Merge branch 'dev' into feature/restore-progress

.gitignore

@@ -32,4 +32,5 @@ notification.mp3
 /extensions
 /test/stdout.txt
 /test/stderr.txt
-/cache.json
+/cache.json*
+/config_states/

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)

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

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)

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]))
 

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}")

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),
 }))

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
-

extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js

 // 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 = [];
+
+  function checkPair(open, close, kind) {
+    if (counts[open] !== counts[close]) {
+      errors.push(
+        `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
+      );
     }
-  } else {
-    counterElt.title = counterElt.title.replace(errorStringCurly, '');
   }
 
-  if(counterElt.title != '') {
-    counterElt.classList.add('error');
-  } else {
-    counterElt.classList.remove('error');
-  }
+  checkPair('(', ')', 'round brackets');
+  checkPair('[', ']', 'square brackets');
+  checkPair('{', '}', 'curly brackets');
+  counterElt.title = errors.join('\n');
+  counterElt.classList.toggle('error', errors.length !== 0);
 }
 
-function setupBracketChecking(id_prompt, id_counter){
-    var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
-    var counter = gradioApp().getElementById(id_counter)
+function setupBracketChecking(id_prompt, id_counter) {
+  var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
+  var counter = gradioApp().getElementById(id_counter)
 
-    textarea.addEventListener("input", function(evt){
-        checkBrackets(evt, textarea, counter)
-    });
+  if (textarea && counter) {
+    textarea.addEventListener("input", () => checkBrackets(textarea, counter));
+  }
 }
 
-onUiLoaded(function(){
-    setupBracketChecking('txt2img_prompt', 'txt2img_token_counter')
-    setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter')
-    setupBracketChecking('img2img_prompt', 'img2img_token_counter')
-    setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter')
-})
\ No newline at end of file
+onUiLoaded(function () {
+  setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
+  setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
+  setupBracketChecking('img2img_prompt', 'img2img_token_counter');
+  setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
+});

javascript/contextMenus.js

@@ -161,14 +161,6 @@ addContextMenuEventListener = initResponse[2];
   appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
   appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
 
-  appendContextMenuOption('#roll','Roll three',
-    function(){
-      let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
-      setTimeout(function(){rollbutton.click()},100)
-      setTimeout(function(){rollbutton.click()},200)
-      setTimeout(function(){rollbutton.click()},300)
-    }
-  )
 })();
 //End example Context Menu Items
 

javascript/edit-attention.js

@@ -17,7 +17,7 @@ function keyupEditAttention(event){
 		// Find opening parenthesis around current cursor
 		const before = text.substring(0, selectionStart);
 		let beforeParen = before.lastIndexOf(OPEN);
-		if (beforeParen == -1) return  false;
+		if (beforeParen == -1) return false;
 		let beforeParenClose = before.lastIndexOf(CLOSE);
 		while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
 			beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
@@ -27,7 +27,7 @@ function keyupEditAttention(event){
 		// Find closing parenthesis around current cursor
 		const after = text.substring(selectionStart);
 		let afterParen = after.indexOf(CLOSE);
-		if (afterParen == -1) return  false;
+		if (afterParen == -1) return false;
 		let afterParenOpen = after.indexOf(OPEN);
 		while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
 			afterParen = after.indexOf(CLOSE, afterParen + 1);
@@ -43,10 +43,28 @@ function keyupEditAttention(event){
 		target.setSelectionRange(selectionStart, selectionEnd);
 		return true;
     }
+    
+    function selectCurrentWord(){
+        if (selectionStart !== selectionEnd) return false;
+        const delimiters = opts.keyedit_delimiters + " \r\n\t";
+        
+        // seek backward until to find beggining
+        while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
+            selectionStart--;
+        }
+        
+        // seek forward to find end
+        while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
+            selectionEnd++;
+        }
 
-	// If the user hasn't selected anything, let's select their current parenthesis block
-    if(! selectCurrentParenthesisBlock('<', '>')){
-        selectCurrentParenthesisBlock('(', ')')
+        target.setSelectionRange(selectionStart, selectionEnd);
+        return true;
+    }
+
+	// If the user hasn't selected anything, let's select their current parenthesis block or word
+    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
+        selectCurrentWord();
     }
 
 	event.preventDefault();
@@ -81,7 +99,13 @@ function keyupEditAttention(event){
 	weight = parseFloat(weight.toPrecision(12));
 	if(String(weight).length == 1) weight += ".0"
 
-	text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+    if (closeCharacter == ')' && weight == 1) {
+        text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
+        selectionStart--;
+        selectionEnd--;
+    } else {
+        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+    }
 
 	target.focus();
 	target.value = text;
@@ -93,4 +117,4 @@ function keyupEditAttention(event){
 
 addEventListener('keydown', (event) => {
     keyupEditAttention(event);
-});
\ No newline at end of file
+});

javascript/extensions.js

@@ -47,3 +47,25 @@ function install_extension_from_index(button, url){
 
     gradioApp().querySelector('#install_extension_button').click()
 }
+
+function config_state_confirm_restore(_, config_state_name, config_restore_type) {
+    if (config_state_name == "Current") {
+        return [false, config_state_name, config_restore_type];
+    }
+    let restored = "";
+    if (config_restore_type == "extensions") {
+        restored = "all saved extension versions";
+    } else if (config_restore_type == "webui") {
+        restored = "the webui version";
+    } else {
+        restored = "the webui version and all saved extension versions";
+    }
+    let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + ".");
+    if (confirmed) {
+        restart_reload();
+        gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
+            x.innerHTML = "Loading..."
+        })
+    }
+    return [confirmed, config_state_name, config_restore_type];
+}

javascript/generationParams.js

@@ -16,9 +16,9 @@ onUiUpdate(function(){
 
 let modalObserver = new MutationObserver(function(mutations) {
 	mutations.forEach(function(mutationRecord) {
-		let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
-		if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
-			gradioApp().getElementById(selectedTab+"_generation_info_button").click()
+		let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText
+		if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img'))
+			gradioApp().getElementById(selectedTab+"_generation_info_button")?.click()
 	});
 });
 

javascript/hints.js

@@ -65,8 +65,8 @@ titles = {
 
     "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
 
-    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
-    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
+    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [clip_skip], [batch_number], [generation_number], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
+    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [clip_skip], [batch_number], [generation_number], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
     "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
 
     "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
@@ -85,7 +85,6 @@ titles = {
     "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).",
 
     "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
-    "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
 
     "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
     "Filename join string": "This string will be used to join split words into a single line if the option above is enabled.",
@@ -111,7 +110,8 @@ titles = {
     "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
     "Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.",
     "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
-    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited."
+    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.",
+    "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
 }
 
 

javascript/imageviewer.js

@@ -251,8 +251,11 @@ document.addEventListener("DOMContentLoaded", function() {
 
     modal.appendChild(modalNext)
 
-    gradioApp().appendChild(modal)
-
+    try {
+		gradioApp().appendChild(modal);
+	} catch (e) {
+		gradioApp().body.appendChild(modal);
+	}
 
     document.body.appendChild(modal);
 

javascript/imageviewerGamepad.js

+    let delay = 350//ms
+    window.addEventListener('gamepadconnected', (e) => {
+        console.log("Gamepad connected!")
+        const gamepad = e.gamepad;
+        setInterval(() => {
+            const xValue = gamepad.axes[0].toFixed(2);
+            if (xValue < -0.3) {
+                modalPrevImage(e);
+            } else if (xValue > 0.3) {
+                modalNextImage(e);
+            }
+
+        }, delay);
+    });
+
+
+    /*
+    Primarily for vr controller type pointer devices.
+    I use the wheel event because there's currently no way to do it properly with web xr.
+     */
+
+    let isScrolling = false;
+    window.addEventListener('wheel', (e) => {
+        if (isScrolling) return;
+        isScrolling = true;
+
+        if (e.deltaX <= -0.6) {
+            modalPrevImage(e);
+        } else if (e.deltaX >= 0.6) {
+            modalNextImage(e);
+        }
+
+        setTimeout(() => {
+            isScrolling = false;
+        }, delay);
+    });
\ No newline at end of file

javascript/progressbar.js

@@ -138,7 +138,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                 return
             }
 
-            if(elapsedFromStart > 5 && !res.queued && !res.active){
+            if(elapsedFromStart > 40 && !res.queued && !res.active){
                 removeProgressBar()
                 return
             }

javascript/ui.js

@@ -386,4 +386,21 @@ function restoreProgress (task_tag) {
   res[0] = 0
   return res
 
-}
\ No newline at end of file
+}
+
+function currentImg2imgSourceResolution(_, _, scaleBy){
+    var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img')
+    return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy]
+}
+
+function updateImg2imgResizeToTextAfterChangingImage(){
+    // At the time this is called from gradio, the image has no yet been replaced.
+    // There may be a better solution, but this is simple and straightforward so I'm going with it.
+
+    setTimeout(function() {
+        gradioApp().getElementById('img2img_update_resize_to').click()
+    }, 500);
+
+    return []
+}
+

launch.py

@@ -19,7 +19,6 @@ python = sys.executable
 git = os.environ.get('GIT', "git")
 index_url = os.environ.get('INDEX_URL', "")
 stored_commit_hash = None
-skip_install = False
 dir_repos = "repositories"
 
 if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
@@ -49,7 +48,7 @@ or any other error regarding unsuccessful package (library) installation,
 please downgrade (or upgrade) to the latest version of 3.10 Python
 and delete current Python and "venv" folder in WebUI's directory.
 
