginfo"),
-        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
-        (train_interface, "Train", "train"),
-    ]
-
-    interfaces += script_callbacks.ui_tabs_callback()
-    interfaces += [(settings.interface, "Settings", "settings")]
-
-    extensions_interface = ui_extensions.create_ui()
-    interfaces += [(extensions_interface, "Extensions", "extensions")]
-
-    shared.tab_names = []
-    for _interface, label, _ifid in interfaces:
-        shared.tab_names.append(label)
-
-    with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
-        settings.add_quicksettings()
-
-        parameters_copypaste.connect_paste_params_buttons()
-
-        with gr.Tabs(elem_id="tabs") as tabs:
-            tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
-            sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999))
-
-            for interface, label, ifid in sorted_interfaces:
-                if label in shared.opts.hidden_tabs:
-                    continue
-                with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
-                    interface.render()
-
-            for interface, _label, ifid in interfaces:
-                if ifid in ["extensions", "settings"]:
-                    continue
-
-                loadsave.add_block(interface, ifid)
-
-            loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
-
-            loadsave.setup_ui()
+                        self.modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
 
-        if os.path.exists(os.path.join(script_path, "notification.mp3")):
-            gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+                with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
+                    with gr.Group(elem_id="modelmerger_results_panel"):
+                        self.modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
 
-        footer = shared.html("footer.html")
-        footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
-        gr.HTML(footer, elem_id="footer")
+        self.blocks = modelmerger_interface
 
-        settings.add_functionality(demo)
-
-        update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
-        settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
-        demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
-
-        def modelmerger(*args):
-            try:
-                results = modules.extras.run_modelmerger(*args)
-            except Exception as e:
-                errors.report("Error loading/saving model file", exc_info=True)
-                modules.sd_models.list_models()  # to remove the potentially missing models from the list
-                return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
-            return results
-
-        modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
-        modelmerger_merge.click(
-            fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
+    def setup_ui(self, dummy_component, sd_model_checkpoint_component):
+        self.modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[self.modelmerger_result])
+        self.modelmerger_merge.click(
+            fn=call_queue.wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
             _js='modelmerger',
             inputs=[
                 dummy_component,
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                interp_method,
-                interp_amount,
-                save_as_half,
-                custom_name,
-                checkpoint_format,
-                config_source,
-                bake_in_vae,
-                discard_weights,
-                save_metadata,
+                self.primary_model_name,
+                self.secondary_model_name,
+                self.tertiary_model_name,
+                self.interp_method,
+                self.interp_amount,
+                self.save_as_half,
+                self.custom_name,
+                self.checkpoint_format,
+                self.config_source,
+                self.bake_in_vae,
+                self.discard_weights,
+                self.save_metadata,
             ],
             outputs=[
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                settings.component_dict['sd_model_checkpoint'],
-                modelmerger_result,
+                self.primary_model_name,
+                self.secondary_model_name,
+                self.tertiary_model_name,
+                sd_model_checkpoint_component,
+                self.modelmerger_result,
             ]
         )
 
-    loadsave.dump_defaults()
-    demo.ui_loadsave = loadsave
-
-    # Required as a workaround for change() event not triggering when loading values from ui-config.json
-    interp_description.value = update_interp_description(interp_method.value)
-
-    return demo
-
-
-def versions_html():
-    import torch
-    import launch
-
-    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
-    commit = launch.commit_hash()
-    tag = launch.git_tag()
-
-    if shared.xformers_available:
-        import xformers
-        xformers_version = xformers.__version__
-    else:
-        xformers_version = "N/A"
-
-    return f"""
-version: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{tag}</a>
-&#x2000;•&#x2000;
-python: <span title="{sys.version}">{python_version}</span>
-&#x2000;•&#x2000;
-torch: {getattr(torch, '__long_version__',torch.__version__)}
-&#x2000;•&#x2000;
-xformers: {xformers_version}
-&#x2000;•&#x2000;
-gradio: {gr.__version__}
-&#x2000;•&#x2000;
-checkpoint: <a id="sd_checkpoint_hash">N/A</a>
-"""
-
-
-def setup_ui_api(app):
-    from pydantic import BaseModel, Field
-    from typing import List
-
-    class QuicksettingsHint(BaseModel):
-        name: str = Field(title="Name of the quicksettings field")
-        label: str = Field(title="Label of the quicksettings field")
-
-    def quicksettings_hint():
-        return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
-
-    app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
-
-    app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
-
-    app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
-
-    def download_sysinfo(attachment=False):
-        from fastapi.responses import PlainTextResponse
-
-        text = sysinfo.get()
-        filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
-
-        return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
-
-    app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
-    app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
+        # Required as a workaround for change() event not triggering when loading values from ui-config.json
+        self.interp_description.value = update_interp_description(self.interp_method.value)
 
-- 
cgit v1.2.3


From 07be13caa357b14f6afa247566d53339522b8e66 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 1 Aug 2023 08:27:54 +0300
Subject: add metadata to checkpoint merger

---
 modules/extras.py               | 39 +++++++++++++++++++++++++++++++++------
 modules/sd_models.py            |  2 +-
 modules/ui_checkpoint_merger.py | 20 ++++++++++++++++++--
 3 files changed, 52 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index e9c0263e..2a310ae3 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -7,7 +7,7 @@ import json
 import torch
 import tqdm
 
-from modules import shared, images, sd_models, sd_vae, sd_models_config
+from modules import shared, images, sd_models, sd_vae, sd_models_config, errors
 from modules.ui_common import plaintext_to_html
 import gradio as gr
 import safetensors.torch
@@ -72,7 +72,20 @@ def to_half(tensor, enable):
     return tensor
 
 
-def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
+def read_metadata(primary_model_name, secondary_model_name, tertiary_model_name):
+    metadata = {}
+
+    for checkpoint_name in [primary_model_name, secondary_model_name, tertiary_model_name]:
+        checkpoint_info = sd_models.checkpoints_list.get(checkpoint_name, None)
+        if checkpoint_info is None:
+            continue
+
+        metadata.update(checkpoint_info.metadata)
+
+    return json.dumps(metadata, indent=4, ensure_ascii=False)
+
+
+def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata, add_merge_recipe, copy_metadata_fields, metadata_json):
     shared.state.begin(job="model-merge")
 
     def fail(message):
@@ -241,11 +254,25 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
     shared.state.textinfo = "Saving"
     print(f"Saving to {output_modelname}...")
 
-    metadata = None
+    metadata = {}
+
+    if save_metadata and copy_metadata_fields:
+        if primary_model_info:
+            metadata.update(primary_model_info.metadata)
+        if secondary_model_info:
+            metadata.update(secondary_model_info.metadata)
+        if tertiary_model_info:
+            metadata.update(tertiary_model_info.metadata)
 
     if save_metadata:
-        metadata = {"format": "pt"}
+        try:
+            metadata.update(json.loads(metadata_json))
+        except Exception as e:
+            errors.display(e, "readin metadata from json")
+
+        metadata["format"] = "pt"
 
+    if save_metadata and add_merge_recipe:
         merge_recipe = {
             "type": "webui", # indicate this model was merged with webui's built-in merger
             "primary_model_hash": primary_model_info.sha256,
@@ -261,7 +288,6 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
             "is_inpainting": result_is_inpainting_model,
             "is_instruct_pix2pix": result_is_instruct_pix2pix_model
         }
-        metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
 
         sd_merge_models = {}
 
@@ -281,11 +307,12 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
         if tertiary_model_info:
             add_model_metadata(tertiary_model_info)
 
+        metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
         metadata["sd_merge_models"] = json.dumps(sd_merge_models)
 
     _, extension = os.path.splitext(output_modelname)
     if extension.lower() == ".safetensors":
-        safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
+        safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata if len(metadata)>0 else None)
     else:
         torch.save(theta_0, output_modelname)
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 1af7fd78..8f72f21d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -85,7 +85,7 @@ class CheckpointInfo:
         if self.shorthash not in self.ids:
             self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']
 
-        checkpoints_list.pop(self.title)
+        checkpoints_list.pop(self.title, None)
         self.title = f'{self.name} [{self.shorthash}]'
         self.register()
 
diff --git a/modules/ui_checkpoint_merger.py b/modules/ui_checkpoint_merger.py
index 8e72258a..4863d861 100644
--- a/modules/ui_checkpoint_merger.py
+++ b/modules/ui_checkpoint_merger.py
@@ -51,7 +51,6 @@ class UiCheckpointMerger:
                     with FormRow():
                         self.checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
                         self.save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
-                        self.save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
 
                     with FormRow():
                         with gr.Column():
@@ -65,16 +64,30 @@ class UiCheckpointMerger:
                     with FormRow():
                         self.discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
 
-                    with gr.Row():
+                    with gr.Accordion("Metadata", open=False) as metadata_editor:
+                        with FormRow():
+                            self.save_metadata = gr.Checkbox(value=True, label="Save metadata", elem_id="modelmerger_save_metadata")
+                            self.add_merge_recipe = gr.Checkbox(value=True, label="Add merge recipe metadata", elem_id="modelmerger_add_recipe")
+                            self.copy_metadata_fields = gr.Checkbox(value=True, label="Copy metadata from merged models", elem_id="modelmerger_copy_metadata")
+
+                        self.metadata_json = gr.TextArea('{}', label="Metadata in JSON format")
+                        self.read_metadata = gr.Button("Read metadata from selected checkpoints")
+
+                    with FormRow():
                         self.modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
 
                 with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
                     with gr.Group(elem_id="modelmerger_results_panel"):
                         self.modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
 
