From 740070ea9cdb254209f66417418f2a4af8b099d6 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Mon, 26 Sep 2022 09:29:50 -0500
Subject: Re-implement universal model loading

---
 modules/shared.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

(limited to 'modules/shared.py')

diff --git a/modules/shared.py b/modules/shared.py
index c32da110..1444040d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -16,11 +16,11 @@ import modules.sd_models
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
-
+model_path = os.path.join(script_path, 'models')
 parser = argparse.ArgumentParser()
 parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), 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; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
-parser.add_argument("--ckpt-dir", type=str, default=os.path.join(script_path, 'models'), help="path to directory with stable diffusion checkpoints",)
+parser.add_argument("--ckpt-dir", type=str, default=model_path, help="path to directory with stable diffusion checkpoints",)
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
 parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
 parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
@@ -34,8 +34,12 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
-parser.add_argument("--esrgan-models-path", type=str, help="path to directory with ESRGAN models", default=os.path.join(script_path, 'ESRGAN'))
-parser.add_argument("--swinir-models-path", type=str, help="path to directory with SwinIR models", default=os.path.join(script_path, 'SwinIR'))
+parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model(s)", default=os.path.join(model_path, 'Codeformer'))
+parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model(s)", default=os.path.join(model_path, 'GFPGAN'))
+parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN models", default=os.path.join(model_path, 'ESRGAN'))
+parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN models", default=os.path.join(model_path, 'RealESRGAN'))
+parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR models", default=os.path.join(model_path, 'SwinIR'))
+parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR models", default=os.path.join(model_path, 'LDSR'))
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-- 
cgit v1.2.3


From 11875f586323cea7c5b8398976449788a83dee76 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Tue, 27 Sep 2022 11:01:13 -0500
Subject: Use model loader with stable-diffusion too.

Hook the model loader into the SD_models file.
Add default url/download if checkpoint is not found.
Add matching stablediffusion-models-path argument.
Add message that --ckpt-dir will be removed in the future, but have it pipe to stablediffusion-models-path for now.
Update help strings for models-path args so they're more or less uniform.
Move sd_model "setup" call to webUI with the others.
Ensure "cleanup_models" method moves existing models to the new locations, including SD, and that we aren't deleting folders that still have stuff in them.
---
 modules/modelloader.py | 25 ++++++++++++++++++-------
 modules/sd_models.py   | 48 +++++++++++++++++++++++++++++-------------------
 modules/shared.py      | 28 +++++++++++++++++-----------
 webui.py               |  1 +
 4 files changed, 65 insertions(+), 37 deletions(-)

(limited to 'modules/shared.py')

diff --git a/modules/modelloader.py b/modules/modelloader.py
index 9520a681..2ee364f0 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -45,7 +45,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                     if file not in existing:
                         path = os.path.join(place, file)
                         existing.append(path)
-        if model_url is not None:
+        if model_url is not None and len(existing) == 0:
             if dl_name is not None:
                 model_file = load_file_from_url(url=model_url, model_dir=model_path, file_name=dl_name, progress=True)
             else:
@@ -69,7 +69,13 @@ def friendly_name(file: str):
 
 
 def cleanup_models():
+    # This code could probably be more efficient if we used a tuple list or something to store the src/destinations
+    # and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler
+    # somehow auto-register and just do these things...
     root_path = script_path
+    src_path = models_path
+    dest_path = os.path.join(models_path, "Stable-diffusion")
+    move_files(src_path, dest_path, ".ckpt")
     src_path = os.path.join(root_path, "ESRGAN")
     dest_path = os.path.join(models_path, "ESRGAN")
     move_files(src_path, dest_path)
@@ -84,20 +90,25 @@ def cleanup_models():
     move_files(src_path, dest_path)
 
 
-def move_files(src_path: str, dest_path: str):
+def move_files(src_path: str, dest_path: str, ext_filter: str = None):
     try:
         if not os.path.exists(dest_path):
             os.makedirs(dest_path)
         if os.path.exists(src_path):
             for file in os.listdir(src_path):
-                if os.path.isfile(file):
-                    fullpath = os.path.join(src_path, file)
-                    print("Moving file: %s to %s" % (fullpath, dest_path))
+                fullpath = os.path.join(src_path, file)
+                if os.path.isfile(fullpath):
+                    print(f"Checking file {file} in {src_path}")
+                    if ext_filter is not None:
+                        if ext_filter not in file:
+                            continue
+                    print(f"Moving {file} from {src_path} to {dest_path}.")
                     try:
                         shutil.move(fullpath, dest_path)
                     except:
                         pass
-            print("Removing folder: %s" % src_path)
-            shutil.rmtree(src_path, True)
+            if len(os.listdir(src_path)) == 0:
+                print(f"Removing empty folder: {src_path}")
+                shutil.rmtree(src_path, True)
     except:
         pass
\ No newline at end of file
diff --git a/modules/sd_models.py b/modules/sd_models.py
index dc81b0dc..89b7d276 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -8,7 +8,13 @@ from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
 
-from modules import shared
+from modules import shared, modelloader
+from modules.paths import models_path
+
+model_dir = "Stable-diffusion"
+model_path = os.path.join(models_path, model_dir)
+model_name = "sd-v1-4.ckpt"
+model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1"
 
 CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash'])
 checkpoints_list = {}
@@ -23,23 +29,28 @@ except Exception:
     pass
 
 
-def list_models():
-    checkpoints_list.clear()
+def modeltitle(path, h):
+    abspath = os.path.abspath(path)
 
-    model_dir = os.path.abspath(shared.cmd_opts.ckpt_dir)
+    if abspath.startswith(model_dir):
+        name = abspath.replace(model_dir, '')
+    else:
+        name = os.path.basename(path)
 
-    def modeltitle(path, h):
-        abspath = os.path.abspath(path)
+    if name.startswith("\\") or name.startswith("/"):
+        name = name[1:]
 
-        if abspath.startswith(model_dir):
-            name = abspath.replace(model_dir, '')
-        else:
-            name = os.path.basename(path)
+    return f'{name} [{h}]'
 
-        if name.startswith("\\") or name.startswith("/"):
-            name = name[1:]
 
-        return f'{name} [{h}]'
+def setup_model(dirname):
+    global model_path
+    global model_name
+    global model_url
+    if not os.path.exists(model_path):
+        os.makedirs(model_path)
+    checkpoints_list.clear()
+    model_list = modelloader.load_models(model_path, model_url, dirname, model_name, ext_filter=".ckpt")
 
     cmd_ckpt = shared.cmd_opts.ckpt
     if os.path.exists(cmd_ckpt):
@@ -47,13 +58,12 @@ def list_models():
         title = modeltitle(cmd_ckpt, h)
         checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h)
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
-        print(f"Checkpoint in --ckpt argument not found: {cmd_ckpt}", file=sys.stderr)
+        print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
 
-    if os.path.exists(model_dir):
-        for filename in glob.glob(model_dir + '/**/*.ckpt', recursive=True):
-            h = model_hash(filename)
-            title = modeltitle(filename, h)
-            checkpoints_list[title] = CheckpointInfo(filename, title, h)
+    for filename in model_list:
+        h = model_hash(filename)
+        title = modeltitle(filename, h)
+        checkpoints_list[title] = CheckpointInfo(filename, title, h)
 
 
 def model_hash(filename):
diff --git a/modules/shared.py b/modules/shared.py
index 1444040d..e617bce4 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -20,7 +20,8 @@ model_path = os.path.join(script_path, 'models')
 parser = argparse.ArgumentParser()
 parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), 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; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
-parser.add_argument("--ckpt-dir", type=str, default=model_path, help="path to directory with stable diffusion checkpoints",)
+# This should be deprecated, but we'll leave it for a few iterations
+parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints (Deprecated, use '--stablediffusion-models-path'", )
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
 parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
 parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
@@ -34,12 +35,13 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
-parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model(s)", default=os.path.join(model_path, 'Codeformer'))
-parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model(s)", default=os.path.join(model_path, 'GFPGAN'))
-parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN models", default=os.path.join(model_path, 'ESRGAN'))
-parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN models", default=os.path.join(model_path, 'RealESRGAN'))
-parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR models", default=os.path.join(model_path, 'SwinIR'))
-parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR models", default=os.path.join(model_path, 'LDSR'))
+parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(model_path, 'Codeformer'))
+parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(model_path, 'GFPGAN'))
+parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
+parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
+parser.add_argument("--stablediffusion-models-path", type=str, help="Path to directory with Stable-diffusion checkpoints.", default=os.path.join(model_path, 'SwinIR'))
+parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
+parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
@@ -57,7 +59,10 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 
 cmd_opts = parser.parse_args()
-
+if cmd_opts.ckpt_dir is not None:
+    print("The 'ckpt-dir' arg is deprecated in favor of the 'stablediffusion-models-path' argument and will be "
+          "removed in a future release. Please use the new option if you wish to use a custom checkpoint directory.")
+    cmd_opts.__setattr__("stablediffusion-models-path", cmd_opts.ckpt_dir)
 device = get_optimal_device()
 
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
@@ -65,6 +70,7 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 
 config_filename = cmd_opts.ui_settings_file
 
+
 class State:
     interrupted = False
     job = ""
@@ -98,8 +104,8 @@ prompt_styles = modules.styles.StyleDatabase(styles_filename)
 interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
-
-modules.sd_models.list_models()
+# This was moved to webui.py with the other model "setup" calls.
+# modules.sd_models.list_models()
 
 
 def realesrgan_models_names():
@@ -195,7 +201,7 @@ options_templates.update(options_section(('system', "System"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),    
+    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
     "enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text and [text] to make it pay less attention"),
diff --git a/webui.py b/webui.py
index e71a217c..15e49bf6 100644
--- a/webui.py
+++ b/webui.py
@@ -23,6 +23,7 @@ from modules.paths import script_path
 from modules.shared import cmd_opts
 
 modelloader.cleanup_models()
+modules.sd_models.setup_model(cmd_opts.stablediffusion_models_path)
 codeformer.setup_model(cmd_opts.codeformer_models_path)
 gfpgan.setup_model(cmd_opts.gfpgan_models_path)
 shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-- 
cgit v1.2.3


From 0dce0df1ee63b2f158805c1a1f1a3743cc4a104b Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Thu, 29 Sep 2022 17:46:23 -0500
Subject: Holy $hit.

Yep.

Fix gfpgan_model_arch requirement(s).
Add Upscaler base class, move from images.
Add a lot of methods to Upscaler.
Re-work all the child upscalers to be proper classes.
Add BSRGAN scaler.
Add ldsr_model_arch class, removing the dependency for another repo that just uses regular latent-diffusion stuff.
Add one universal method that will always find and load new upscaler models without having to add new "setup_model" calls. Still need to add command line params, but that could probably be automated.
Add a "self.scale" property to all Upscalers so the scalers themselves can do "things" in response to the requested upscaling size.
Ensure LDSR doesn't get stuck in a longer loop of "upscale/downscale/upscale" as we try to reach the target upscale size.
Add typehints for IDE sanity.
PEP-8 improvements.
Moar.
---
 launch.py                    |  11 ++-
 modules/bsrgan_model.py      |  79 +++++++++++++++
 modules/bsrgan_model_arch.py | 103 ++++++++++++++++++++
 modules/esrgan_model.py      | 227 +++++++++++++++++++++----------------------
 modules/extras.py            |  35 ++++---
 modules/gfpgan_model.py      |  58 +++++++----
 modules/gfpgan_model_arch.py | 150 ----------------------------
 modules/images.py            |  84 +++++++---------
 modules/ldsr_model.py        | 103 +++++++-------------
 modules/ldsr_model_arch.py   | 223 ++++++++++++++++++++++++++++++++++++++++++
 modules/modelloader.py       |  74 ++++++++++----
 modules/realesrgan_model.py  | 209 ++++++++++++++++++++-------------------
 modules/sd_models.py         |   3 +-
 modules/sd_samplers.py       |   4 +-
 modules/shared.py            |  18 ++--
 modules/swinir_model.py      | 157 +++++++++++++-----------------
 modules/upscaler.py          | 121 +++++++++++++++++++++++
 webui.py                     |   9 +-
 18 files changed, 1018 insertions(+), 650 deletions(-)
 create mode 100644 modules/bsrgan_model.py
 create mode 100644 modules/bsrgan_model_arch.py
 delete mode 100644 modules/gfpgan_model_arch.py
 create mode 100644 modules/ldsr_model_arch.py
 create mode 100644 modules/upscaler.py

(limited to 'modules/shared.py')

diff --git a/launch.py b/launch.py
index 4462631c..77edea26 100644
--- a/launch.py
+++ b/launch.py
@@ -1,5 +1,5 @@
 # this scripts installs necessary requirements and launches main program in webui.py
-
+import shutil
 import subprocess
 import os
 import sys
@@ -22,7 +22,6 @@ stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "6
 taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
 codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
 blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
-ldsr_commit_hash = os.environ.get('LDSR_COMMIT_HASH',"abf33e7002d59d9085081bce93ec798dcabd49af")
 
 args = shlex.split(commandline_args)
 
@@ -122,9 +121,11 @@ git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-di
 git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
 git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
 git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
-# Using my repo until my changes are merged, as this makes interfacing with our version of SD-web a lot easier
-git_clone("https://github.com/Hafiidz/latent-diffusion", repo_dir('latent-diffusion'), "LDSR", ldsr_commit_hash)
-
+if os.path.isdir(repo_dir('latent-diffusion')):
+    try:
+        shutil.rmtree(repo_dir('latent-diffusion'))
+    except:
+        pass
 if not is_installed("lpips"):
     run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
 
diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
new file mode 100644
index 00000000..77141545
--- /dev/null
+++ b/modules/bsrgan_model.py
@@ -0,0 +1,79 @@
+import os.path
+import sys
+import traceback
+
+import PIL.Image
+import numpy as np
+import torch
+from basicsr.utils.download_util import load_file_from_url
+
+import modules.upscaler
+from modules import shared, modelloader
+from modules.bsrgan_model_arch import RRDBNet
+from modules.paths import models_path
+
+
+class UpscalerBSRGAN(modules.upscaler.Upscaler):
+    def __init__(self, dirname):
+        self.name = "BSRGAN"
+        self.model_path = os.path.join(models_path, self.name)
+        self.model_name = "BSRGAN 4x"
+        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
+        self.user_path = dirname
+        super().__init__()
+        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
+        scalers = []
+        if len(model_paths) == 0:
+            scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4)
+            scalers.append(scaler_data)
+        for file in model_paths:
+            if "http" in file:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(file)
+            try:
+                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
+                scalers.append(scaler_data)
+            except Exception:
+                print(f"Error loading BSRGAN model: {file}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+        self.scalers = scalers
+
+    def do_upscale(self, img: PIL.Image, selected_file):
+        torch.cuda.empty_cache()
+        model = self.load_model(selected_file)
+        if model is None:
+            return img
+        model.to(shared.device)
+        torch.cuda.empty_cache()
+        img = np.array(img)
+        img = img[:, :, ::-1]
+        img = np.moveaxis(img, 2, 0) / 255
+        img = torch.from_numpy(img).float()
+        img = img.unsqueeze(0).to(shared.device)
+        with torch.no_grad():
+            output = model(img)
+        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
+        output = 255. * np.moveaxis(output, 0, 2)
+        output = output.astype(np.uint8)
+        output = output[:, :, ::-1]
+        torch.cuda.empty_cache()
+        return PIL.Image.fromarray(output, 'RGB')
+
+    def load_model(self, path: str):
+        if "http" in path:
+            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
+                                          progress=True)
+        else:
+            filename = path
+        if not os.path.exists(filename) or filename is None:
+            print("Unable to load %s from %s" % (self.model_dir, filename))
+            return None
+        print("Loading %s from %s" % (self.model_dir, filename))
+        model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=2)  # define network
+        model.load_state_dict(torch.load(filename), strict=True)
+        model.eval()
+        for k, v in model.named_parameters():
+            v.requires_grad = False
+        return model
+
diff --git a/modules/bsrgan_model_arch.py b/modules/bsrgan_model_arch.py
new file mode 100644
index 00000000..d72647db
--- /dev/null
+++ b/modules/bsrgan_model_arch.py
@@ -0,0 +1,103 @@
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+
+
+def initialize_weights(net_l, scale=1):
+    if not isinstance(net_l, list):
+        net_l = [net_l]
+    for net in net_l:
+        for m in net.modules():
+            if isinstance(m, nn.Conv2d):
+                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
+                m.weight.data *= scale  # for residual block
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                init.constant_(m.weight, 1)
+                init.constant_(m.bias.data, 0.0)
+
+
+def make_layer(block, n_layers):
+    layers = []
+    for _ in range(n_layers):
+        layers.append(block())
+    return nn.Sequential(*layers)
+
+
+class ResidualDenseBlock_5C(nn.Module):
+    def __init__(self, nf=64, gc=32, bias=True):
+        super(ResidualDenseBlock_5C, self).__init__()
+        # gc: growth channel, i.e. intermediate channels
+        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
+        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
+        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+        # initialization
+        initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    def forward(self, x):
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        return x5 * 0.2 + x
+
+
+class RRDB(nn.Module):
+    '''Residual in Residual Dense Block'''
+
+    def __init__(self, nf, gc=32):
+        super(RRDB, self).__init__()
+        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
+
+    def forward(self, x):
+        out = self.RDB1(x)
+        out = self.RDB2(out)
+        out = self.RDB3(out)
+        return out * 0.2 + x
+
+
+class RRDBNet(nn.Module):
+    def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4):
+        super(RRDBNet, self).__init__()
+        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
+        self.sf = sf
+        print([in_nc, out_nc, nf, nb, gc, sf])
+
+        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
+        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
+        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        #### upsampling
+        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        if self.sf==4:
+            self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        fea = self.conv_first(x)
+        trunk = self.trunk_conv(self.RRDB_trunk(fea))
+        fea = fea + trunk
+
+        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
+        if self.sf==4:
+            fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
+        out = self.conv_last(self.lrelu(self.HRconv(fea)))
+
+        return out
\ No newline at end of file
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 5e10c49c..ce841aa4 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -1,6 +1,4 @@
 import os
-import sys
-import traceback
 