-You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3109/
+You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3106/
 
 {"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
 
@@ -121,12 +120,12 @@ def run_python(code, desc=None, errdesc=None):
     return run(f'"{python}" -c "{code}"', desc, errdesc)
 
 
-def run_pip(args, desc=None):
-    if skip_install:
+def run_pip(command, desc=None, live=False):
+    if args.skip_install:
         return
 
     index_url_line = f' --index-url {index_url}' if index_url != '' else ''
-    return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+    return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
 
 
 def check_run_python(code):
@@ -223,12 +222,10 @@ def run_extensions_installers(settings_file):
 
 
 def prepare_environment():
-    global skip_install
-
-    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117")
+    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.0 torchvision==0.15.1 --index-url https://download.pytorch.org/whl/cu118")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
 
-    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
+    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17')
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
     openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
@@ -271,7 +268,7 @@ def prepare_environment():
     if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
         if platform.system() == "Windows":
             if platform.python_version().startswith("3.10"):
-                run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+                run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
             else:
                 print("Installation of xformers is not supported in this version of Python.")
                 print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
@@ -296,7 +293,7 @@ def prepare_environment():
 
     if not os.path.isfile(requirements_file):
         requirements_file = os.path.join(script_path, requirements_file)
-    run_pip(f"install -r \"{requirements_file}\"", "requirements for Web UI")
+    run_pip(f"install -r \"{requirements_file}\"", "requirements")
 
     run_extensions_installers(settings_file=args.ui_settings_file)
 

modules/api/api.py

@@ -6,7 +6,6 @@ import uvicorn
 import gradio as gr
 from threading import Lock
 from io import BytesIO
-from gradio.processing_utils import decode_base64_to_file
 from fastapi import APIRouter, Depends, FastAPI, Request, Response
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from fastapi.exceptions import HTTPException
@@ -131,8 +130,8 @@ def api_middleware(app: FastAPI):
             "body": vars(e).get('body', ''),
             "errors": str(e),
         }
-        print(f"API error: {request.method}: {request.url} {err}")
         if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions
+            print(f"API error: {request.method}: {request.url} {err}")
             if rich_available:
                 console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200]))
             else:
@@ -272,7 +271,9 @@ class Api:
                     raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
                 # always on script with no arg should always run so you don't really need to add them to the requests
                 if "args" in request.alwayson_scripts[alwayson_script_name]:
-                    script_args[alwayson_script.args_from:alwayson_script.args_to] = request.alwayson_scripts[alwayson_script_name]["args"]
+                    # min between arg length in scriptrunner and arg length in the request
+                    for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))):
+                        script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
         return script_args
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
@@ -395,16 +396,11 @@ class Api:
     def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
         reqDict = setUpscalers(req)
 
-        def prepareFiles(file):
-            file = decode_base64_to_file(file.data, file_path=file.name)
-            file.orig_name = file.name
-            return file
-
-        reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
-        reqDict.pop('imageList')
+        image_list = reqDict.pop('imageList', [])
+        image_folder = [decode_base64_to_image(x.data) for x in image_list]
 
         with self.queue_lock:
-            result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
+            result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)
 
         return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
 

modules/cmd_args.py

@@ -95,6 +95,7 @@ parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(
 parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
 parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
 parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
+parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
 parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")

modules/config_states.py

+"""
+Supports saving and restoring webui and extensions from a known working set of commits
+"""
+
+import os
+import sys
+import traceback
+import json
+import time
+import tqdm
+
+from datetime import datetime
+from collections import OrderedDict
+import git
+
+from modules import shared, extensions
+from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path, config_states_dir
+
+
+all_config_states = OrderedDict()
+
+
+def list_config_states():
+    global all_config_states
+
+    all_config_states.clear()
+    os.makedirs(config_states_dir, exist_ok=True)
+
+    config_states = []
+    for filename in os.listdir(config_states_dir):
+        if filename.endswith(".json"):
+            path = os.path.join(config_states_dir, filename)
+            with open(path, "r", encoding="utf-8") as f:
+                j = json.load(f)
+                j["filepath"] = path
+                config_states.append(j)
+
+    config_states = list(sorted(config_states, key=lambda cs: cs["created_at"], reverse=True))
+
+    for cs in config_states:
+        timestamp = time.asctime(time.gmtime(cs["created_at"]))
+        name = cs.get("name", "Config")
+        full_name = f"{name}: {timestamp}"
+        all_config_states[full_name] = cs
+
+    return all_config_states
+
+
+def get_webui_config():
+    webui_repo = None
+
+    try:
+        if os.path.exists(os.path.join(script_path, ".git")):
+            webui_repo = git.Repo(script_path)
+    except Exception:
+        print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+    webui_remote = None
+    webui_commit_hash = None
+    webui_commit_date = None
+    webui_branch = None
+    if webui_repo and not webui_repo.bare:
+        try:
+            webui_remote = next(webui_repo.remote().urls, None)
+            head = webui_repo.head.commit
+            webui_commit_date = webui_repo.head.commit.committed_date
+            webui_commit_hash = head.hexsha
+            webui_branch = webui_repo.active_branch.name
+
+        except Exception:
+            webui_remote = None
+
+    return {
+        "remote": webui_remote,
+        "commit_hash": webui_commit_hash,
+        "commit_date": webui_commit_date,
+        "branch": webui_branch,
+    }
+
+
+def get_extension_config():
+    ext_config = {}
+
+    for ext in extensions.extensions:
+        entry = {
+            "name": ext.name,
+            "path": ext.path,
+            "enabled": ext.enabled,
+            "is_builtin": ext.is_builtin,
+            "remote": ext.remote,
+            "commit_hash": ext.commit_hash,
+            "commit_date": ext.commit_date,
+            "branch": ext.branch,
+            "have_info_from_repo": ext.have_info_from_repo
+        }
+
+        ext_config[ext.name] = entry
+
+    return ext_config
+
+
+def get_config():
+    creation_time = datetime.now().timestamp()
+    webui_config = get_webui_config()
+    ext_config = get_extension_config()
+
+    return {
+        "created_at": creation_time,
+        "webui": webui_config,
+        "extensions": ext_config
+    }
+
+
+def restore_webui_config(config):
+    print("* Restoring webui state...")
+
+    if "webui" not in config:
+        print("Error: No webui data saved to config")
+        return
+
+    webui_config = config["webui"]
+
+    if "commit_hash" not in webui_config:
+        print("Error: No commit saved to webui config")
+        return
+
+    webui_commit_hash = webui_config.get("commit_hash", None)
+    webui_repo = None
+
+    try:
+        if os.path.exists(os.path.join(script_path, ".git")):
+            webui_repo = git.Repo(script_path)
+    except Exception:
+        print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+        return
+
+    try:
+        webui_repo.git.fetch(all=True)
+        webui_repo.git.reset(webui_commit_hash, hard=True)
+        print(f"* Restored webui to commit {webui_commit_hash}.")
+    except Exception:
+        print(f"Error restoring webui to commit {webui_commit_hash}:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+
+def restore_extension_config(config):
+    print("* Restoring extension state...")
+
+    if "extensions" not in config:
+        print("Error: No extension data saved to config")
+        return
+
+    ext_config = config["extensions"]
+
+    results = []
+    disabled = []
+
+    for ext in tqdm.tqdm(extensions.extensions):
+        if ext.is_builtin:
+            continue
+
+        ext.read_info_from_repo()
+        current_commit = ext.commit_hash
+
+        if ext.name not in ext_config:
+            ext.disabled = True
+            disabled.append(ext.name)
+            results.append((ext, current_commit[:8], False, "Saved extension state not found in config, marking as disabled"))
+            continue
+
+        entry = ext_config[ext.name]
+
+        if "commit_hash" in entry and entry["commit_hash"]:
+            try:
+                ext.fetch_and_reset_hard(entry["commit_hash"])
+                ext.read_info_from_repo()
+                if current_commit != entry["commit_hash"]:
+                    results.append((ext, current_commit[:8], True, entry["commit_hash"][:8]))
+            except Exception as ex:
+                results.append((ext, current_commit[:8], False, ex))
+        else:
+            results.append((ext, current_commit[:8], False, "No commit hash found in config"))
+
+        if not entry.get("enabled", False):
+            ext.disabled = True
+            disabled.append(ext.name)
+        else:
+            ext.disabled = False
+
+    shared.opts.disabled_extensions = disabled
+    shared.opts.save(shared.config_filename)
+
+    print("* Finished restoring extensions. Results:")
+    for ext, prev_commit, success, result in results:
+        if success:
+            print(f"  + {ext.name}: {prev_commit} -> {result}")
+        else:
+            print(f"  ! {ext.name}: FAILURE ({result})")