+        self.metadata_editor = metadata_editor
         self.blocks = modelmerger_interface
 
     def setup_ui(self, dummy_component, sd_model_checkpoint_component):
+        self.checkpoint_format.change(lambda fmt: gr.update(visible=fmt == 'safetensors'), inputs=[self.checkpoint_format], outputs=[self.metadata_editor], show_progress=False)
+
+        self.read_metadata.click(extras.read_metadata, inputs=[self.primary_model_name, self.secondary_model_name, self.tertiary_model_name], outputs=[self.metadata_json])
+
         self.modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[self.modelmerger_result])
         self.modelmerger_merge.click(
             fn=call_queue.wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
@@ -93,6 +106,9 @@ class UiCheckpointMerger:
                 self.bake_in_vae,
                 self.discard_weights,
                 self.save_metadata,
+                self.add_merge_recipe,
+                self.copy_metadata_fields,
+                self.metadata_json,
             ],
             outputs=[
                 self.primary_model_name,
-- 
cgit v1.2.3


From 6a0d498c8ec5287a75e2a3bc8a4fa79e82e64c18 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 1 Aug 2023 12:50:23 +0300
Subject: support tooltip kwarg for gradio elements

---
 modules/scripts.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 5b4edcac..edf7347e 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -646,6 +646,8 @@ def add_classes_to_gradio_component(comp):
 
 
 def IOComponent_init(self, *args, **kwargs):
+    self.webui_tooltip = kwargs.pop('tooltip', None)
+
     if scripts_current is not None:
         scripts_current.before_component(self, **kwargs)
 
@@ -663,8 +665,20 @@ def IOComponent_init(self, *args, **kwargs):
     return res
 
 
+def Block_get_config(self):
+    config = original_Block_get_config(self)
+
+    webui_tooltip = getattr(self, 'webui_tooltip', None)
+    if webui_tooltip:
+        config["webui_tooltip"] = webui_tooltip
+
+    return config
+
+
 original_IOComponent_init = gr.components.IOComponent.__init__
+original_Block_get_config = gr.components.Block.get_config
 gr.components.IOComponent.__init__ = IOComponent_init
+gr.components.Block.get_config = Block_get_config
 
 
 def BlockContext_init(self, *args, **kwargs):
-- 
cgit v1.2.3


From 10ff071e3368a028ddb6d68d3c3bfd028094aabb Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 2 Aug 2023 18:37:16 +0300
Subject: update doggettx cross attention optimization to not use an
 unreasonable amount of memory in some edge cases -- suggestion by MorkTheOrk

---
 modules/sd_hijack_optimizations.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index b5f85ba5..0e810eec 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -256,9 +256,9 @@ def split_cross_attention_forward(self, x, context=None, mask=None, **kwargs):
             raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
                                f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
 
-        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+        slice_size = q.shape[1] // steps
         for i in range(0, q.shape[1], slice_size):
-            end = i + slice_size
+            end = min(i + slice_size, q.shape[1])
             s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
 
             s2 = s1.softmax(dim=-1, dtype=q.dtype)
-- 
cgit v1.2.3


From ccb92339348f6973de39cde062982a51a4cd0818 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 2 Aug 2023 18:53:09 +0300
Subject: add yet another torch_gc to reclaim some of VRAM after the initial
 stage of img2img

---
 modules/processing.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index b0992ee1..0b66cd2a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1348,6 +1348,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         image = image.to(shared.device, dtype=devices.dtype_vae)
 
         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
+        devices.torch_gc()
 
         if self.resize_mode == 3:
             self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
-- 
cgit v1.2.3


From 84b6fcd02ca6d6ab48c4b6be4bb8724b1c2e7014 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 3 Aug 2023 00:00:23 +0300
Subject: add NV option for Random number generator source setting, which
 allows to generate same pictures on CPU/AMD/Mac as on NVidia  videocards.

---
 modules/devices.py                |  39 ++++++++++++++-
 modules/processing.py             |   6 +--
 modules/rng_philox.py             | 100 ++++++++++++++++++++++++++++++++++++++
 modules/sd_samplers_kdiffusion.py |   5 +-
 modules/shared.py                 |   2 +-
 5 files changed, 142 insertions(+), 10 deletions(-)
 create mode 100644 modules/rng_philox.py

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 57e51da3..b58776d8 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -3,7 +3,7 @@ import contextlib
 from functools import lru_cache
 
 import torch
-from modules import errors
+from modules import errors, rng_philox
 
 if sys.platform == "darwin":
     from modules import mac_specific
@@ -90,23 +90,58 @@ def cond_cast_float(input):
     return input.float() if unet_needs_upcast else input
 
 
+nv_rng = None
+
+
 def randn(seed, shape):
     from modules.shared import opts
 
-    torch.manual_seed(seed)
+    manual_seed(seed)
+
+    if opts.randn_source == "NV":
+        return torch.asarray(nv_rng.randn(shape), device=device)
+
     if opts.randn_source == "CPU" or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
+
     return torch.randn(shape, device=device)
 
 
+def randn_like(x):
+    from modules.shared import opts
+
+    if opts.randn_source == "NV":
+        return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
+
+    if opts.randn_source == "CPU" or x.device.type == 'mps':
+        return torch.randn_like(x, device=cpu).to(x.device)
+
+    return torch.randn_like(x)
+
+
 def randn_without_seed(shape):
     from modules.shared import opts
 
+    if opts.randn_source == "NV":
+        return torch.asarray(nv_rng.randn(shape), device=device)
+
     if opts.randn_source == "CPU" or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
+
     return torch.randn(shape, device=device)
 
 
+def manual_seed(seed):
+    from modules.shared import opts
+
+    if opts.randn_source == "NV":
+        global nv_rng
+        nv_rng = rng_philox.Generator(seed)
+        return
+
+    torch.manual_seed(seed)
+
+
 def autocast(disable=False):
     from modules import shared
 
diff --git a/modules/processing.py b/modules/processing.py
index 0b66cd2a..8f34c8b4 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -492,7 +492,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
         noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
 
         subnoise = None
-        if subseeds is not None:
+        if subseeds is not None and subseed_strength != 0:
             subseed = 0 if i >= len(subseeds) else subseeds[i]
 
             subnoise = devices.randn(subseed, noise_shape)
@@ -524,7 +524,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
             cnt = p.sampler.number_of_needed_noises(p)
 
             if eta_noise_seed_delta > 0:
-                torch.manual_seed(seed + eta_noise_seed_delta)
+                devices.manual_seed(seed + eta_noise_seed_delta)
 
             for j in range(cnt):
                 sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
@@ -636,7 +636,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
         "Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
         "Init image hash": getattr(p, 'init_img_hash', None),
-        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+        "RNG": opts.randn_source if opts.randn_source != "GPU" and opts.randn_source != "NV" else None,
         "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
         **p.extra_generation_params,
         "Version": program_version() if opts.add_version_to_infotext else None,
diff --git a/modules/rng_philox.py b/modules/rng_philox.py
new file mode 100644
index 00000000..b5c02483
--- /dev/null
+++ b/modules/rng_philox.py
@@ -0,0 +1,100 @@
+"""RNG imitiating torch cuda randn on CPU. You are welcome.
+
+Usage:
+
+```
+g = Generator(seed=0)
+print(g.randn(shape=(3, 4)))
+```
+
+Expected output:
+```
+[[-0.92466259 -0.42534415 -2.6438457   0.14518388]
+ [-0.12086647 -0.57972564 -0.62285122 -0.32838709]
+ [-1.07454231 -0.36314407 -1.67105067  2.26550497]]
+```
+"""
+
+import numpy as np
+
+philox_m = [0xD2511F53, 0xCD9E8D57]
+philox_w = [0x9E3779B9, 0xBB67AE85]
+
+two_pow32_inv = np.array([2.3283064e-10], dtype=np.float32)
+two_pow32_inv_2pi = np.array([2.3283064e-10 * 6.2831855], dtype=np.float32)
+
+
+def uint32(x):
+    """Converts (N,) np.uint64 array into (2, N) np.unit32 array."""
+    return np.moveaxis(x.view(np.uint32).reshape(-1, 2), 0, 1)
+
+
+def philox4_round(counter, key):
+    """A single round of the Philox 4x32 random number generator."""
+
+    v1 = uint32(counter[0].astype(np.uint64) * philox_m[0])
+    v2 = uint32(counter[2].astype(np.uint64) * philox_m[1])
+
+    counter[0] = v2[1] ^ counter[1] ^ key[0]
+    counter[1] = v2[0]
+    counter[2] = v1[1] ^ counter[3] ^ key[1]
+    counter[3] = v1[0]
+
+
+def philox4_32(counter, key, rounds=10):
+    """Generates 32-bit random numbers using the Philox 4x32 random number generator.
+
+    Parameters:
+        counter (numpy.ndarray): A 4xN array of 32-bit integers representing the counter values (offset into generation).
+        key (numpy.ndarray): A 2xN array of 32-bit integers representing the key values (seed).
+        rounds (int): The number of rounds to perform.
+
+    Returns:
+        numpy.ndarray: A 4xN array of 32-bit integers containing the generated random numbers.
+    """
+
+    for _ in range(rounds - 1):
+        philox4_round(counter, key)
+
+        key[0] = key[0] + philox_w[0]
+        key[1] = key[1] + philox_w[1]
+
+    philox4_round(counter, key)
+    return counter
+
+
+def box_muller(x, y):
+    """Returns just the first out of two numbers generated by Box–Muller transform algorithm."""
+    u = x.astype(np.float32) * two_pow32_inv + two_pow32_inv / 2
+    v = y.astype(np.float32) * two_pow32_inv_2pi + two_pow32_inv_2pi / 2
+
+    s = np.sqrt(-2.0 * np.log(u))
+
+    r1 = s * np.sin(v)
+    return r1.astype(np.float32)
+
+
+class Generator:
+    """RNG that produces same outputs as torch.randn(..., device='cuda') on CPU"""
+
+    def __init__(self, seed):
+        self.seed = seed
+        self.offset = 0
+
+    def randn(self, shape):
+        """Generate a sequence of n standard normal random variables using the Philox 4x32 random number generator and the Box-Muller transform."""
+
+        n = 1
+        for x in shape:
+            n *= x
+
+        counter = np.zeros((4, n), dtype=np.uint32)
+        counter[0] = self.offset
+        counter[2] = np.arange(n, dtype=np.uint32)  # up to 2^32 numbers can be generated - if you want more you'd need to spill into counter[3]
+        self.offset += 1
+
+        key = uint32(np.array([[self.seed] * n], dtype=np.uint64))
+
+        g = philox4_32(counter, key)
+
+        return box_muller(g[0], g[1]).reshape(shape)  # discard g[2] and g[3]
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index e0da3425..d72c1b5f 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -260,10 +260,7 @@ class TorchHijack:
             if noise.shape == x.shape:
                 return noise
 
