From 64311faa6848d641cc452115e4e1eb47d2a7b519 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 12 Aug 2023 12:39:59 +0300
Subject: put refiner into main UI, into the new accordions section add VAE
 from main model into infotext, not from refiner model option to make scripts
 UI without gr.Group fix inconsistencies with refiner when usings samplers
 that do more denoising than steps

---
 modules/sd_samplers_common.py | 40 +++++++++++++++++++++++-----------------
 1 file changed, 23 insertions(+), 17 deletions(-)

(limited to 'modules/sd_samplers_common.py')

diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 35c4d657..85f3c7e0 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -7,7 +7,16 @@ from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, s
 from modules.shared import opts, state
 import k_diffusion.sampling
 
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+SamplerDataTuple = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+
+class SamplerData(SamplerDataTuple):
+    def total_steps(self, steps):
+        if self.options.get("second_order", False):
+            steps = steps * 2
+
+        return steps
 
 
 def setup_img2img_steps(p, steps=None):
@@ -131,31 +140,26 @@ def replace_torchsde_browinan():
 replace_torchsde_browinan()
 
 
-def apply_refiner(sampler):
-    completed_ratio = sampler.step / sampler.steps
+def apply_refiner(cfg_denoiser):
+    completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps
+    refiner_switch_at = cfg_denoiser.p.refiner_switch_at
+    refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info
 
-    if completed_ratio <= shared.opts.sd_refiner_switch_at:
+    if refiner_switch_at is not None and completed_ratio <= refiner_switch_at:
         return False
 
-    if shared.opts.sd_refiner_checkpoint == "None":
+    if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info:
         return False
 
-    if shared.sd_model.sd_checkpoint_info.title == shared.opts.sd_refiner_checkpoint:
-        return False
-
-    refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(shared.opts.sd_refiner_checkpoint)
-    if refiner_checkpoint_info is None:
-        raise Exception(f'Could not find checkpoint with name {shared.opts.sd_refiner_checkpoint}')
-
-    sampler.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
-    sampler.p.extra_generation_params['Refiner switch at'] = shared.opts.sd_refiner_switch_at
+    cfg_denoiser.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
+    cfg_denoiser.p.extra_generation_params['Refiner switch at'] = refiner_switch_at
 
     with sd_models.SkipWritingToConfig():
         sd_models.reload_model_weights(info=refiner_checkpoint_info)
 
     devices.torch_gc()
-    sampler.p.setup_conds()
-    sampler.update_inner_model()
+    cfg_denoiser.p.setup_conds()
+    cfg_denoiser.update_inner_model()
 
     return True
 
@@ -192,7 +196,7 @@ class Sampler:
         self.sampler_noises = None
         self.stop_at = None
         self.eta = None
-        self.config = None  # set by the function calling the constructor
+        self.config: SamplerData = None  # set by the function calling the constructor
         self.last_latent = None
         self.s_min_uncond = None
         self.s_churn = 0.0
@@ -208,6 +212,7 @@ class Sampler:
         self.p = None
         self.model_wrap_cfg = None
         self.sampler_extra_args = None
+        self.options = {}
 
     def callback_state(self, d):
         step = d['i']
@@ -220,6 +225,7 @@ class Sampler:
 
     def launch_sampling(self, steps, func):
         self.model_wrap_cfg.steps = steps
+        self.model_wrap_cfg.total_steps = self.config.total_steps(steps)
         state.sampling_steps = steps
         state.sampling_step = 0
 
-- 
cgit v1.2.3


From b293ed30610c040e621e1840d63047ae298f0650 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 12 Aug 2023 12:54:32 +0300
Subject: make it possible to use hires fix together with refiner

---
 modules/processing.py                 | 6 ++++++
 modules/processing_scripts/refiner.py | 2 +-
 modules/sd_samplers_common.py         | 3 +++
 3 files changed, 10 insertions(+), 1 deletion(-)