modules/devices.py

@@ -92,14 +92,18 @@ def cond_cast_float(input):
 
 
 def randn(seed, shape):
+    from modules.shared import opts
+
     torch.manual_seed(seed)
-    if device.type == 'mps':
+    if opts.randn_source == "CPU" or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 
 
 def randn_without_seed(shape):
-    if device.type == 'mps':
+    from modules.shared import opts
+
+    if opts.randn_source == "CPU" or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 

modules/extensions.py

@@ -3,10 +3,11 @@ import sys
 import traceback
 
 import time
+from datetime import datetime
 import git
 
 from modules import shared
-from modules.paths_internal import extensions_dir, extensions_builtin_dir
+from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path
 
 extensions = []
 
@@ -31,12 +32,15 @@ class Extension:
         self.status = ''
         self.can_update = False
         self.is_builtin = is_builtin
+        self.commit_hash = ''
+        self.commit_date = None
         self.version = ''
+        self.branch = None
         self.remote = None
         self.have_info_from_repo = False
 
     def read_info_from_repo(self):
-        if self.have_info_from_repo:
+        if self.is_builtin or self.have_info_from_repo:
             return
 
         self.have_info_from_repo = True
@@ -56,10 +60,15 @@ class Extension:
                 self.status = 'unknown'
                 self.remote = next(repo.remote().urls, None)
                 head = repo.head.commit
-                ts = time.asctime(time.gmtime(repo.head.commit.committed_date))
-                self.version = f'{head.hexsha[:8]} ({ts})'
-
-            except Exception:
+                self.commit_date = repo.head.commit.committed_date
+                ts = time.asctime(time.gmtime(self.commit_date))
+                if repo.active_branch:
+                    self.branch = repo.active_branch.name
+                self.commit_hash = head.hexsha
+                self.version = f'{self.commit_hash[:8]} ({ts})'
+
+            except Exception as ex:
+                print(f"Failed reading extension data from Git repository ({self.name}): {ex}", file=sys.stderr)
                 self.remote = None
 
     def list_files(self, subdir, extension):
@@ -82,18 +91,30 @@ class Extension:
         for fetch in repo.remote().fetch(dry_run=True):
             if fetch.flags != fetch.HEAD_UPTODATE:
                 self.can_update = True
-                self.status = "behind"
+                self.status = "new commits"
+                return
+
+        try:
+            origin = repo.rev_parse('origin')
+            if repo.head.commit != origin:
+                self.can_update = True
+                self.status = "behind HEAD"
                 return
+        except Exception:
+            self.can_update = False
+            self.status = "unknown (remote error)"
+            return
 
         self.can_update = False
         self.status = "latest"
 
-    def fetch_and_reset_hard(self):
+    def fetch_and_reset_hard(self, commit='origin'):
         repo = git.Repo(self.path)
         # Fix: `error: Your local changes to the following files would be overwritten by merge`,
         # because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
         repo.git.fetch(all=True)
-        repo.git.reset('origin', hard=True)
+        repo.git.reset(commit, hard=True)
+        self.have_info_from_repo = False
 
 
 def list_extensions():

modules/extra_networks_hypernet.py

@@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
     def activate(self, p, params_list):
         additional = shared.opts.sd_hypernetwork
 
-        if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+        if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
             p.all_prompts = [x + f"<hypernet:{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]))
 

modules/extras.py

 import os
 import re
 import shutil
+import json
 
 
 import torch
@@ -71,7 +72,7 @@ def to_half(tensor, enable):
     return tensor
 
 
-def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights):
+def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
     shared.state.begin()
     shared.state.job = 'model-merge'
 
@@ -241,13 +242,54 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
     shared.state.textinfo = "Saving"
     print(f"Saving to {output_modelname}...")
 
+    metadata = {"format": "pt", "sd_merge_models": {}, "sd_merge_recipe": None}
+
+    if save_metadata:
+        merge_recipe = {
+            "type": "webui", # indicate this model was merged with webui's built-in merger
+            "primary_model_hash": primary_model_info.sha256,
+            "secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None,
+            "tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None,
+            "interp_method": interp_method,
+            "multiplier": multiplier,
+            "save_as_half": save_as_half,
+            "custom_name": custom_name,
+            "config_source": config_source,
+            "bake_in_vae": bake_in_vae,
+            "discard_weights": discard_weights,
+            "is_inpainting": result_is_inpainting_model,
+            "is_instruct_pix2pix": result_is_instruct_pix2pix_model
+        }
+        metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
+
+        def add_model_metadata(checkpoint_info):
+            checkpoint_info.calculate_shorthash()
+            metadata["sd_merge_models"][checkpoint_info.sha256] = {
+                "name": checkpoint_info.name,
+                "legacy_hash": checkpoint_info.hash,
+                "sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
+            }
+
+            metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
+
+        add_model_metadata(primary_model_info)
+        if secondary_model_info:
+            add_model_metadata(secondary_model_info)
+        if tertiary_model_info:
+            add_model_metadata(tertiary_model_info)
+
+        metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
+
     _, extension = os.path.splitext(output_modelname)
     if extension.lower() == ".safetensors":
-        safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
+        safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
     else:
         torch.save(theta_0, output_modelname)
 
     sd_models.list_models()
+    created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None)
+    if created_model:
+        created_model.calculate_shorthash()
 
     create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
 

modules/generation_parameters_copypaste.py

@@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
     restore_old_hires_fix_params(res)
 
+    # Missing RNG means the default was set, which is GPU RNG
+    if "RNG" not in res:
+        res["RNG"] = "GPU"
+
     return res
 
 
@@ -304,6 +308,8 @@ infotext_to_setting_name_mapping = [
     ('UniPC skip type', 'uni_pc_skip_type'),
     ('UniPC order', 'uni_pc_order'),
     ('UniPC lower order final', 'uni_pc_lower_order_final'),
+    ('RNG', 'randn_source'),
+    ('NGMS', 's_min_uncond'),
 ]
 
 

modules/images.py

@@ -318,6 +318,7 @@ re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
 re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
 re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
 max_filename_part_length = 128
+NOTHING_AND_SKIP_PREVIOUS_TEXT = object()
 
 
 def sanitize_filename_part(text, replace_spaces=True):
@@ -352,6 +353,10 @@ class FilenameGenerator:
         'prompt_no_styles': lambda self: self.prompt_no_style(),
         'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
         'prompt_words': lambda self: self.prompt_words(),
+        'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.batch_index + 1,
+        'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
+        'hasprompt': lambda self, *args: self.hasprompt(*args),  # accepts formats:[hasprompt<prompt1|default><prompt2>..]
+        'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
     }
     default_time_format = '%Y%m%d%H%M%S'
 