-        if opts.randn_source == "CPU" or x.device.type == 'mps':
-            return torch.randn_like(x, device=devices.cpu).to(x.device)
-        else:
-            return torch.randn_like(x)
+        return devices.randn_like(x)
 
 
 class KDiffusionSampler:
diff --git a/modules/shared.py b/modules/shared.py
index aa72c9c8..7103b4ca 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -428,7 +428,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
     "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
     "auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
-    "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
+    "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
 }))
 
 options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
-- 
cgit v1.2.3


From fca42949a3593c5a2f646e30cc99be2c02566aa2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 3 Aug 2023 07:18:55 +0300
Subject: rework torchsde._brownian.brownian_interval replacement to use
 device.randn_local and respect the NV setting.

---
 modules/devices.py            | 44 +++++++++++++++++++++++++++++++++++++------
 modules/sd_samplers_common.py | 12 ++++++------
 2 files changed, 44 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index b58776d8..00a00b18 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -71,14 +71,17 @@ def enable_tf32():
         torch.backends.cudnn.allow_tf32 = True
 
 
-
 errors.run(enable_tf32, "Enabling TF32")
 
-cpu = torch.device("cpu")
-device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
-dtype = torch.float16
-dtype_vae = torch.float16
-dtype_unet = torch.float16
+cpu: torch.device = torch.device("cpu")
+device: torch.device = None
+device_interrogate: torch.device = None
+device_gfpgan: torch.device = None
+device_esrgan: torch.device = None
+device_codeformer: torch.device = None
+dtype: torch.dtype = torch.float16
+dtype_vae: torch.dtype = torch.float16
+dtype_unet: torch.dtype = torch.float16
 unet_needs_upcast = False
 
 
@@ -94,6 +97,10 @@ nv_rng = None
 
 
 def randn(seed, shape):
+    """Generate a tensor with random numbers from a normal distribution using seed.
+
+    Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
+
     from modules.shared import opts
 
     manual_seed(seed)
@@ -107,7 +114,27 @@ def randn(seed, shape):
     return torch.randn(shape, device=device)
 
 
+def randn_local(seed, shape):
+    """Generate a tensor with random numbers from a normal distribution using seed.
+
+    Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
+
+    from modules.shared import opts
+
+    if opts.randn_source == "NV":
+        rng = rng_philox.Generator(seed)
+        return torch.asarray(rng.randn(shape), device=device)
+
+    local_device = cpu if opts.randn_source == "CPU" or device.type == 'mps' else device
+    local_generator = torch.Generator(local_device).manual_seed(int(seed))
+    return torch.randn(shape, device=local_device, generator=local_generator).to(device)
+
+
 def randn_like(x):
+    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+
+    Use either randn() or manual_seed() to initialize the generator."""
+
     from modules.shared import opts
 
     if opts.randn_source == "NV":
@@ -120,6 +147,10 @@ def randn_like(x):
 
 
 def randn_without_seed(shape):
+    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+
+    Use either randn() or manual_seed() to initialize the generator."""
+
     from modules.shared import opts
 
     if opts.randn_source == "NV":
@@ -132,6 +163,7 @@ def randn_without_seed(shape):
 
 
 def manual_seed(seed):
+    """Set up a global random number generator using the specified seed."""
     from modules.shared import opts
 
     if opts.randn_source == "NV":
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 763829f1..5deda761 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -2,10 +2,8 @@ from collections import namedtuple
 import numpy as np
 import torch
 from PIL import Image
-from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd
-
+from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared
 from modules.shared import opts, state
-import modules.shared as shared
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
 
@@ -85,11 +83,13 @@ class InterruptedException(BaseException):
     pass
 
 
-if opts.randn_source == "CPU":
+def replace_torchsde_browinan():
     import torchsde._brownian.brownian_interval
 
     def torchsde_randn(size, dtype, device, seed):
-        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
-        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+        return devices.randn_local(seed, size).to(device=device, dtype=dtype)
 
     torchsde._brownian.brownian_interval._randn = torchsde_randn
+
+
+replace_torchsde_browinan()
-- 
cgit v1.2.3


From 0904df84e296c10fe0ccb37a3b689e6b96d87e41 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 3 Aug 2023 07:53:03 +0300
Subject: minor performance improvements for philox

---
 modules/rng_philox.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/rng_philox.py b/modules/rng_philox.py
index b5c02483..5532cf9d 100644
--- a/modules/rng_philox.py
+++ b/modules/rng_philox.py
@@ -26,7 +26,7 @@ two_pow32_inv_2pi = np.array([2.3283064e-10 * 6.2831855], dtype=np.float32)
 
 def uint32(x):
     """Converts (N,) np.uint64 array into (2, N) np.unit32 array."""
-    return np.moveaxis(x.view(np.uint32).reshape(-1, 2), 0, 1)
+    return x.view(np.uint32).reshape(-1, 2).transpose(1, 0)
 
 
 def philox4_round(counter, key):
@@ -65,8 +65,8 @@ def philox4_32(counter, key, rounds=10):
 
 def box_muller(x, y):
     """Returns just the first out of two numbers generated by Box–Muller transform algorithm."""
-    u = x.astype(np.float32) * two_pow32_inv + two_pow32_inv / 2
-    v = y.astype(np.float32) * two_pow32_inv_2pi + two_pow32_inv_2pi / 2
+    u = x * two_pow32_inv + two_pow32_inv / 2
+    v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2
 
     s = np.sqrt(-2.0 * np.log(u))
 
@@ -93,7 +93,9 @@ class Generator:
         counter[2] = np.arange(n, dtype=np.uint32)  # up to 2^32 numbers can be generated - if you want more you'd need to spill into counter[3]
         self.offset += 1
 
-        key = uint32(np.array([[self.seed] * n], dtype=np.uint64))
+        key = np.empty(n, dtype=np.uint64)
+        key.fill(self.seed)
+        key = uint32(key)
 
         g = philox4_32(counter, key)
 
-- 
cgit v1.2.3


From f56a309432996936a41040ea723d76a8cb488f87 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Thu, 3 Aug 2023 10:25:49 +0900
Subject: fix missing TI hash

---
 modules/sd_hijack_clip.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 2f9d569b..8f29057a 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -245,6 +245,8 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                 hashes.append(f"{name}: {shorthash}")
 
             if hashes:
+                if self.hijack.extra_generation_params.get("TI hashes"):
+                    hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
                 self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
 
         if getattr(self.wrapped, 'return_pooled', False):
-- 
cgit v1.2.3


From 20549a50cb3c41868ce561c6658bfaa0d20ac7ba Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 3 Aug 2023 22:46:57 +0300
Subject: add style editor dialog rework toprow for img2img and txt2img to use
 a class with fields fix the console error when editing checkpoint user
 metadata

---
 modules/sd_models.py                           |   2 +-
 modules/styles.py                              |   5 +-
 modules/ui.py                                  | 230 ++++++++++---------------
 modules/ui_common.py                           |  32 +++-
 modules/ui_extra_networks_checkpoints.py       |   2 +-
 modules/ui_extra_networks_hypernets.py         |   2 +-
 modules/ui_extra_networks_textual_inversion.py |   2 +-
 modules/ui_prompt_styles.py                    | 110 ++++++++++++
 8 files changed, 235 insertions(+), 150 deletions(-)
 create mode 100644 modules/ui_prompt_styles.py