(limited to 'modules/sd_samplers_common.py')

diff --git a/modules/processing.py b/modules/processing.py
index 5996cbac..6ad105d7 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1200,6 +1200,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, total_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
 
     def setup_conds(self):
+        if self.is_hr_pass:
+            # if we are in hr pass right now, the call is being made from the refiner, and we don't need to setup firstpass cons or switch model
+            self.hr_c = None
+            self.calculate_hr_conds()
+            return
+
         super().setup_conds()
 
         self.hr_uc = None
diff --git a/modules/processing_scripts/refiner.py b/modules/processing_scripts/refiner.py
index 5a82991a..773ec5d0 100644
--- a/modules/processing_scripts/refiner.py
+++ b/modules/processing_scripts/refiner.py
@@ -24,7 +24,7 @@ class ScriptRefiner(scripts.Script):
                 refiner_checkpoint = gr.Dropdown(label='Checkpoint', elem_id=self.elem_id("checkpoint"), choices=sd_models.checkpoint_tiles(), value='', tooltip="switch to another model in the middle of generation")
                 create_refresh_button(refiner_checkpoint, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, self.elem_id("checkpoint_refresh"))
 
-                refiner_switch_at = gr.Slider(value=0.8, label="Switch at", minimum=0.01, maximum=1.0, step=0.01, elem_id=self.elem_id("switch_at"), tooltip="fraction of sampling steps when the swtch to refiner model should happen; 1=never, 0.5=switch in the middle of generation")
+                refiner_switch_at = gr.Slider(value=0.8, label="Switch at", minimum=0.01, maximum=1.0, step=0.01, elem_id=self.elem_id("switch_at"), tooltip="fraction of sampling steps when the switch to refiner model should happen; 1=never, 0.5=switch in the middle of generation")
 
         def lookup_checkpoint(title):
             info = sd_models.get_closet_checkpoint_match(title)
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 85f3c7e0..40c7aae0 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -151,6 +151,9 @@ def apply_refiner(cfg_denoiser):
     if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info:
         return False
 
+    if getattr(cfg_denoiser.p, "enable_hr", False) and not cfg_denoiser.p.is_hr_pass:
+        return False
+
     cfg_denoiser.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
     cfg_denoiser.p.extra_generation_params['Refiner switch at'] = refiner_switch_at
 
-- 
cgit v1.2.3


From fa9370b7411166c19e8e386400dc4e6082f47b2d Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 13 Aug 2023 06:07:30 +0300
Subject: add refiner to StableDiffusionProcessing class write out correct
 model name in infotext, rather than the refiner model

---
 modules/processing.py                 | 38 +++++++++++++++++++++++++++--------
 modules/processing_scripts/refiner.py | 16 +++++----------
 modules/sd_samplers_common.py         |  2 +-
 3 files changed, 36 insertions(+), 20 deletions(-)

(limited to 'modules/sd_samplers_common.py')

diff --git a/modules/processing.py b/modules/processing.py
index 6ad105d7..b47ddaa8 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -111,7 +111,7 @@ class StableDiffusionProcessing:
     cached_uc = [None, None]
     cached_c = [None, None]
 
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, refiner_checkpoint: str = None, refiner_switch_at: float = None, script_args: list = None):
         if sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
@@ -153,10 +153,14 @@ class StableDiffusionProcessing:
         self.s_noise = s_noise if s_noise is not None else opts.s_noise
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
         self.override_settings_restore_afterwards = override_settings_restore_afterwards
+        self.refiner_checkpoint = refiner_checkpoint
+        self.refiner_switch_at = refiner_switch_at
+
         self.is_using_inpainting_conditioning = False
         self.disable_extra_networks = False
         self.token_merging_ratio = 0
         self.token_merging_ratio_hr = 0
+        self.refiner_checkpoint_info = None
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -191,6 +195,11 @@ class StableDiffusionProcessing:
 