 import numpy as np
 import torch
@@ -8,93 +6,119 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgam_model_arch as arch
-import modules.images
-from modules import shared
-from modules import shared, modelloader
+from modules import shared, modelloader, images
 from modules.devices import has_mps
 from modules.paths import models_path
+from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
-model_dir = "ESRGAN"
-model_path = os.path.join(models_path, model_dir)
-model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
-model_name = "ESRGAN_x4"
-
-
-def load_model(path: str, name: str):
-    global model_path
-    global model_url
-    global model_dir
-    global model_name
-    if "http" in path:
-        filename = load_file_from_url(url=model_url, model_dir=model_path, file_name="%s.pth" % model_name, progress=True)
-    else:
-        filename = path
-    if not os.path.exists(filename) or filename is None:
-        print("Unable to load %s from %s" % (model_dir, filename))
-        return None
-    print("Loading %s from %s" % (model_dir, filename))
-    # this code is adapted from https://github.com/xinntao/ESRGAN
-    pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
-    crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
-
-    if 'conv_first.weight' in pretrained_net:
-        crt_model.load_state_dict(pretrained_net)
-        return crt_model
 
-    if 'model.0.weight' not in pretrained_net:
-        is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
-        if is_realesrgan:
-            raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
-        else:
-            raise Exception("The file is not a ESRGAN model.")
+class UpscalerESRGAN(Upscaler):
+    def __init__(self, dirname):
+        self.name = "ESRGAN"
+        self.model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
+        self.model_name = "ESRGAN 4x"
+        self.scalers = []
+        self.user_path = dirname
+        self.model_path = os.path.join(models_path, self.name)
+        super().__init__()
+        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
+        scalers = []
+        if len(model_paths) == 0:
+            scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
+            scalers.append(scaler_data)
+        for file in model_paths:
+            print(f"File: {file}")
+            if "http" in file:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(file)
+
+            scaler_data = UpscalerData(name, file, self, 4)
+            print(f"ESRGAN: Adding scaler {name}")
+            self.scalers.append(scaler_data)
+
+    def do_upscale(self, img, selected_model):
+        model = self.load_model(selected_model)
+        if model is None:
+            return img
+        model.to(shared.device)
+        img = esrgan_upscale(model, img)
+        return img
 
-    crt_net = crt_model.state_dict()
-    load_net_clean = {}
-    for k, v in pretrained_net.items():
-        if k.startswith('module.'):
-            load_net_clean[k[7:]] = v
+    def load_model(self, path: str):
+        if "http" in path:
+            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+                                          file_name="%s.pth" % self.model_name,
+                                          progress=True)
         else:
-            load_net_clean[k] = v
-    pretrained_net = load_net_clean
-
-    tbd = []
-    for k, v in crt_net.items():
-        tbd.append(k)
-
-    # directly copy
-    for k, v in crt_net.items():
-        if k in pretrained_net and pretrained_net[k].size() == v.size():
-            crt_net[k] = pretrained_net[k]
-            tbd.remove(k)
-
-    crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
-    crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
-
-    for k in tbd.copy():
-        if 'RDB' in k:
-            ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
-            if '.weight' in k:
-                ori_k = ori_k.replace('.weight', '.0.weight')
-            elif '.bias' in k:
-                ori_k = ori_k.replace('.bias', '.0.bias')
-            crt_net[k] = pretrained_net[ori_k]
-            tbd.remove(k)
-
-    crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
-    crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
-    crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
-    crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
-    crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
-    crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
-    crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
-    crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
-    crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
-    crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
-
-    crt_model.load_state_dict(crt_net)
-    crt_model.eval()
-    return crt_model
+            filename = path
+        if not os.path.exists(filename) or filename is None:
+            print("Unable to load %s from %s" % (self.model_path, filename))
+            return None
+        # this code is adapted from https://github.com/xinntao/ESRGAN
+        pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
+        crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
+
+        if 'conv_first.weight' in pretrained_net:
+            crt_model.load_state_dict(pretrained_net)
+            return crt_model
+
+        if 'model.0.weight' not in pretrained_net:
+            is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net[
+                "params_ema"]
+            if is_realesrgan:
+                raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
+            else:
+                raise Exception("The file is not a ESRGAN model.")
+
+        crt_net = crt_model.state_dict()
+        load_net_clean = {}
+        for k, v in pretrained_net.items():
+            if k.startswith('module.'):
+                load_net_clean[k[7:]] = v
+            else:
+                load_net_clean[k] = v
+        pretrained_net = load_net_clean
+
+        tbd = []
+        for k, v in crt_net.items():
+            tbd.append(k)
+
+        # directly copy
+        for k, v in crt_net.items():
+            if k in pretrained_net and pretrained_net[k].size() == v.size():
+                crt_net[k] = pretrained_net[k]
+                tbd.remove(k)
+
+        crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
+        crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
+
+        for k in tbd.copy():
+            if 'RDB' in k:
+                ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
+                if '.weight' in k:
+                    ori_k = ori_k.replace('.weight', '.0.weight')
+                elif '.bias' in k:
+                    ori_k = ori_k.replace('.bias', '.0.bias')
+                crt_net[k] = pretrained_net[ori_k]
+                tbd.remove(k)
+
+        crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
+        crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
+        crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
+        crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
+        crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
+        crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
+        crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
+        crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
+        crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
+        crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
+
+        crt_model.load_state_dict(crt_net)
+        crt_model.eval()
+        return crt_model
+
 
 def upscale_without_tiling(model, img):
     img = np.array(img)
@@ -115,7 +139,7 @@ def esrgan_upscale(model, img):
     if opts.ESRGAN_tile == 0:
         return upscale_without_tiling(model, img)
 
-    grid = modules.images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
+    grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
     newtiles = []
     scale_factor = 1
 
@@ -130,38 +154,7 @@ def esrgan_upscale(model, img):
             newrow.append([x * scale_factor, w * scale_factor, output])
         newtiles.append([y * scale_factor, h * scale_factor, newrow])
 
-    newgrid = modules.images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
-    output = modules.images.combine_grid(newgrid)
+    newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor,
+                                  grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
+    output = images.combine_grid(newgrid)
     return output
-
-
-class UpscalerESRGAN(modules.images.Upscaler):
-    def __init__(self, filename, title):
-        self.name = title
-        self.filename = filename
-
-    def do_upscale(self, img):
-        model = load_model(self.filename, self.name)
-        if model is None:
-            return img
-        model.to(shared.device)
-        img = esrgan_upscale(model, img)
-        return img
-
-
-def setup_model(dirname):
-    global model_path
-    global model_name
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
-
-    model_paths = modelloader.load_models(model_path, command_path=dirname, ext_filter=[".pt", ".pth"])
-    if len(model_paths) == 0:
-        modules.shared.sd_upscalers.append(UpscalerESRGAN(model_url, model_name))
-    for file in model_paths:
-        name = modelloader.friendly_name(file)
-        try:
-            modules.shared.sd_upscalers.append(UpscalerESRGAN(file, name))
-        except Exception:
-            print(f"Error loading ESRGAN model: {file}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
diff --git a/modules/extras.py b/modules/extras.py
index af6e631f..d7d0fa54 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -66,29 +66,28 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
             info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
             image = res
 
-        if upscaling_resize != 1.0:
-            def upscale(image, scaler_index, resize):
-                small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
-                pixels = tuple(np.array(small).flatten().tolist())
-                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
+        def upscale(image, scaler_index, resize):
+            small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
+            pixels = tuple(np.array(small).flatten().tolist())
+            key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
 
-                c = cached_images.get(key)
-                if c is None:
-                    upscaler = shared.sd_upscalers[scaler_index]
-                    c = upscaler.upscale(image, image.width * resize, image.height * resize)
-                    cached_images[key] = c
+            c = cached_images.get(key)
+            if c is None:
+                upscaler = shared.sd_upscalers[scaler_index]
+                c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+                cached_images[key] = c
 
-                return c
+            return c
 
-            info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
-            res = upscale(image, extras_upscaler_1, upscaling_resize)
+        info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
+        res = upscale(image, extras_upscaler_1, upscaling_resize)
 
-            if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-                res2 = upscale(image, extras_upscaler_2, upscaling_resize)
-                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
-                res = Image.blend(res, res2, extras_upscaler_2_visibility)
+        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
+            res2 = upscale(image, extras_upscaler_2, upscaling_resize)
+            info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
+            res = Image.blend(res, res2, extras_upscaler_2_visibility)
 
-            image = res
+        image = res
 
         while len(cached_images) > 2:
             del cached_images[next(iter(cached_images.keys()))]
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index ffb6960d..2bf8a1ee 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -1,24 +1,23 @@
 import os
 import sys
 import traceback
-from glob import glob
 
-from modules import shared, devices
-from modules.shared import cmd_opts
-from modules.paths import script_path
+import facexlib
+import gfpgan
+
 import modules.face_restoration
 from modules import shared, devices, modelloader
 from modules.paths import models_path
 
 model_dir = "GFPGAN"
-cmd_dir = None
+user_path = None
 model_path = os.path.join(models_path, model_dir)
 model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
-
+have_gfpgan = False
 loaded_gfpgan_model = None
 
 
-def gfpgan():
+def gfpgann():
     global loaded_gfpgan_model
     global model_path
     if loaded_gfpgan_model is not None:
@@ -28,14 +27,16 @@ def gfpgan():
     if gfpgan_constructor is None:
         return None
 
-    models = modelloader.load_models(model_path, model_url, cmd_dir)
-    if len(models) != 0:
+    models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
+    if len(models) == 1 and "http" in models[0]:
+        model_file = models[0]
+    elif len(models) != 0:
         latest_file = max(models, key=os.path.getctime)
         model_file = latest_file
     else:
         print("Unable to load gfpgan model!")
         return None
-    model = gfpgan_constructor(model_path=model_file, model_dir=model_path, upscale=1, arch='clean', channel_multiplier=2,
+    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2,
                                bg_upsampler=None)
     model.gfpgan.to(shared.device)
     loaded_gfpgan_model = model
@@ -44,11 +45,12 @@ def gfpgan():
 
 
 def gfpgan_fix_faces(np_image):
-    model = gfpgan()
+    model = gfpgann()
     if model is None:
         return np_image
     np_image_bgr = np_image[:, :, ::-1]
-    cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
+    cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False,
+                                                                     only_center_face=False, paste_back=True)
     np_image = gfpgan_output_bgr[:, :, ::-1]
 
     if shared.opts.face_restoration_unload:
@@ -57,7 +59,6 @@ def gfpgan_fix_faces(np_image):
     return np_image
 
 
-have_gfpgan = False
 gfpgan_constructor = None
 
 
@@ -67,14 +68,33 @@ def setup_model(dirname):
         os.makedirs(model_path)
 
     try:
-        from modules.gfpgan_model_arch import GFPGANerr
-        global cmd_dir
+        from gfpgan import GFPGANer
+        from facexlib import detection, parsing
+        global user_path
         global have_gfpgan
         global gfpgan_constructor
 
-        cmd_dir = dirname
+        load_file_from_url_orig = gfpgan.utils.load_file_from_url
+        facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
+        facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
+
+        def my_load_file_from_url(**kwargs):
+            print("Setting model_dir to " + model_path)
+            return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
+
+        def facex_load_file_from_url(**kwargs):
+            return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
+
+        def facex_load_file_from_url2(**kwargs):
+            return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
+
+        gfpgan.utils.load_file_from_url = my_load_file_from_url
+        facexlib.detection.load_file_from_url = facex_load_file_from_url
+        facexlib.parsing.load_file_from_url = facex_load_file_from_url2
+        user_path = dirname
+        print("Have gfpgan should be true?")
         have_gfpgan = True
-        gfpgan_constructor = GFPGANerr
+        gfpgan_constructor = GFPGANer
 
         class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
             def name(self):
@@ -82,7 +102,9 @@ def setup_model(dirname):
 
             def restore(self, np_image):
                 np_image_bgr = np_image[:, :, ::-1]
-                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgan().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
+                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgann().enhance(np_image_bgr, has_aligned=False,
+                                                                                     only_center_face=False,
+                                                                                     paste_back=True)
                 np_image = gfpgan_output_bgr[:, :, ::-1]
 
                 return np_image
diff --git a/modules/gfpgan_model_arch.py b/modules/gfpgan_model_arch.py
deleted file mode 100644
index d81cea96..00000000
--- a/modules/gfpgan_model_arch.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# GFPGAN likes to download stuff "wherever", and we're trying to fix that, so this is a copy of the original...
-
-import cv2
-import os
-import torch
-from basicsr.utils import img2tensor, tensor2img
-from basicsr.utils.download_util import load_file_from_url
-from facexlib.utils.face_restoration_helper import FaceRestoreHelper
-from torchvision.transforms.functional import normalize
-
-from gfpgan.archs.gfpgan_bilinear_arch import GFPGANBilinear
-from gfpgan.archs.gfpganv1_arch import GFPGANv1
-from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
-
-ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-class GFPGANerr():
-    """Helper for restoration with GFPGAN.
-
-    It will detect and crop faces, and then resize the faces to 512x512.
-    GFPGAN is used to restored the resized faces.
-    The background is upsampled with the bg_upsampler.
-    Finally, the faces will be pasted back to the upsample background image.
-
-    Args:
-        model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically).
-        upscale (float): The upscale of the final output. Default: 2.
-        arch (str): The GFPGAN architecture. Option: clean | original. Default: clean.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        bg_upsampler (nn.Module): The upsampler for the background. Default: None.
-    """
-
-    def __init__(self, model_path, model_dir, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None, device=None):
-        self.upscale = upscale
-        self.bg_upsampler = bg_upsampler
-
-        # initialize model
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
-        # initialize the GFP-GAN
-        if arch == 'clean':
-            self.gfpgan = GFPGANv1Clean(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=False,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        elif arch == 'bilinear':
-            self.gfpgan = GFPGANBilinear(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=False,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        elif arch == 'original':
-            self.gfpgan = GFPGANv1(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=True,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        elif arch == 'RestoreFormer':
-            from gfpgan.archs.restoreformer_arch import RestoreFormer
-            self.gfpgan = RestoreFormer()
-        # initialize face helper
-        self.face_helper = FaceRestoreHelper(
-            upscale,
-            face_size=512,
-            crop_ratio=(1, 1),
-            det_model='retinaface_resnet50',
-            save_ext='png',
-            use_parse=True,
-            device=self.device,
-            model_rootpath=model_dir)
-
-        if model_path.startswith('https://'):
-            model_path = load_file_from_url(
-                url=model_path, model_dir=model_dir, progress=True, file_name=None)
-        loadnet = torch.load(model_path)
-        if 'params_ema' in loadnet:
-            keyname = 'params_ema'
-        else:
-            keyname = 'params'
-        self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
-        self.gfpgan.eval()
-        self.gfpgan = self.gfpgan.to(self.device)
-
-    @torch.no_grad()
-    def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True, weight=0.5):
-        self.face_helper.clean_all()
-
-        if has_aligned:  # the inputs are already aligned
-            img = cv2.resize(img, (512, 512))
-            self.face_helper.cropped_faces = [img]
-        else:
-            self.face_helper.read_image(img)
-            # get face landmarks for each face
-            self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5)
-            # eye_dist_threshold=5: skip faces whose eye distance is smaller than 5 pixels
-            # TODO: even with eye_dist_threshold, it will still introduce wrong detections and restorations.
-            # align and warp each face
-            self.face_helper.align_warp_face()
-
-        # face restoration
-        for cropped_face in self.face_helper.cropped_faces:
-            # prepare data
-            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-            cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device)
-
-            try:
-                output = self.gfpgan(cropped_face_t, return_rgb=False, weight=weight)[0]
-                # convert to image
-                restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
-            except RuntimeError as error:
-                print(f'\tFailed inference for GFPGAN: {error}.')
-                restored_face = cropped_face
-
-            restored_face = restored_face.astype('uint8')
-            self.face_helper.add_restored_face(restored_face)
-
-        if not has_aligned and paste_back:
-            # upsample the background
-            if self.bg_upsampler is not None:
-                # Now only support RealESRGAN for upsampling background
-                bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0]
-            else:
-                bg_img = None
-
-            self.face_helper.get_inverse_affine(None)
-            # paste each restored face to the input image
-            restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img)
-            return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img
-        else:
-            return self.face_helper.cropped_faces, self.face_helper.restored_faces, None
diff --git a/modules/images.py b/modules/images.py
index 9458bf8d..a6538dbe 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -11,7 +11,6 @@ from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
 from fonts.ttf import Roboto
 import string
 