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 8f72f21d..1d93d893 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -68,7 +68,7 @@ class CheckpointInfo:
 
         self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
 
-        self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
+        self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
 
     def register(self):
         checkpoints_list[self.title] = self
diff --git a/modules/styles.py b/modules/styles.py
index ec0e1bc5..0740fe1b 100644
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -106,10 +106,7 @@ class StyleDatabase:
         if os.path.exists(path):
             shutil.copy(path, f"{path}.bak")
 
-        fd = os.open(path, os.O_RDWR | os.O_CREAT)
-        with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
-            # _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
-            # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
+        with open(path, "w", encoding="utf-8-sig", newline='') as file:
             writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
             writer.writeheader()
             writer.writerows(style._asdict() for k, style in self.styles.items())
diff --git a/modules/ui.py b/modules/ui.py
index ac2787eb..c059dcec 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -12,7 +12,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin  # noqa: F401
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles
 from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
 from modules.paths import script_path
 from modules.ui_common import create_refresh_button
@@ -92,19 +92,6 @@ def send_gradio_gallery_to_image(x):
     return image_from_url_text(x[0])
 
 
-def add_style(name: str, prompt: str, negative_prompt: str):
-    if name is None:
-        return [gr_show() for x in range(4)]
-
-    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
-    shared.prompt_styles.styles[style.name] = style
-    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
-    # reserialize all styles every time we save them
-    shared.prompt_styles.save_styles(shared.styles_filename)
-
-    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
-
-
 def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
     from modules import processing, devices
 
@@ -129,13 +116,6 @@ def resize_from_to_html(width, height, scale_by):
     return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
 
 
-def apply_styles(prompt, prompt_neg, styles):
-    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
-    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
-
-    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
-
-
 def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
     if mode in {0, 1, 3, 4}:
         return [interrogation_function(ii_singles[mode]), None]
@@ -267,71 +247,67 @@ def update_token_counter(text, steps):
     return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
 
 
-def create_toprow(is_img2img):
-    id_part = "img2img" if is_img2img else "txt2img"
+class Toprow:
+    def __init__(self, is_img2img):
+        id_part = "img2img" if is_img2img else "txt2img"
+        self.id_part = id_part
 
-    with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
-        with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
-
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
-
-        button_interrogate = None
-        button_deepbooru = None
-        if is_img2img:
-            with gr.Column(scale=1, elem_classes="interrogate-col"):
-                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
-        with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
-            with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
-                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
-                skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
-                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
-                skip.click(
-                    fn=lambda: shared.state.skip(),
-                    inputs=[],
-                    outputs=[],
-                )
+        with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
+            with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
+                with gr.Row():
+                    with gr.Column(scale=80):
+                        with gr.Row():
+                            self.prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
 
-                interrupt.click(
-                    fn=lambda: shared.state.interrupt(),
-                    inputs=[],
-                    outputs=[],
-                )
+                with gr.Row():
+                    with gr.Column(scale=80):
+                        with gr.Row():
+                            self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+
+            self.button_interrogate = None
+            self.button_deepbooru = None
+            if is_img2img:
+                with gr.Column(scale=1, elem_classes="interrogate-col"):
+                    self.button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+                    self.button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+            with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
+                with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+                    self.interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+                    self.skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
+                    self.submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+
+                    self.skip.click(
+                        fn=lambda: shared.state.skip(),
+                        inputs=[],
+                        outputs=[],
+                    )
 
-            with gr.Row(elem_id=f"{id_part}_tools"):
-                paste = ToolButton(value=paste_symbol, elem_id="paste")
-                clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
-                extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
-                prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
-                save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
-                restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
-
-                token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
-                token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-                negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
-                negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
-
-                clear_prompt_button.click(
-                    fn=lambda *x: x,
-                    _js="confirm_clear_prompt",
-                    inputs=[prompt, negative_prompt],
-                    outputs=[prompt, negative_prompt],
-                )
+                    self.interrupt.click(
+                        fn=lambda: shared.state.interrupt(),
+                        inputs=[],
+                        outputs=[],
+                    )
 
-            with gr.Row(elem_id=f"{id_part}_styles_row"):
-                prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
-                create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")
+                with gr.Row(elem_id=f"{id_part}_tools"):
+                    self.paste = ToolButton(value=paste_symbol, elem_id="paste")
+                    self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
+                    self.extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
+                    self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
+
+                    self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
+                    self.token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+                    self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
+                    self.negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
+
+                    self.clear_prompt_button.click(
+                        fn=lambda *x: x,
+                        _js="confirm_clear_prompt",
+                        inputs=[self.prompt, self.negative_prompt],
+                        outputs=[self.prompt, self.negative_prompt],
+                    )
 
-    return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button
+                self.ui_styles = ui_prompt_styles.UiPromptStyles(id_part, self.prompt, self.negative_prompt)
 
 
 def setup_progressbar(*args, **kwargs):
@@ -419,14 +395,14 @@ def create_ui():
     modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=False)
+        toprow = txt2img_toprow = Toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
 
         with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
             from modules import ui_extra_networks
-            extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img')
+            extra_networks_ui = ui_extra_networks.create_ui(extra_networks, toprow.extra_networks_button, 'txt2img')
 
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='compact', elem_id="txt2img_settings"):
@@ -532,9 +508,9 @@ def create_ui():
                 _js="submit",
                 inputs=[
                     dummy_component,
-                    txt2img_prompt,
-                    txt2img_negative_prompt,
-                    txt2img_prompt_styles,
+                    toprow.prompt,
+                    toprow.negative_prompt,
+                    toprow.ui_styles.dropdown,
                     steps,
                     sampler_index,
                     restore_faces,
@@ -569,12 +545,12 @@ def create_ui():
                 show_progress=False,
             )
 
-            txt2img_prompt.submit(**txt2img_args)
-            submit.click(**txt2img_args)
+            toprow.prompt.submit(**txt2img_args)
+            toprow.submit.click(**txt2img_args)
 
             res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
 
-            restore_progress_button.click(
+            toprow.restore_progress_button.click(
                 fn=progress.restore_progress,
                 _js="restoreProgressTxt2img",
                 inputs=[dummy_component],
@@ -593,7 +569,7 @@ def create_ui():
                     txt_prompt_img
                 ],
                 outputs=[
-                    txt2img_prompt,
+                    toprow.prompt,
                     txt_prompt_img
                 ],
                 show_progress=False,
@@ -607,8 +583,8 @@ def create_ui():
             )
 
             txt2img_paste_fields = [
-                (txt2img_prompt, "Prompt"),
-                (txt2img_negative_prompt, "Negative prompt"),
+                (toprow.prompt, "Prompt"),
+                (toprow.negative_prompt, "Negative prompt"),
                 (steps, "Steps"),
                 (sampler_index, "Sampler"),
                 (restore_faces, "Face restoration"),
@@ -621,7 +597,7 @@ def create_ui():
                 (subseed_strength, "Variation seed strength"),
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
-                (txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+                (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
                 (denoising_strength, "Denoising strength"),
                 (enable_hr, lambda d: "Denoising strength" in d),
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
@@ -639,12 +615,12 @@ def create_ui():
             ]
             parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
             parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
+                paste_button=toprow.paste, tabname="txt2img", source_text_component=toprow.prompt, source_image_component=None,
             ))
 
             txt2img_preview_params = [
-                txt2img_prompt,
-                txt2img_negative_prompt,
+                toprow.prompt,
+                toprow.negative_prompt,
                 steps,
                 sampler_index,
                 cfg_scale,
@@ -653,8 +629,8 @@ def create_ui():
                 height,
             ]
 
-            token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
-            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
+            toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
+            toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
 
             ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
 
@@ -662,13 +638,13 @@ def create_ui():
     modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=True)
+        toprow = img2img_toprow = Toprow(is_img2img=True)
 
         img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
 
         with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks:
             from modules import ui_extra_networks
-            extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'img2img')
+            extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, toprow.extra_networks_button, 'img2img')
 
         with FormRow().style(equal_height=False):
             with gr.Column(variant='compact', elem_id="img2img_settings"):
@@ -889,7 +865,7 @@ def create_ui():
                     img2img_prompt_img
                 ],
                 outputs=[
-                    img2img_prompt,
+                    toprow.prompt,
                     img2img_prompt_img
                 ],
                 show_progress=False,
@@ -901,9 +877,9 @@ def create_ui():
                 inputs=[
                     dummy_component,
                     dummy_component,
-                    img2img_prompt,
-                    img2img_negative_prompt,
-                    img2img_prompt_styles,
+                    toprow.prompt,
+                    toprow.negative_prompt,
+                    toprow.ui_styles.dropdown,
                     init_img,
                     sketch,
                     init_img_with_mask,
@@ -962,11 +938,11 @@ def create_ui():
                     inpaint_color_sketch,
                     init_img_inpaint,
                 ],
-                outputs=[img2img_prompt, dummy_component],
+                outputs=[toprow.prompt, dummy_component],
             )
 