         self.user = None
 
+        self.sd_model_name = None
+        self.sd_model_hash = None
+        self.sd_vae_name = None
+        self.sd_vae_hash = None
+
     @property
     def sd_model(self):
         return shared.sd_model
@@ -408,7 +417,10 @@ class Processed:
         self.batch_size = p.batch_size
         self.restore_faces = p.restore_faces
         self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None
-        self.sd_model_hash = shared.sd_model.sd_model_hash
+        self.sd_model_name = p.sd_model_name
+        self.sd_model_hash = p.sd_model_hash
+        self.sd_vae_name = p.sd_vae_name
+        self.sd_vae_hash = p.sd_vae_hash
         self.seed_resize_from_w = p.seed_resize_from_w
         self.seed_resize_from_h = p.seed_resize_from_h
         self.denoising_strength = getattr(p, 'denoising_strength', None)
@@ -459,7 +471,10 @@ class Processed:
             "batch_size": self.batch_size,
             "restore_faces": self.restore_faces,
             "face_restoration_model": self.face_restoration_model,
+            "sd_model_name": self.sd_model_name,
             "sd_model_hash": self.sd_model_hash,
+            "sd_vae_name": self.sd_vae_name,
+            "sd_vae_hash": self.sd_vae_hash,
             "seed_resize_from_w": self.seed_resize_from_w,
             "seed_resize_from_h": self.seed_resize_from_h,
             "denoising_strength": self.denoising_strength,
@@ -578,10 +593,10 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "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),
-        "VAE hash": p.loaded_vae_hash if opts.add_model_hash_to_info else None,
-        "VAE": p.loaded_vae_name if opts.add_model_name_to_info else None,
+        "Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
+        "Model": p.sd_model_name if opts.add_model_name_to_info else None,
+        "VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
+        "VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
         "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}"),
@@ -670,8 +685,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.tiling is None:
         p.tiling = opts.tiling
 
-    p.loaded_vae_name = sd_vae.get_loaded_vae_name()
-    p.loaded_vae_hash = sd_vae.get_loaded_vae_hash()
+    if p.refiner_checkpoint not in (None, "", "None"):
+        p.refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(p.refiner_checkpoint)
+        if p.refiner_checkpoint_info is None:
+            raise Exception(f'Could not find checkpoint with name {p.refiner_checkpoint}')
+
+    p.sd_model_name = shared.sd_model.sd_checkpoint_info.name_for_extra
+    p.sd_model_hash = shared.sd_model.sd_model_hash
+    p.sd_vae_name = sd_vae.get_loaded_vae_name()
+    p.sd_vae_hash = sd_vae.get_loaded_vae_hash()
 
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
     modules.sd_hijack.model_hijack.clear_comments()
diff --git a/modules/processing_scripts/refiner.py b/modules/processing_scripts/refiner.py
index 773ec5d0..7b946d05 100644
--- a/modules/processing_scripts/refiner.py
+++ b/modules/processing_scripts/refiner.py
@@ -41,15 +41,9 @@ class ScriptRefiner(scripts.Script):
     def before_process(self, p, enable_refiner, refiner_checkpoint, refiner_switch_at):
         # the actual implementation is in sd_samplers_common.py, apply_refiner
 
-        p.refiner_checkpoint_info = None
-        p.refiner_switch_at = None
-
         if not enable_refiner or refiner_checkpoint in (None, "", "None"):
-            return
-
-        refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(refiner_checkpoint)
-        if refiner_checkpoint_info is None:
-            raise Exception(f'Could not find checkpoint with name {refiner_checkpoint}')
-
-        p.refiner_checkpoint_info = refiner_checkpoint_info
-        p.refiner_switch_at = refiner_switch_at
+            p.refiner_checkpoint_info = None
+            p.refiner_switch_at = None
+        else:
+            p.refiner_checkpoint = refiner_checkpoint
+            p.refiner_switch_at = refiner_switch_at
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 40c7aae0..380cdd5f 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -145,7 +145,7 @@ def apply_refiner(cfg_denoiser):
     refiner_switch_at = cfg_denoiser.p.refiner_switch_at
     refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info
 
-    if refiner_switch_at is not None and completed_ratio <= refiner_switch_at:
+    if refiner_switch_at is not None and completed_ratio < refiner_switch_at:
         return False
 
     if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info:
-- 
cgit v1.2.3


From 599f61a1e0bddf463dd3c6adb84509b3d9db1941 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 13 Aug 2023 08:24:16 +0300
Subject: use dataclass for StableDiffusionProcessing

---
 modules/processing.py         | 318 +++++++++++++++++++++++-------------------
 modules/sd_samplers_common.py |   5 +-
 2 files changed, 176 insertions(+), 147 deletions(-)

(limited to 'modules/sd_samplers_common.py')

diff --git a/modules/processing.py b/modules/processing.py
index b47ddaa8..007a4e05 100755
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1,9 +1,11 @@
+from __future__ import annotations
 import json
 import logging
 import math
 import os
 import sys
 import hashlib
+from dataclasses import dataclass, field
 
 import torch
 import numpy as np
@@ -11,7 +13,7 @@ from PIL import Image, ImageOps
 import random
 import cv2
 from skimage import exposure
-from typing import Any, Dict, List
+from typing import Any
 