-import modules.shared
 from modules import sd_samplers, shared
 from modules.shared import opts, cmd_opts
 
@@ -52,8 +51,8 @@ def split_grid(image, tile_w=512, tile_h=512, overlap=64):
     cols = math.ceil((w - overlap) / non_overlap_width)
     rows = math.ceil((h - overlap) / non_overlap_height)
 
-    dx = (w - tile_w) / (cols-1) if cols > 1 else 0
-    dy = (h - tile_h) / (rows-1) if rows > 1 else 0
+    dx = (w - tile_w) / (cols - 1) if cols > 1 else 0
+    dy = (h - tile_h) / (rows - 1) if rows > 1 else 0
 
     grid = Grid([], tile_w, tile_h, w, h, overlap)
     for row in range(rows):
@@ -67,7 +66,7 @@ def split_grid(image, tile_w=512, tile_h=512, overlap=64):
         for col in range(cols):
             x = int(col * dx)
 
-            if x+tile_w >= w:
+            if x + tile_w >= w:
                 x = w - tile_w
 
             tile = image.crop((x, y, x + tile_w, y + tile_h))
@@ -85,8 +84,10 @@ def combine_grid(grid):
         r = r.astype(np.uint8)
         return Image.fromarray(r, 'L')
 
-    mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
-    mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
+    mask_w = make_mask_image(
+        np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
+    mask_h = make_mask_image(
+        np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
 
     combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
     for y, h, row in grid.tiles:
@@ -129,10 +130,12 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
 
     def draw_texts(drawing, draw_x, draw_y, lines):
         for i, line in enumerate(lines):
-            drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
+            drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt,
+                                   fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
 
             if not line.is_active:
-                drawing.line((draw_x - line.size[0]//2, draw_y + line.size[1]//2, draw_x + line.size[0]//2, draw_y + line.size[1]//2), fill=color_inactive, width=4)
+                drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2,
+                              draw_y + line.size[1] // 2), fill=color_inactive, width=4)
 
             draw_y += line.size[1] + line_spacing
 
@@ -171,7 +174,8 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
             line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1])
 
     hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
-    ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]
+    ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in
+                        ver_texts]
 
     pad_top = max(hor_text_heights) + line_spacing * 2
 
@@ -202,8 +206,10 @@ def draw_prompt_matrix(im, width, height, all_prompts):
     prompts_horiz = prompts[:boundary]
     prompts_vert = prompts[boundary:]
 
-    hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]
-    ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]
+    hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in
+                 range(1 << len(prompts_horiz))]
+    ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in
+                 range(1 << len(prompts_vert))]
 
     return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
 
@@ -214,7 +220,8 @@ def resize_image(resize_mode, im, width, height):
             return im.resize((w, h), resample=LANCZOS)
 
         upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]
-        return upscaler.upscale(im, w, h)
+        scale = w / im.width
+        return upscaler.scaler.upscale(im, scale)
 
     if resize_mode == 0:
         res = resize(im, width, height)
@@ -244,11 +251,13 @@ def resize_image(resize_mode, im, width, height):
         if ratio < src_ratio:
             fill_height = height // 2 - src_h // 2
             res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
-            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
+            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)),
+                      box=(0, fill_height + src_h))
         elif ratio > src_ratio:
             fill_width = width // 2 - src_w // 2
             res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
-            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
+            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)),
+                      box=(fill_width + src_w, 0))
 
     return res
 
@@ -256,7 +265,7 @@ def resize_image(resize_mode, im, width, height):
 invalid_filename_chars = '<>:"/\\|?*\n'
 invalid_filename_prefix = ' '
 invalid_filename_postfix = ' .'
-re_nonletters = re.compile(r'[\s'+string.punctuation+']+')
+re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
 max_filename_part_length = 128
 
 
@@ -283,7 +292,8 @@ def apply_filename_pattern(x, p, seed, prompt):
             words = [x for x in re_nonletters.split(prompt or "") if len(x) > 0]
             if len(words) == 0:
                 words = ["empty"]
-            x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))
+            x = x.replace("[prompt_words]",
+                          sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))
 
     if p is not None:
         x = x.replace("[steps]", str(p.steps))
@@ -291,7 +301,8 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[width]", str(p.width))
         x = x.replace("[height]", str(p.height))
         x = x.replace("[styles]", sanitize_filename_part(", ".join(p.styles), replace_spaces=False))
-        x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
+        x = x.replace("[sampler]",
+                      sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
     x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
     x = x.replace("[date]", datetime.date.today().isoformat())
@@ -303,6 +314,7 @@ def apply_filename_pattern(x, p, seed, prompt):
 
     return x
 
+
 def get_next_sequence_number(path, basename):
     """
     Determines and returns the next sequence number to use when saving an image in the specified directory.
@@ -316,7 +328,8 @@ def get_next_sequence_number(path, basename):
     prefix_length = len(basename)
     for p in os.listdir(path):
         if p.startswith(basename):
-            l = os.path.splitext(p[prefix_length:])[0].split('-') #splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
+            l = os.path.splitext(p[prefix_length:])[0].split(
+                '-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
             try:
                 result = max(int(l[0]), result)
             except ValueError:
@@ -324,7 +337,10 @@ def get_next_sequence_number(path, basename):
 
     return result + 1
 
-def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix=""):
+
+def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False,
+               no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None,
+               forced_filename=None, suffix=""):
     if short_filename or prompt is None or seed is None:
         file_decoration = ""
     elif opts.save_to_dirs:
@@ -361,7 +377,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         fullfn = "a.png"
         fullfn_without_extension = "a"
         for i in range(500):
-            fn = f"{basecount+i:05}" if basename == '' else f"{basename}-{basecount+i:04}"
+            fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
             fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
             fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
             if not os.path.exists(fullfn):
@@ -403,31 +419,3 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             file.write(info + "\n")
 
 
-class Upscaler:
-    name = "Lanczos"
-
-    def do_upscale(self, img):
-        return img
-
-    def upscale(self, img, w, h):
-        for i in range(3):
-            if img.width >= w and img.height >= h:
-                break
-
-            img = self.do_upscale(img)
-
-        if img.width != w or img.height != h:
-            img = img.resize((int(w), int(h)), resample=LANCZOS)
-
-        return img
-
-
-class UpscalerNone(Upscaler):
-    name = "None"
-
-    def upscale(self, img, w, h):
-        return img
-
-
-modules.shared.sd_upscalers.append(UpscalerNone())
-modules.shared.sd_upscalers.append(Upscaler())
diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
index 4f9b1657..969d1a0d 100644
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@@ -1,74 +1,45 @@
 import os
 import sys
 import traceback
-from collections import namedtuple
 
-from modules import shared, images, modelloader, paths
-from modules.paths import models_path
-
-model_dir = "LDSR"
-model_path = os.path.join(models_path, model_dir)
-cmd_path = None
-model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
-yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
+from basicsr.utils.download_util import load_file_from_url
 
-LDSRModelInfo = namedtuple("LDSRModelInfo", ["name", "location", "model", "netscale"])
-
-ldsr_models = []
-have_ldsr = False
-LDSR_obj = None
+from modules.upscaler import Upscaler, UpscalerData
+from modules.ldsr_model_arch import LDSR
+from modules import shared
+from modules.paths import models_path
 
 
-class UpscalerLDSR(images.Upscaler):
-    def __init__(self, steps):
-        self.steps = steps
+class UpscalerLDSR(Upscaler):
+    def __init__(self, user_path):
         self.name = "LDSR"
-
-    def do_upscale(self, img):
-        return upscale_with_ldsr(img)
-
-
-def setup_model(dirname):
-    global cmd_path
-    global model_path
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
-    cmd_path = dirname
-    shared.sd_upscalers.append(UpscalerLDSR(100))
-
-
-def prepare_ldsr():
-    path = paths.paths.get("LDSR", None)
-    if path is None:
-        return
-    global have_ldsr
-    global LDSR_obj
-    try:
-        from LDSR import LDSR
-        model_files = modelloader.load_models(model_path, model_url, cmd_path, dl_name="model.ckpt", ext_filter=[".ckpt"])
-        yaml_files = modelloader.load_models(model_path, yaml_url, cmd_path, dl_name="project.yaml", ext_filter=[".yaml"])
-        if len(model_files) != 0 and len(yaml_files) != 0:
-            model_file = model_files[0]
-            yaml_file = yaml_files[0]
-            have_ldsr = True
-            LDSR_obj = LDSR(model_file, yaml_file)
-        else:
-            return
-
-    except Exception:
-        print("Error importing LDSR:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-        have_ldsr = False
-
-
-def upscale_with_ldsr(image):
-    prepare_ldsr()
-    if not have_ldsr or LDSR_obj is None:
-        return image
-
-    ddim_steps = shared.opts.ldsr_steps
-    pre_scale = shared.opts.ldsr_pre_down
-    post_scale = shared.opts.ldsr_post_down
-
-    image = LDSR_obj.super_resolution(image, ddim_steps, pre_scale, post_scale)
-    return image
+        self.model_path = os.path.join(models_path, self.name)
+        self.user_path = user_path
+        self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
+        self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
+        super().__init__()
+        scaler_data = UpscalerData("LDSR", None, self)
+        self.scalers = [scaler_data]
+
+    def load_model(self, path: str):
+        model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+                                   file_name="model.pth", progress=True)
+        yaml = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+                                  file_name="project.yaml", progress=True)
+
+        try:
+            return LDSR(model, yaml)
+
+        except Exception:
+            print("Error importing LDSR:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+        return None
+
+    def do_upscale(self, img, path):
+        ldsr = self.load_model(path)
+        if ldsr is None:
+            print("NO LDSR!")
+            return img
+        ddim_steps = shared.opts.ldsr_steps
+        pre_scale = shared.opts.ldsr_pre_down
+        return ldsr.super_resolution(img, ddim_steps, self.scale)
diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
new file mode 100644
index 00000000..8fe87c6a
--- /dev/null
+++ b/modules/ldsr_model_arch.py
@@ -0,0 +1,223 @@
+import gc
+import time
+import warnings
+
+import numpy as np
+import torch
+import torchvision
+from PIL import Image
+from einops import rearrange, repeat
+from omegaconf import OmegaConf
+
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.util import instantiate_from_config, ismap
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+# Create LDSR Class
+class LDSR:
+    def load_model_from_config(self, half_attention):
+        print(f"Loading model from {self.modelPath}")
+        pl_sd = torch.load(self.modelPath, map_location="cpu")
+        sd = pl_sd["state_dict"]
+        config = OmegaConf.load(self.yamlPath)
+        model = instantiate_from_config(config.model)
+        model.load_state_dict(sd, strict=False)
+        model.cuda()
+        if half_attention:
+            model = model.half()
+
+        model.eval()
+        return {"model": model}
+
+    def __init__(self, model_path, yaml_path):
+        self.modelPath = model_path
+        self.yamlPath = yaml_path
+
+    @staticmethod
+    def run(model, selected_path, custom_steps, eta):
+        example = get_cond(selected_path)
+
+        n_runs = 1
+        guider = None
+        ckwargs = None
+        ddim_use_x0_pred = False
+        temperature = 1.
+        eta = eta
+        custom_shape = None
+
+        height, width = example["image"].shape[1:3]
+        split_input = height >= 128 and width >= 128
+
+        if split_input:
+            ks = 128
+            stride = 64
+            vqf = 4  #
+            model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
+                                        "vqf": vqf,
+                                        "patch_distributed_vq": True,
+                                        "tie_braker": False,
+                                        "clip_max_weight": 0.5,
+                                        "clip_min_weight": 0.01,
+                                        "clip_max_tie_weight": 0.5,
+                                        "clip_min_tie_weight": 0.01}
+        else:
+            if hasattr(model, "split_input_params"):
+                delattr(model, "split_input_params")
+
+        x_t = None
+        logs = None
+        for n in range(n_runs):
+            if custom_shape is not None:
+                x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
+                x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
+
+            logs = make_convolutional_sample(example, model,
+                                             custom_steps=custom_steps,
+                                             eta=eta, quantize_x0=False,
+                                             custom_shape=custom_shape,
+                                             temperature=temperature, noise_dropout=0.,
+                                             corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
+                                             ddim_use_x0_pred=ddim_use_x0_pred
+                                             )
+        return logs
+
+    def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
+        model = self.load_model_from_config(half_attention)
+
+        # Run settings
+        diffusion_steps = int(steps)
+        eta = 1.0
+
+        down_sample_method = 'Lanczos'
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        im_og = image
+        width_og, height_og = im_og.size
+        # If we can adjust the max upscale size, then the 4 below should be our variable
+        print("Foo")
+        down_sample_rate = target_scale / 4
+        print(f"Downsample rate is {down_sample_rate}")
+        width_downsampled_pre = width_og * down_sample_rate
+        height_downsampled_pre = height_og * down_sample_method
+
+        if down_sample_rate != 1:
+            print(
+                f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
+            im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
+        else:
+            print(f"Down sample rate is 1 from {target_scale} / 4")
+        logs = self.run(model["model"], im_og, diffusion_steps, eta)
+
+        sample = logs["sample"]
+        sample = sample.detach().cpu()
+        sample = torch.clamp(sample, -1., 1.)
+        sample = (sample + 1.) / 2. * 255
+        sample = sample.numpy().astype(np.uint8)
+        sample = np.transpose(sample, (0, 2, 3, 1))
+        a = Image.fromarray(sample[0])
+
+        del model
+        gc.collect()
+        torch.cuda.empty_cache()
+        print(f'Processing finished!')
+        return a
+
+
+def get_cond(selected_path):
+    example = dict()
+    up_f = 4
+    c = selected_path.convert('RGB')
+    c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
+    c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
+                                                    antialias=True)
+    c_up = rearrange(c_up, '1 c h w -> 1 h w c')
+    c = rearrange(c, '1 c h w -> 1 h w c')
+    c = 2. * c - 1.
+
+    c = c.to(torch.device("cuda"))
+    example["LR_image"] = c
+    example["image"] = c_up
+
+    return example
+
+
+@torch.no_grad()
+def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
+                    mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
+                    corrector_kwargs=None, x_t=None
+                    ):
+    ddim = DDIMSampler(model)
+    bs = shape[0]
+    shape = shape[1:]
+    print(f"Sampling with eta = {eta}; steps: {steps}")
+    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
+                                         normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
+                                         mask=mask, x0=x0, temperature=temperature, verbose=False,
+                                         score_corrector=score_corrector,
+                                         corrector_kwargs=corrector_kwargs, x_t=x_t)
+
+    return samples, intermediates
+
+
+@torch.no_grad()
+def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
+                              corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
+    log = dict()
+
+    z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
+                                        return_first_stage_outputs=True,
+                                        force_c_encode=not (hasattr(model, 'split_input_params')
+                                                            and model.cond_stage_key == 'coordinates_bbox'),
+                                        return_original_cond=True)
+
+    if custom_shape is not None:
+        z = torch.randn(custom_shape)
+        print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
+
+    z0 = None
+
+    log["input"] = x
+    log["reconstruction"] = xrec
+
+    if ismap(xc):
+        log["original_conditioning"] = model.to_rgb(xc)
+        if hasattr(model, 'cond_stage_key'):
+            log[model.cond_stage_key] = model.to_rgb(xc)
+
+    else:
+        log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
+        if model.cond_stage_model:
+            log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
+            if model.cond_stage_key == 'class_label':
+                log[model.cond_stage_key] = xc[model.cond_stage_key]
+
+    with model.ema_scope("Plotting"):
+        t0 = time.time()
+
+        sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
+                                                eta=eta,
+                                                quantize_x0=quantize_x0, mask=None, x0=z0,
+                                                temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
+                                                x_t=x_T)
+        t1 = time.time()
+
+        if ddim_use_x0_pred:
+            sample = intermediates['pred_x0'][-1]
+
+    x_sample = model.decode_first_stage(sample)
+
+    try:
+        x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
+        log["sample_noquant"] = x_sample_noquant
+        log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
+    except:
+        pass
+
+    log["sample"] = x_sample
+    log["time"] = t1 - t0
+
+    return log
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 3bd1de4d..6de65c69 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -1,34 +1,36 @@
 import os
 import shutil
+import importlib
 from urllib.parse import urlparse
 
 from basicsr.utils.download_util import load_file_from_url
 
+from modules import shared
+from modules.upscaler import Upscaler
 from modules.paths import script_path, models_path
 
 
-def load_models(model_path: str, model_url: str = None, command_path: str = None, dl_name: str = None, existing=None,
-                ext_filter=None) -> list:
+def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None) -> list:
     """
     A one-and done loader to try finding the desired models in specified directories.
 