-            img2img_prompt.submit(**img2img_args)
-            submit.click(**img2img_args)
+            toprow.prompt.submit(**img2img_args)
+            toprow.submit.click(**img2img_args)
 
             res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
 
@@ -978,7 +954,7 @@ def create_ui():
                 show_progress=False,
             )
 
-            restore_progress_button.click(
+            toprow.restore_progress_button.click(
                 fn=progress.restore_progress,
                 _js="restoreProgressImg2img",
                 inputs=[dummy_component],
@@ -991,46 +967,24 @@ def create_ui():
                 show_progress=False,
             )
 
-            img2img_interrogate.click(
+            toprow.button_interrogate.click(
                 fn=lambda *args: process_interrogate(interrogate, *args),
                 **interrogate_args,
             )
 
-            img2img_deepbooru.click(
+            toprow.button_deepbooru.click(
                 fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
                 **interrogate_args,
             )
 
-            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
-            style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
-            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-
-            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
-                button.click(
-                    fn=add_style,
-                    _js="ask_for_style_name",
-                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
-                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
-                    inputs=[dummy_component, prompt, negative_prompt],
-                    outputs=[txt2img_prompt_styles, img2img_prompt_styles],
-                )
-
-            for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
-                button.click(
-                    fn=apply_styles,
-                    _js=js_func,
-                    inputs=[prompt, negative_prompt, styles],
-                    outputs=[prompt, negative_prompt, styles],
-                )
-
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
+            toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
+            toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
 
             ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
 
             img2img_paste_fields = [
-                (img2img_prompt, "Prompt"),
-                (img2img_negative_prompt, "Negative prompt"),
+                (toprow.prompt, "Prompt"),
+                (toprow.negative_prompt, "Negative prompt"),
                 (steps, "Steps"),
                 (sampler_index, "Sampler"),
                 (restore_faces, "Face restoration"),
@@ -1044,7 +998,7 @@ def create_ui():
                 (subseed_strength, "Variation seed strength"),
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
-                (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+                (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
                 (denoising_strength, "Denoising strength"),
                 (mask_blur, "Mask blur"),
                 *modules.scripts.scripts_img2img.infotext_fields
@@ -1052,7 +1006,7 @@ def create_ui():
             parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
             parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
             parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
+                paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
             ))
 
     modules.scripts.scripts_current = None
diff --git a/modules/ui_common.py b/modules/ui_common.py
index 11eb2a4b..ba75fa73 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -223,20 +223,44 @@ Requested path was: {f}
 
 
 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+    refresh_components = refresh_component if isinstance(refresh_component, list) else [refresh_component]
+
+    label = None
+    for comp in refresh_components:
+        label = getattr(comp, 'label', None)
+        if label is not None:
+            break
+
     def refresh():
         refresh_method()
         args = refreshed_args() if callable(refreshed_args) else refreshed_args
 
         for k, v in args.items():
-            setattr(refresh_component, k, v)
+            for comp in refresh_components:
+                setattr(comp, k, v)
 
-        return gr.update(**(args or {}))
+        return [gr.update(**(args or {})) for _ in refresh_components]
 
-    refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
+    refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id, tooltip=f"{label}: refresh" if label else "Refresh")
     refresh_button.click(
         fn=refresh,
         inputs=[],
-        outputs=[refresh_component]
+        outputs=[*refresh_components]
     )
     return refresh_button
 
+
+def setup_dialog(button_show, dialog, *, button_close=None):
+    """Sets up the UI so that the dialog (gr.Box) is invisible, and is only shown when buttons_show is clicked, in a fullscreen modal window."""
+
+    dialog.visible = False
+
+    button_show.click(
+        fn=lambda: gr.update(visible=True),
+        inputs=[],
+        outputs=[dialog],
+    ).then(fn=None, _js="function(){ popup(gradioApp().getElementById('" + dialog.elem_id + "')); }")
+
+    if button_close:
+        button_close.click(fn=None, _js="closePopup")
+
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py
index 2bb0a222..891d8f2c 100644
--- a/modules/ui_extra_networks_checkpoints.py
+++ b/modules/ui_extra_networks_checkpoints.py
@@ -12,7 +12,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
     def refresh(self):
         shared.refresh_checkpoints()
 
-    def create_item(self, name, index=None):
+    def create_item(self, name, index=None, enable_filter=True):
         checkpoint: sd_models.CheckpointInfo = sd_models.checkpoint_aliases.get(name)
         path, ext = os.path.splitext(checkpoint.filename)
         return {
diff --git a/modules/ui_extra_networks_hypernets.py b/modules/ui_extra_networks_hypernets.py
index e53ccb42..514a4562 100644
--- a/modules/ui_extra_networks_hypernets.py
+++ b/modules/ui_extra_networks_hypernets.py
@@ -11,7 +11,7 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
     def refresh(self):
         shared.reload_hypernetworks()
 
-    def create_item(self, name, index=None):
+    def create_item(self, name, index=None, enable_filter=True):
         full_path = shared.hypernetworks[name]
         path, ext = os.path.splitext(full_path)
 
diff --git a/modules/ui_extra_networks_textual_inversion.py b/modules/ui_extra_networks_textual_inversion.py
index d1794e50..73134698 100644
--- a/modules/ui_extra_networks_textual_inversion.py
+++ b/modules/ui_extra_networks_textual_inversion.py
@@ -12,7 +12,7 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
     def refresh(self):
         sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
 
-    def create_item(self, name, index=None):
+    def create_item(self, name, index=None, enable_filter=True):
         embedding = sd_hijack.model_hijack.embedding_db.word_embeddings.get(name)
 
         path, ext = os.path.splitext(embedding.filename)
diff --git a/modules/ui_prompt_styles.py b/modules/ui_prompt_styles.py
new file mode 100644
index 00000000..85eb3a64
--- /dev/null
+++ b/modules/ui_prompt_styles.py
@@ -0,0 +1,110 @@
+import gradio as gr
+
+from modules import shared, ui_common, ui_components, styles
+
+styles_edit_symbol = '\U0001f58c\uFE0F'  # 🖌️
+styles_materialize_symbol = '\U0001f4cb'  # 📋
+
+
+def select_style(name):
+    style = shared.prompt_styles.styles.get(name)
+    existing = style is not None
+    empty = not name
+
+    prompt = style.prompt if style else gr.update()
+    negative_prompt = style.negative_prompt if style else gr.update()
+
+    return prompt, negative_prompt, gr.update(visible=existing), gr.update(visible=not empty)
+
+
+def save_style(name, prompt, negative_prompt):
+    if not name:
+        return gr.update(visible=False)
+
+    style = styles.PromptStyle(name, prompt, negative_prompt)
+    shared.prompt_styles.styles[style.name] = style
+    shared.prompt_styles.save_styles(shared.styles_filename)
+
+    return gr.update(visible=True)
+
+
+def delete_style(name):
+    if name == "":
+        return
+
+    shared.prompt_styles.styles.pop(name, None)
+    shared.prompt_styles.save_styles(shared.styles_filename)
+
+    return '', '', ''
+
+
+def materialize_styles(prompt, negative_prompt, styles):
+    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
+    negative_prompt = shared.prompt_styles.apply_negative_styles_to_prompt(negative_prompt, styles)
+
+    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=negative_prompt), gr.Dropdown.update(value=[])]
+
+
+def refresh_styles():
+    return gr.update(choices=list(shared.prompt_styles.styles)), gr.update(choices=list(shared.prompt_styles.styles))
+
+
+class UiPromptStyles:
+    def __init__(self, tabname, main_ui_prompt, main_ui_negative_prompt):
+        self.tabname = tabname
+
+        with gr.Row(elem_id=f"{tabname}_styles_row"):
+            self.dropdown = gr.Dropdown(label="Styles", show_label=False, elem_id=f"{tabname}_styles", choices=list(shared.prompt_styles.styles), value=[], multiselect=True, tooltip="Styles")
+            edit_button = ui_components.ToolButton(value=styles_edit_symbol, elem_id=f"{tabname}_styles_edit_button", tooltip="Edit styles")
+
+        with gr.Box(elem_id=f"{tabname}_styles_dialog", elem_classes="popup-dialog") as styles_dialog:
+            with gr.Row():
+                self.selection = gr.Dropdown(label="Styles", elem_id=f"{tabname}_styles_edit_select", choices=list(shared.prompt_styles.styles), value=[], allow_custom_value=True, info="Styles allow you to add custom text to prompt. Use the {prompt} token in style text, and it will be replaced with user's prompt when applying style. Otherwise, style's text will be added to the end of the prompt.")
+                ui_common.create_refresh_button([self.dropdown, self.selection], shared.prompt_styles.reload, lambda: {"choices": list(shared.prompt_styles.styles)}, f"refresh_{tabname}_styles")
+                self.materialize = ui_components.ToolButton(value=styles_materialize_symbol, elem_id=f"{tabname}_style_apply", tooltip="Apply all selected styles from the style selction dropdown in main UI to the prompt.")
+
+            with gr.Row():
+                self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3)
+
+            with gr.Row():
+                self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3)
+
+            with gr.Row():
+                self.save = gr.Button('Save', variant='primary', elem_id=f'{tabname}_edit_style_save', visible=False)
+                self.delete = gr.Button('Delete', variant='primary', elem_id=f'{tabname}_edit_style_delete', visible=False)
+                self.close = gr.Button('Close', variant='secondary', elem_id=f'{tabname}_edit_style_close')
+
+        self.selection.change(
+            fn=select_style,
+            inputs=[self.selection],
+            outputs=[self.prompt, self.neg_prompt, self.delete, self.save],
+            show_progress=False,
+        )
+
+        self.save.click(
+            fn=save_style,
+            inputs=[self.selection, self.prompt, self.neg_prompt],
+            outputs=[self.delete],
+            show_progress=False,
+        ).then(refresh_styles, outputs=[self.dropdown, self.selection], show_progress=False)
+
+        self.delete.click(
+            fn=delete_style,
+            _js='function(name){ if(name == "") return ""; return confirm("Delete style " + name + "?") ? name : ""; }',
+            inputs=[self.selection],
+            outputs=[self.selection, self.prompt, self.neg_prompt],
+            show_progress=False,
+        ).then(refresh_styles, outputs=[self.dropdown, self.selection], show_progress=False)
+
+        self.materialize.click(
+            fn=materialize_styles,
+            inputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
+            outputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
+            show_progress=False,
+        ).then(fn=None, _js="function(){update_"+tabname+"_tokens(); closePopup();}", show_progress=False)
+
+        ui_common.setup_dialog(button_show=edit_button, dialog=styles_dialog, button_close=self.close)
+
+
+
+
-- 
cgit v1.2.3