 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, errors, rng
@@ -104,106 +106,126 @@ def txt2img_image_conditioning(sd_model, x, width, height):
         return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
 
 
+@dataclass(repr=False)
 class StableDiffusionProcessing:
-    """
-    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
-    """
+    sd_model: object = None
+    outpath_samples: str = None
+    outpath_grids: str = None
+    prompt: str = ""
+    prompt_for_display: str = None
+    negative_prompt: str = ""
+    styles: list[str] = field(default_factory=list)
+    seed: int = -1
+    subseed: int = -1
+    subseed_strength: float = 0
+    seed_resize_from_h: int = -1
+    seed_resize_from_w: int = -1
+    seed_enable_extras: bool = True
+    sampler_name: str = None
+    batch_size: int = 1
+    n_iter: int = 1
+    steps: int = 50
+    cfg_scale: float = 7.0
+    width: int = 512
+    height: int = 512
+    restore_faces: bool = None
+    tiling: bool = None
+    do_not_save_samples: bool = False
+    do_not_save_grid: bool = False
+    extra_generation_params: dict[str, Any] = None
+    overlay_images: list = None
+    eta: float = None
+    do_not_reload_embeddings: bool = False
+    denoising_strength: float = 0
+    ddim_discretize: str = None
+    s_min_uncond: float = None
+    s_churn: float = None
+    s_tmax: float = None
+    s_tmin: float = None
+    s_noise: float = None
+    override_settings: dict[str, Any] = None
+    override_settings_restore_afterwards: bool = True
+    sampler_index: int = None
+    refiner_checkpoint: str = None
+    refiner_switch_at: float = None
+    token_merging_ratio = 0
+    token_merging_ratio_hr = 0
+    disable_extra_networks: bool = False
+
+    script_args: list = None
+
     cached_uc = [None, None]
     cached_c = [None, None]
 
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, refiner_checkpoint: str = None, refiner_switch_at: float = None, script_args: list = None):
-        if sampler_index is not None:
+    sampler: sd_samplers_common.Sampler | None = field(default=None, init=False)
+    is_using_inpainting_conditioning: bool = field(default=False, init=False)
+    paste_to: tuple | None = field(default=None, init=False)
+
+    is_hr_pass: bool = field(default=False, init=False)
+
+    c: tuple = field(default=None, init=False)
+    uc: tuple = field(default=None, init=False)
+
+    rng: rng.ImageRNG | None = field(default=None, init=False)
+    step_multiplier: int = field(default=1, init=False)
+    color_corrections: list = field(default=None, init=False)
+
+    scripts: list = field(default=None, init=False)
+    all_prompts: list = field(default=None, init=False)
+    all_negative_prompts: list = field(default=None, init=False)
+    all_seeds: list = field(default=None, init=False)
+    all_subseeds: list = field(default=None, init=False)
+    iteration: int = field(default=0, init=False)
+    main_prompt: str = field(default=None, init=False)
+    main_negative_prompt: str = field(default=None, init=False)
+
+    prompts: list = field(default=None, init=False)
+    negative_prompts: list = field(default=None, init=False)
+    seeds: list = field(default=None, init=False)
+    subseeds: list = field(default=None, init=False)
+    extra_network_data: dict = field(default=None, init=False)
+
+    user: str = field(default=None, init=False)
+
+    sd_model_name: str = field(default=None, init=False)
+    sd_model_hash: str = field(default=None, init=False)
+    sd_vae_name: str = field(default=None, init=False)
+    sd_vae_hash: str = field(default=None, init=False)
+
+    def __post_init__(self):
+        if self.sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
-        self.outpath_samples: str = outpath_samples
-        self.outpath_grids: str = outpath_grids
-        self.prompt: str = prompt
-        self.prompt_for_display: str = None
-        self.negative_prompt: str = (negative_prompt or "")
-        self.styles: list = styles or []
-        self.seed: int = seed
-        self.subseed: int = subseed
-        self.subseed_strength: float = subseed_strength
-        self.seed_resize_from_h: int = seed_resize_from_h
-        self.seed_resize_from_w: int = seed_resize_from_w
-        self.sampler_name: str = sampler_name
-        self.batch_size: int = batch_size
-        self.n_iter: int = n_iter
-        self.steps: int = steps
-        self.cfg_scale: float = cfg_scale
-        self.width: int = width
-        self.height: int = height
-        self.restore_faces: bool = restore_faces
-        self.tiling: bool = tiling
-        self.do_not_save_samples: bool = do_not_save_samples
-        self.do_not_save_grid: bool = do_not_save_grid
-        self.extra_generation_params: dict = extra_generation_params or {}
-        self.overlay_images = overlay_images
-        self.eta = eta
-        self.do_not_reload_embeddings = do_not_reload_embeddings
-        self.paste_to = None
-        self.color_corrections = None
-        self.denoising_strength: float = denoising_strength
         self.sampler_noise_scheduler_override = None
-        self.ddim_discretize = ddim_discretize or opts.ddim_discretize
-        self.s_min_uncond = s_min_uncond or opts.s_min_uncond
-        self.s_churn = s_churn or opts.s_churn
-        self.s_tmin = s_tmin or opts.s_tmin
-        self.s_tmax = (s_tmax if s_tmax is not None else opts.s_tmax) or float('inf')
-        self.s_noise = s_noise if s_noise is not None else opts.s_noise
-        self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
-        self.override_settings_restore_afterwards = override_settings_restore_afterwards
-        self.refiner_checkpoint = refiner_checkpoint
-        self.refiner_switch_at = refiner_switch_at
-
-        self.is_using_inpainting_conditioning = False
-        self.disable_extra_networks = False
-        self.token_merging_ratio = 0
-        self.token_merging_ratio_hr = 0
+        self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
+        self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
+        self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin
+        self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf')
+        self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise
+
+        self.extra_generation_params = self.extra_generation_params or {}
+        self.override_settings = self.override_settings or {}
+        self.script_args = self.script_args or {}
+
         self.refiner_checkpoint_info = None
 
