Merge branch 'dev' into pr/xyz-sort-001

43 files changed, 556 insertions, 250 deletions

diff --git a/.gitignore b/.gitignore
index 0b1d17ca..3b48ba9a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -32,4 +32,4 @@ notification.mp3
 /extensions
 /test/stdout.txt
 /test/stderr.txt
-/cache.json
+/cache.json*
diff --git a/README.md b/README.md
index b67e2296..20f74531 100644
--- a/README.md
+++ b/README.md
@@ -120,6 +120,7 @@ sudo pacman -S wget git python3
 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).

diff --git a/environment-wsl2.yaml b/environment-wsl2.yaml
index f8872750..0c4ae680 100644
--- a/environment-wsl2.yaml
+++ b/environment-wsl2.yaml
@@ -4,8 +4,8 @@ channels:
   - defaults
 dependencies:
   - python=3.10
-  - pip=22.2.2
-  - cudatoolkit=11.3
-  - pytorch=1.12.1
-  - torchvision=0.13.1
-  - numpy=1.23.1
\ No newline at end of file
+  - pip=23.0
+  - cudatoolkit=11.8
+  - pytorch=2.0
+  - torchvision=0.15
+  - numpy=1.23
diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py
index 6be6ef73..45f899fc 100644
--- a/extensions-builtin/Lora/extra_networks_lora.py
+++ b/extensions-builtin/Lora/extra_networks_lora.py
@@ -8,7 +8,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def activate(self, p, params_list):

         additional = shared.opts.sd_lora

 

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

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

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

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

 

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

 

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

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

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

 }))

diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py
index e0fbf3a3..c7fd5739 100644
--- a/extensions-builtin/ScuNET/scripts/scunet_model.py
+++ b/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -5,11 +5,15 @@ import traceback
 import PIL.Image
 import numpy as np
 import torch
+from tqdm import tqdm
+
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
 from modules import devices, modelloader
 from scunet_model_arch import SCUNet as net
+from modules.shared import opts
+from modules import images
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
             scalers.append(scaler_data2)
         self.scalers = scalers
 
-    def do_upscale(self, img: PIL.Image, selected_file):
+    @staticmethod
+    @torch.no_grad()
+    def tiled_inference(img, model):
+        # test the image tile by tile
+        h, w = img.shape[2:]
+        tile = opts.SCUNET_tile
+        tile_overlap = opts.SCUNET_tile_overlap
+        if tile == 0:
+            return model(img)
+
+        device = devices.get_device_for('scunet')
+        assert tile % 8 == 0, "tile size should be a multiple of window_size"
+        sf = 1
+
+        stride = tile - tile_overlap
+        h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+        w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+        E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
+        W = torch.zeros_like(E, dtype=devices.dtype, device=device)
+
+        with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
+            for h_idx in h_idx_list:
+
+                for w_idx in w_idx_list:
+
+                    in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+
+                    out_patch = model(in_patch)
+                    out_patch_mask = torch.ones_like(out_patch)
+
+                    E[
+                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                    ].add_(out_patch)
+                    W[
+                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                    ].add_(out_patch_mask)
+                    pbar.update(1)
+        output = E.div_(W)
+
+        return output
+
+    def do_upscale(self, img: PIL.Image.Image, selected_file):
+
         torch.cuda.empty_cache()
 
         model = self.load_model(selected_file)
         if model is None:
+            print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
             return img
 
         device = devices.get_device_for('scunet')
-        img = np.array(img)
-        img = img[:, :, ::-1]
-        img = np.moveaxis(img, 2, 0) / 255
-        img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(device)
-
-        with torch.no_grad():
-            output = model(img)
-        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-        output = 255. * np.moveaxis(output, 0, 2)
-        output = output.astype(np.uint8)
-        output = output[:, :, ::-1]
+        tile = opts.SCUNET_tile
+        h, w = img.height, img.width
+        np_img = np.array(img)
+        np_img = np_img[:, :, ::-1]  # RGB to BGR
+        np_img = np_img.transpose((2, 0, 1)) / 255  # HWC to CHW
+        torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device)  # type: ignore
+
+        if tile > h or tile > w:
+            _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
+            _img[:, :, :h, :w] = torch_img # pad image
+            torch_img = _img
+
+        torch_output = self.tiled_inference(torch_img, model).squeeze(0)
+        torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
+        np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
+        del torch_img, torch_output
         torch.cuda.empty_cache()
-        return PIL.Image.fromarray(output, 'RGB')
+
+        output = np_output.transpose((1, 2, 0))  # CHW to HWC
+        output = output[:, :, ::-1]  # BGR to RGB
+        return PIL.Image.fromarray((output * 255).astype(np.uint8))
 
     def load_model(self, path: str):
         device = devices.get_device_for('scunet')
@@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         model = model.to(device)
 
         return model
-
diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
index f0918e26..5c7a836a 100644
--- a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
+++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
@@ -1,103 +1,42 @@
 // Stable Diffusion WebUI - Bracket checker
-// Version 1.0
-// By Hingashi no Florin/Bwin4L
+// By Hingashi no Florin/Bwin4L & @akx
 // Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
 // If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
 