From af528552d6006268e352f422d533a69a6a718a5d Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 3 Aug 2023 23:18:50 +0300
Subject: fix linter issues

---
 modules/ui.py | 57 +++++++++++++++++++++++++--------------------------------
 1 file changed, 25 insertions(+), 32 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index c059dcec..70364a8b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -12,30 +12,23 @@ import numpy as np
 from PIL import Image, PngImagePlugin  # noqa: F401
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts
 from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
 from modules.paths import script_path
 from modules.ui_common import create_refresh_button
 from modules.ui_gradio_extensions import reload_javascript
 
-
 from modules.shared import opts, cmd_opts
 
-import modules.codeformer_model
 import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.scripts
+import modules.hypernetworks.ui as hypernetworks_ui
+import modules.textual_inversion.ui as textual_inversion_ui
+import modules.textual_inversion.textual_inversion as textual_inversion
 import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
 from modules import prompt_parser
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
-from modules.textual_inversion import textual_inversion
-import modules.hypernetworks.ui
 from modules.generation_parameters_copypaste import image_from_url_text
-import modules.extras
 
 create_setting_component = ui_settings.create_setting_component
 
@@ -391,11 +384,11 @@ def create_ui():
 
     parameters_copypaste.reset()
 
-    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
-    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+    scripts.scripts_current = scripts.scripts_txt2img
+    scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        toprow = txt2img_toprow = Toprow(is_img2img=False)
+        toprow = Toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
@@ -406,7 +399,7 @@ def create_ui():
 
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='compact', elem_id="txt2img_settings"):
-                modules.scripts.scripts_txt2img.prepare_ui()
+                scripts.scripts_txt2img.prepare_ui()
 
                 for category in ordered_ui_categories():
                     if category == "sampler":
@@ -474,10 +467,10 @@ def create_ui():
 
                     elif category == "scripts":
                         with FormGroup(elem_id="txt2img_script_container"):
-                            custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+                            custom_inputs = scripts.scripts_txt2img.setup_ui()
 
                     else:
-                        modules.scripts.scripts_txt2img.setup_ui_for_section(category)
+                        scripts.scripts_txt2img.setup_ui_for_section(category)
 
             hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
 
@@ -611,7 +604,7 @@ def create_ui():
                 (hr_prompt, "Hires prompt"),
                 (hr_negative_prompt, "Hires negative prompt"),
                 (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
-                *modules.scripts.scripts_txt2img.infotext_fields
+                *scripts.scripts_txt2img.infotext_fields
             ]
             parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
             parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
@@ -634,11 +627,11 @@ def create_ui():
 
             ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
 
-    modules.scripts.scripts_current = modules.scripts.scripts_img2img
-    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+    scripts.scripts_current = scripts.scripts_img2img
+    scripts.scripts_img2img.initialize_scripts(is_img2img=True)
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        toprow = img2img_toprow = Toprow(is_img2img=True)
+        toprow = Toprow(is_img2img=True)
 
         img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
 
@@ -740,7 +733,7 @@ def create_ui():
                 with FormRow():
                     resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
-                modules.scripts.scripts_img2img.prepare_ui()
+                scripts.scripts_img2img.prepare_ui()
 
                 for category in ordered_ui_categories():
                     if category == "sampler":
@@ -821,7 +814,7 @@ def create_ui():
 
                     elif category == "scripts":
                         with FormGroup(elem_id="img2img_script_container"):
-                            custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+                            custom_inputs = scripts.scripts_img2img.setup_ui()
 
                     elif category == "inpaint":
                         with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
@@ -852,7 +845,7 @@ def create_ui():
                                     outputs=[inpaint_controls, mask_alpha],
                                 )
                     else:
-                        modules.scripts.scripts_img2img.setup_ui_for_section(category)
+                        scripts.scripts_img2img.setup_ui_for_section(category)
 
             img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
 
@@ -1001,7 +994,7 @@ def create_ui():
                 (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
                 (denoising_strength, "Denoising strength"),
                 (mask_blur, "Mask blur"),
-                *modules.scripts.scripts_img2img.infotext_fields
+                *scripts.scripts_img2img.infotext_fields
             ]
             parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
             parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
@@ -1009,7 +1002,7 @@ def create_ui():
                 paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
             ))
 
-    modules.scripts.scripts_current = None
+    scripts.scripts_current = None
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         ui_postprocessing.create_ui()
@@ -1063,7 +1056,7 @@ def create_ui():
                     new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=hypernetworks_ui.keys, elem_id="train_new_hypernetwork_activation_func")
                     new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
@@ -1208,7 +1201,7 @@ def create_ui():
                 ti_outcome = gr.HTML(elem_id="ti_error", value="")
 
         create_embedding.click(
-            fn=modules.textual_inversion.ui.create_embedding,
+            fn=textual_inversion_ui.create_embedding,
             inputs=[
                 new_embedding_name,
                 initialization_text,
@@ -1223,7 +1216,7 @@ def create_ui():
         )
 
         create_hypernetwork.click(
-            fn=modules.hypernetworks.ui.create_hypernetwork,
+            fn=hypernetworks_ui.create_hypernetwork,
             inputs=[
                 new_hypernetwork_name,
                 new_hypernetwork_sizes,
@@ -1243,7 +1236,7 @@ def create_ui():
         )
 
         run_preprocess.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
             inputs=[
                 dummy_component,
@@ -1279,7 +1272,7 @@ def create_ui():
         )
 
         train_embedding.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+            fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
             inputs=[
                 dummy_component,
@@ -1313,7 +1306,7 @@ def create_ui():
         )
 
         train_hypernetwork.click(
-            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            fn=wrap_gradio_gpu_call(hypernetworks_ui.train_hypernetwork, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
             inputs=[
                 dummy_component,
-- 
cgit v1.2.3


From 09c1be96748584b08b6299024bb7b64bafb09d09 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Thu, 3 Aug 2023 23:31:04 +0300
Subject: put some of the shared functionality into toprow write a comment for
 the toprow

---
 modules/ui.py | 39 ++++++++++++---------------------------
 1 file changed, 12 insertions(+), 27 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 70364a8b..03306ba9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -25,6 +25,7 @@ import modules.hypernetworks.ui as hypernetworks_ui
 import modules.textual_inversion.ui as textual_inversion_ui
 import modules.textual_inversion.textual_inversion as textual_inversion
 import modules.shared as shared
+import modules.images
 from modules import prompt_parser
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
@@ -241,6 +242,8 @@ def update_token_counter(text, steps):
 
 
 class Toprow:
+    """Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation"""
+
     def __init__(self, is_img2img):
         id_part = "img2img" if is_img2img else "txt2img"
         self.id_part = id_part
@@ -251,12 +254,14 @@ class Toprow:
                     with gr.Column(scale=80):
                         with gr.Row():
                             self.prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+                            self.prompt_img = gr.File(label="", elem_id=f"{id_part}_prompt_image", file_count="single", type="binary", visible=False)
 
                 with gr.Row():
                     with gr.Column(scale=80):
                         with gr.Row():
                             self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
 
+
             self.button_interrogate = None
             self.button_deepbooru = None
             if is_img2img:
@@ -302,6 +307,13 @@ class Toprow:
 
                 self.ui_styles = ui_prompt_styles.UiPromptStyles(id_part, self.prompt, self.negative_prompt)
 
+        self.prompt_img.change(
+            fn=modules.images.image_data,
+            inputs=[self.prompt_img],
+            outputs=[self.prompt, self.prompt_img],
+            show_progress=False,
+        )
+
 
 def setup_progressbar(*args, **kwargs):
     pass
@@ -391,7 +403,6 @@ def create_ui():
         toprow = Toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
-        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
 
         with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks:
             from modules import ui_extra_networks
@@ -556,18 +567,6 @@ def create_ui():
                 show_progress=False,
             )
 