@@ -360,6 +365,22 @@ class FilenameGenerator:
         self.seed = seed
         self.prompt = prompt
         self.image = image
+        
+    def hasprompt(self, *args):
+        lower = self.prompt.lower()
+        if self.p is None or self.prompt is None:
+            return None
+        outres = ""
+        for arg in args:
+            if arg != "":
+                division = arg.split("|")
+                expected = division[0].lower()
+                default = division[1] if len(division) > 1 else ""
+                if lower.find(expected) >= 0:
+                    outres = f'{outres}{expected}'
+                else:
+                    outres = outres if default == "" else f'{outres}{default}'
+        return sanitize_filename_part(outres)
 
     def prompt_no_style(self):
         if self.p is None or self.prompt is None:
@@ -403,9 +424,9 @@ class FilenameGenerator:
 
         for m in re_pattern.finditer(x):
             text, pattern = m.groups()
-            res += text
 
             if pattern is None:
+                res += text
                 continue
 
             pattern_args = []
@@ -426,11 +447,13 @@ class FilenameGenerator:
                     print(f"Error adding [{pattern}] to filename", file=sys.stderr)
                     print(traceback.format_exc(), file=sys.stderr)
 
-                if replacement is not None:
-                    res += str(replacement)
+                if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
+                    continue
+                elif replacement is not None:
+                    res += text + str(replacement)
                     continue
 
-            res += f'[{pattern}]'
+            res += f'{text}[{pattern}]'
 
         return res
 

modules/img2img.py

@@ -4,7 +4,7 @@ import sys
 import traceback
 
 import numpy as np
-from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
 
 from modules import devices, sd_samplers
 from modules.generation_parameters_copypaste import create_override_settings_dict
@@ -46,7 +46,10 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
         if state.interrupted:
             break
 
-        img = Image.open(image)
+        try:
+            img = Image.open(image)
+        except UnidentifiedImageError:
+            continue
         # Use the EXIF orientation of photos taken by smartphones.
         img = ImageOps.exif_transpose(img)
         p.init_images = [img] * p.batch_size
@@ -78,7 +81,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     is_batch = mode == 5
@@ -114,6 +117,12 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
     if image is not None:
         image = ImageOps.exif_transpose(image)
 
+    if selected_scale_tab == 1:
+        assert image, "Can't scale by because no image is selected"
+
+        width = int(image.width * scale_by)
+        height = int(image.height * scale_by)
+
     assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
 
     p = StableDiffusionProcessingImg2Img(
@@ -151,7 +160,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         override_settings=override_settings,
     )
 
-    p.scripts = modules.scripts.scripts_txt2img
+    p.scripts = modules.scripts.scripts_img2img
     p.script_args = args
 
     if shared.cmd_opts.enable_console_prompts:

modules/interrogate.py

@@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir):
     category_types = ["artists", "flavors", "mediums", "movements"]
 
     try:
-        os.makedirs(tmpdir)
+        os.makedirs(tmpdir, exist_ok=True)
         for category_type in category_types:
             torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
         os.rename(tmpdir, content_dir)
@@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir):
         errors.display(e, "downloading default CLIP interrogate categories")
     finally:
         if os.path.exists(tmpdir):
-            os.remove(tmpdir)
+            os.removedirs(tmpdir)
 
 
 class InterrogateModels:

modules/ngrok.py

@@ -13,6 +13,18 @@ def connect(token, port, region):
     config = conf.PyngrokConfig(
         auth_token=token, region=region
     )
+    
+    # Guard for existing tunnels
+    existing = ngrok.get_tunnels(pyngrok_config=config)
+    if existing:
+        for established in existing:
+            # Extra configuration in the case that the user is also using ngrok for other tunnels
+            if established.config['addr'][-4:] == str(port):
+                public_url = existing[0].public_url
+                print(f'ngrok has already been connected to localhost:{port}! URL: {public_url}\n'
+                    'You can use this link after the launch is complete.')
+                return
+    
     try:
         if account is None:
             public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url

modules/paths_internal.py

@@ -20,3 +20,4 @@ data_path = cmd_opts_pre.data_dir
 models_path = os.path.join(data_path, "models")
 extensions_dir = os.path.join(data_path, "extensions")
 extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
+config_states_dir = os.path.join(script_path, "config_states")

modules/postprocessing.py

@@ -18,9 +18,14 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
 
     if extras_mode == 1:
         for img in image_folder:
-            image = Image.open(img)
+            if isinstance(img, Image.Image):
+                image = img
+                fn = ''
+            else:
+                image = Image.open(os.path.abspath(img.name))
+                fn = os.path.splitext(img.orig_name)[0]
             image_data.append(image)
-            image_names.append(os.path.splitext(img.orig_name)[0])
+            image_names.append(fn)
     elif extras_mode == 2:
         assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
         assert input_dir, 'input directory not selected'

modules/processing.py

@@ -3,6 +3,7 @@ import math
 import os
 import sys
 import warnings
+import hashlib
 
 import torch
 import numpy as np
@@ -105,7 +106,7 @@ class StableDiffusionProcessing:
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
     """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
         if sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
@@ -140,6 +141,7 @@ class StableDiffusionProcessing:
         self.denoising_strength: float = denoising_strength
         self.sampler_noise_scheduler_override = None
         self.ddim_discretize = ddim_discretize or opts.ddim_discretize
+        self.s_min_uncond = s_min_uncond or opts.s_min_uncond
         self.s_churn = s_churn or opts.s_churn
         self.s_tmin = s_tmin or opts.s_tmin
         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
@@ -162,6 +164,8 @@ class StableDiffusionProcessing:
         self.all_seeds = None
         self.all_subseeds = None
         self.iteration = 0
+        self.is_hr_pass = False
+        
 
     @property
     def sd_model(self):
@@ -476,6 +480,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
+        "Init image hash": getattr(p, 'init_img_hash', None),
+        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+        "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
     }
 
     generation_params.update(p.extra_generation_params)
@@ -639,8 +646,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     processed = Processed(p, [], p.seed, "")
                     file.write(processed.infotext(p, 0))
 
-            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
-            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
+            step_multiplier = 1
+            if not shared.opts.dont_fix_second_order_samplers_schedule:
+                try:
+                    step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
+                except:
+                    pass
+            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
+            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
 
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
@@ -670,6 +683,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
             for i, x_sample in enumerate(x_samples_ddim):
+                p.batch_index = i
+
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
 
@@ -706,9 +721,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image.info["parameters"] = text
                 output_images.append(image)
 
-                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
+                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
                     image_mask = p.mask_for_overlay.convert('RGB')
-                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA')
+                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
 
                     if opts.save_mask:
                         images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
@@ -718,7 +733,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
                     if opts.return_mask:
                         output_images.append(image_mask)
-                    
+
                     if opts.return_mask_composite:
                         output_images.append(image_mask_composite)
 
@@ -871,6 +886,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if not self.enable_hr:
             return samples
 
+        self.is_hr_pass = True
+
         target_width = self.hr_upscale_to_x
         target_height = self.hr_upscale_to_y
 
@@ -940,6 +957,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
 
+        self.is_hr_pass = False
+
         return samples
 
 
@@ -1007,6 +1026,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.color_corrections = []
         imgs = []
         for img in self.init_images:
+
+            # Save init image
+            if opts.save_init_img:
+                self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
+                images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
+
             image = images.flatten(img, opts.img2img_background_color)
 
             if crop_region is None and self.resize_mode != 3:

modules/realesrgan_model.py

@@ -9,7 +9,7 @@ from realesrgan import RealESRGANer
 
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import cmd_opts, opts
-
+from modules import modelloader
 
 class UpscalerRealESRGAN(Upscaler):
     def __init__(self, path):
@@ -23,7 +23,15 @@ class UpscalerRealESRGAN(Upscaler):
             self.enable = True
             self.scalers = []
             scalers = self.load_models(path)
+
+            local_model_paths = self.find_models(ext_filter=[".pth"])
             for scaler in scalers:
+                if scaler.local_data_path.startswith("http"):
+                    filename = modelloader.friendly_name(scaler.local_data_path)
+                    local = next(iter([local_model for local_model in local_model_paths if local_model.endswith(filename + '.pth')]), None)
+                    if local:
+                        scaler.local_data_path = local
+
                 if scaler.name in opts.realesrgan_enabled_models:
                     self.scalers.append(scaler)
 
@@ -64,7 +72,9 @@ class UpscalerRealESRGAN(Upscaler):
                 print(f"Unable to find model info: {path}")
                 return None
 
-            info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
+            if info.local_data_path.startswith("http"):
+                info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
+
             return info
         except Exception as e:
             print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)

modules/safe.py

 # this code is adapted from the script contributed by anon from /h/
 
-import io
 import pickle
 import collections
 import sys
@@ -12,11 +11,9 @@ import _codecs
 import zipfile
 import re
 
-
 # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
 TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
 
