Merge branch 'release_candidate'

66 files changed, 1561 insertions, 342 deletions

diff --git a/.gitignore b/.gitignore
index 0b1d17ca..7328401f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -32,4 +32,5 @@ notification.mp3
 /extensions
 /test/stdout.txt
 /test/stderr.txt
-/cache.json
+/cache.json*
+/config_states/
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 00000000..73b25eaf
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,58 @@
+## Upcoming version:

+### Features:

+ * switch to torch 2.0.0 (except for AMD GPUs)

+ * visual improvements to custom code scripts

+ * add filename patterns: [clip_skip], [hasprompt<>], [batch_number], [generation_number]

+ * add support for saving init images in img2img, and record their hashes in infotext for reproducability

+ * automatically select current word when adjusting weight with ctrl+up/down

+ * add dropdowns for X/Y/Z plot

+ * setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs

+ * support Gradio's theme API

+ * use TCMalloc on Linux by default; possible fix for memory leaks

+ * (optimization) option to remove negative conditioning at low sigma values #9177

+ * embed model merge metadata in .safetensors file

+ * extension settings backup/restore feature #9169

+ * add "resize by" and "resize to" tabs to img2img

+ * add option "keep original size" to textual inversion images preprocess

+ * image viewer scrolling via analog stick

+ * button to restore the progress from session lost / tab reload

+

+### Minor:

+ * gradio bumped to 3.28.1

+ * in extra tab, change extras "scale to" to sliders

+ * add labels to tool buttons to make it possible to hide them

+ * add tiled inference support for ScuNET

+ * add branch support for extension installation

+ * change linux installation script to insall into current directory rather than /home/username

+ * sort textual inversion embeddings by name (case insensitive)

+ * allow styles.csv to be symlinked or mounted in docker

+ * remove the "do not add watermark to images" option

+ * make selected tab configurable with UI config

+ * extra networks UI in now fixed height and scrollable

+ * add disable_tls_verify arg for use with self-signed certs

+

+### Extensions:

+ * Add reload callback

+ * add is_hr_pass field for processing

+

+### Bug Fixes:

+ * fix broken batch image processing on 'Extras/Batch Process' tab

+ * add "None" option to extra networks dropdowns

+ * fix FileExistsError for CLIP Interrogator

+ * fix /sdapi/v1/txt2img endpoint not working on Linux #9319

+ * fix disappearing live previews and progressbar during slow tasks

+ * fix fullscreen image view not working properly in some cases

+ * prevent alwayson_scripts args param resizing script_arg list when they are inserted in it

+ * fix prompt schedule for second order samplers

+ * fix image mask/composite for weird resolutions #9628

+ * use correct images for previews when using AND (see #9491)

+ * one broken image in img2img batch won't stop all processing

+ * fix image orientation bug in train/preprocess

+ * fix Ngrok recreating tunnels every reload

+ * fix --realesrgan-models-path and --ldsr-models-path not working

+ * fix --skip-install not working

+ * outpainting Mk2 & Poorman should use the SAMPLE file format to save images, not GRID file format

+ * do not fail all Loras if some have failed to load when making a picture

+

+## Before versions:

+  * everything

diff --git a/README.md b/README.md
index b67e2296..67a1a83a 100644
--- a/README.md
+++ b/README.md
@@ -100,7 +100,7 @@ Alternatively, use online services (like Google Colab):
 - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)

 

 ### Automatic Installation on Windows

-1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH".

+1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".

 2. Install [git](https://git-scm.com/download/win).

 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.

 4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.

@@ -115,11 +115,12 @@ sudo dnf install wget git python3
 # Arch-based:

 sudo pacman -S wget git python3

 ```

-2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run:

+2. Navigate to the directory you would like the webui to be installed and execute the following command:

 ```bash

 bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)

 ```

 3. Run `webui.sh`.

+4. Check `webui-user.sh` for options.

 ### Installation on Apple Silicon

 

 Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).

@@ -158,4 +159,4 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - Security advice - RyotaK

 - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC

 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.

-- (You)
\ No newline at end of file
+- (You)

diff --git a/environment-wsl2.yaml b/environment-wsl2.yaml
index f8872750..0c4ae680 100644
--- a/environment-wsl2.yaml
+++ b/environment-wsl2.yaml
@@ -4,8 +4,8 @@ channels:
   - defaults
 dependencies:
   - python=3.10
-  - pip=22.2.2
-  - cudatoolkit=11.3
-  - pytorch=1.12.1
-  - torchvision=0.13.1
-  - numpy=1.23.1
\ No newline at end of file
+  - pip=23.0
+  - cudatoolkit=11.8
+  - pytorch=2.0
+  - torchvision=0.15
+  - numpy=1.23
diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py
index b8cff29b..da19cff1 100644
--- a/extensions-builtin/LDSR/scripts/ldsr_model.py
+++ b/extensions-builtin/LDSR/scripts/ldsr_model.py
@@ -25,22 +25,28 @@ class UpscalerLDSR(Upscaler):
         yaml_path = os.path.join(self.model_path, "project.yaml")
         old_model_path = os.path.join(self.model_path, "model.pth")
         new_model_path = os.path.join(self.model_path, "model.ckpt")
-        safetensors_model_path = os.path.join(self.model_path, "model.safetensors")
+
+        local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
+        local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
+        local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
+        local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
+
         if os.path.exists(yaml_path):
             statinfo = os.stat(yaml_path)
             if statinfo.st_size >= 10485760:
                 print("Removing invalid LDSR YAML file.")
                 os.remove(yaml_path)
+
         if os.path.exists(old_model_path):
             print("Renaming model from model.pth to model.ckpt")
             os.rename(old_model_path, new_model_path)
-        if os.path.exists(safetensors_model_path):
-            model = safetensors_model_path
+
+        if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
+            model = local_safetensors_path
         else:
-            model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
-                                       file_name="model.ckpt", progress=True)
-        yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
-                                  file_name="project.yaml", progress=True)
+            model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="model.ckpt", progress=True)
+
+        yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_path, file_name="project.yaml", progress=True)
 
         try:
             return LDSR(model, yaml)
diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py
index 6be6ef73..45f899fc 100644
--- a/extensions-builtin/Lora/extra_networks_lora.py
+++ b/extensions-builtin/Lora/extra_networks_lora.py
@@ -8,7 +8,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def activate(self, p, params_list):

         additional = shared.opts.sd_lora

 

-        if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:

+        if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:

             p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]

             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))

 

diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py
index d3eb0d3b..6f246921 100644
--- a/extensions-builtin/Lora/lora.py
+++ b/extensions-builtin/Lora/lora.py
@@ -211,7 +211,11 @@ def load_loras(names, multipliers=None):
         lora_on_disk = loras_on_disk[i]

         if lora_on_disk is not None:

             if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime:

-                lora = load_lora(name, lora_on_disk.filename)

+                try:

+                    lora = load_lora(name, lora_on_disk.filename)

+                except Exception as e:

+                    errors.display(e, f"loading Lora {lora_on_disk.filename}")

+                    continue

 

         if lora is None:

             print(f"Couldn't find Lora with name {name}")

diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py
index 0adab225..3fc38ab9 100644
--- a/extensions-builtin/Lora/scripts/lora_script.py
+++ b/extensions-builtin/Lora/scripts/lora_script.py
@@ -52,5 +52,5 @@ script_callbacks.on_before_ui(before_ui)
 

 

 shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {

-    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),

+    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),

 }))

diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py
index e0fbf3a3..c7fd5739 100644
--- a/extensions-builtin/ScuNET/scripts/scunet_model.py
+++ b/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -5,11 +5,15 @@ import traceback
 import PIL.Image
 import numpy as np
 import torch
+from tqdm import tqdm
+
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
 from modules import devices, modelloader
 from scunet_model_arch import SCUNet as net
+from modules.shared import opts
+from modules import images
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
             scalers.append(scaler_data2)
         self.scalers = scalers
 
-    def do_upscale(self, img: PIL.Image, selected_file):
+    @staticmethod
+    @torch.no_grad()
+    def tiled_inference(img, model):
+        # test the image tile by tile
+        h, w = img.shape[2:]
+        tile = opts.SCUNET_tile
+        tile_overlap = opts.SCUNET_tile_overlap
+        if tile == 0:
+            return model(img)
+
+        device = devices.get_device_for('scunet')