-            txt_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    txt_prompt_img
-                ],
-                outputs=[
-                    toprow.prompt,
-                    txt_prompt_img
-                ],
-                show_progress=False,
-            )
-
             enable_hr.change(
                 fn=lambda x: gr_show(x),
                 inputs=[enable_hr],
@@ -633,8 +632,6 @@ def create_ui():
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         toprow = Toprow(is_img2img=True)
 
-        img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
-
         with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks:
             from modules import ui_extra_networks
             extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, toprow.extra_networks_button, 'img2img')
@@ -852,18 +849,6 @@ def create_ui():
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
-            img2img_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    img2img_prompt_img
-                ],
-                outputs=[
-                    toprow.prompt,
-                    img2img_prompt_img
-                ],
-                show_progress=False,
-            )
-
             img2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
                 _js="submit_img2img",
-- 
cgit v1.2.3


From bbfff771d7337707bf501b27f98da2f7a7c06f73 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 29 Jul 2023 01:07:35 +0900
Subject: --disable-all-extensions --disable-extra-extensions

---
 modules/cmd_args.py      |  2 ++
 modules/extensions.py    | 10 +++++++---
 modules/ui_extensions.py | 18 +++++++++++-------
 3 files changed, 20 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/cmd_args.py b/modules/cmd_args.py
index dd5fadc4..1262f1a4 100644
--- a/modules/cmd_args.py
+++ b/modules/cmd_args.py
@@ -111,3 +111,5 @@ parser.add_argument('--subpath', type=str, help='customize the subpath for gradi
 parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
 parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
 parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')
+parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False)
+parser.add_argument("--disable-extra-extensions", action='store_true', help=" prevent all extensions except built-in from running regardless of any other settings", default=False)
diff --git a/modules/extensions.py b/modules/extensions.py
index 3ad5ed53..e4633af4 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -11,9 +11,9 @@ os.makedirs(extensions_dir, exist_ok=True)
 
 
 def active():
-    if shared.opts.disable_all_extensions == "all":
+    if shared.cmd_opts.disable_all_extensions or shared.opts.disable_all_extensions == "all":
         return []
-    elif shared.opts.disable_all_extensions == "extra":
+    elif shared.cmd_opts.disable_extra_extensions or shared.opts.disable_all_extensions == "extra":
         return [x for x in extensions if x.enabled and x.is_builtin]
     else:
         return [x for x in extensions if x.enabled]
@@ -141,8 +141,12 @@ def list_extensions():
     if not os.path.isdir(extensions_dir):
         return
 
-    if shared.opts.disable_all_extensions == "all":
+    if shared.cmd_opts.disable_all_extensions:
+        print("*** \"--disable-all-extensions\" arg was used, will not load any extensions ***")
+    elif shared.opts.disable_all_extensions == "all":
         print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
+    elif shared.cmd_opts.disable_extra_extensions:
+        print("*** \"--disable-extra-extensions\" arg was used, will only load built-in extensions ***")
     elif shared.opts.disable_all_extensions == "extra":
         print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
 
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index f3e4fba7..bd28bfcf 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -164,7 +164,7 @@ def extension_table():
             ext_status = ext.status
 
         style = ""
-        if shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.opts.disable_all_extensions == "all":
+        if shared.cmd_opts.disable_extra_extensions and not ext.is_builtin or shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.cmd_opts.disable_all_extensions or shared.opts.disable_all_extensions == "all":
             style = STYLE_PRIMARY
 
         version_link = ext.version
@@ -537,12 +537,16 @@ def create_ui():
                     extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
 
                 html = ""
-                if shared.opts.disable_all_extensions != "none":
-                    html = """
-<span style="color: var(--primary-400);">
-    "Disable all extensions" was set, change it to "none" to load all extensions again
-</span>
-                    """
+
+                if shared.cmd_opts.disable_all_extensions or shared.cmd_opts.disable_extra_extensions or shared.opts.disable_all_extensions != "none":
+                    if shared.cmd_opts.disable_all_extensions:
+                        msg = '"--disable-all-extensions" was used, remove it to load all extensions again'
+                    elif shared.opts.disable_all_extensions != "none":
+                        msg = '"Disable all extensions" was set, change it to "none" to load all extensions again'
+                    elif shared.cmd_opts.disable_extra_extensions:
+                        msg = '"--disable-extra-extensions" was used, remove it to load all extensions again'
+                    html = f'<span style="color: var(--primary-400);">{msg}</span>'
+
                 info = gr.HTML(html)
                 extensions_table = gr.HTML('Loading...')
                 ui.load(fn=extension_table, inputs=[], outputs=[extensions_table])
-- 
cgit v1.2.3


From 362789a3793025c698fa42372fd66c3c4f2d6413 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Fri, 4 Aug 2023 07:50:17 +0300
Subject: gradio 3.39

---
 modules/gradio_extensons.py     | 60 +++++++++++++++++++++++++++++++++++++++++
 modules/scripts.py              | 60 -----------------------------------------
 modules/shared.py               |  3 ++-
 modules/ui.py                   | 24 ++++++++---------
 modules/ui_checkpoint_merger.py |  2 +-
 modules/ui_common.py            |  2 +-
 modules/ui_components.py        |  2 +-
 modules/ui_extensions.py        |  8 +++---
 modules/ui_postprocessing.py    |  2 +-
 9 files changed, 82 insertions(+), 81 deletions(-)
 create mode 100644 modules/gradio_extensons.py

(limited to 'modules')

diff --git a/modules/gradio_extensons.py b/modules/gradio_extensons.py
new file mode 100644
index 00000000..5af7fd8e
--- /dev/null
+++ b/modules/gradio_extensons.py
@@ -0,0 +1,60 @@
+import gradio as gr
+
+from modules import scripts
+
+def add_classes_to_gradio_component(comp):
+    """
+    this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
+    """
+
+    comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
+
+    if getattr(comp, 'multiselect', False):
+        comp.elem_classes.append('multiselect')
+
+
+def IOComponent_init(self, *args, **kwargs):
+    self.webui_tooltip = kwargs.pop('tooltip', None)
+
+    if scripts.scripts_current is not None:
+        scripts.scripts_current.before_component(self, **kwargs)
+
+    scripts.script_callbacks.before_component_callback(self, **kwargs)
+
+    res = original_IOComponent_init(self, *args, **kwargs)
+
+    add_classes_to_gradio_component(self)
+
+    scripts.script_callbacks.after_component_callback(self, **kwargs)
+
+    if scripts.scripts_current is not None:
+        scripts.scripts_current.after_component(self, **kwargs)
+
+    return res
+
+
+def Block_get_config(self):
+    config = original_Block_get_config(self)
+
+    webui_tooltip = getattr(self, 'webui_tooltip', None)
+    if webui_tooltip:
+        config["webui_tooltip"] = webui_tooltip
+
+    return config
+
+
+def BlockContext_init(self, *args, **kwargs):
+    res = original_BlockContext_init(self, *args, **kwargs)
+
+    add_classes_to_gradio_component(self)
+
+    return res
+
+
+original_IOComponent_init = gr.components.IOComponent.__init__
+original_Block_get_config = gr.blocks.Block.get_config
+original_BlockContext_init = gr.blocks.BlockContext.__init__
+
+gr.components.IOComponent.__init__ = IOComponent_init
+gr.blocks.Block.get_config = Block_get_config
+gr.blocks.BlockContext.__init__ = BlockContext_init
diff --git a/modules/scripts.py b/modules/scripts.py
index edf7347e..f7d060aa 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -631,63 +631,3 @@ def reload_script_body_only():
 
 
 reload_scripts = load_scripts  # compatibility alias
-
-
-def add_classes_to_gradio_component(comp):
-    """
-    this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
-    """
-
-    comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
-
-    if getattr(comp, 'multiselect', False):
-        comp.elem_classes.append('multiselect')
-
-
-
-def IOComponent_init(self, *args, **kwargs):
-    self.webui_tooltip = kwargs.pop('tooltip', None)
-
-    if scripts_current is not None:
-        scripts_current.before_component(self, **kwargs)
-
-    script_callbacks.before_component_callback(self, **kwargs)
-
-    res = original_IOComponent_init(self, *args, **kwargs)
-
-    add_classes_to_gradio_component(self)
-
-    script_callbacks.after_component_callback(self, **kwargs)
-
-    if scripts_current is not None:
-        scripts_current.after_component(self, **kwargs)
-
-    return res
-
-
-def Block_get_config(self):
-    config = original_Block_get_config(self)
-
-    webui_tooltip = getattr(self, 'webui_tooltip', None)
-    if webui_tooltip:
-        config["webui_tooltip"] = webui_tooltip
-
-    return config
-
-
-original_IOComponent_init = gr.components.IOComponent.__init__
-original_Block_get_config = gr.components.Block.get_config
-gr.components.IOComponent.__init__ = IOComponent_init
-gr.components.Block.get_config = Block_get_config
-
-
-def BlockContext_init(self, *args, **kwargs):
-    res = original_BlockContext_init(self, *args, **kwargs)
-
-    add_classes_to_gradio_component(self)
-
-    return res
-
-
-original_BlockContext_init = gr.blocks.BlockContext.__init__
-gr.blocks.BlockContext.__init__ = BlockContext_init
diff --git a/modules/shared.py b/modules/shared.py
index 7103b4ca..cec030f7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -385,7 +385,8 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
 }))
 
 options_templates.update(options_section(('system', "System"), {
-    "show_warnings": OptionInfo(False, "Show warnings in console."),
+    "show_warnings": OptionInfo(False, "Show warnings in console.").needs_restart(),
+    "show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_restart(),
     "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
     "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
     "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
diff --git a/modules/ui.py b/modules/ui.py
index 03306ba9..822a7660 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -12,6 +12,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin  # noqa: F401
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
+from modules import gradio_extensons  # noqa: F401
 from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts
 from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
 from modules.paths import script_path
@@ -34,6 +35,7 @@ from modules.generation_parameters_copypaste import image_from_url_text
 create_setting_component = ui_settings.create_setting_component
 
 warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
+warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else "ignore", category=gr.deprecation.GradioDeprecationWarning)
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -146,7 +148,6 @@ def interrogate_deepbooru(image):
 def create_seed_inputs(target_interface):
     with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
         seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
-        seed.style(container=False)
         random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
         reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
 