-
 def encode(*args):
     out = _codecs.encode(*args)
     return out
@@ -27,7 +24,7 @@ class RestrictedUnpickler(pickle.Unpickler):
 
     def persistent_load(self, saved_id):
         assert saved_id[0] == 'storage'
-        return TypedStorage()
+        return TypedStorage(_internal=True)
 
     def find_class(self, module, name):
         if self.extra_handler is not None:

modules/script_callbacks.py

@@ -93,6 +93,7 @@ callback_map = dict(
     callbacks_infotext_pasted=[],
     callbacks_script_unloaded=[],
     callbacks_before_ui=[],
+    callbacks_on_reload=[],
 )
 
 
@@ -109,6 +110,14 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
             report_exception(c, 'app_started_callback')
 
 
+def app_reload_callback():
+    for c in callback_map['callbacks_on_reload']:
+        try:
+            c.callback()
+        except Exception:
+            report_exception(c, 'callbacks_on_reload')
+
+
 def model_loaded_callback(sd_model):
     for c in callback_map['callbacks_model_loaded']:
         try:
@@ -254,6 +263,11 @@ def on_app_started(callback):
     add_callback(callback_map['callbacks_app_started'], callback)
 
 
+def on_before_reload(callback):
+    """register a function to be called just before the server reloads."""
+    add_callback(callback_map['callbacks_on_reload'], callback)
+
+
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument; this function is also called when the script is reloaded. """

modules/sd_models.py

@@ -52,6 +52,15 @@ class CheckpointInfo:
 
         self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
 
+        self.metadata = {}
+
+        _, ext = os.path.splitext(self.filename)
+        if ext.lower() == ".safetensors":
+            try:
+                self.metadata = read_metadata_from_safetensors(filename)
+            except Exception as e:
+                errors.display(e, f"reading checkpoint metadata: {filename}")
+
     def register(self):
         checkpoints_list[self.title] = self
         for id in self.ids:
@@ -544,4 +553,4 @@ def unload_model_weights(sd_model=None, info=None):
 
     print(f"Unloaded weights {timer.summary()}.")
 
-    return sd_model
\ No newline at end of file
+    return sd_model

modules/sd_samplers_common.py

@@ -60,3 +60,13 @@ def store_latent(decoded):
 
 class InterruptedException(BaseException):
     pass
+
+
+if opts.randn_source == "CPU":
+    import torchsde._brownian.brownian_interval
+
+    def torchsde_randn(size, dtype, device, seed):
+        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+
+    torchsde._brownian.brownian_interval._randn = torchsde_randn

modules/sd_samplers_kdiffusion.py

@@ -76,7 +76,7 @@ class CFGDenoiser(torch.nn.Module):
 
         return denoised
 
-    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
+    def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
         if state.interrupted or state.skipped:
             raise sd_samplers_common.InterruptedException
 
@@ -115,12 +115,21 @@ class CFGDenoiser(torch.nn.Module):
         sigma_in = denoiser_params.sigma
         tensor = denoiser_params.text_cond
         uncond = denoiser_params.text_uncond
+        skip_uncond = False
 
-        if tensor.shape[1] == uncond.shape[1]:
-            if not is_edit_model:
-                cond_in = torch.cat([tensor, uncond])
-            else:
+        # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
+        if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+            skip_uncond = True
+            x_in = x_in[:-batch_size]
+            sigma_in = sigma_in[:-batch_size]
+
+        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+            if is_edit_model:
                 cond_in = torch.cat([tensor, uncond, uncond])
+            elif skip_uncond:
+                cond_in = tensor
+            else:
+                cond_in = torch.cat([tensor, uncond])
 
             if shared.batch_cond_uncond:
                 x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
@@ -144,7 +153,13 @@ class CFGDenoiser(torch.nn.Module):
 
                 x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
 
-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
+            if not skip_uncond:
+                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
+
+        denoised_image_indexes = [x[0][0] for x in conds_list]
+        if skip_uncond:
+            fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
+            x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
 
         denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
         cfg_denoised_callback(denoised_params)
@@ -152,20 +167,21 @@ class CFGDenoiser(torch.nn.Module):
         devices.test_for_nans(x_out, "unet")
 
         if opts.live_preview_content == "Prompt":
-            sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
+            sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
         elif opts.live_preview_content == "Negative prompt":
             sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
 
-        if not is_edit_model:
-            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-        else:
+        if is_edit_model:
             denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
+        elif skip_uncond:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
+        else:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
 
         if self.mask is not None:
             denoised = self.init_latent * self.mask + self.nmask * denoised
 
         self.step += 1
-
         return denoised
 
 
@@ -190,7 +206,7 @@ class TorchHijack:
             if noise.shape == x.shape:
                 return noise
 
-        if x.device.type == 'mps':
+        if opts.randn_source == "CPU" or x.device.type == 'mps':
             return torch.randn_like(x, device=devices.cpu).to(x.device)
         else:
             return torch.randn_like(x)
@@ -210,6 +226,7 @@ class KDiffusionSampler:
         self.eta = None
         self.config = None
         self.last_latent = None
+        self.s_min_uncond = None
 
         self.conditioning_key = sd_model.model.conditioning_key
 
@@ -244,6 +261,7 @@ class KDiffusionSampler:
         self.model_wrap_cfg.step = 0
         self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
         self.eta = p.eta if p.eta is not None else opts.eta_ancestral
+        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
 
         k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
 
@@ -326,6 +344,7 @@ class KDiffusionSampler:
             'image_cond': image_conditioning, 
             'uncond': unconditional_conditioning, 
             'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
         }
 
         samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
@@ -359,7 +378,8 @@ class KDiffusionSampler:
             'cond': conditioning, 
             'image_cond': image_conditioning, 
             'uncond': unconditional_conditioning, 
-            'cond_scale': p.cfg_scale
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
         }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples

modules/shared.py

@@ -4,6 +4,7 @@ import json
 import os
 import sys
 import time
+import requests
 
 from PIL import Image
 import gradio as gr
@@ -39,6 +40,7 @@ restricted_opts = {
     "outdir_grids",
     "outdir_txt2img_grids",
     "outdir_save",
+    "outdir_init_images"
 }
 
 ui_reorder_categories = [
@@ -54,6 +56,21 @@ ui_reorder_categories = [
     "scripts",
 ]
 
+# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
+gradio_hf_hub_themes = [
+    "gradio/glass",
+    "gradio/monochrome",
+    "gradio/seafoam",
+    "gradio/soft",
+    "freddyaboulton/dracula_revamped",
+    "gradio/dracula_test",
+    "abidlabs/dracula_test",
+    "abidlabs/pakistan",
+    "dawood/microsoft_windows",
+    "ysharma/steampunk"
+]
+
+
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
@@ -252,7 +269,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
     "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
-    "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
+    "save_init_img": OptionInfo(False, "Save init images when using img2img"),
 
     "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
     "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
@@ -268,6 +285,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
     "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
     "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
     "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
+    "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
 }))
 
 options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
@@ -283,6 +301,8 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "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(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+    "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -331,6 +351,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
+    "randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
 }))
 
 options_templates.update(options_section(('compatibility', "Compatibility"), {
@@ -338,6 +359,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
     "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
     "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
     "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
+    "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -361,7 +383,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
     "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
     "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
     "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
-    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
 }))
 
 options_templates.update(options_section(('ui', "User interface"), {
@@ -382,11 +404,13 @@ options_templates.update(options_section(('ui', "User interface"), {
     "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
     "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
     "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
+    "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
     "quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
     "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
     "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
     "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
     "localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
+    "gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
 }))
 
 options_templates.update(options_section(('ui', "Live previews"), {
@@ -405,6 +429,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
     's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
@@ -424,6 +449,7 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
 options_templates.update(options_section((None, "Hidden options"), {
     "disabled_extensions": OptionInfo([], "Disable these extensions"),
     "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
+    "restore_config_state_file": OptionInfo("", "Config state file to restore from, under 'config-states/' folder"),
     "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
 }))
 