-        if not seed_enable_extras:
+        if not self.seed_enable_extras:
             self.subseed = -1
             self.subseed_strength = 0
             self.seed_resize_from_h = 0
             self.seed_resize_from_w = 0
 
-        self.scripts = None
-        self.script_args = script_args
-        self.all_prompts = None
-        self.all_negative_prompts = None
-        self.all_seeds = None
-        self.all_subseeds = None
-        self.iteration = 0
-        self.is_hr_pass = False
-        self.sampler = None
-        self.main_prompt = None
-        self.main_negative_prompt = None
-
-        self.prompts = None
-        self.negative_prompts = None
-        self.extra_network_data = None
-        self.seeds = None
-        self.subseeds = None
-
-        self.step_multiplier = 1
         self.cached_uc = StableDiffusionProcessing.cached_uc
         self.cached_c = StableDiffusionProcessing.cached_c
-        self.uc = None
-        self.c = None
-        self.rng: rng.ImageRNG = None
-
-        self.user = None
-
-        self.sd_model_name = None
-        self.sd_model_hash = None
-        self.sd_vae_name = None
-        self.sd_vae_hash = None
 
     @property
     def sd_model(self):
         return shared.sd_model
 
+    @sd_model.setter
+    def sd_model(self, value):
+        pass
+
     def txt2img_image_conditioning(self, x, width=None, height=None):
         self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
 
@@ -932,49 +954,51 @@ def old_hires_fix_first_pass_dimensions(width, height):
     return width, height
 
 
+@dataclass(repr=False)
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
-    sampler = None
+    enable_hr: bool = False
+    denoising_strength: float = 0.75
+    firstphase_width: int = 0
+    firstphase_height: int = 0
+    hr_scale: float = 2.0
+    hr_upscaler: str = None
+    hr_second_pass_steps: int = 0
+    hr_resize_x: int = 0
+    hr_resize_y: int = 0
+    hr_checkpoint_name: str = None
+    hr_sampler_name: str = None
+    hr_prompt: str = ''
+    hr_negative_prompt: str = ''
+
     cached_hr_uc = [None, None]
     cached_hr_c = [None, None]
 