-    @param dl_name: The file name to use for downloading a model. If not specified, it will be used from the URL.
-    @param model_url: If specified, attempt to download model from the given URL.
+    @param download_name: Specify to download from model_url immediately.
+    @param model_url: If no other models are found, this will be downloaded on upscale.
     @param model_path: The location to store/find models in.
     @param command_path: A command-line argument to search for models in first.
-    @param existing: An array of existing model paths.
     @param ext_filter: An optional list of filename extensions to filter by
     @return: A list of paths containing the desired model(s)
     """
+    output = []
+
     if ext_filter is None:
         ext_filter = []
-    if existing is None:
-        existing = []
     try:
         places = []
         if command_path is not None and command_path != model_path:
             pretrained_path = os.path.join(command_path, 'experiments/pretrained_models')
             if os.path.exists(pretrained_path):
+                print(f"Appending path: {pretrained_path}")
                 places.append(pretrained_path)
             elif os.path.exists(command_path):
                 places.append(command_path)
@@ -36,26 +38,24 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
         for place in places:
             if os.path.exists(place):
                 for file in os.listdir(place):
-                    if os.path.isdir(file):
+                    full_path = os.path.join(place, file)
+                    if os.path.isdir(full_path):
                         continue
                     if len(ext_filter) != 0:
                         model_name, extension = os.path.splitext(file)
                         if extension not in ext_filter:
                             continue
-                    if file not in existing:
-                        path = os.path.join(place, file)
-                        existing.append(path)
-        if model_url is not None and len(existing) == 0:
-            if dl_name is not None:
-                model_file = load_file_from_url(url=model_url, model_dir=model_path, file_name=dl_name, progress=True)
+                    if file not in output:
+                        output.append(full_path)
+        if model_url is not None and len(output) == 0:
+            if download_name is not None:
+                dl = load_file_from_url(model_url, model_path, True, download_name)
+                output.append(dl)
             else:
-                model_file = load_file_from_url(url=model_url, model_dir=model_path, progress=True)
-
-            if os.path.exists(model_file) and os.path.isfile(model_file) and model_file not in existing:
-                existing.append(model_file)
+                output.append(model_url)
     except:
         pass
-    return existing
+    return output
 
 
 def friendly_name(file: str):
@@ -110,4 +110,38 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
                 print(f"Removing empty folder: {src_path}")
                 shutil.rmtree(src_path, True)
     except:
-        pass
\ No newline at end of file
+        pass
+
+
+def load_upscalers():
+    datas = []
+    for cls in Upscaler.__subclasses__():
+        name = cls.__name__
+        module_name = cls.__module__
+        print(f"Class: {name} and {module_name}")
+        module = importlib.import_module(module_name)
+        class_ = getattr(module, name)
+        cmd_name = f"{name.lower().replace('upscaler', '')}-models-path"
+        print(f"CMD Name: {cmd_name}")
+        opt_string = None
+        try:
+            opt_string = shared.opts.__getattr__(cmd_name)
+        except:
+            pass
+        scaler = class_(opt_string)
+        for child in scaler.scalers:
+            print(f"Appending {child.name}")
+            datas.append(child)
+
+    shared.sd_upscalers = datas
+
+    # for scaler in subclasses:
+    #     print(f"Found scaler: {type(scaler).__name__}")
+    #     try:
+    #         scaler = scaler()
+    #         for child in scaler.scalers:
+    #             print(f"Appending {child.name}")
+    #             datas.append[child]
+    #     except:
+    #         pass
+    # shared.sd_upscalers = datas
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py
index 458bf678..0a2eb896 100644
--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@@ -1,64 +1,135 @@
 import os
 import sys
 import traceback
-from collections import namedtuple
 
 import numpy as np
 from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 from realesrgan import RealESRGANer
 
-import modules.images
+from modules.upscaler import Upscaler, UpscalerData
 from modules.paths import models_path
 from modules.shared import cmd_opts, opts
 
-model_dir = "RealESRGAN"
-model_path = os.path.join(models_path, model_dir)
-cmd_dir = None
-RealesrganModelInfo = namedtuple("RealesrganModelInfo", ["name", "location", "model", "netscale"])
-realesrgan_models = []
-have_realesrgan = False
 
+class UpscalerRealESRGAN(Upscaler):
+    def __init__(self, path):
+        self.name = "RealESRGAN"
+        self.model_path = os.path.join(models_path, self.name)
+        self.user_path = path
+        super().__init__()
+        try:
+            from basicsr.archs.rrdbnet_arch import RRDBNet
+            from realesrgan import RealESRGANer
+            from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+            self.enable = True
+            self.scalers = []
+            scalers = self.load_models(path)
+            for scaler in scalers:
+                if scaler.name in opts.realesrgan_enabled_models:
+                    self.scalers.append(scaler)
+
+        except Exception:
+            print("Error importing Real-ESRGAN:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+            self.enable = False
+            self.scalers = []
+
+    def do_upscale(self, img, path):
+        if not self.enable:
+            return img
+
+        info = self.load_model(path)
+        if not os.path.exists(info.data_path):
+            print("Unable to load RealESRGAN model: %s" % info.name)
+            return img
+
+        upsampler = RealESRGANer(
+            scale=info.scale,
+            model_path=info.data_path,
+            model=info.model(),
+            half=not cmd_opts.no_half,
+            tile=opts.ESRGAN_tile,
+            tile_pad=opts.ESRGAN_tile_overlap,
+        )
+
+        upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
+
+        image = Image.fromarray(upsampled)
+        return image
+
+    def load_model(self, path):
+        try:
+            info = None
+            for scaler in self.scalers:
+                if scaler.data_path == path:
+                    info = scaler
+
+            if info is None:
+                print(f"Unable to find model info: {path}")
+                return None
+
+            model_file = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
+            info.data_path = model_file
+            return info
+        except Exception as e:
+            print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+        return None
 
-def get_realesrgan_models():
+    def load_models(self, _):
+        return get_realesrgan_models(self)
+
+
+def get_realesrgan_models(scaler):
     try:
         from basicsr.archs.rrdbnet_arch import RRDBNet
         from realesrgan.archs.srvgg_arch import SRVGGNetCompact
         models = [
-            RealesrganModelInfo(
-                name="Real-ESRGAN General x4x3",
-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
-                netscale=4,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+            UpscalerData(
+                name="R-ESRGAN General 4xV3",
+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3"
+                     ".pth",
+                scale=4,
+                upscaler=scaler,
+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4,
+                                              act_type='prelu')
             ),
-            RealesrganModelInfo(
-                name="Real-ESRGAN General WDN x4x3",
-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
-                netscale=4,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+            UpscalerData(
+                name="R-ESRGAN General WDN 4xV3",
+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
+                scale=4,
+                upscaler=scaler,
+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4,
+                                              act_type='prelu')
             ),
-            RealesrganModelInfo(
-                name="Real-ESRGAN AnimeVideo",
-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
-                netscale=4,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+            UpscalerData(
+                name="R-ESRGAN AnimeVideo",
+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
+                scale=4,
+                upscaler=scaler,
+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4,
+                                              act_type='prelu')
             ),
-            RealesrganModelInfo(
-                name="Real-ESRGAN 4x plus",
-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
-                netscale=4,
+            UpscalerData(
+                name="R-ESRGAN 4x+",
+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+                scale=4,
+                upscaler=scaler,
                 model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
             ),
-            RealesrganModelInfo(
-                name="Real-ESRGAN 4x plus anime 6B",
-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
-                netscale=4,
+            UpscalerData(
+                name="R-ESRGAN 4x+ Anime6B",
+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+                scale=4,
+                upscaler=scaler,
                 model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
             ),
-            RealesrganModelInfo(
-                name="Real-ESRGAN 2x plus",
-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
-                netscale=2,
+            UpscalerData(
+                name="R-ESRGAN 2x+",
+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+                scale=2,
+                upscaler=scaler,
                 model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
             ),
         ]
@@ -66,69 +137,3 @@ def get_realesrgan_models():
     except Exception as e:
         print("Error making Real-ESRGAN models list:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
-
-
-class UpscalerRealESRGAN(modules.images.Upscaler):
-    def __init__(self, upscaling, model_index):
-        self.upscaling = upscaling
-        self.model_index = model_index
-        self.name = realesrgan_models[model_index].name
-
-    def do_upscale(self, img):
-        return upscale_with_realesrgan(img, self.upscaling, self.model_index)
-
-
-def setup_model(dirname):
-    global model_path
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
-
-    global realesrgan_models
-    global have_realesrgan
-    if model_path != dirname:
-        model_path = dirname
-    try:
-        from basicsr.archs.rrdbnet_arch import RRDBNet
-        from realesrgan import RealESRGANer
-        from realesrgan.archs.srvgg_arch import SRVGGNetCompact
-
-        realesrgan_models = get_realesrgan_models()
-        have_realesrgan = True
-
-        for i, model in enumerate(realesrgan_models):
-            if model.name in opts.realesrgan_enabled_models:
-                modules.shared.sd_upscalers.append(UpscalerRealESRGAN(model.netscale, i))
-
-    except Exception:
-        print("Error importing Real-ESRGAN:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-
-        realesrgan_models = [RealesrganModelInfo('None', '', 0, None)]
-        have_realesrgan = False
-
-
-def upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index):
-    if not have_realesrgan:
-        return image
-
-    info = realesrgan_models[RealESRGAN_model_index]
-
-    model = info.model()
-    model_file = load_file_from_url(url=info.location, model_dir=model_path, progress=True)
-    if not os.path.exists(model_file):
-        print("Unable to load RealESRGAN model: %s" % info.name)
-        return image
-
-    upsampler = RealESRGANer(
-        scale=info.netscale,
-        model_path=info.location,
-        model=model,
-        half=not cmd_opts.no_half,
-        tile=opts.ESRGAN_tile,
-        tile_pad=opts.ESRGAN_tile_overlap,
-    )
-
-    upsampled = upsampler.enhance(np.array(image), outscale=RealESRGAN_upscaling)[0]
-
-    image = Image.fromarray(upsampled)
-    return image
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 89b7d276..23826727 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -50,7 +50,7 @@ def setup_model(dirname):
     if not os.path.exists(model_path):
         os.makedirs(model_path)
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path, model_url, dirname, model_name, ext_filter=".ckpt")
+    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=dirname, download_name=model_name, ext_filter=".ckpt")
 
     cmd_ckpt = shared.cmd_opts.ckpt
     if os.path.exists(cmd_ckpt):
@@ -68,6 +68,7 @@ def setup_model(dirname):
 
 def model_hash(filename):
     try:
+        print(f"Opening: {filename}")
         with open(filename, "rb") as file:
             import hashlib
             m = hashlib.sha256()
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 666ee1ee..cfc3ee40 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -154,9 +154,9 @@ class VanillaStableDiffusionSampler:
 
         # existing code fails with cetin step counts, like 9
         try:
-            samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)
+            samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_t=x, eta=p.ddim_eta)
         except Exception:
-            samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=p.ddim_eta)
+            samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_t=x, eta=p.ddim_eta)
 
         return samples_ddim
 
diff --git a/modules/shared.py b/modules/shared.py
index c27079eb..4c31039d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -1,18 +1,19 @@
-import sys
 import argparse
+import datetime
 import json
 import os
+import sys
+
 import gradio as gr
 import tqdm
-import datetime
 
 import modules.artists
-from modules.paths import script_path, sd_path
-from modules.devices import get_optimal_device
-import modules.styles
 import modules.interrogate
 import modules.memmon
 import modules.sd_models
+import modules.styles
+from modules.devices import get_optimal_device
+from modules.paths import script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
@@ -38,6 +39,7 @@ parser.add_argument("--share", action='store_true', help="use share=True for gra
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(model_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(model_path, 'GFPGAN'))
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
+parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))
 parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
 parser.add_argument("--stablediffusion-models-path", type=str, help="Path to directory with Stable-diffusion checkpoints.", default=os.path.join(model_path, 'SwinIR'))
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
@@ -111,7 +113,7 @@ face_restorers = []
 
 def realesrgan_models_names():
     import modules.realesrgan_model
-    return [x.name for x in modules.realesrgan_model.get_realesrgan_models()]
+    return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
 
 
 class OptionInfo:
@@ -176,13 +178,11 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
 options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-    "realesrgan_enabled_models": OptionInfo(["Real-ESRGAN 4x plus", "Real-ESRGAN 4x plus anime 6B"], "Select which RealESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
+    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN x4+", "R-ESRGAN x4+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
     "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
     "ldsr_pre_down": OptionInfo(1, "LDSR Pre-process downssample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
-    "ldsr_post_down": OptionInfo(1, "LDSR Post-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
-
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
 }))
 
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index f515779e..ea7b6301 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -1,92 +1,91 @@
 import contextlib
 import os
-import sys
-import traceback
 
 import numpy as np
 import torch
 from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
-import modules.images
 from modules import modelloader
 from modules.paths import models_path
 from modules.shared import cmd_opts, opts, device
 from modules.swinir_model_arch import SwinIR as net
+from modules.upscaler import Upscaler, UpscalerData
 
-model_dir = "SwinIR"
-model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
-model_name = "SwinIR x4"
-model_path = os.path.join(models_path, model_dir)
-cmd_path = ""
 precision_scope = (
     torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
 )
 
 
-def load_model(path, scale=4):
-    global model_path
-    global model_name
-    if "http" in path:
-        dl_name = "%s%s" % (model_name.replace(" ", "_"), ".pth")
-        filename = load_file_from_url(url=path, model_dir=model_path, file_name=dl_name, progress=True)
-    else:
-        filename = path
-    if filename is None or not os.path.exists(filename):
-        return None
-    model = net(
-        upscale=scale,
-        in_chans=3,
-        img_size=64,
-        window_size=8,
-        img_range=1.0,
-        depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
-        embed_dim=240,
-        num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
-        mlp_ratio=2,
-        upsampler="nearest+conv",
-        resi_connection="3conv",
-    )
-
-    pretrained_model = torch.load(filename)
-    model.load_state_dict(pretrained_model["params_ema"], strict=True)
-    if not cmd_opts.no_half:
-        model = model.half()
-    return model
-
-
-def setup_model(dirname):
-    global model_path
-    global model_name
-    global cmd_path
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
-    cmd_path = dirname
-    model_file = ""
-    try:
-        models = modelloader.load_models(model_path, ext_filter=[".pt", ".pth"], command_path=cmd_path)
-
-        if len(models) != 0:
-            model_file = models[0]
-            name = modelloader.friendly_name(model_file)
-        else:
-            # Add the "default" model if none are found.
-            model_file = model_url
-            name = model_name
+class UpscalerSwinIR(Upscaler):
+    def __init__(self, dirname):
+        self.name = "SwinIR"
+        self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
+                         "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
+                         "-L_x4_GAN.pth "
+        self.model_name = "SwinIR 4x"
+        self.model_path = os.path.join(models_path, self.name)
+        self.user_path = dirname
+        super().__init__()
+        scalers = []
+        model_files = self.find_models(ext_filter=[".pt", ".pth"])
+        for model in model_files:
+            if "http" in model:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(model)
+            model_data = UpscalerData(name, model, self)
+            scalers.append(model_data)
+        self.scalers = scalers
+
+    def do_upscale(self, img, model_file):
+        model = self.load_model(model_file)
+        if model is None:
+            return img
+        model = model.to(device)
+        img = upscale(img, model)
+        try:
+            torch.cuda.empty_cache()
+        except:
+            pass
+        return img
 