@@ -600,6 +626,24 @@ clip_model = None
 
 progress_print_out = sys.stdout
 
+gradio_theme = gr.themes.Base()
+
+
+def reload_gradio_theme(theme_name=None):
+    global gradio_theme
+    if not theme_name:
+        theme_name = opts.gradio_theme
+
+    if theme_name == "Default":
+        gradio_theme = gr.themes.Default()
+    else:
+        try:
+            gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
+        except requests.exceptions.ConnectionError:
+            print("Can't access HuggingFace Hub, falling back to default Gradio theme")
+            gradio_theme = gr.themes.Default()
+
+
 
 class TotalTQDM:
     def __init__(self):

modules/styles.py

@@ -72,16 +72,14 @@ class StyleDatabase:
         return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
 
     def save_styles(self, path: str) -> None:
-        # Write to temporary file first, so we don't nuke the file if something goes wrong
-        fd, temp_path = tempfile.mkstemp(".csv")
+        # Always keep a backup file around
+        if os.path.exists(path):
+            shutil.copy(path, path + ".bak")
+
+        fd = os.open(path, os.O_RDWR|os.O_CREAT)
         with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
             # _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
             # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
             writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
             writer.writeheader()
             writer.writerows(style._asdict() for k,     style in self.styles.items())
-
-        # Always keep a backup file around
-        if os.path.exists(path):
-            shutil.move(path, path + ".bak")
-        shutil.move(temp_path, path)

modules/textual_inversion/preprocess.py

@@ -11,7 +11,7 @@ from modules.shared import opts, cmd_opts
 from modules.textual_inversion import autocrop
 
 
-def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
+def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
     try:
         if process_caption:
             shared.interrogator.load()
@@ -19,7 +19,7 @@ def preprocess(id_task, process_src, process_dst, process_width, process_height,
         if process_caption_deepbooru:
             deepbooru.model.start()
 
-        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
+        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
 
     finally:
 
@@ -131,7 +131,7 @@ def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, thr
     return wh and center_crop(image, *wh)
     
 
-def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
+def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
@@ -161,7 +161,9 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
         params.subindex = 0
         filename = os.path.join(src, imagefile)
         try:
-            img = Image.open(filename).convert("RGB")
+            img = Image.open(filename)
+            img = ImageOps.exif_transpose(img)
+            img = img.convert("RGB")
         except Exception:
             continue
 
@@ -223,6 +225,10 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
                 print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)")
             process_default_resize = False
 
+        if process_keep_original_size:
+            save_pic(img, index, params, existing_caption=existing_caption)
+            process_default_resize = False
+
         if process_default_resize:
             img = images.resize_image(1, img, width, height)
             save_pic(img, index, params, existing_caption=existing_caption)

modules/textual_inversion/textual_inversion.py

@@ -233,6 +233,12 @@ class EmbeddingDatabase:
             self.load_from_dir(embdir)
             embdir.update()
 
+        # re-sort word_embeddings because load_from_dir may not load in alphabetic order.
+        # using a temporary copy so we don't reinitialize self.word_embeddings in case other objects have a reference to it.
+        sorted_word_embeddings = {e.name: e for e in sorted(self.word_embeddings.values(), key=lambda e: e.name.lower())}
+        self.word_embeddings.clear()
+        self.word_embeddings.update(sorted_word_embeddings)
+
         displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
         if self.previously_displayed_embeddings != displayed_embeddings:
             self.previously_displayed_embeddings = displayed_embeddings

modules/ui_common.py

@@ -125,7 +125,7 @@ Requested path was: {f}
 
     with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
         with gr.Group(elem_id=f"{tabname}_gallery_container"):
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4)
 
         generation_info = None
         with gr.Column():

modules/ui_components.py

@@ -62,3 +62,13 @@ class DropdownMulti(FormComponent, gr.Dropdown):
 
     def get_block_name(self):
         return "dropdown"
+
+
+class DropdownEditable(FormComponent, gr.Dropdown):
+    """Same as gr.Dropdown but allows editing value"""
+    def __init__(self, **kwargs):
+        super().__init__(allow_custom_value=True, **kwargs)
+
+    def get_block_name(self):
+        return "dropdown"
+

modules/ui_extra_networks.py

@@ -241,7 +241,7 @@ def create_ui(container, button, tabname):
 
     with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
         for page in ui.stored_extra_pages:
-            with gr.Tab(page.title):
+            with gr.Tab(page.title, id=page.title.lower().replace(" ", "_")):
 
                 page_elem = gr.HTML(page.create_html(ui.tabname))
                 ui.pages.append(page_elem)

modules/ui_postprocessing.py

@@ -9,13 +9,13 @@ def create_ui():
     with gr.Row().style(equal_height=False, variant='compact'):
         with gr.Column(variant='compact'):
             with gr.Tabs(elem_id="mode_extras"):
-                with gr.TabItem('Single Image', elem_id="extras_single_tab") as tab_single:
+                with gr.TabItem('Single Image', id="single_image", elem_id="extras_single_tab") as tab_single:
                     extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
 
-                with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch:
-                    image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
+                with gr.TabItem('Batch Process', id="batch_process", elem_id="extras_batch_process_tab") as tab_batch:
+                    image_batch = gr.Files(label="Batch Process", interactive=True, elem_id="extras_image_batch")
 
-                with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir:
+                with gr.TabItem('Batch from Directory', id="batch_from_directory", elem_id="extras_batch_directory_tab") as tab_batch_dir:
                     extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
                     extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
                     show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")

requirements.txt

+astunparse
 blendmodes
 accelerate
 basicsr
 fonts
 font-roboto
 gfpgan
-gradio==3.23
-invisible-watermark
+gradio==3.28.1
 numpy
 omegaconf
 opencv-contrib-python

requirements_versions.txt

 blendmodes==2022
 transformers==4.25.1
-accelerate==0.12.0
+accelerate==0.18.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.23
-numpy==1.23.3
+gradio==3.28.1
+numpy==1.23.5
 Pillow==9.4.0
 realesrgan==0.3.0
 torch
@@ -25,6 +25,6 @@ lark==1.1.2
 inflection==0.5.1
 GitPython==3.1.30
 torchsde==0.2.5
-safetensors==0.3.0
+safetensors==0.3.1
 httpcore<=0.15
 fastapi==0.94.0

script.js

@@ -7,7 +7,7 @@ function gradioApp() {
 }
 
 function get_uiCurrentTab() {
-    return gradioApp().querySelector('#tabs button:not(.border-transparent)')
+    return gradioApp().querySelector('#tabs button.selected')
 }
 
 function get_uiCurrentTabContent() {

scripts/custom_code.py

 import modules.scripts as scripts
 import gradio as gr
+import ast
+import copy
 
 from modules.processing import Processed
 from modules.shared import opts, cmd_opts, state
 
+
+def convertExpr2Expression(expr):
+    expr.lineno = 0
+    expr.col_offset = 0
+    result = ast.Expression(expr.value, lineno=0, col_offset = 0)
+
+    return result
+
+
+def exec_with_return(code, module):
+    """
+    like exec() but can return values
+    https://stackoverflow.com/a/52361938/5862977
+    """
+    code_ast = ast.parse(code)
+
+    init_ast = copy.deepcopy(code_ast)
+    init_ast.body = code_ast.body[:-1]
+
+    last_ast = copy.deepcopy(code_ast)
+    last_ast.body = code_ast.body[-1:]
+
+    exec(compile(init_ast, "<ast>", "exec"), module.__dict__)
+    if type(last_ast.body[0]) == ast.Expr:
+        return eval(compile(convertExpr2Expression(last_ast.body[0]), "<ast>", "eval"), module.__dict__)
+    else:
+        exec(compile(last_ast, "<ast>", "exec"), module.__dict__)
+
+
 class Script(scripts.Script):
 
     def title(self):
@@ -13,12 +44,23 @@ class Script(scripts.Script):
         return cmd_opts.allow_code
 
     def ui(self, is_img2img):
-        code = gr.Textbox(label="Python code", lines=1, elem_id=self.elem_id("code"))
+        example = """from modules.processing import process_images
+
+p.width = 768
+p.height = 768
+p.batch_size = 2
+p.steps = 10
+
+return process_images(p)
+"""
+
 
-        return [code]
+        code = gr.Code(value=example, language="python", label="Python code", elem_id=self.elem_id("code"))
+        indent_level = gr.Number(label='Indent level', value=2, precision=0, elem_id=self.elem_id("indent_level"))
 
+        return [code, indent_level]
 