-function checkBrackets(evt, textArea, counterElt) {
-  errorStringParen  = '(...) - Different number of opening and closing parentheses detected.\n';
-  errorStringSquare = '[...] - Different number of opening and closing square brackets detected.\n';
-  errorStringCurly  = '{...} - Different number of opening and closing curly brackets detected.\n';
-
-  openBracketRegExp = /\(/g;
-  closeBracketRegExp = /\)/g;
-
-  openSquareBracketRegExp = /\[/g;
-  closeSquareBracketRegExp = /\]/g;
-
-  openCurlyBracketRegExp = /\{/g;
-  closeCurlyBracketRegExp = /\}/g;
-
-  totalOpenBracketMatches = 0;
-  totalCloseBracketMatches = 0;
-  totalOpenSquareBracketMatches = 0;
-  totalCloseSquareBracketMatches = 0;
-  totalOpenCurlyBracketMatches = 0;
-  totalCloseCurlyBracketMatches = 0;
-
-  openBracketMatches = textArea.value.match(openBracketRegExp);
-  if(openBracketMatches) {
-    totalOpenBracketMatches = openBracketMatches.length;
-  }
-
-  closeBracketMatches = textArea.value.match(closeBracketRegExp);
-  if(closeBracketMatches) {
-    totalCloseBracketMatches = closeBracketMatches.length;
-  }
-
-  openSquareBracketMatches = textArea.value.match(openSquareBracketRegExp);
-  if(openSquareBracketMatches) {
-    totalOpenSquareBracketMatches = openSquareBracketMatches.length;
-  }
-
-  closeSquareBracketMatches = textArea.value.match(closeSquareBracketRegExp);
-  if(closeSquareBracketMatches) {
-    totalCloseSquareBracketMatches = closeSquareBracketMatches.length;
-  }
-
-  openCurlyBracketMatches = textArea.value.match(openCurlyBracketRegExp);
-  if(openCurlyBracketMatches) {
-    totalOpenCurlyBracketMatches = openCurlyBracketMatches.length;
-  }
-
-  closeCurlyBracketMatches = textArea.value.match(closeCurlyBracketRegExp);
-  if(closeCurlyBracketMatches) {
-    totalCloseCurlyBracketMatches = closeCurlyBracketMatches.length;
-  }
-
-  if(totalOpenBracketMatches != totalCloseBracketMatches) {
-    if(!counterElt.title.includes(errorStringParen)) {
-      counterElt.title += errorStringParen;
-    }
-  } else {
-    counterElt.title = counterElt.title.replace(errorStringParen, '');
-  }
-
-  if(totalOpenSquareBracketMatches != totalCloseSquareBracketMatches) {
-    if(!counterElt.title.includes(errorStringSquare)) {
-      counterElt.title += errorStringSquare;
-    }
-  } else {
-    counterElt.title = counterElt.title.replace(errorStringSquare, '');
-  }
-
-  if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) {
-    if(!counterElt.title.includes(errorStringCurly)) {
-      counterElt.title += errorStringCurly;
+function checkBrackets(textArea, counterElt) {
+  var counts = {};
+  (textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => {
+    counts[bracket] = (counts[bracket] || 0) + 1;
+  });
+  var errors = [];
+
+  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');
+});
diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js
index 06f505b0..9468c107 100644
--- a/javascript/contextMenus.js
+++ b/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

 

diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js
index 20a5aadf..588c7b77 100644
--- a/javascript/edit-attention.js
+++ b/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
+});

diff --git a/javascript/generationParams.js b/javascript/generationParams.js
index 95f05093..1266a266 100644
--- a/javascript/generationParams.js
+++ b/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()
 	});
 });
 
diff --git a/javascript/hints.js b/javascript/hints.js
index f48a0eb6..44d418da 100644
--- a/javascript/hints.js
+++ b/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], [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],[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.",
@@ -111,7 +111,8 @@ 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."
 }
 
 
diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index d6483562..3deffa9b 100644
--- a/javascript/imageviewer.js
+++ b/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);
 
diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 4ac9b8db..8df3f569 100644
--- a/javascript/progressbar.js
+++ b/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
             }
diff --git a/launch.py b/launch.py
index 68e08114..af1c8309 100644
--- a/launch.py
+++ b/launch.py
@@ -121,12 +121,12 @@ def run_python(code, desc=None, errdesc=None):
     return run(f'"{python}" -c "{code}"', desc, errdesc)

 

 

-def run_pip(args, desc=None):