-    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_checkpoint_name: str = None, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
-        super().__init__(**kwargs)
-        self.enable_hr = enable_hr
-        self.denoising_strength = denoising_strength
-        self.hr_scale = hr_scale
-        self.hr_upscaler = hr_upscaler
-        self.hr_second_pass_steps = hr_second_pass_steps
-        self.hr_resize_x = hr_resize_x
-        self.hr_resize_y = hr_resize_y
-        self.hr_upscale_to_x = hr_resize_x
-        self.hr_upscale_to_y = hr_resize_y
-        self.hr_checkpoint_name = hr_checkpoint_name
-        self.hr_checkpoint_info = None
-        self.hr_sampler_name = hr_sampler_name
-        self.hr_prompt = hr_prompt
-        self.hr_negative_prompt = hr_negative_prompt
-        self.all_hr_prompts = None
-        self.all_hr_negative_prompts = None
-        self.latent_scale_mode = None
-
-        if firstphase_width != 0 or firstphase_height != 0:
+    hr_checkpoint_info: dict = field(default=None, init=False)
+    hr_upscale_to_x: int = field(default=0, init=False)
+    hr_upscale_to_y: int = field(default=0, init=False)
+    truncate_x: int = field(default=0, init=False)
+    truncate_y: int = field(default=0, init=False)
+    applied_old_hires_behavior_to: tuple = field(default=None, init=False)
+    latent_scale_mode: dict = field(default=None, init=False)
+    hr_c: tuple | None = field(default=None, init=False)
+    hr_uc: tuple | None = field(default=None, init=False)
+    all_hr_prompts: list = field(default=None, init=False)
+    all_hr_negative_prompts: list = field(default=None, init=False)
+    hr_prompts: list = field(default=None, init=False)
+    hr_negative_prompts: list = field(default=None, init=False)
+    hr_extra_network_data: list = field(default=None, init=False)
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        if self.firstphase_width != 0 or self.firstphase_height != 0:
             self.hr_upscale_to_x = self.width
             self.hr_upscale_to_y = self.height
-            self.width = firstphase_width
-            self.height = firstphase_height
-
-        self.truncate_x = 0
-        self.truncate_y = 0
-        self.applied_old_hires_behavior_to = None
-
-        self.hr_prompts = None
-        self.hr_negative_prompts = None
-        self.hr_extra_network_data = None
+            self.width = self.firstphase_width
+            self.height = self.firstphase_height
 
         self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc
         self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c
-        self.hr_c = None
-        self.hr_uc = None
 
     def calculate_target_resolution(self):
         if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
@@ -1252,7 +1276,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         return super().get_conds()
 
-
     def parse_extra_network_prompts(self):
         res = super().parse_extra_network_prompts()
 
@@ -1265,32 +1288,37 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         return res
 
 
+@dataclass(repr=False)
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
-    sampler = None
-
-    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = None, mask_blur_x: int = 4, mask_blur_y: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
-        super().__init__(**kwargs)
-
-        self.init_images = init_images
-        self.resize_mode: int = resize_mode
-        self.denoising_strength: float = denoising_strength
-        self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
-        self.init_latent = None
-        self.image_mask = mask
-        self.latent_mask = None
-        self.mask_for_overlay = None
-        self.mask_blur_x = mask_blur_x
-        self.mask_blur_y = mask_blur_y
-        if mask_blur is not None:
-            self.mask_blur = mask_blur
-        self.inpainting_fill = inpainting_fill
-        self.inpaint_full_res = inpaint_full_res
-        self.inpaint_full_res_padding = inpaint_full_res_padding
-        self.inpainting_mask_invert = inpainting_mask_invert
-        self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
+    init_images: list = None
+    resize_mode: int = 0
+    denoising_strength: float = 0.75
+    image_cfg_scale: float = None
+    mask: Any = None
+    mask_blur_x: int = 4
+    mask_blur_y: int = 4
+    mask_blur: int = None
+    inpainting_fill: int = 0
+    inpaint_full_res: bool = True
+    inpaint_full_res_padding: int = 0
+    inpainting_mask_invert: int = 0
+    initial_noise_multiplier: float = None
+    latent_mask: Image = None
+
+    image_mask: Any = field(default=None, init=False)
+
+    nmask: torch.Tensor = field(default=None, init=False)
+    image_conditioning: torch.Tensor = field(default=None, init=False)
+    init_img_hash: str = field(default=None, init=False)
+    mask_for_overlay: Image = field(default=None, init=False)
+    init_latent: torch.Tensor = field(default=None, init=False)
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        self.image_mask = self.mask
         self.mask = None
-        self.nmask = None
-        self.image_conditioning = None
+        self.initial_noise_multiplier = opts.initial_noise_multiplier if self.initial_noise_multiplier is None else self.initial_noise_multiplier
 