-    def run(self, p, code):
+    def run(self, p, code, indent_level):
         assert cmd_opts.allow_code, '--allow-code option must be enabled'
 
         display_result_data = [[], -1, ""]
@@ -29,13 +71,20 @@ class Script(scripts.Script):
             display_result_data[2] = i
 
         from types import ModuleType
-        compiled = compile(code, '', 'exec')
         module = ModuleType("testmodule")
         module.__dict__.update(globals())
         module.p = p
         module.display = display
-        exec(compiled, module.__dict__)
+
+        indent = " " * indent_level
+        indented = code.replace('\n', '\n' + indent)
+        body = f"""def __webuitemp__():
+{indent}{indented}
+__webuitemp__()"""
+
+        result = exec_with_return(body, module)
+
+        if isinstance(result, Processed):
+            return result
 
         return Processed(p, *display_result_data)
-    
-    
\ No newline at end of file

scripts/outpainting_mk_2.py

@@ -275,7 +275,7 @@ class Script(scripts.Script):
 
         if opts.samples_save:
             for img in all_processed_images:
-                images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
+                images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.samples_format, info=res.info, p=p)
 
         if opts.grid_save and not unwanted_grid_because_of_img_count:
             images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)

scripts/poor_mans_outpainting.py

@@ -138,7 +138,7 @@ class Script(scripts.Script):
         combined_image = images.combine_grid(grid)
 
         if opts.samples_save:
-            images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.grid_format, info=initial_info, p=p)
+            images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
 
         processed = Processed(p, [combined_image], initial_seed, initial_info)
 

scripts/postprocessing_upscale.py

@@ -4,8 +4,8 @@ import numpy as np
 from modules import scripts_postprocessing, shared
 import gradio as gr
 
-from modules.ui_components import FormRow
-
+from modules.ui_components import FormRow, ToolButton
+from modules.ui import switch_values_symbol
 
 upscale_cache = {}
 
@@ -25,9 +25,12 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
 
                     with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
                         with FormRow():
-                            upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
-                            upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+                            with gr.Column(elem_id="upscaling_column_size", scale=4):
+                                upscaling_resize_w = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
+                                upscaling_resize_h = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h")
+                            with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                                upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn")
+                                upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
 
             with FormRow():
                 extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
@@ -36,6 +39,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
                 extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
                 extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
 
+        upscaling_res_switch_btn.click(lambda w, h: (h, w), inputs=[upscaling_resize_w, upscaling_resize_h], outputs=[upscaling_resize_w, upscaling_resize_h], show_progress=False)
         tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
         tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
 

scripts/xyz_grid.py

@@ -211,7 +211,8 @@ axis_options = [
     AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
     AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
     AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
-    AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),
+    AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list, key=str.casefold)),
+    AxisOption("Negative Guidance minimum sigma", float, apply_field("s_min_uncond")),
     AxisOption("Sigma Churn", float, apply_field("s_churn")),
     AxisOption("Sigma min", float, apply_field("s_tmin")),
     AxisOption("Sigma max", float, apply_field("s_tmax")),
@@ -374,16 +375,19 @@ class Script(scripts.Script):
                 with gr.Row():
                     x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
                     x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
+                    x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True)
                     fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
 
                 with gr.Row():
                     y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
                     y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
+                    y_values_dropdown = gr.Dropdown(label="Y values",visible=False,multiselect=True,interactive=True)
                     fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
 
                 with gr.Row():
                     z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
                     z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
+                    z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True)
                     fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
 
         with gr.Row(variant="compact", elem_id="axis_options"):
@@ -401,54 +405,74 @@ class Script(scripts.Script):
             swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
             swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")
 
-        def swap_axes(axis1_type, axis1_values, axis2_type, axis2_values):
-            return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values
+        def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown):
+            return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown
 
-        xy_swap_args = [x_type, x_values, y_type, y_values]
+        xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown]
         swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
-        yz_swap_args = [y_type, y_values, z_type, z_values]
+        yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown]
         swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
-        xz_swap_args = [x_type, x_values, z_type, z_values]
+        xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
         swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
 
         def fill(x_type):
             axis = self.current_axis_options[x_type]
-            return ", ".join(axis.choices()) if axis.choices else gr.update()
-
-        fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
-        fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
-        fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])
-
-        def select_axis(x_type):
-            return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
-
-        x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
-        y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
-        z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
+            return axis.choices() if axis.choices else gr.update()
+
+        fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown])
+        fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values_dropdown])
+        fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown])
+
+        def select_axis(axis_type,axis_values_dropdown):
+            choices = self.current_axis_options[axis_type].choices
+            has_choices = choices is not None
+            current_values = axis_values_dropdown
+            if has_choices:
+                choices = choices()
+                if isinstance(current_values,str):
+                    current_values = current_values.split(",")
+                current_values = list(filter(lambda x: x in choices, current_values))
+            return gr.Button.update(visible=has_choices),gr.Textbox.update(visible=not has_choices),gr.update(choices=choices if has_choices else None,visible=has_choices,value=current_values)
+
+        x_type.change(fn=select_axis, inputs=[x_type,x_values_dropdown], outputs=[fill_x_button,x_values,x_values_dropdown])
+        y_type.change(fn=select_axis, inputs=[y_type,y_values_dropdown], outputs=[fill_y_button,y_values,y_values_dropdown])
+        z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown])
+
+        def get_dropdown_update_from_params(axis,params):
+            val_key = axis + " Values"
+            vals = params.get(val_key,"")
+            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
+            return gr.update(value = valslist)
 
         self.infotext_fields = (
             (x_type, "X Type"),
             (x_values, "X Values"),
+            (x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
             (y_type, "Y Type"),
             (y_values, "Y Values"),
+            (y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
             (z_type, "Z Type"),
             (z_values, "Z Values"),
+            (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
         )
 
-        return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
+        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
 
-    def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
+    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
         if not no_fixed_seeds:
             modules.processing.fix_seed(p)
 
         if not opts.return_grid:
             p.batch_size = 1
 
-        def process_axis(opt, vals):
+        def process_axis(opt, vals, vals_dropdown):
             if opt.label == 'Nothing':
                 return [0]
 
-            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
+            if opt.choices is not None:
+                valslist = vals_dropdown
+            else:
+                valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
 
             if opt.type == int:
                 valslist_ext = []
@@ -506,13 +530,19 @@ class Script(scripts.Script):
             return valslist
 
         x_opt = self.current_axis_options[x_type]
-        xs = process_axis(x_opt, x_values)
+        if x_opt.choices is not None:
+            x_values = ",".join(x_values_dropdown)
+        xs = process_axis(x_opt, x_values, x_values_dropdown)
 
         y_opt = self.current_axis_options[y_type]
-        ys = process_axis(y_opt, y_values)
+        if y_opt.choices is not None:
+            y_values = ",".join(y_values_dropdown)
+        ys = process_axis(y_opt, y_values, y_values_dropdown)
 
         z_opt = self.current_axis_options[z_type]
-        zs = process_axis(z_opt, z_values)
+        if z_opt.choices is not None:
+            z_values = ",".join(z_values_dropdown)
+        zs = process_axis(z_opt, z_values, z_values_dropdown)
 
         # this could be moved to common code, but unlikely to be ever triggered anywhere else
         Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes

style.css

@@ -293,7 +293,12 @@ button.custom-button{
     margin-left: -0.75em
 }
 
-#txtimg_hr_finalres .resolution{
+#img2img_scale_resolution_preview.block{
+    display: flex;
+    align-items: end;
+}
+
+#txtimg_hr_finalres .resolution, #img2img_scale_resolution_preview .resolution{
     font-weight: bold;
 }
 
@@ -312,6 +317,10 @@ div.dimensions-tools{
     align-content: center;
 }
 
+div#extras_scale_to_tab div.form{
+    flex-direction: row;
+}
+
 #mode_img2img .gradio-image > div.fixed-height, #mode_img2img .gradio-image > div.fixed-height img{
     height: 480px !important;
     max-height: 480px !important;
@@ -333,6 +342,18 @@ div.dimensions-tools{
     overflow-wrap: break-word;
 }
 
+#img2img_column_batch{
+    align-self: end;
+    margin-bottom: 0.9em;
+}
+
+#img2img_unused_scale_by_slider{
+    visibility: hidden;
+    width: 0.5em;
+    max-width: 0.5em;
+    min-width: 0.5em;
+}
+
 /* settings */
 #quicksettings {
     width: fit-content;
@@ -642,6 +663,12 @@ footer {
 