+        assert tile % 8 == 0, "tile size should be a multiple of window_size"
+        sf = 1
+
+        stride = tile - tile_overlap
+        h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+        w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+        E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
+        W = torch.zeros_like(E, dtype=devices.dtype, device=device)
+
+        with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
+            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]
+
+                    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
+                    ].add_(out_patch)
+                    W[
+                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                    ].add_(out_patch_mask)
+                    pbar.update(1)
+        output = E.div_(W)
+
+        return output
+
+    def do_upscale(self, img: PIL.Image.Image, selected_file):
+
         torch.cuda.empty_cache()
 
         model = self.load_model(selected_file)
         if model is None:
+            print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
             return img
 
         device = devices.get_device_for('scunet')
-        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(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]
+        tile = opts.SCUNET_tile
+        h, w = img.height, img.width
+        np_img = np.array(img)
+        np_img = np_img[:, :, ::-1]  # RGB to BGR
+        np_img = np_img.transpose((2, 0, 1)) / 255  # HWC to CHW
+        torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device)  # type: ignore
+
+        if tile > h or tile > w:
+            _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
+            _img[:, :, :h, :w] = torch_img # pad image
+            torch_img = _img
+
+        torch_output = self.tiled_inference(torch_img, model).squeeze(0)
+        torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
+        np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
+        del torch_img, torch_output
         torch.cuda.empty_cache()
-        return PIL.Image.fromarray(output, 'RGB')
+
+        output = np_output.transpose((1, 2, 0))  # CHW to HWC
+        output = output[:, :, ::-1]  # BGR to RGB
+        return PIL.Image.fromarray((output * 255).astype(np.uint8))
 
     def load_model(self, path: str):
         device = devices.get_device_for('scunet')
@@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         model = model.to(device)
 
         return model
-
diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
index f0918e26..5c7a836a 100644
--- a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
+++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
@@ -1,103 +1,42 @@
 // Stable Diffusion WebUI - Bracket checker
-// Version 1.0
-// By Hingashi no Florin/Bwin4L
+// By Hingashi no Florin/Bwin4L & @akx
 // Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
 // If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
 
-function checkBrackets(evt, textArea, counterElt) {
-  errorStringParen  = '(...) - Different number of opening and closing parentheses detected.\n';
-  errorStringSquare = '[...] - Different number of opening and closing square brackets detected.\n';
-  errorStringCurly  = '{...} - Different number of opening and closing curly brackets detected.\n';
-
-  openBracketRegExp = /\(/g;
-  closeBracketRegExp = /\)/g;
-
-  openSquareBracketRegExp = /\[/g;
-  closeSquareBracketRegExp = /\]/g;
-
-  openCurlyBracketRegExp = /\{/g;
-  closeCurlyBracketRegExp = /\}/g;
-
-  totalOpenBracketMatches = 0;
-  totalCloseBracketMatches = 0;
-  totalOpenSquareBracketMatches = 0;
-  totalCloseSquareBracketMatches = 0;
-  totalOpenCurlyBracketMatches = 0;
-  totalCloseCurlyBracketMatches = 0;
-
-  openBracketMatches = textArea.value.match(openBracketRegExp);
-  if(openBracketMatches) {
-    totalOpenBracketMatches = openBracketMatches.length;
-  }
-
-  closeBracketMatches = textArea.value.match(closeBracketRegExp);
-  if(closeBracketMatches) {
-    totalCloseBracketMatches = closeBracketMatches.length;
-  }
-
-  openSquareBracketMatches = textArea.value.match(openSquareBracketRegExp);
-  if(openSquareBracketMatches) {
-    totalOpenSquareBracketMatches = openSquareBracketMatches.length;
-  }
-
-  closeSquareBracketMatches = textArea.value.match(closeSquareBracketRegExp);
-  if(closeSquareBracketMatches) {
-    totalCloseSquareBracketMatches = closeSquareBracketMatches.length;
-  }
-
-  openCurlyBracketMatches = textArea.value.match(openCurlyBracketRegExp);
-  if(openCurlyBracketMatches) {
-    totalOpenCurlyBracketMatches = openCurlyBracketMatches.length;
-  }
-
-  closeCurlyBracketMatches = textArea.value.match(closeCurlyBracketRegExp);
-  if(closeCurlyBracketMatches) {
-    totalCloseCurlyBracketMatches = closeCurlyBracketMatches.length;
-  }
-
-  if(totalOpenBracketMatches != totalCloseBracketMatches) {
-    if(!counterElt.title.includes(errorStringParen)) {
-      counterElt.title += errorStringParen;
-    }
-  } else {
-    counterElt.title = counterElt.title.replace(errorStringParen, '');
-  }
-
-  if(totalOpenSquareBracketMatches != totalCloseSquareBracketMatches) {
-    if(!counterElt.title.includes(errorStringSquare)) {
-      counterElt.title += errorStringSquare;
-    }
-  } else {
-    counterElt.title = counterElt.title.replace(errorStringSquare, '');
-  }
-
-  if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) {
-    if(!counterElt.title.includes(errorStringCurly)) {
-      counterElt.title += errorStringCurly;
+function checkBrackets(textArea, counterElt) {
+  var counts = {};
+  (textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => {
+    counts[bracket] = (counts[bracket] || 0) + 1;
+  });
+  var errors = [];
+