     @property
     def mask_blur(self):
@@ -1300,15 +1328,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
     @mask_blur.setter
     def mask_blur(self, value):
-        self.mask_blur_x = value
-        self.mask_blur_y = value
-
-    @mask_blur.deleter
-    def mask_blur(self):
-        del self.mask_blur_x
-        del self.mask_blur_y
+        if isinstance(value, int):
+            self.mask_blur_x = value
+            self.mask_blur_y = value
 
     def init(self, all_prompts, all_seeds, all_subseeds):
+        self.image_cfg_scale: float = self.image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
+
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
         crop_region = None
 
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 380cdd5f..09d1e11e 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -305,5 +305,8 @@ class Sampler:
         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(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        raise NotImplementedError()
 
-
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        raise NotImplementedError()
-- 
cgit v1.2.3


From 822597db49218de17e105e62075096284dfcfd41 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Sun, 13 Aug 2023 04:16:48 -0400
Subject: Encode batches separately

Significantly reduces VRAM.
This makes encoding more inline with how decoding currently functions.
---
 modules/sd_samplers_common.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

(limited to 'modules/sd_samplers_common.py')

diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 09d1e11e..f9d034ca 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -92,7 +92,15 @@ def images_tensor_to_samples(image, approximation=None, model=None):
             model = shared.sd_model
         image = image.to(shared.device, dtype=devices.dtype_vae)
         image = image * 2 - 1
-        x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
+        if len(image) > 1:
+            x_latent = torch.stack([
+                model.get_first_stage_encoding(
+                    model.encode_first_stage(torch.unsqueeze(img, 0))
+                )[0]
+                for img in image
+            ])
+        else:
+            x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
 
     return x_latent
 
-- 
cgit v1.2.3


From d1a70c3f0534b88665d55bdac1e6b48a63f7f035 Mon Sep 17 00:00:00 2001
From: catboxanon <122327233+catboxanon@users.noreply.github.com>
Date: Sun, 13 Aug 2023 08:22:24 -0400
Subject: Add s_noise param to more samplers

---
 modules/sd_samplers_common.py     | 8 ++++----
 modules/sd_samplers_kdiffusion.py | 6 ++++++
 2 files changed, 10 insertions(+), 4 deletions(-)

(limited to 'modules/sd_samplers_common.py')

diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 09d1e11e..d2fb21f4 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -276,19 +276,19 @@ class Sampler:
             s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
             s_noise = getattr(opts, 's_noise', p.s_noise)
 
-            if s_churn != self.s_churn:
+            if 's_churn' in extra_params_kwargs and s_churn != self.s_churn:
                 extra_params_kwargs['s_churn'] = s_churn
                 p.s_churn = s_churn
                 p.extra_generation_params['Sigma churn'] = s_churn
-            if s_tmin != self.s_tmin:
+            if 's_tmin' in extra_params_kwargs and s_tmin != self.s_tmin:
                 extra_params_kwargs['s_tmin'] = s_tmin
                 p.s_tmin = s_tmin
                 p.extra_generation_params['Sigma tmin'] = s_tmin
-            if s_tmax != self.s_tmax:
+            if 's_tmax' in extra_params_kwargs and s_tmax != self.s_tmax:
                 extra_params_kwargs['s_tmax'] = s_tmax
                 p.s_tmax = s_tmax
                 p.extra_generation_params['Sigma tmax'] = s_tmax
-            if s_noise != self.s_noise:
+            if 's_noise' in extra_params_kwargs and s_noise != self.s_noise:
                 extra_params_kwargs['s_noise'] = s_noise
                 p.s_noise = s_noise
                 p.extra_generation_params['Sigma noise'] = s_noise
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index a48a563f..9f5dfd6d 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -45,6 +45,12 @@ 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'],
+    'sample_dpm_fast': ['s_noise'],
+    'sample_dpm_2_ancestral': ['s_noise'],
+    'sample_dpmpp_2s_ancestral': ['s_noise'],
+    'sample_dpmpp_sde': ['s_noise'],
+    'sample_dpmpp_2m_sde': ['s_noise'],
+    'sample_dpmpp_3m_sde': ['s_noise'],
 }
 
 k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
-- 
cgit v1.2.3