 /* extra networks UI */
 
+.extra-network-cards{
+    height: 400px;
+    overflow: scroll;
+    resize: vertical;
+}
+
 .extra-networks > div > [id *= '_extra_']{
     margin: 0.3em;
 }

webui-macos-env.sh

@@ -11,7 +11,7 @@ fi
 
 export install_dir="$HOME"
 export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
-export TORCH_COMMAND="pip install torch==1.12.1 torchvision==0.13.1"
+export TORCH_COMMAND="pip install torch torchvision"
 export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git"
 export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71"
 export PYTORCH_ENABLE_MPS_FALLBACK=1

webui-user.sh

@@ -43,4 +43,7 @@
 # Uncomment to enable accelerated launch
 #export ACCELERATE="True"
 
+# Uncomment to disable TCMalloc
+#export NO_TCMALLOC="True"
+
 ###########################################

webui.py

@@ -5,6 +5,7 @@ import importlib
 import signal
 import re
 import warnings
+import json
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
@@ -20,6 +21,9 @@ startup_timer = timer.Timer()
 import torch
 import pytorch_lightning # pytorch_lightning should be imported after torch, but it re-enables warnings on import so import once to disable them
 warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning")
+warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
+
+
 startup_timer.record("import torch")
 
 import gradio
@@ -37,7 +41,7 @@ if ".dev" in torch.__version__ or "+git" in torch.__version__:
     torch.__long_version__ = torch.__version__
     torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
 
-from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks
+from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks, config_states
 import modules.codeformer_model as codeformer
 import modules.face_restoration
 import modules.gfpgan_model as gfpgan
@@ -67,11 +71,51 @@ else:
     server_name = "0.0.0.0" if cmd_opts.listen else None
 
 
+def fix_asyncio_event_loop_policy():
+    """
+        The default `asyncio` event loop policy only automatically creates
+        event loops in the main threads. Other threads must create event
+        loops explicitly or `asyncio.get_event_loop` (and therefore
+        `.IOLoop.current`) will fail. Installing this policy allows event
+        loops to be created automatically on any thread, matching the
+        behavior of Tornado versions prior to 5.0 (or 5.0 on Python 2).
+    """
+
+    import asyncio
+
+    if sys.platform == "win32" and hasattr(asyncio, "WindowsSelectorEventLoopPolicy"):
+        # "Any thread" and "selector" should be orthogonal, but there's not a clean
+        # interface for composing policies so pick the right base.
+        _BasePolicy = asyncio.WindowsSelectorEventLoopPolicy  # type: ignore
+    else:
+        _BasePolicy = asyncio.DefaultEventLoopPolicy
+
+    class AnyThreadEventLoopPolicy(_BasePolicy):  # type: ignore
+        """Event loop policy that allows loop creation on any thread.
+        Usage::
+
+            asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+        """
+
+        def get_event_loop(self) -> asyncio.AbstractEventLoop:
+            try:
+                return super().get_event_loop()
+            except (RuntimeError, AssertionError):
+                # This was an AssertionError in python 3.4.2 (which ships with debian jessie)
+                # and changed to a RuntimeError in 3.4.3.
+                # "There is no current event loop in thread %r"
+                loop = self.new_event_loop()
+                self.set_event_loop(loop)
+                return loop
+
+    asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+
+
 def check_versions():
     if shared.cmd_opts.skip_version_check:
         return
 
-    expected_torch_version = "1.13.1"
+    expected_torch_version = "2.0.0"
 
     if version.parse(torch.__version__) < version.parse(expected_torch_version):
         errors.print_error_explanation(f"""
@@ -84,7 +128,7 @@ there are reports of issues with training tab on the latest version.
 Use --skip-version-check commandline argument to disable this check.
         """.strip())
 
-    expected_xformers_version = "0.0.16rc425"
+    expected_xformers_version = "0.0.17"
     if shared.xformers_available:
         import xformers
 
@@ -99,12 +143,27 @@ Use --skip-version-check commandline argument to disable this check.
 
 
 def initialize():
+    fix_asyncio_event_loop_policy()
+
     check_versions()
 
     extensions.list_extensions()
     localization.list_localizations(cmd_opts.localizations_dir)
     startup_timer.record("list extensions")
 
+    config_state_file = shared.opts.restore_config_state_file
+    shared.opts.restore_config_state_file = ""
+    shared.opts.save(shared.config_filename)
+
+    if os.path.isfile(config_state_file):
+        print(f"*** About to restore extension state from file: {config_state_file}")
+        with open(config_state_file, "r", encoding="utf-8") as f:
+            config_state = json.load(f)
+            config_states.restore_extension_config(config_state)
+        startup_timer.record("restore extension config")
+    elif config_state_file:
+        print(f"!!! Config state backup not found: {config_state_file}")
+
     if cmd_opts.ui_debug_mode:
         shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
         modules.scripts.load_scripts()
@@ -126,9 +185,6 @@ def initialize():
     modules.scripts.load_scripts()
     startup_timer.record("load scripts")
 
-    modelloader.load_upscalers()
-    startup_timer.record("load upscalers")
-
     modules.sd_vae.refresh_vae_list()
     startup_timer.record("refresh VAE")
 
@@ -150,6 +206,7 @@ def initialize():
     shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
+    shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
     startup_timer.record("opts onchange")
 
     shared.reload_hypernetworks()
@@ -212,6 +269,8 @@ def wait_on_server(demo=None):
             time.sleep(0.5)
             demo.close()
             time.sleep(0.5)
+
+            modules.script_callbacks.app_reload_callback()
             break
 
 
@@ -260,6 +319,7 @@ def webui():
             server_port=cmd_opts.port,
             ssl_keyfile=cmd_opts.tls_keyfile,
             ssl_certfile=cmd_opts.tls_certfile,
+            ssl_verify=cmd_opts.disable_tls_verify,
             debug=cmd_opts.gradio_debug,
             auth=[tuple(cred.split(':')) for cred in gradio_auth_creds] if gradio_auth_creds else None,
             inbrowser=cmd_opts.autolaunch,
@@ -302,6 +362,19 @@ def webui():
         extensions.list_extensions()
         startup_timer.record("list extensions")
 
+        config_state_file = shared.opts.restore_config_state_file
+        shared.opts.restore_config_state_file = ""
+        shared.opts.save(shared.config_filename)
+
+        if os.path.isfile(config_state_file):
+            print(f"*** About to restore extension state from file: {config_state_file}")
+            with open(config_state_file, "r", encoding="utf-8") as f:
+                config_state = json.load(f)
+                config_states.restore_extension_config(config_state)
+            startup_timer.record("restore extension config")
+        elif config_state_file:
+            print(f"!!! Config state backup not found: {config_state_file}")
+
         localization.list_localizations(cmd_opts.localizations_dir)
 
         modelloader.forbid_loaded_nonbuiltin_upscalers()

webui.sh

@@ -23,7 +23,7 @@ fi
 # Install directory without trailing slash
 if [[ -z "${install_dir}" ]]
 then
-    install_dir="/home/$(whoami)"
+    install_dir="$(pwd)"
 fi
 
 # Name of the subdirectory (defaults to stable-diffusion-webui)
@@ -113,12 +113,13 @@ case "$gpu_info" in
         printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half"
         printf "\n%s\n" "${delimiter}"
     ;;
-    *) 
+    *)
     ;;
 esac
 if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
 then
-    export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.2"
+    # AMD users will still use torch 1.13 because 2.0 does not seem to work.
+    export TORCH_COMMAND="pip install torch==1.13.1+rocm5.2 torchvision==0.14.1+rocm5.2 --index-url https://download.pytorch.org/whl/rocm5.2"
 fi  
 
 for preq in "${GIT}" "${python_cmd}"
@@ -172,15 +173,30 @@ else
     exit 1
 fi
 
+# Try using TCMalloc on Linux
+prepare_tcmalloc() {
+    if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then
+        TCMALLOC="$(ldconfig -p | grep -Po "libtcmalloc.so.\d" | head -n 1)"
+        if [[ ! -z "${TCMALLOC}" ]]; then
+            echo "Using TCMalloc: ${TCMALLOC}"
+            export LD_PRELOAD="${TCMALLOC}"
+        else
+            printf "\e[1m\e[31mCannot locate TCMalloc (improves CPU memory usage)\e[0m\n"
+        fi
+    fi
+}
+
 if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]
 then
     printf "\n%s\n" "${delimiter}"
     printf "Accelerating launch.py..."
     printf "\n%s\n" "${delimiter}"
+    prepare_tcmalloc
     exec accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@"
 else
     printf "\n%s\n" "${delimiter}"
     printf "Launching launch.py..."
-    printf "\n%s\n" "${delimiter}"      
+    printf "\n%s\n" "${delimiter}"
+    prepare_tcmalloc
     exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
 fi