-        modules.shared.sd_upscalers.append(UpscalerSwin(model_file, name))
-    except Exception:
-        print(f"Error loading SwinIR model: {model_file}", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+    def load_model(self, path, scale=4):
+        if "http" in path:
+            dl_name = "%s%s" % (self.name.replace(" ", "_"), ".pth")
+            filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
+        else:
+            filename = path
+        if filename is None or not os.path.exists(filename):
+            return None
+        model = net(
+            upscale=scale,
+            in_chans=3,
+            img_size=64,
+            window_size=8,
+            img_range=1.0,
+            depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
+            embed_dim=240,
+            num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+            mlp_ratio=2,
+            upsampler="nearest+conv",
+            resi_connection="3conv",
+        )
+
+        pretrained_model = torch.load(filename)
+        model.load_state_dict(pretrained_model["params_ema"], strict=True)
+        if not cmd_opts.no_half:
+            model = model.half()
+        return model
 
 
 def upscale(
-    img,
-    model,
-    tile=opts.SWIN_tile,
-    tile_overlap=opts.SWIN_tile_overlap,
-    window_size=8,
-    scale=4,
+        img,
+        model,
+        tile=opts.SWIN_tile,
+        tile_overlap=opts.SWIN_tile_overlap,
+        window_size=8,
+        scale=4,
 ):
     img = np.array(img)
     img = img[:, :, ::-1]
@@ -125,34 +124,16 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
 
     for h_idx in h_idx_list:
         for w_idx in w_idx_list:
-            in_patch = img[..., h_idx : h_idx + tile, w_idx : w_idx + tile]
+            in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
             out_patch = model(in_patch)
             out_patch_mask = torch.ones_like(out_patch)
 
             E[
-                ..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf
+            ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
             ].add_(out_patch)
             W[
-                ..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf
+            ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
             ].add_(out_patch_mask)
     output = E.div_(W)
 
     return output
-
-
-class UpscalerSwin(modules.images.Upscaler):
-    def __init__(self, filename, title):
-        self.name = title
-        self.filename = filename
-
-    def do_upscale(self, img):
-        model = load_model(self.filename)
-        if model is None:
-            return img
-        model = model.to(device)
-        img = upscale(img, model)
-        try:
-            torch.cuda.empty_cache()
-        except:
-            pass
-        return img
\ No newline at end of file
diff --git a/modules/upscaler.py b/modules/upscaler.py
new file mode 100644
index 00000000..d698282f
--- /dev/null
+++ b/modules/upscaler.py
@@ -0,0 +1,121 @@
+import os
+from abc import abstractmethod
+
+import PIL
+import numpy as np
+import torch
+from PIL import Image
+
+import modules.shared
+from modules import modelloader, shared
+
+LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+from modules.paths import models_path
+
+
+class Upscaler:
+    name = None
+    model_path = None
+    model_name = None
+    model_url = None
+    enable = True
+    filter = None
+    model = None
+    user_path = None
+    scalers: []
+    tile = True
+
+    def __init__(self, create_dirs=False):
+        self.mod_pad_h = None
+        self.tile_size = modules.shared.opts.ESRGAN_tile
+        self.tile_pad = modules.shared.opts.ESRGAN_tile_overlap
+        self.device = modules.shared.device
+        self.img = None
+        self.output = None
+        self.scale = 1
+        self.half = not modules.shared.cmd_opts.no_half
+        self.pre_pad = 0
+        self.mod_scale = None
+        if self.name is not None and create_dirs:
+            self.model_path = os.path.join(models_path, self.name)
+            if not os.path.exists(self.model_path):
+                os.makedirs(self.model_path)
+
+        try:
+            import cv2
+            self.can_tile = True
+        except:
+            pass
+
+    @abstractmethod
+    def do_upscale(self, img: PIL.Image, selected_model: str):
+        return img
+
+    def upscale(self, img: PIL.Image, scale: int, selected_model: str = None):
+        self.scale = scale
+        dest_w = img.width * scale
+        dest_h = img.height * scale
+        for i in range(3):
+            if img.width >= dest_w and img.height >= dest_h:
+                break
+            img = self.do_upscale(img, selected_model)
+        if img.width != dest_w or img.height != dest_h:
+            img = img.resize(dest_w, dest_h, resample=LANCZOS)
+
+        return img
+
+    @abstractmethod
+    def load_model(self, path: str):
+        pass
+
+    def find_models(self, ext_filter=None) -> list:
+        return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path)
+
+    def update_status(self, prompt):
+        print(f"\nextras: {prompt}", file=shared.progress_print_out)
+
+
+class UpscalerData:
+    name = None
+    data_path = None
+    scale: int = 4
+    scaler: Upscaler = None
+    model: None
+
+    def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None):
+        self.name = name
+        self.data_path = path
+        self.scaler = upscaler
+        self.scale = scale
+        self.model = model
+
+
+class UpscalerNone(Upscaler):
+    name = "None"
+    scalers = []
+
+    def load_model(self, path):
+        pass
+
+    def do_upscale(self, img, selected_model=None):
+        return img
+
+    def __init__(self, dirname=None):
+        super().__init__(False)
+        self.scalers = [UpscalerData("None", None, self)]
+
+
+class UpscalerLanczos(Upscaler):
+    scalers = []
+
+    def do_upscale(self, img, selected_model=None):
+        return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=LANCZOS)
+
+    def load_model(self, _):
+        pass
+
+    def __init__(self, dirname=None):
+        super().__init__(False)
+        self.name = "Lanczos"
+        self.scalers = [UpscalerData("Lanczos", None, self)]
+
diff --git a/webui.py b/webui.py
index 76d392a2..be1bc769 100644
--- a/webui.py
+++ b/webui.py
@@ -1,9 +1,10 @@
 import os
 import signal
 import threading
-
+import modules.paths
 import modules.codeformer_model as codeformer
 import modules.esrgan_model as esrgan
+import modules.bsrgan_model as bsrgan
 import modules.extras
 import modules.face_restoration
 import modules.gfpgan_model as gfpgan
@@ -27,11 +28,7 @@ modules.sd_models.setup_model(cmd_opts.stablediffusion_models_path)
 codeformer.setup_model(cmd_opts.codeformer_models_path)
 gfpgan.setup_model(cmd_opts.gfpgan_models_path)
 shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-
-esrgan.setup_model(cmd_opts.esrgan_models_path)
-swinir.setup_model(cmd_opts.swinir_models_path)
-realesrgan.setup_model(cmd_opts.realesrgan_models_path)
-ldsr.setup_model(cmd_opts.ldsr_models_path)
+modelloader.load_upscalers()
 queue_lock = threading.Lock()
 
 
-- 
cgit v1.2.3


From d1f098540ad1dbc2abb8d04322634efba650b631 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 11:42:40 +0300
Subject: remove unwanted formatting/functionality from the PR

---
 launch.py                   |   7 +--
 modules/esrgan_model.py     | 123 ++++++++++++++++++++++----------------------
 modules/extras.py           |  35 +++++++------
 modules/gfpgan_model.py     |  12 ++---
 modules/images.py           |  37 +++++--------
 modules/ldsr_model_arch.py  |   1 -
 modules/modelloader.py      |   9 +++-
 modules/realesrgan_model.py |  12 ++---
 modules/sd_models.py        |  56 ++++++--------------
 modules/shared.py           |   8 +--
 webui.py                    |   2 +-
 11 files changed, 127 insertions(+), 175 deletions(-)

(limited to 'modules/shared.py')

diff --git a/launch.py b/launch.py
index 3b8d8f23..d2793ed2 100644
--- a/launch.py
+++ b/launch.py
@@ -1,5 +1,4 @@
 # this scripts installs necessary requirements and launches main program in webui.py
-import shutil
 import subprocess
 import os
 import sys
@@ -119,11 +118,7 @@ git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming
 git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
 git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
 git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
-if os.path.isdir(repo_dir('latent-diffusion')):
-    try:
-        shutil.rmtree(repo_dir('latent-diffusion'))
-    except:
-        pass
+
 if not is_installed("lpips"):
     run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
 
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index ce841aa4..ea91abfe 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -13,6 +13,63 @@ from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
 
+def fix_model_layers(crt_model, pretrained_net):
+    # this code is adapted from https://github.com/xinntao/ESRGAN
+    if 'conv_first.weight' in pretrained_net:
+        return pretrained_net
+
+    if 'model.0.weight' not in pretrained_net:
+        is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
+        if is_realesrgan:
+            raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
+        else:
+            raise Exception("The file is not a ESRGAN model.")
+
+    crt_net = crt_model.state_dict()
+    load_net_clean = {}
+    for k, v in pretrained_net.items():
+        if k.startswith('module.'):
+            load_net_clean[k[7:]] = v
+        else:
+            load_net_clean[k] = v
+    pretrained_net = load_net_clean
+
+    tbd = []
+    for k, v in crt_net.items():
+        tbd.append(k)
+
+    # directly copy
+    for k, v in crt_net.items():
+        if k in pretrained_net and pretrained_net[k].size() == v.size():
+            crt_net[k] = pretrained_net[k]
+            tbd.remove(k)
+
+    crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
+    crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
+
+    for k in tbd.copy():
+        if 'RDB' in k:
+            ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
+            if '.weight' in k:
+                ori_k = ori_k.replace('.weight', '.0.weight')
+            elif '.bias' in k:
+                ori_k = ori_k.replace('.bias', '.0.bias')
+            crt_net[k] = pretrained_net[ori_k]
+            tbd.remove(k)
+
+    crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
+    crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
+    crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
+    crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
+    crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
+    crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
+    crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
+    crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
+    crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
+    crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
+
+    return crt_net
+
 class UpscalerESRGAN(Upscaler):
     def __init__(self, dirname):
         self.name = "ESRGAN"
@@ -28,14 +85,12 @@ class UpscalerESRGAN(Upscaler):
             scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
             scalers.append(scaler_data)
         for file in model_paths:
-            print(f"File: {file}")
             if "http" in file:
                 name = self.model_name
             else:
                 name = modelloader.friendly_name(file)
 
             scaler_data = UpscalerData(name, file, self, 4)
-            print(f"ESRGAN: Adding scaler {name}")
             self.scalers.append(scaler_data)
 
     def do_upscale(self, img, selected_model):
@@ -56,67 +111,14 @@ class UpscalerESRGAN(Upscaler):
         if not os.path.exists(filename) or filename is None:
             print("Unable to load %s from %s" % (self.model_path, filename))
             return None
-        # this code is adapted from https://github.com/xinntao/ESRGAN
+
         pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
         crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
 
-        if 'conv_first.weight' in pretrained_net:
-            crt_model.load_state_dict(pretrained_net)
-            return crt_model
-
-        if 'model.0.weight' not in pretrained_net:
-            is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net[
-                "params_ema"]
-            if is_realesrgan:
-                raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
-            else:
-                raise Exception("The file is not a ESRGAN model.")
-
-        crt_net = crt_model.state_dict()
-        load_net_clean = {}
-        for k, v in pretrained_net.items():
-            if k.startswith('module.'):
-                load_net_clean[k[7:]] = v
-            else:
-                load_net_clean[k] = v
-        pretrained_net = load_net_clean
-
-        tbd = []
-        for k, v in crt_net.items():
-            tbd.append(k)
-
-        # directly copy
-        for k, v in crt_net.items():
-            if k in pretrained_net and pretrained_net[k].size() == v.size():
-                crt_net[k] = pretrained_net[k]
-                tbd.remove(k)
-
-        crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
-        crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
-
-        for k in tbd.copy():
-            if 'RDB' in k:
-                ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
-                if '.weight' in k:
-                    ori_k = ori_k.replace('.weight', '.0.weight')
-                elif '.bias' in k:
-                    ori_k = ori_k.replace('.bias', '.0.bias')
-                crt_net[k] = pretrained_net[ori_k]
-                tbd.remove(k)
-
-        crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
-        crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
-        crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
-        crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
-        crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
-        crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
-        crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
-        crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
-        crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
-        crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
-
-        crt_model.load_state_dict(crt_net)
+        pretrained_net = fix_model_layers(crt_model, pretrained_net)
+        crt_model.load_state_dict(pretrained_net)
         crt_model.eval()
+
         return crt_model
 
 
@@ -154,7 +156,6 @@ def esrgan_upscale(model, img):
             newrow.append([x * scale_factor, w * scale_factor, output])
         newtiles.append([y * scale_factor, h * scale_factor, newrow])
 
-    newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor,
-                                  grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
+    newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
     output = images.combine_grid(newgrid)
     return output
diff --git a/modules/extras.py b/modules/extras.py
index 1d4e9fa8..1bff5874 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -67,28 +67,29 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
             info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
             image = res
 
-        def upscale(image, scaler_index, resize):
-            small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
-            pixels = tuple(np.array(small).flatten().tolist())
-            key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
+        if upscaling_resize != 1.0:
+            def upscale(image, scaler_index, resize):
+                small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
+                pixels = tuple(np.array(small).flatten().tolist())
+                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
 
-            c = cached_images.get(key)
-            if c is None:
-                upscaler = shared.sd_upscalers[scaler_index]
-                c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
-                cached_images[key] = c
+                c = cached_images.get(key)
+                if c is None:
+                    upscaler = shared.sd_upscalers[scaler_index]
+                    c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+                    cached_images[key] = c
 
-            return c
+                return c
 
-        info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
-        res = upscale(image, extras_upscaler_1, upscaling_resize)
+            info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
+            res = upscale(image, extras_upscaler_1, upscaling_resize)
 
-        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-            res2 = upscale(image, extras_upscaler_2, upscaling_resize)
-            info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
-            res = Image.blend(res, res2, extras_upscaler_2_visibility)
+            if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
+                res2 = upscale(image, extras_upscaler_2, upscaling_resize)
+                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
+                res = Image.blend(res, res2, extras_upscaler_2_visibility)
 
-        image = res
+            image = res
 
         while len(cached_images) > 2:
             del cached_images[next(iter(cached_images.keys()))]
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index 2bf8a1ee..bb30d733 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -36,8 +36,7 @@ def gfpgann():
     else:
         print("Unable to load gfpgan model!")
         return None
-    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2,
-                               bg_upsampler=None)
+    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
     model.gfpgan.to(shared.device)
     loaded_gfpgan_model = model
 
@@ -49,8 +48,7 @@ def gfpgan_fix_faces(np_image):
     if model is None:
         return np_image
     np_image_bgr = np_image[:, :, ::-1]
-    cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False,
-                                                                     only_center_face=False, paste_back=True)
+    cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
     np_image = gfpgan_output_bgr[:, :, ::-1]
 
     if shared.opts.face_restoration_unload:
@@ -79,7 +77,6 @@ def setup_model(dirname):
         facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
 
         def my_load_file_from_url(**kwargs):
-            print("Setting model_dir to " + model_path)
             return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
 
         def facex_load_file_from_url(**kwargs):
@@ -92,7 +89,6 @@ def setup_model(dirname):
         facexlib.detection.load_file_from_url = facex_load_file_from_url
         facexlib.parsing.load_file_from_url = facex_load_file_from_url2
         user_path = dirname
-        print("Have gfpgan should be true?")
         have_gfpgan = True
         gfpgan_constructor = GFPGANer
 
@@ -102,9 +98,7 @@ def setup_model(dirname):
 
             def restore(self, np_image):
                 np_image_bgr = np_image[:, :, ::-1]
-                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgann().enhance(np_image_bgr, has_aligned=False,
-                                                                                     only_center_face=False,
-                                                                                     paste_back=True)
+                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgann().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
                 np_image = gfpgan_output_bgr[:, :, ::-1]
 
                 return np_image
diff --git a/modules/images.py b/modules/images.py
index 6430cfec..e89c44b2 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -84,10 +84,8 @@ def combine_grid(grid):
         r = r.astype(np.uint8)
         return Image.fromarray(r, 'L')
 
-    mask_w = make_mask_image(
-        np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
-    mask_h = make_mask_image(
-        np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
+    mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
+    mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
 
     combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
     for y, h, row in grid.tiles:
@@ -130,12 +128,10 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
 
     def draw_texts(drawing, draw_x, draw_y, lines):
         for i, line in enumerate(lines):
-            drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt,
-                                   fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
+            drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
 
             if not line.is_active:
-                drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2,
-                              draw_y + line.size[1] // 2), fill=color_inactive, width=4)
+                drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2, draw_y + line.size[1] // 2), fill=color_inactive, width=4)
 
             draw_y += line.size[1] + line_spacing
 
@@ -206,10 +202,8 @@ def draw_prompt_matrix(im, width, height, all_prompts):
     prompts_horiz = prompts[:boundary]
     prompts_vert = prompts[boundary:]
 