+def run_pip(args, desc=None, live=False):

     if 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 {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)

 

 

 def check_run_python(code):

@@ -225,10 +225,10 @@ 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 +271,7 @@ 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 +296,7 @@ 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)

 

diff --git a/modules/api/api.py b/modules/api/api.py
index 518b2a61..9ffcbd5f 100644
--- a/modules/api/api.py
+++ b/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
@@ -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])
 
diff --git a/modules/devices.py b/modules/devices.py
index 52c3e7cd..c705a3cb 100644
--- a/modules/devices.py
+++ b/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)
 
diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py
index d3a4d7ad..33d100dd 100644
--- a/modules/extra_networks_hypernet.py
+++ b/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]))

 

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 6df76858..99f1a0d3 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/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'),

 ]

 

 

diff --git a/modules/images.py b/modules/images.py
index b3535070..559de810 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -352,6 +352,8 @@ 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(),

+        'hasprompt': lambda self, *args: self.hasprompt(*args), #accept 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 +362,22 @@ 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:

diff --git a/modules/img2img.py b/modules/img2img.py
index 953ac5d2..d22d9a49 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -151,7 +151,7 @@ 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:

diff --git a/modules/interrogate.py b/modules/interrogate.py
index cbb80683..e1665708 100644
--- a/modules/interrogate.py
+++ b/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:

diff --git a/modules/postprocessing.py b/modules/postprocessing.py
index 09d8e605..4dc1a2ab 100644
--- a/modules/postprocessing.py
+++ b/modules/postprocessing.py
@@ -18,9 +18,15 @@ 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'

diff --git a/modules/processing.py b/modules/processing.py
index 6d9c6a8d..c50784f4 100644
--- a/modules/processing.py
+++ b/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 +164,7 @@ class StableDiffusionProcessing:
         self.all_seeds = None

         self.all_subseeds = None

         self.iteration = 0

+        

 

     @property

     def sd_model(self):

@@ -476,6 +479,9 @@ 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 +645,14 @@ 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:

@@ -706,9 +718,9 @@ 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")

@@ -1007,6 +1019,12 @@ 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:

diff --git a/modules/safe.py b/modules/safe.py
index 82d44be3..dadf319c 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -1,6 +1,5 @@
 # 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:

diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index a1aac7cf..bc074238 100644
--- a/modules/sd_samplers_common.py
+++ b/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

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index e9f08518..eb98e599 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/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

diff --git a/modules/shared.py b/modules/shared.py
index 5fd0eecb..ec964e3f 100644
--- a/modules/shared.py
+++ b/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 = \

@@ -253,6 +270,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "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 +286,7 @@ 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 +302,8 @@ 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 +352,7 @@ 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 +360,7 @@ 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 +384,7 @@ 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 +405,13 @@ 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 +430,7 @@ 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}),

@@ -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):

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index d2e62e58..379df243 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/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

diff --git a/modules/ui.py b/modules/ui.py
index 627fbe0b..dab94b93 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -171,8 +171,8 @@ def create_seed_inputs(target_interface):
     with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):

         seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')

         seed.style(container=False)

-        random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed')

-        reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed')

+        random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed')

+        reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed')

 

         seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)

 

@@ -468,7 +468,7 @@ def create_ui():
                                 height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")

 

                             with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):

-                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")

+                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")

 

                             if opts.dimensions_and_batch_together:

                                 with gr.Column(elem_id="txt2img_column_batch"):

@@ -1204,7 +1204,7 @@ def create_ui():
 

             with gr.Column(elem_id='ti_gallery_container'):

                 ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)

-                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)

+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)

                 ti_progress = gr.HTML(elem_id="ti_progress", value="")

                 ti_outcome = gr.HTML(elem_id="ti_error", value="")

 

@@ -1565,7 +1565,7 @@ def create_ui():
     for _interface, label, _ifid in interfaces:

         shared.tab_names.append(label)

 

-    with gr.Blocks(analytics_enabled=False, title="Stable Diffusion") as demo:

+    with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:

         with gr.Row(elem_id="quicksettings", variant="compact"):

             for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):

                 component = create_setting_component(k, is_quicksettings=True)

@@ -1705,7 +1705,7 @@ def create_ui():
                 if init_field is not None:

                     init_field(saved_value)

 

-        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:

+        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:

             apply_field(x, 'visible')

 

         if type(x) == gr.Slider:

diff --git a/modules/ui_common.py b/modules/ui_common.py
index 3b11dcc8..27ab3ebb 100644
--- a/modules/ui_common.py
+++ b/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():

diff --git a/modules/ui_components.py b/modules/ui_components.py
index 2b1da2cb..64451df7 100644
--- a/modules/ui_components.py
+++ b/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"

+

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index efd6cda2..e90bedc8 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -129,7 +129,7 @@ def normalize_git_url(url):
     return url