@@ -158,7 +159,6 @@ def create_seed_inputs(target_interface):
     with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
         seed_extras.append(seed_extra_row_1)
         subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
-        subseed.style(container=False)
         random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
         reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
         subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
@@ -408,7 +408,7 @@ def create_ui():
             from modules import ui_extra_networks
             extra_networks_ui = ui_extra_networks.create_ui(extra_networks, toprow.extra_networks_button, 'txt2img')
 
-        with gr.Row().style(equal_height=False):
+        with gr.Row(equal_height=False):
             with gr.Column(variant='compact', elem_id="txt2img_settings"):
                 scripts.scripts_txt2img.prepare_ui()
 
@@ -636,7 +636,7 @@ def create_ui():
             from modules import ui_extra_networks
             extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, toprow.extra_networks_button, 'img2img')
 
-        with FormRow().style(equal_height=False):
+        with FormRow(equal_height=False):
             with gr.Column(variant='compact', elem_id="img2img_settings"):
                 copy_image_buttons = []
                 copy_image_destinations = {}
@@ -658,19 +658,19 @@ def create_ui():
                     img2img_selected_tab = gr.State(0)
 
                     with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height)
                         add_copy_image_controls('img2img', init_img)
 
                     with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
-                        sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA", height=opts.img2img_editor_height)
                         add_copy_image_controls('sketch', sketch)
 
                     with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height)
                         add_copy_image_controls('inpaint', init_img_with_mask)
 
                     with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
-                        inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA", height=opts.img2img_editor_height)
                         inpaint_color_sketch_orig = gr.State(None)
                         add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
 
@@ -993,7 +993,7 @@ def create_ui():
         ui_postprocessing.create_ui()
 
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
-        with gr.Row().style(equal_height=False):
+        with gr.Row(equal_height=False):
             with gr.Column(variant='panel'):
                 image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
 
@@ -1018,10 +1018,10 @@ def create_ui():
     modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger()
 
     with gr.Blocks(analytics_enabled=False) as train_interface:
-        with gr.Row().style(equal_height=False):
+        with gr.Row(equal_height=False):
             gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
 
-        with gr.Row(variant="compact").style(equal_height=False):
+        with gr.Row(variant="compact", equal_height=False):
             with gr.Tabs(elem_id="train_tabs"):
 
                 with gr.Tab(label="Create embedding", id="create_embedding"):
@@ -1181,7 +1181,7 @@ def create_ui():
 
             with gr.Column(elem_id='ti_gallery_container'):
                 ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
+                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery', columns=4)
                 gr.HTML(elem_id="ti_progress", value="")
                 ti_outcome = gr.HTML(elem_id="ti_error", value="")
 
diff --git a/modules/ui_checkpoint_merger.py b/modules/ui_checkpoint_merger.py
index 4863d861..f9c5dd6b 100644
--- a/modules/ui_checkpoint_merger.py
+++ b/modules/ui_checkpoint_merger.py
@@ -29,7 +29,7 @@ def modelmerger(*args):
 class UiCheckpointMerger:
     def __init__(self):
         with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
-            with gr.Row().style(equal_height=False):
+            with gr.Row(equal_height=False):
                 with gr.Column(variant='compact'):
                     self.interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
 
diff --git a/modules/ui_common.py b/modules/ui_common.py
index ba75fa73..eefe0c0e 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -134,7 +134,7 @@ Requested path was: {f}
 
     with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
         with gr.Group(elem_id=f"{tabname}_gallery_container"):
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4)
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4)
 
         generation_info = None
         with gr.Column():
diff --git a/modules/ui_components.py b/modules/ui_components.py
index 64451df7..8f8a7088 100644
--- a/modules/ui_components.py
+++ b/modules/ui_components.py
@@ -35,7 +35,7 @@ class FormColumn(FormComponent, gr.Column):
 
 
 class FormGroup(FormComponent, gr.Group):
-    """Same as gr.Row but fits inside gradio forms"""
+    """Same as gr.Group but fits inside gradio forms"""
 
     def get_block_name(self):
         return "group"
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index bd28bfcf..15a8b0bf 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -533,8 +533,8 @@ def create_ui():
                     apply = gr.Button(value=apply_label, variant="primary")
                     check = gr.Button(value="Check for updates")
                     extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
-                    extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
-                    extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
+                    extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False, container=False)
+                    extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False, container=False)
 
                 html = ""
 
@@ -569,7 +569,7 @@ def create_ui():
                 with gr.Row():
                     refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
                     extensions_index_url = os.environ.get('WEBUI_EXTENSIONS_INDEX', "https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui-extensions/master/index.json")
-                    available_extensions_index = gr.Text(value=extensions_index_url, label="Extension index URL").style(container=False)
+                    available_extensions_index = gr.Text(value=extensions_index_url, label="Extension index URL", container=False)
                     extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
                     install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
 
@@ -578,7 +578,7 @@ def create_ui():
                     sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order",'update time', 'create time', "stars"], type="index")
 
                 with gr.Row():
-                    search_extensions_text = gr.Text(label="Search").style(container=False)
+                    search_extensions_text = gr.Text(label="Search", container=False)
 
                 install_result = gr.HTML()
                 available_extensions_table = gr.HTML()
diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py
index c7dc1154..802e1ce7 100644
--- a/modules/ui_postprocessing.py
+++ b/modules/ui_postprocessing.py
@@ -6,7 +6,7 @@ import modules.generation_parameters_copypaste as parameters_copypaste
 def create_ui():
     tab_index = gr.State(value=0)
 
-    with gr.Row().style(equal_height=False, variant='compact'):
+    with gr.Row(equal_height=False, variant='compact'):
         with gr.Column(variant='compact'):
             with gr.Tabs(elem_id="mode_extras"):
                 with gr.TabItem('Single Image', id="single_image", elem_id="extras_single_tab") as tab_single:
-- 
cgit v1.2.3


From 073c0ebba380acbd73be8262feba41212165ff84 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Fri, 4 Aug 2023 08:04:06 +0300
Subject: add gradio version warning

---
 modules/errors.py | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 50 insertions(+)

(limited to 'modules')

diff --git a/modules/errors.py b/modules/errors.py
index dffabe45..192cd8ff 100644
--- a/modules/errors.py
+++ b/modules/errors.py
@@ -84,3 +84,53 @@ def run(code, task):
         code()
     except Exception as e:
         display(task, e)
+
+
+def check_versions():
+    from packaging import version
+    from modules import shared
+
+    import torch
+    import gradio
+
+    expected_torch_version = "2.0.0"
+    expected_xformers_version = "0.0.20"
+    expected_gradio_version = "3.39.0"
+
+    if version.parse(torch.__version__) < version.parse(expected_torch_version):
+        print_error_explanation(f"""
+You are running torch {torch.__version__}.
+The program is tested to work with torch {expected_torch_version}.
+To reinstall the desired version, run with commandline flag --reinstall-torch.
+Beware that this will cause a lot of large files to be downloaded, as well as
+there are reports of issues with training tab on the latest version.
+
+Use --skip-version-check commandline argument to disable this check.
+        """.strip())
+
+    if shared.xformers_available:
+        import xformers
+
+        if version.parse(xformers.__version__) < version.parse(expected_xformers_version):
+            print_error_explanation(f"""
+You are running xformers {xformers.__version__}.
+The program is tested to work with xformers {expected_xformers_version}.
+To reinstall the desired version, run with commandline flag --reinstall-xformers.
+
+Use --skip-version-check commandline argument to disable this check.
+            """.strip())
+
+    if gradio.__version__ != expected_gradio_version:
+        print_error_explanation(f"""
+You are running gradio {gradio.__version__}.
+The program is designed to work with gradio {expected_gradio_version}.
+Using a different version of gradio is extremely likely to break the program.
+
+Reasons why you have the mismatched gradio version can be:
+  - you use --skip-install flag.
+  - you use webui.py to start the program instead of launch.py.
+  - an extension installs the incompatible gradio version.
+
+Use --skip-version-check commandline argument to disable this check.
+        """.strip())
+
-- 
cgit v1.2.3