-    hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in
-                 range(1 << len(prompts_horiz))]
-    ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in
-                 range(1 << len(prompts_vert))]
+    hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]
+    ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]
 
     return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
 
@@ -259,13 +253,11 @@ def resize_image(resize_mode, im, width, height):
         if ratio < src_ratio:
             fill_height = height // 2 - src_h // 2
             res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
-            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)),
-                      box=(0, fill_height + src_h))
+            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
         elif ratio > src_ratio:
             fill_width = width // 2 - src_w // 2
             res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
-            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)),
-                      box=(fill_width + src_w, 0))
+            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
 
     return res
 
@@ -300,8 +292,7 @@ def apply_filename_pattern(x, p, seed, prompt):
             words = [x for x in re_nonletters.split(prompt or "") if len(x) > 0]
             if len(words) == 0:
                 words = ["empty"]
-            x = x.replace("[prompt_words]",
-                          sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))
+            x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))
 
     if p is not None:
         x = x.replace("[steps]", str(p.steps))
@@ -309,8 +300,7 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[width]", str(p.width))
         x = x.replace("[height]", str(p.height))
         x = x.replace("[styles]", sanitize_filename_part(", ".join(p.styles), replace_spaces=False))
-        x = x.replace("[sampler]",
-                      sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
+        x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
     x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
     x = x.replace("[date]", datetime.date.today().isoformat())
@@ -336,8 +326,7 @@ def get_next_sequence_number(path, basename):
     prefix_length = len(basename)
     for p in os.listdir(path):
         if p.startswith(basename):
-            l = os.path.splitext(p[prefix_length:])[0].split(
-                '-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
+            l = os.path.splitext(p[prefix_length:])[0].split('-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
             try:
                 result = max(int(l[0]), result)
             except ValueError:
@@ -346,9 +335,7 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
-def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False,
-               no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None,
-               forced_filename=None, suffix=""):
+def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix=""):
     if short_filename or prompt is None or seed is None:
         file_decoration = ""
     elif opts.save_to_dirs:
diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
index f8f3c3d3..7faac6e1 100644
--- a/modules/ldsr_model_arch.py
+++ b/modules/ldsr_model_arch.py
@@ -125,7 +125,6 @@ class LDSR:
         del model
         gc.collect()
         torch.cuda.empty_cache()
-        print(f'Processing finished!')
         return a
 
 
diff --git a/modules/modelloader.py b/modules/modelloader.py
index b3e6dc36..1106aeb7 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -25,8 +25,10 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
 
     if ext_filter is None:
         ext_filter = []
+
     try:
         places = []
+
         if command_path is not None and command_path != model_path:
             pretrained_path = os.path.join(command_path, 'experiments/pretrained_models')
             if os.path.exists(pretrained_path):
@@ -34,7 +36,9 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                 places.append(pretrained_path)
             elif os.path.exists(command_path):
                 places.append(command_path)
+
         places.append(model_path)
+
         for place in places:
             if os.path.exists(place):
                 for file in os.listdir(place):
@@ -47,14 +51,17 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                             continue
                     if file not in output:
                         output.append(full_path)
+
         if model_url is not None and len(output) == 0:
             if download_name is not None:
                 dl = load_file_from_url(model_url, model_path, True, download_name)
                 output.append(dl)
             else:
                 output.append(model_url)
-    except:
+
+    except Exception:
         pass
+
     return output
 
 
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py
index 0a2eb896..dc0123e0 100644
--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@@ -88,28 +88,24 @@ def get_realesrgan_models(scaler):
         models = [
             UpscalerData(
                 name="R-ESRGAN General 4xV3",
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3"
-                     ".pth",
+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
                 scale=4,
                 upscaler=scaler,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4,
-                                              act_type='prelu')
+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
             ),
             UpscalerData(
                 name="R-ESRGAN General WDN 4xV3",
                 path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
                 scale=4,
                 upscaler=scaler,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4,
-                                              act_type='prelu')
+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
             ),
             UpscalerData(
                 name="R-ESRGAN AnimeVideo",
                 path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
                 scale=4,
                 upscaler=scaler,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4,
-                                              act_type='prelu')
+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
             ),
             UpscalerData(
                 name="R-ESRGAN 4x+",
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4b9000a4..caa85d5e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -12,10 +12,10 @@ from modules import shared, modelloader
 from modules.paths import models_path
 
 model_dir = "Stable-diffusion"
-model_path = os.path.join(models_path, model_dir)
+model_path = os.path.abspath(os.path.join(models_path, model_dir))
 model_name = "sd-v1-4.ckpt"
 model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1"
-user_dir = None
+user_dir: (str | None) = None
 
 CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
 checkpoints_list = {}
@@ -30,26 +30,8 @@ except Exception:
     pass
 
 
-def modeltitle(path, h):
-    abspath = os.path.abspath(path)
-
-    if abspath.startswith(model_dir):
-        name = abspath.replace(model_dir, '')
-    else:
-        name = os.path.basename(path)
-
-    if name.startswith("\\") or name.startswith("/"):
-        name = name[1:]
-
-    return f'{name} [{h}]'
-
-
 def setup_model(dirname):
-    global model_path
-    global model_name
-    global model_url
     global user_dir
-    global model_list
     user_dir = dirname
     if not os.path.exists(model_path):
         os.makedirs(model_path)
@@ -62,21 +44,16 @@ def checkpoint_tiles():
 
 
 def list_models():
-    global model_path
-    global model_url
-    global model_name
-    global user_dir
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path=model_path,model_url=model_url,command_path= user_dir,
-                                         ext_filter=[".ckpt"], download_name=model_name)
-    print(f"Model list: {model_list}")
-    model_dir = os.path.abspath(model_path)
+    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=user_dir, ext_filter=[".ckpt"], download_name=model_name)
 
-    def modeltitle(path, h):
+    def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
 
-        if abspath.startswith(model_dir):
-            name = abspath.replace(model_dir, '')
+        if user_dir is not None and abspath.startswith(user_dir):
+            name = abspath.replace(user_dir, '')
+        elif abspath.startswith(model_path):
+            name = abspath.replace(model_path, '')
         else:
             name = os.path.basename(path)
 
@@ -85,29 +62,30 @@ def list_models():
 
         shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
 
-        return f'{name} [{h}]', shortname
+        return f'{name} [{shorthash}]', shortname
 
     cmd_ckpt = shared.cmd_opts.ckpt
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
-        title, model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, model_name)
+        title, short_model_name = modeltitle(cmd_ckpt, h)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
     for filename in model_list:
         h = model_hash(filename)
-        title, model_name = modeltitle(filename, h)
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, model_name)
+        title, short_model_name = modeltitle(filename, h)
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
+
 
 def get_closet_checkpoint_match(searchString):
     applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title))
-    if len(applicable)>0:
+    if len(applicable) > 0:
         return applicable[0]
     return None
 
+
 def model_hash(filename):
     try:
-        print(f"Opening: {filename}")
         with open(filename, "rb") as file:
             import hashlib
             m = hashlib.sha256()
@@ -128,7 +106,7 @@ def select_checkpoint():
     if len(checkpoints_list) == 0:
         print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
         print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
-        print(f" - directory {os.path.abspath(shared.cmd_opts.stablediffusion_models_path)}", file=sys.stderr)
+        print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
         print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
         exit(1)
 
diff --git a/modules/shared.py b/modules/shared.py
index 69002158..03a1a4d3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -21,8 +21,7 @@ model_path = os.path.join(script_path, 'models')
 parser = argparse.ArgumentParser()
 parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), 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; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
-# This should be deprecated, but we'll leave it for a few iterations
-parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints (Deprecated, use '--stablediffusion-models-path'", )
+parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
 parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
 parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
@@ -41,7 +40,6 @@ parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory wi
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
 parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))
 parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
-parser.add_argument("--stablediffusion-models-path", type=str, help="Path to directory with Stable-diffusion checkpoints.", default=os.path.join(model_path, 'SwinIR'))
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
@@ -61,10 +59,6 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 
 cmd_opts = parser.parse_args()
-if cmd_opts.ckpt_dir is not None:
-    print("The 'ckpt-dir' arg is deprecated in favor of the 'stablediffusion-models-path' argument and will be "
-          "removed in a future release. Please use the new option if you wish to use a custom checkpoint directory.")
-    cmd_opts.__setattr__("stablediffusion-models-path", cmd_opts.ckpt_dir)
 device = get_optimal_device()
 
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
diff --git a/webui.py b/webui.py
index 5fd65edc..b8cccd54 100644
--- a/webui.py
+++ b/webui.py
@@ -28,7 +28,7 @@ from modules.paths import script_path
 from modules.shared import cmd_opts
 
 modelloader.cleanup_models()
-modules.sd_models.setup_model(cmd_opts.stablediffusion_models_path)
+modules.sd_models.setup_model(cmd_opts.ckpt_dir)
 codeformer.setup_model(cmd_opts.codeformer_models_path)
 gfpgan.setup_model(cmd_opts.gfpgan_models_path)
 shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-- 
cgit v1.2.3


From b60cd0809f6dc99ff0751593f562b64609b3117c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 12:56:36 +0300
Subject: return the dropdown that mysteriously disappeared

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

(limited to 'modules/shared.py')

diff --git a/modules/shared.py b/modules/shared.py
index 03a1a4d3..8428c7a3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -193,7 +193,7 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
-- 
cgit v1.2.3


From 2b03f0bbda1229dff6e7ab6f656b28587eba8308 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 22:16:03 +0300
Subject: if --ckpt option is specified, load that model

---
 modules/sd_models.py | 1 +
 modules/shared.py    | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules/shared.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ab014efb..2539f14c 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -69,6 +69,7 @@ def list_models():
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
         checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
+        shared.opts.sd_model_checkpoint = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
     for filename in model_list:
diff --git a/modules/shared.py b/modules/shared.py
index 8428c7a3..ac968b2d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -20,7 +20,7 @@ default_sd_model_file = sd_model_file
 model_path = os.path.join(script_path, 'models')
 parser = argparse.ArgumentParser()
 parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), 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; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
+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",)
 parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
 parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
-- 
cgit v1.2.3


From 820f1dc96b1979d7e92170c161db281ee8bd988b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 15:03:39 +0300
Subject: initial support for training textual inversion

---
 .gitignore                                        |   1 +
 javascript/progressbar.js                         |   1 +
 javascript/textualInversion.js                    |   8 +
 modules/devices.py                                |   3 +-
 modules/processing.py                             |  13 +-
 modules/sd_hijack.py                              | 324 ++++------------------
 modules/sd_hijack_optimizations.py                | 164 +++++++++++
 modules/sd_models.py                              |   4 +-
 modules/shared.py                                 |   3 +-
 modules/textual_inversion/dataset.py              |  76 +++++
 modules/textual_inversion/textual_inversion.py    | 258 +++++++++++++++++
 modules/textual_inversion/ui.py                   |  32 +++
 modules/ui.py                                     | 139 ++++++++--
 style.css                                         |  10 +-
 textual_inversion_templates/style.txt             |  19 ++
 textual_inversion_templates/style_filewords.txt   |  19 ++
 textual_inversion_templates/subject.txt           |  27 ++
 textual_inversion_templates/subject_filewords.txt |  27 ++
 webui.py                                          |  15 +-
 19 files changed, 828 insertions(+), 315 deletions(-)
 create mode 100644 javascript/textualInversion.js
 create mode 100644 modules/sd_hijack_optimizations.py
 create mode 100644 modules/textual_inversion/dataset.py
 create mode 100644 modules/textual_inversion/textual_inversion.py
 create mode 100644 modules/textual_inversion/ui.py
 create mode 100644 textual_inversion_templates/style.txt
 create mode 100644 textual_inversion_templates/style_filewords.txt
 create mode 100644 textual_inversion_templates/subject.txt
 create mode 100644 textual_inversion_templates/subject_filewords.txt

(limited to 'modules/shared.py')

diff --git a/.gitignore b/.gitignore
index 3532dab3..7afc9395 100644
--- a/.gitignore
+++ b/.gitignore
@@ -25,3 +25,4 @@ __pycache__
 /.idea
 notification.mp3
 /SwinIR
+/textual_inversion
diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 21f25b38..1e297abb 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -30,6 +30,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_inte
 onUiUpdate(function(){
     check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
     check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')
+    check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', 'ti_interrupt', 'ti_preview', 'ti_gallery')
 })
 
 function requestMoreProgress(id_part, id_progressbar_span, id_interrupt){
diff --git a/javascript/textualInversion.js b/javascript/textualInversion.js
new file mode 100644
index 00000000..8061be08
--- /dev/null
+++ b/javascript/textualInversion.js
@@ -0,0 +1,8 @@
+
+
+function start_training_textual_inversion(){
+    requestProgress('ti')
+    gradioApp().querySelector('#ti_error').innerHTML=''
+
+    return args_to_array(arguments)
+}
diff --git a/modules/devices.py b/modules/devices.py
index 07bb2339..ff82f2f6 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -32,10 +32,9 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-
 device = get_optimal_device()
 device_codeformer = cpu if has_mps else device
-
+dtype = torch.float16
 
 def randn(seed, shape):
     # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
diff --git a/modules/processing.py b/modules/processing.py
index 7eeb5191..8223423a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -56,7 +56,7 @@ class StableDiffusionProcessing:
         self.prompt: str = prompt
         self.prompt_for_display: str = None
         self.negative_prompt: str = (negative_prompt or "")
-        self.styles: str = styles
+        self.styles: list = styles or []
         self.seed: int = seed
         self.subseed: int = subseed
         self.subseed_strength: float = subseed_strength
@@ -271,7 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "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),
-        "Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
+        "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
     }
 
     generation_params.update(p.extra_generation_params)
@@ -295,8 +295,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     fix_seed(p)
 
-    os.makedirs(p.outpath_samples, exist_ok=True)
-    os.makedirs(p.outpath_grids, exist_ok=True)
+    if p.outpath_samples is not None:
+        os.makedirs(p.outpath_samples, exist_ok=True)
+
+    if p.outpath_grids is not None:
+        os.makedirs(p.outpath_grids, exist_ok=True)
 
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
 
@@ -323,7 +326,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
 
     if os.path.exists(cmd_opts.embeddings_dir):
-        model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)
+        model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     infotexts = []
     output_images = []
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index fa7eaeb8..fd57e5c5 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -6,244 +6,41 @@ import torch
 import numpy as np
 from torch import einsum
 
-from modules import prompt_parser
+import modules.textual_inversion.textual_inversion
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared
 from modules.shared import opts, device, cmd_opts
 
-from ldm.util import default
-from einops import rearrange
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 
+attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
+diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
+diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
 
-# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
-def split_cross_attention_forward_v1(self, x, context=None, mask=None):
-    h = self.heads
 
-    q = self.to_q(x)
-    context = default(context, x)
-    k = self.to_k(context)
-    v = self.to_v(context)
-    del context, x
+def apply_optimizations():
+    if cmd_opts.opt_split_attention_v1:
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
+        ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
 
-    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
-    for i in range(0, q.shape[0], 2):
-        end = i + 2
-        s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
-        s1 *= self.scale
+def undo_optimizations():
+    ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward
+    ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
+    ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
-        s2 = s1.softmax(dim=-1)
-        del s1
-
-        r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
-        del s2
-
-    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
-    del r1
-
-    return self.to_out(r2)
-
-
-# taken from https://github.com/Doggettx/stable-diffusion
-def split_cross_attention_forward(self, x, context=None, mask=None):
-    h = self.heads
-
-    q_in = self.to_q(x)
-    context = default(context, x)
-    k_in = self.to_k(context) * self.scale
-    v_in = self.to_v(context)
-    del context, x
-
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
-    del q_in, k_in, v_in
-
-    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-
-    stats = torch.cuda.memory_stats(q.device)
-    mem_active = stats['active_bytes.all.current']
-    mem_reserved = stats['reserved_bytes.all.current']
-    mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
-    mem_free_torch = mem_reserved - mem_active
-    mem_free_total = mem_free_cuda + mem_free_torch
-
-    gb = 1024 ** 3
-    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
-    modifier = 3 if q.element_size() == 2 else 2.5
-    mem_required = tensor_size * modifier
-    steps = 1
-
-    if mem_required > mem_free_total:
-        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
-        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
-        #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
-
-    if steps > 64:
-        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
-        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]
-    for i in range(0, q.shape[1], slice_size):
-        end = i + slice_size
-        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
-
-        s2 = s1.softmax(dim=-1, dtype=q.dtype)
-        del s1
-
-        r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
-        del s2
-
-    del q, k, v
-
-    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
-    del r1
-
-    return self.to_out(r2)
-
-def nonlinearity_hijack(x):
-    # swish
-    t = torch.sigmoid(x)
-    x *= t
-    del t
-
-    return x
-
-def cross_attention_attnblock_forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q1 = self.q(h_)
-        k1 = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q1.shape
-
-        q2 = q1.reshape(b, c, h*w)
-        del q1
-
-        q = q2.permute(0, 2, 1)   # b,hw,c
-        del q2
-
-        k = k1.reshape(b, c, h*w) # b,c,hw
-        del k1
-
-        h_ = torch.zeros_like(k, device=q.device)
-
-        stats = torch.cuda.memory_stats(q.device)
-        mem_active = stats['active_bytes.all.current']
-        mem_reserved = stats['reserved_bytes.all.current']
-        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
-        mem_free_torch = mem_reserved - mem_active
-        mem_free_total = mem_free_cuda + mem_free_torch
-
-        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
-        mem_required = tensor_size * 2.5
-        steps = 1
-
-        if mem_required > mem_free_total:
-            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
-
-        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-        for i in range(0, q.shape[1], slice_size):
-            end = i + slice_size
-
-            w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-            w2 = w1 * (int(c)**(-0.5))
-            del w1
-            w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
-            del w2
-
-            # attend to values
-            v1 = v.reshape(b, c, h*w)
-            w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)
-            del w3
-
-            h_[:, :, i:end] = torch.bmm(v1, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-            del v1, w4
-
-        h2 = h_.reshape(b, c, h, w)
-        del h_
-
-        h3 = self.proj_out(h2)
-        del h2
-
-        h3 += x
-
-        return h3
 
 class StableDiffusionModelHijack:
-    ids_lookup = {}
-    word_embeddings = {}
-    word_embeddings_checksums = {}
     fixes = None
     comments = []
-    dir_mtime = None
     layers = None
     circular_enabled = False
     clip = None
 