 

 

-def install_extension_from_url(dirname, url):

+def install_extension_from_url(dirname, url, branch_name=None):

     check_access()

 

     assert url, 'No URL specified'

@@ -150,10 +150,17 @@ def install_extension_from_url(dirname, url):
 

     try:

         shutil.rmtree(tmpdir, True)

-        with git.Repo.clone_from(url, tmpdir) as repo:

-            repo.remote().fetch()

-            for submodule in repo.submodules:

-                submodule.update()

+        if not branch_name:

+            # if no branch is specified, use the default branch

+            with git.Repo.clone_from(url, tmpdir) as repo:

+                repo.remote().fetch()

+                for submodule in repo.submodules:

+                    submodule.update()

+        else:

+            with git.Repo.clone_from(url, tmpdir, branch=branch_name) as repo:

+                repo.remote().fetch()

+                for submodule in repo.submodules:

+                    submodule.update()

         try:

             os.rename(tmpdir, target_dir)

         except OSError as err:

@@ -376,13 +383,14 @@ def create_ui():
 

             with gr.TabItem("Install from URL"):

                 install_url = gr.Text(label="URL for extension's git repository")

+                install_branch = gr.Text(label="Specific branch name", placeholder="Leave empty for default main branch")

                 install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")

                 install_button = gr.Button(value="Install", variant="primary")

                 install_result = gr.HTML(elem_id="extension_install_result")

 

                 install_button.click(

                     fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),

-                    inputs=[install_dirname, install_url],

+                    inputs=[install_dirname, install_url, install_branch],

                     outputs=[extensions_table, install_result],

                 )

 

diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py
index b418d955..d278e1b6 100644
--- a/modules/ui_postprocessing.py
+++ b/modules/ui_postprocessing.py
@@ -13,7 +13,7 @@ def create_ui():
                     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")

+                    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:

                     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")

diff --git a/requirements.txt b/requirements.txt
index c72b2927..9091f612 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,11 @@
+astunparse

 blendmodes

 accelerate

 basicsr

 fonts

 font-roboto

 gfpgan

-gradio==3.23

+gradio==3.27

 invisible-watermark

 numpy

 omegaconf

diff --git a/requirements_versions.txt b/requirements_versions.txt
index df65431a..94d32d3d 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -1,10 +1,10 @@
 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==3.27

+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

diff --git a/scripts/custom_code.py b/scripts/custom_code.py
index d29113e6..4071d86d 100644
--- a/scripts/custom_code.py
+++ b/scripts/custom_code.py
@@ -1,9 +1,40 @@
 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
diff --git a/scripts/postprocessing_upscale.py b/scripts/postprocessing_upscale.py
index 11eab31a..ef1186ac 100644
--- a/scripts/postprocessing_upscale.py
+++ b/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])

 

diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py
index 8aaee244..398065d9 100644
--- a/scripts/xyz_grid.py
+++ b/scripts/xyz_grid.py
@@ -212,6 +212,7 @@ axis_options = [
     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: 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

diff --git a/style.css b/style.css
index de16a7f2..aafc2362 100644
--- a/style.css
+++ b/style.css
@@ -312,6 +312,10 @@ 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;

diff --git a/webui-macos-env.sh b/webui-macos-env.sh
index 37cac4fb..65d80413 100644
--- a/webui-macos-env.sh
+++ b/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 --extra-index-url https://download.pytorch.org/whl/cu118"
 export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git"
 export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71"
 export PYTORCH_ENABLE_MPS_FALLBACK=1
diff --git a/webui-user.sh b/webui-user.sh
index bfa53cb7..49a426ff 100644
--- a/webui-user.sh
+++ b/webui-user.sh
@@ -43,4 +43,7 @@
 # Uncomment to enable accelerated launch
 #export ACCELERATE="True"
 
+# Uncomment to disable TCMalloc
+#export NO_TCMALLOC="True"
+
 ###########################################
diff --git a/webui.py b/webui.py
index b570895f..95623c6f 100644
--- a/webui.py
+++ b/webui.py
@@ -20,6 +20,9 @@ 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

@@ -67,11 +70,51 @@ 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 +127,7 @@ 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

 

@@ -99,6 +142,8 @@ Use --skip-version-check commandline argument to disable this check.
 

 

 def initialize():

+    fix_asyncio_event_loop_policy()

+

     check_versions()

 

     extensions.list_extensions()

@@ -126,9 +171,6 @@ 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 +192,7 @@ 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()

diff --git a/webui.sh b/webui.sh
index 8cdad22d..35e7b74e 100755
--- a/webui.sh
+++ b/webui.sh
@@ -23,7 +23,7 @@ fi
 # Install directory without trailing slash
 if [[ -z "${install_dir}" ]]
 then
-    install_dir="/home/$(whoami)"
+    install_dir="${HOME}"
 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