-    def load_textual_inversion_embeddings(self, dirname, model):
-        mt = os.path.getmtime(dirname)
-        if self.dir_mtime is not None and mt <= self.dir_mtime:
-            return
-
-        self.dir_mtime = mt
-        self.ids_lookup.clear()
-        self.word_embeddings.clear()
-
-        tokenizer = model.cond_stage_model.tokenizer
-
-        def const_hash(a):
-            r = 0
-            for v in a:
-                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
-            return r
-
-        def process_file(path, filename):
-            name = os.path.splitext(filename)[0]
-
-            data = torch.load(path, map_location="cpu")
-
-            # textual inversion embeddings
-            if 'string_to_param' in data:
-                param_dict = data['string_to_param']
-                if hasattr(param_dict, '_parameters'):
-                    param_dict = getattr(param_dict, '_parameters')  # 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:
-                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)
-
-            self.word_embeddings[name] = emb.detach().to(device)
-            self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1)*100)&0xffff:04x}'
-
-            ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
-
-            first_id = ids[0]
-            if first_id not in self.ids_lookup:
-                self.ids_lookup[first_id] = []
-            self.ids_lookup[first_id].append((ids, name))
-
-        for fn in os.listdir(dirname):
-            try:
-                fullfn = os.path.join(dirname, fn)
-
-                if os.stat(fullfn).st_size == 0:
-                    continue
-
-                process_file(fullfn, fn)
-            except Exception:
-                print(f"Error loading emedding {fn}:", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-                continue
-
-        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
+    embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
 
     def hijack(self, m):
         model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
@@ -253,12 +50,7 @@ class StableDiffusionModelHijack:
 
         self.clip = m.cond_stage_model
 
-        if cmd_opts.opt_split_attention_v1:
-            ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
-        elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-            ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
-            ldm.modules.diffusionmodules.model.nonlinearity = nonlinearity_hijack
-            ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
+        apply_optimizations()
 
         def flatten(el):
             flattened = [flatten(children) for children in el.children()]
@@ -296,7 +88,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
         super().__init__()
         self.wrapped = wrapped
-        self.hijack = hijack
+        self.hijack: StableDiffusionModelHijack = hijack
         self.tokenizer = wrapped.tokenizer
         self.max_length = wrapped.max_length
         self.token_mults = {}
@@ -317,7 +109,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
@@ -339,28 +130,19 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             while i < len(tokens):
                 token = tokens[i]
 
-                possible_matches = self.hijack.ids_lookup.get(token, None)
+                embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
-                if possible_matches is None:
+                if embedding is None:
                     remade_tokens.append(token)
                     multipliers.append(weight)
+                    i += 1
                 else:
-                    found = False
-                    for ids, word in possible_matches:
-                        if tokens[i:i + len(ids)] == ids:
-                            emb_len = int(self.hijack.word_embeddings[word].shape[0])
-                            fixes.append((len(remade_tokens), word))
-                            remade_tokens += [0] * emb_len
-                            multipliers += [weight] * emb_len
-                            i += len(ids) - 1
-                            found = True
-                            used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
-                            break
-
-                    if not found:
-                        remade_tokens.append(token)
-                        multipliers.append(weight)
-                i += 1
+                    emb_len = int(embedding.vec.shape[0])
+                    fixes.append((len(remade_tokens), embedding))
+                    remade_tokens += [0] * emb_len
+                    multipliers += [weight] * emb_len
+                    used_custom_terms.append((embedding.name, embedding.checksum()))
+                    i += emb_len
 
         if len(remade_tokens) > maxlen - 2:
             vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
@@ -431,32 +213,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 while i < len(tokens):
                     token = tokens[i]
 
-                    possible_matches = self.hijack.ids_lookup.get(token, None)
+                    embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
                     mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
                     if mult_change is not None:
                         mult *= mult_change
-                    elif possible_matches is None:
+                        i += 1
+                    elif embedding is None:
                         remade_tokens.append(token)
                         multipliers.append(mult)
+                        i += 1
                     else:
-                        found = False
-                        for ids, word in possible_matches:
-                            if tokens[i:i+len(ids)] == ids:
-                                emb_len = int(self.hijack.word_embeddings[word].shape[0])
-                                fixes.append((len(remade_tokens), word))
-                                remade_tokens += [0] * emb_len
-                                multipliers += [mult] * emb_len
-                                i += len(ids) - 1
-                                found = True
-                                used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
-                                break
-
-                        if not found:
-                            remade_tokens.append(token)
-                            multipliers.append(mult)
-
-                    i += 1
+                        emb_len = int(embedding.vec.shape[0])
+                        fixes.append((len(remade_tokens), embedding))
+                        remade_tokens += [0] * emb_len
+                        multipliers += [mult] * emb_len
+                        used_custom_terms.append((embedding.name, embedding.checksum()))
+                        i += emb_len
 
                 if len(remade_tokens) > maxlen - 2:
                     vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
@@ -464,6 +237,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     overflowing_words = [vocab.get(int(x), "") for x in ovf]
                     overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
                     hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+
                 token_count = len(remade_tokens)
                 remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
                 remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
@@ -484,7 +258,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         else:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
 
-
         self.hijack.fixes = hijack_fixes
         self.hijack.comments = hijack_comments
 
@@ -517,14 +290,19 @@ class EmbeddingsWithFixes(torch.nn.Module):
 
         inputs_embeds = self.wrapped(input_ids)
 
-        if batch_fixes is not None:
-            for fixes, tensor in zip(batch_fixes, inputs_embeds):
-                for offset, word in fixes:
-                    emb = self.embeddings.word_embeddings[word]
-                    emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
-                    tensor[offset+1:offset+1+emb_len] = self.embeddings.word_embeddings[word][0:emb_len]
+        if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
+            return inputs_embeds
+
+        vecs = []
+        for fixes, tensor in zip(batch_fixes, inputs_embeds):
+            for offset, embedding in fixes:
+                emb = embedding.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:]])
+
+            vecs.append(tensor)
 
-        return inputs_embeds
+        return torch.stack(vecs)
 
 
 def add_circular_option_to_conv_2d():
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
new file mode 100644
index 00000000..9c079e57
--- /dev/null
+++ b/modules/sd_hijack_optimizations.py
@@ -0,0 +1,164 @@
+import math
+import torch
+from torch import einsum
+
+from ldm.util import default
+from einops import rearrange
+
+
+# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
+def split_cross_attention_forward_v1(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+    k = self.to_k(context)
+    v = self.to_v(context)
+    del context, x
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
+    for i in range(0, q.shape[0], 2):
+        end = i + 2
+        s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
+        s1 *= self.scale
+
+        s2 = s1.softmax(dim=-1)
+        del s1
+
+        r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
+        del s2
+
+    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+    del r1
+
+    return self.to_out(r2)
+
+
+# taken from https://github.com/Doggettx/stable-diffusion
+def split_cross_attention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q_in = self.to_q(x)
+    context = default(context, x)
+    k_in = self.to_k(context) * self.scale
+    v_in = self.to_v(context)
+    del context, x
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    del q_in, k_in, v_in
+
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+
+    stats = torch.cuda.memory_stats(q.device)
+    mem_active = stats['active_bytes.all.current']
+    mem_reserved = stats['reserved_bytes.all.current']
+    mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
+    mem_free_torch = mem_reserved - mem_active
+    mem_free_total = mem_free_cuda + mem_free_torch
+
+    gb = 1024 ** 3
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
+    modifier = 3 if q.element_size() == 2 else 2.5
+    mem_required = tensor_size * modifier
+    steps = 1
+
+    if mem_required > mem_free_total:
+        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
+        #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
+
+    if steps > 64:
+        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+        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]
+    for i in range(0, q.shape[1], slice_size):
+        end = i + slice_size
+        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
+
+        s2 = s1.softmax(dim=-1, dtype=q.dtype)
+        del s1
+
+        r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+        del s2
+
+    del q, k, v
+
+    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+    del r1
+
+    return self.to_out(r2)
+
+def nonlinearity_hijack(x):
+    # swish
+    t = torch.sigmoid(x)
+    x *= t
+    del t
+
+    return x
+
+def cross_attention_attnblock_forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q1 = self.q(h_)
+        k1 = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q1.shape
+
+        q2 = q1.reshape(b, c, h*w)
+        del q1
+
+        q = q2.permute(0, 2, 1)   # b,hw,c
+        del q2
+
+        k = k1.reshape(b, c, h*w) # b,c,hw
+        del k1
+
+        h_ = torch.zeros_like(k, device=q.device)
+
+        stats = torch.cuda.memory_stats(q.device)
+        mem_active = stats['active_bytes.all.current']
+        mem_reserved = stats['reserved_bytes.all.current']
+        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
+        mem_free_torch = mem_reserved - mem_active
+        mem_free_total = mem_free_cuda + mem_free_torch
+
+        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
+        mem_required = tensor_size * 2.5
+        steps = 1
+
+        if mem_required > mem_free_total:
+            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+
+        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+        for i in range(0, q.shape[1], slice_size):
+            end = i + slice_size
+
+            w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+            w2 = w1 * (int(c)**(-0.5))
+            del w1
+            w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
+            del w2
+
+            # attend to values
+            v1 = v.reshape(b, c, h*w)
+            w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)
+            del w3
+
+            h_[:, :, i:end] = torch.bmm(v1, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+            del v1, w4
+
+        h2 = h_.reshape(b, c, h, w)
+        del h_
+
+        h3 = self.proj_out(h2)
+        del h2
+
+        h3 += x
+
+        return h3
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2539f14c..5b3dbdc7 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -8,7 +8,7 @@ from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader
+from modules import shared, modelloader, devices
 from modules.paths import models_path
 
 model_dir = "Stable-diffusion"
@@ -134,6 +134,8 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
     if not shared.cmd_opts.no_half:
         model.half()
 
+    devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
+
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpint = checkpoint_file
 
diff --git a/modules/shared.py b/modules/shared.py
index ac968b2d..ac0bc480 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -78,6 +78,7 @@ class State:
     current_latent = None
     current_image = None
     current_image_sampling_step = 0
+    textinfo = None
 
     def interrupt(self):
         self.interrupted = True
@@ -88,7 +89,7 @@ class State:
         self.current_image_sampling_step = 0
         
     def get_job_timestamp(self):
-        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
 
 
 state = State()
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
new file mode 100644
index 00000000..7e134a08
--- /dev/null
+++ b/modules/textual_inversion/dataset.py
@@ -0,0 +1,76 @@
+import os
+import numpy as np
+import PIL
+import torch
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+import random
+import tqdm
+
+
+class PersonalizedBase(Dataset):
+    def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
+
+        self.placeholder_token = placeholder_token
+
+        self.size = size
+        self.width = width
+        self.height = height
+        self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+
+        self.dataset = []
+
+        with open(template_file, "r") as file:
+            lines = [x.strip() for x in file.readlines()]
+
+        self.lines = lines
+
+        assert data_root, 'dataset directory not specified'
+
+        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
+        print("Preparing dataset...")
+        for path in tqdm.tqdm(self.image_paths):
+            image = Image.open(path)
+            image = image.convert('RGB')
+            image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
+
+            filename = os.path.basename(path)
+            filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')
+            filename_tokens = [token for token in filename_tokens if token.isalpha()]
+
+            npimage = np.array(image).astype(np.uint8)
+            npimage = (npimage / 127.5 - 1.0).astype(np.float32)
+
+            torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
+            torchdata = torch.moveaxis(torchdata, 2, 0)
+
+            init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
+
+            self.dataset.append((init_latent, filename_tokens))
+
+        self.length = len(self.dataset) * repeats
+
+        self.initial_indexes = np.arange(self.length) % len(self.dataset)
+        self.indexes = None
+        self.shuffle()
+
+    def shuffle(self):
+        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, i):
+        if i % len(self.dataset) == 0:
+            self.shuffle()
+
+        index = self.indexes[i % len(self.indexes)]
+        x, filename_tokens = self.dataset[index]
+
+        text = random.choice(self.lines)
+        text = text.replace("[name]", self.placeholder_token)
+        text = text.replace("[filewords]", ' '.join(filename_tokens))
+
+        return x, text
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
new file mode 100644
index 00000000..c0baaace
--- /dev/null
+++ b/modules/textual_inversion/textual_inversion.py
@@ -0,0 +1,258 @@
+import os
+import sys
+import traceback
+
+import torch
+import tqdm
+import html
+import datetime
+
+from modules import shared, devices, sd_hijack, processing
+import modules.textual_inversion.dataset
+
+
+class Embedding:
+    def __init__(self, vec, name, step=None):
+        self.vec = vec
+        self.name = name
+        self.step = step
+        self.cached_checksum = None
+
+    def save(self, filename):
+        embedding_data = {
+            "string_to_token": {"*": 265},
+            "string_to_param": {"*": self.vec},
+            "name": self.name,
+            "step": self.step,
+        }
+
+        torch.save(embedding_data, filename)
+
+    def checksum(self):
+        if self.cached_checksum is not None:
+            return self.cached_checksum
+
+        def const_hash(a):
+            r = 0
+            for v in a:
+                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
+            return r
+
+        self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
+        return self.cached_checksum
+
+class EmbeddingDatabase:
+    def __init__(self, embeddings_dir):
+        self.ids_lookup = {}
+        self.word_embeddings = {}
+        self.dir_mtime = None
+        self.embeddings_dir = embeddings_dir
+
+    def register_embedding(self, embedding, model):
+
+        self.word_embeddings[embedding.name] = embedding
+
+        ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0]
+
+        first_id = ids[0]
+        if first_id not in self.ids_lookup:
+            self.ids_lookup[first_id] = []
+        self.ids_lookup[first_id].append((ids, embedding))
+
+        return embedding
+
+    def load_textual_inversion_embeddings(self):
+        mt = os.path.getmtime(self.embeddings_dir)
+        if self.dir_mtime is not None and mt <= self.dir_mtime:
+            return
+
+        self.dir_mtime = mt
+        self.ids_lookup.clear()
+        self.word_embeddings.clear()
+
+        def process_file(path, filename):
+            name = os.path.splitext(filename)[0]
+
+            data = torch.load(path, map_location="cpu")
+
+            # textual inversion embeddings
+            if 'string_to_param' in data:
+                param_dict = data['string_to_param']
+                if hasattr(param_dict, '_parameters'):
+                    param_dict = getattr(param_dict, '_parameters')  # 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:
+                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)
+            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)
+            self.register_embedding(embedding, shared.sd_model)
+
+        for fn in os.listdir(self.embeddings_dir):
+            try:
+                fullfn = os.path.join(self.embeddings_dir, fn)
+
+                if os.stat(fullfn).st_size == 0:
+                    continue
+
+                process_file(fullfn, fn)
+            except Exception:
+                print(f"Error loading emedding {fn}:", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+                continue
+
+        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
+
+    def find_embedding_at_position(self, tokens, offset):
+        token = tokens[offset]
+        possible_matches = self.ids_lookup.get(token, None)
+
+        if possible_matches is None:
+            return None
+
+        for ids, embedding in possible_matches:
+            if tokens[offset:offset + len(ids)] == ids:
+                return embedding
+
+        return None
+
+
+
+def create_embedding(name, num_vectors_per_token):
+    init_text = '*'
+
+    cond_model = shared.sd_model.cond_stage_model
+    embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
+
+    ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
+    embedded = embedding_layer(ids.to(devices.device)).squeeze(0)
+    vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
+
+    for i in range(num_vectors_per_token):
+        vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
+
+    fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
+    assert not os.path.exists(fn), f"file {fn} already exists"
+
+    embedding = Embedding(vec, name)
+    embedding.step = 0
+    embedding.save(fn)
+
+    return fn
+
+
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
+    assert embedding_name, 'embedding not selected'
+
+    shared.state.textinfo = "Initializing textual inversion training..."
+    shared.state.job_count = steps
+
+    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+
+    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name)
+
+    if save_embedding_every > 0:
+        embedding_dir = os.path.join(log_directory, "embeddings")
+        os.makedirs(embedding_dir, exist_ok=True)
+    else:
+        embedding_dir = None
+
+    if create_image_every > 0:
+        images_dir = os.path.join(log_directory, "images")
+        os.makedirs(images_dir, exist_ok=True)
+    else:
+        images_dir = None
+
+    cond_model = shared.sd_model.cond_stage_model
+
+    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
+    with torch.autocast("cuda"):
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+
+    hijack = sd_hijack.model_hijack
+
+    embedding = hijack.embedding_db.word_embeddings[embedding_name]
+    embedding.vec.requires_grad = True
+
+    optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
+
+    losses = torch.zeros((32,))
+
+    last_saved_file = "<none>"
+    last_saved_image = "<none>"
+
+    ititial_step = embedding.step or 0
+    if ititial_step > steps:
+        return embedding, filename
+
+    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    for i, (x, text) in pbar:
+        embedding.step = i + ititial_step
+
+        if embedding.step > steps:
+            break
+
+        if shared.state.interrupted:
+            break
+
+        with torch.autocast("cuda"):
+            c = cond_model([text])
+            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+
+            losses[embedding.step % losses.shape[0]] = loss.item()
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        pbar.set_description(f"loss: {losses.mean():.7f}")
+
+        if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
+            last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
+            embedding.save(last_saved_file)
+
+        if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
+            last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
+
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                prompt=text,
+                steps=20,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+            )
+
+            processed = processing.process_images(p)
+            image = processed.images[0]
+
+            shared.state.current_image = image
+            image.save(last_saved_image)
+
+            last_saved_image += f", prompt: {text}"
+
+        shared.state.job_no = embedding.step
+
+        shared.state.textinfo = f"""
+<p>
+Loss: {losses.mean():.7f}<br/>
+Step: {embedding.step}<br/>
+Last prompt: {html.escape(text)}<br/>
+Last saved embedding: {html.escape(last_saved_file)}<br/>
+Last saved image: {html.escape(last_saved_image)}<br/>
+</p>
+"""
+
+    embedding.cached_checksum = None
+    embedding.save(filename)
+
+    return embedding, filename
+
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
new file mode 100644
index 00000000..ce3677a9
--- /dev/null
+++ b/modules/textual_inversion/ui.py
@@ -0,0 +1,32 @@
+import html
+
+import gradio as gr
+
+import modules.textual_inversion.textual_inversion as ti
+from modules import sd_hijack, shared
+
+
+def create_embedding(name, nvpt):
+    filename = ti.create_embedding(name, nvpt)
+
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+    return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
+
+
+def train_embedding(*args):
+
+    try:
+        sd_hijack.undo_optimizations()
+
+        embedding, filename = ti.train_embedding(*args)
+
+        res = f"""
+Training {'interrupted' if shared.state.interrupted else 'finished'} after {embedding.step} steps.
+Embedding saved to {html.escape(filename)}
+"""
+        return res, ""
+    except Exception:
+        raise
+    finally:
+        sd_hijack.apply_optimizations()
diff --git a/modules/ui.py b/modules/ui.py
index 15572bb0..57aef6ff 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -21,6 +21,7 @@ import gradio as gr
 import gradio.utils
 import gradio.routes
 
+from modules import sd_hijack
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
@@ -32,6 +33,7 @@ import modules.gfpgan_model
 import modules.codeformer_model
 import modules.styles
 import modules.generation_parameters_copypaste
+import modules.textual_inversion.ui
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -142,8 +144,8 @@ def save_files(js_data, images, index):
     return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
-def wrap_gradio_call(func):
-    def f(*args, **kwargs):
+def wrap_gradio_call(func, extra_outputs=None):
+    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
         run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
         if run_memmon:
             shared.mem_mon.monitor()
@@ -159,7 +161,10 @@ def wrap_gradio_call(func):
             shared.state.job = ""
             shared.state.job_count = 0
 
-            res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+            if extra_outputs_array is None:
+                extra_outputs_array = [None, '']
+
+            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
 
         elapsed = time.perf_counter() - t
 
@@ -179,6 +184,7 @@ def wrap_gradio_call(func):
         res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
 
         shared.state.interrupted = False
+        shared.state.job_count = 0
 
         return tuple(res)
 
@@ -187,7 +193,7 @@ def wrap_gradio_call(func):
 
 def check_progress_call(id_part):
     if shared.state.job_count == 0:
-        return "", gr_show(False), gr_show(False)
+        return "", gr_show(False), gr_show(False), gr_show(False)
 
     progress = 0
 
@@ -219,13 +225,19 @@ def check_progress_call(id_part):
         else:
             preview_visibility = gr_show(True)
 
-    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image
+    if shared.state.textinfo is not None:
+        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
+    else:
+        textinfo_result = gr_show(False)
+
+    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
 
 
 def check_progress_call_initial(id_part):
     shared.state.job_count = -1
     shared.state.current_latent = None
     shared.state.current_image = None
+    shared.state.textinfo = None
 
     return check_progress_call(id_part)
 
@@ -399,13 +411,16 @@ def create_toprow(is_img2img):
     return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste
 
 
-def setup_progressbar(progressbar, preview, id_part):
+def setup_progressbar(progressbar, preview, id_part, textinfo=None):
+    if textinfo is None:
+        textinfo = gr.HTML(visible=False)
+
     check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
     check_progress.click(
         fn=lambda: check_progress_call(id_part),
         show_progress=False,
         inputs=[],
-        outputs=[progressbar, preview, preview],
+        outputs=[progressbar, preview, preview, textinfo],
     )
 
     check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
@@ -413,11 +428,14 @@ def setup_progressbar(progressbar, preview, id_part):
         fn=lambda: check_progress_call_initial(id_part),
         show_progress=False,
         inputs=[],
-        outputs=[progressbar, preview, preview],
+        outputs=[progressbar, preview, preview, textinfo],
     )
 
 
-def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
+def create_ui(wrap_gradio_gpu_call):
+    import modules.img2img
+    import modules.txt2img
+
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
@@ -483,7 +501,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
             txt2img_args = dict(
-                fn=txt2img,
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
                 _js="submit",
                 inputs=[
                     txt2img_prompt,
@@ -675,7 +693,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             )
 
             img2img_args = dict(
-                fn=img2img,
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
                 _js="submit_img2img",
                 inputs=[
                     dummy_component,
@@ -828,7 +846,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
 
         submit.click(
-            fn=run_extras,
+            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
             _js="get_extras_tab_index",
             inputs=[
                 dummy_component,
@@ -878,7 +896,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                     pnginfo_send_to_img2img = gr.Button('Send to img2img')
 
         image.change(
-            fn=wrap_gradio_call(run_pnginfo),
+            fn=wrap_gradio_call(modules.extras.run_pnginfo),
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
@@ -887,7 +905,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
                 gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
-                
+
                 with gr.Row():
                     primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")
                     secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
@@ -896,10 +914,96 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
                 save_as_half = gr.Checkbox(value=False, label="Safe as float16")
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
-            
+
             with gr.Column(variant='panel'):
                 submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
 
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+    with gr.Blocks() as textual_inversion_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column():
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
+
+                    new_embedding_name = gr.Textbox(label="Name")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding = gr.Button(value="Create", variant='primary')
+
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
+                    train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                    learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
+                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+                    steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=1000, precision=0)
+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=1000, precision=0)
+
+                    with gr.Row():
+                        with gr.Column(scale=2):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            with gr.Row():
+                                interrupt_training = gr.Button(value="Interrupt")
+                                train_embedding = gr.Button(value="Train", variant='primary')
+
+            with gr.Column():
+                progressbar = gr.HTML(elem_id="ti_progressbar")
+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+
+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
+                ti_progress = gr.HTML(elem_id="ti_progress", value="")
+                ti_outcome = gr.HTML(elem_id="ti_error", value="")
+                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
+
+        create_embedding.click(
+            fn=modules.textual_inversion.ui.create_embedding,
+            inputs=[
+                new_embedding_name,
+                nvpt,
+            ],
+            outputs=[
+                train_embedding_name,
+                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=[
+                train_embedding_name,
+                learn_rate,
+                dataset_directory,
+                log_directory,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        interrupt_training.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
     def create_setting_component(key):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
@@ -1011,6 +1115,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (textual_inversion_interface, "Textual inversion", "ti"),
         (settings_interface, "Settings", "settings"),
     ]
 
@@ -1044,11 +1149,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         
         def modelmerger(*args):
             try:
-                results = run_modelmerger(*args)
+                results = modules.extras.run_modelmerger(*args)
             except Exception as e:
                 print("Error loading/saving model file:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
-                modules.sd_models.list_models() #To remove the potentially missing models from the list
+                modules.sd_models.list_models()  # to remove the potentially missing models from the list
                 return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
             return results
 
diff --git a/style.css b/style.css
index 79d6bb0d..39586bf1 100644
--- a/style.css
+++ b/style.css
@@ -157,7 +157,7 @@ button{
     max-width: 10em;
 }
 
-#txt2img_preview, #img2img_preview{
+#txt2img_preview, #img2img_preview, #ti_preview{
     position: absolute;
     width: 320px;
     left: 0;
@@ -172,18 +172,18 @@ button{
 }
 
 @media screen and (min-width: 768px) {
-    #txt2img_preview, #img2img_preview {
+    #txt2img_preview, #img2img_preview, #ti_preview {
         position: absolute;
     }
 }
 
 @media screen and (max-width: 767px) {
-    #txt2img_preview, #img2img_preview {
+    #txt2img_preview, #img2img_preview, #ti_preview {
         position: relative;
     }
 }
 
-#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0{
+#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0, #ti_preview div.left-0.top-0{
     display: none;
 }
 
@@ -247,7 +247,7 @@ input[type="range"]{
 #txt2img_negative_prompt, #img2img_negative_prompt{
 }
 
-#txt2img_progressbar, #img2img_progressbar{
+#txt2img_progressbar, #img2img_progressbar, #ti_progressbar{
   position: absolute;
   z-index: 1000;
   right: 0;
diff --git a/textual_inversion_templates/style.txt b/textual_inversion_templates/style.txt
new file mode 100644
index 00000000..15af2d6b
--- /dev/null
+++ b/textual_inversion_templates/style.txt
@@ -0,0 +1,19 @@
+a painting, art by [name]
+a rendering, art by [name]
+a cropped painting, art by [name]
+the painting, art by [name]
+a clean painting, art by [name]
+a dirty painting, art by [name]
+a dark painting, art by [name]
+a picture, art by [name]
+a cool painting, art by [name]
+a close-up painting, art by [name]
+a bright painting, art by [name]
+a cropped painting, art by [name]
+a good painting, art by [name]
+a close-up painting, art by [name]
+a rendition, art by [name]
+a nice painting, art by [name]
+a small painting, art by [name]
+a weird painting, art by [name]
+a large painting, art by [name]
diff --git a/textual_inversion_templates/style_filewords.txt b/textual_inversion_templates/style_filewords.txt
new file mode 100644
index 00000000..b3a8159a
--- /dev/null
+++ b/textual_inversion_templates/style_filewords.txt
@@ -0,0 +1,19 @@
+a painting of [filewords], art by [name]
+a rendering of [filewords], art by [name]
+a cropped painting of [filewords], art by [name]
+the painting of [filewords], art by [name]
+a clean painting of [filewords], art by [name]
+a dirty painting of [filewords], art by [name]
+a dark painting of [filewords], art by [name]
+a picture of [filewords], art by [name]
+a cool painting of [filewords], art by [name]
+a close-up painting of [filewords], art by [name]
+a bright painting of [filewords], art by [name]
+a cropped painting of [filewords], art by [name]
+a good painting of [filewords], art by [name]
+a close-up painting of [filewords], art by [name]
+a rendition of [filewords], art by [name]
+a nice painting of [filewords], art by [name]
+a small painting of [filewords], art by [name]
+a weird painting of [filewords], art by [name]
+a large painting of [filewords], art by [name]
diff --git a/textual_inversion_templates/subject.txt b/textual_inversion_templates/subject.txt
new file mode 100644
index 00000000..79f36aa0
--- /dev/null
+++ b/textual_inversion_templates/subject.txt
@@ -0,0 +1,27 @@
+a photo of a [name]
+a rendering of a [name]
+a cropped photo of the [name]
+the photo of a [name]
+a photo of a clean [name]
+a photo of a dirty [name]
+a dark photo of the [name]
+a photo of my [name]
+a photo of the cool [name]
+a close-up photo of a [name]
+a bright photo of the [name]
+a cropped photo of a [name]
+a photo of the [name]
+a good photo of the [name]
+a photo of one [name]
+a close-up photo of the [name]
+a rendition of the [name]
+a photo of the clean [name]
+a rendition of a [name]
+a photo of a nice [name]
+a good photo of a [name]
+a photo of the nice [name]
+a photo of the small [name]
+a photo of the weird [name]
+a photo of the large [name]
+a photo of a cool [name]
+a photo of a small [name]
diff --git a/textual_inversion_templates/subject_filewords.txt b/textual_inversion_templates/subject_filewords.txt
new file mode 100644
index 00000000..008652a6
--- /dev/null
+++ b/textual_inversion_templates/subject_filewords.txt
@@ -0,0 +1,27 @@
+a photo of a [name], [filewords]
+a rendering of a [name], [filewords]
+a cropped photo of the [name], [filewords]
+the photo of a [name], [filewords]
+a photo of a clean [name], [filewords]
+a photo of a dirty [name], [filewords]
+a dark photo of the [name], [filewords]
+a photo of my [name], [filewords]
+a photo of the cool [name], [filewords]
+a close-up photo of a [name], [filewords]
+a bright photo of the [name], [filewords]
+a cropped photo of a [name], [filewords]
+a photo of the [name], [filewords]
+a good photo of the [name], [filewords]
+a photo of one [name], [filewords]
+a close-up photo of the [name], [filewords]
+a rendition of the [name], [filewords]
+a photo of the clean [name], [filewords]
+a rendition of a [name], [filewords]
+a photo of a nice [name], [filewords]
+a good photo of a [name], [filewords]
+a photo of the nice [name], [filewords]
+a photo of the small [name], [filewords]
+a photo of the weird [name], [filewords]
+a photo of the large [name], [filewords]
+a photo of a cool [name], [filewords]
+a photo of a small [name], [filewords]
diff --git a/webui.py b/webui.py
index b8cccd54..19fdcdd4 100644
--- a/webui.py
+++ b/webui.py
@@ -12,7 +12,6 @@ import modules.bsrgan_model as bsrgan
 import modules.extras
 import modules.face_restoration
 import modules.gfpgan_model as gfpgan
-import modules.img2img
 import modules.ldsr_model as ldsr
 import modules.lowvram
 import modules.realesrgan_model as realesrgan
@@ -21,7 +20,6 @@ import modules.sd_hijack
 import modules.sd_models
 import modules.shared as shared
 import modules.swinir_model as swinir
-import modules.txt2img
 import modules.ui
 from modules import modelloader
 from modules.paths import script_path
@@ -46,7 +44,7 @@ def wrap_queued_call(func):
     return f
 
 
-def wrap_gradio_gpu_call(func):
+def wrap_gradio_gpu_call(func, extra_outputs=None):
     def f(*args, **kwargs):
         devices.torch_gc()
 
@@ -58,6 +56,7 @@ def wrap_gradio_gpu_call(func):
         shared.state.current_image = None
         shared.state.current_image_sampling_step = 0
         shared.state.interrupted = False
+        shared.state.textinfo = None
 
         with queue_lock:
             res = func(*args, **kwargs)
@@ -69,7 +68,7 @@ def wrap_gradio_gpu_call(func):
 
         return res
 
-    return modules.ui.wrap_gradio_call(f)
+    return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
 
 
 modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
@@ -86,13 +85,7 @@ def webui():
 
     signal.signal(signal.SIGINT, sigint_handler)
 
-    demo = modules.ui.create_ui(
-        txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
-        img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
-        run_extras=wrap_gradio_gpu_call(modules.extras.run_extras),
-        run_pnginfo=modules.extras.run_pnginfo,
-        run_modelmerger=modules.extras.run_modelmerger
-    )
+    demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
 
     demo.launch(
         share=cmd_opts.share,
-- 
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