Merge branch 'dev' into release_candidate

CHANGELOG.md

-## Upcoming version:
+## Upcoming 1.2.0
+
+### Features:
+ * do not load wait for stable diffusion model to load at startup
+ * add filename patterns: [denoising]
+ * directory hiding for extra networks: dirs starting with . will hide their cards on extra network tabs unless specifically searched for
+ * Lora: for the `<...>` text in prompt, use name of Lora that is in the metdata of the file, if present, instead of filename (both can be used to activate lora)
+ * Lora: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
+ * Lora: Fix some Loras not working (ones that have 3x3 convolution layer)
+ * Lora: add an option to use old method of applying loras (producing same results as with kohya-ss)
+ * add version to infotext, footer and console output when starting
+ * add links to wiki for filename pattern settings
+ * add extended info for quicksettings setting and use multiselect input instead of a text field
+
+### Minor:
+ * --subpath option for gradio for use with reverse proxy
+ * linux/OSX: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
+ * possible frontend optimization: do not apply localizations if there are none
+ * Add extra `None` option for VAE in XYZ plot
+ * print error to console when batch processing in img2img fails
+ * create HTML for extra network pages only on demand
+ * allow directories starting with . to still list their models for lora, checkpoints, etc
+ * put infotext options into their own category in settings tab
+ * do not show licenses page when user selects Show all pages in settings
+ * 
+
+### Extensions:
+ * Tooltip localization support
+ * Add api method to get LoRA models with prompt
+
+### Bug Fixes:
+ * re-add /docs endpoint
+ * fix gamepad navigation
+ * make the lightbox fullscreen image function properly
+ * fix squished thumbnails in extras tab
+ * keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
+ * fix webui showing the same image if you configure the generation to always save results into same file
+ * fix bug with upscalers not working properly
+ * Fix MPS on PyTorch 2.0.1, Intel Macs
+ * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
+ * prevent Reload UI button/link from reloading the page when it's not yet ready
+
+
+## 1.1.1
+### Bug Fixes:
+ * fix an error that prevents running webui on torch<2.0 without --disable-safe-unpickle
+
+## 1.1.0
 ### Features:
  * switch to torch 2.0.0 (except for AMD GPUs)
  * visual improvements to custom code scripts
@@ -54,5 +101,5 @@
  * outpainting Mk2 & Poorman should use the SAMPLE file format to save images, not GRID file format
  * do not fail all Loras if some have failed to load when making a picture
 
-## Before versions:
+## 1.0.0
   * everything

extensions-builtin/Lora/extra_networks_lora.py

 from modules import extra_networks, shared
 import lora
 
+
 class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def __init__(self):
         super().__init__('lora')

extensions-builtin/Lora/lora.py

@@ -4,7 +4,7 @@ import re
 import torch
 from typing import Union
 
-from modules import shared, devices, sd_models, errors
+from modules import shared, devices, sd_models, errors, scripts
 
 metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
 
@@ -93,6 +93,7 @@ class LoraOnDisk:
             self.metadata = m
 
         self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None)  # those are cover images and they are too big to display in UI as text
+        self.alias = self.metadata.get('ss_output_name', self.name)
 
 
 class LoraModule:
@@ -165,8 +166,10 @@ def load_lora(name, filename):
             module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
         elif type(sd_module) == torch.nn.MultiheadAttention:
             module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
-        elif type(sd_module) == torch.nn.Conv2d:
+        elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (1, 1):
             module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+        elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3):
+            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False)
         else:
             print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
             continue
@@ -199,11 +202,11 @@ def load_loras(names, multipliers=None):
 
     loaded_loras.clear()
 
-    loras_on_disk = [available_loras.get(name, None) for name in names]
+    loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
     if any([x is None for x in loras_on_disk]):
         list_available_loras()
 
-        loras_on_disk = [available_loras.get(name, None) for name in names]
+        loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
 
     for i, name in enumerate(names):
         lora = already_loaded.get(name, None)
@@ -232,6 +235,8 @@ def lora_calc_updown(lora, module, target):
 
         if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
             updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
+        elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
+            updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
         else:
             updown = up @ down
 
@@ -240,6 +245,19 @@ def lora_calc_updown(lora, module, target):
         return updown
 
 
+def lora_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+    weights_backup = getattr(self, "lora_weights_backup", None)
+
+    if weights_backup is None:
+        return
+
+    if isinstance(self, torch.nn.MultiheadAttention):
+        self.in_proj_weight.copy_(weights_backup[0])
+        self.out_proj.weight.copy_(weights_backup[1])
+    else:
+        self.weight.copy_(weights_backup)
+
+
 def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
     """
     Applies the currently selected set of Loras to the weights of torch layer self.
@@ -264,12 +282,7 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu
         self.lora_weights_backup = weights_backup
 
     if current_names != wanted_names:
-        if weights_backup is not None:
-            if isinstance(self, torch.nn.MultiheadAttention):
-                self.in_proj_weight.copy_(weights_backup[0])
-                self.out_proj.weight.copy_(weights_backup[1])
-            else:
-                self.weight.copy_(weights_backup)
+        lora_restore_weights_from_backup(self)
 
         for lora in loaded_loras:
             module = lora.modules.get(lora_layer_name, None)
@@ -300,12 +313,45 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu
         setattr(self, "lora_current_names", wanted_names)
 
 
+def lora_forward(module, input, original_forward):
+    """
+    Old way of applying Lora by executing operations during layer's forward.
+    Stacking many loras this way results in big performance degradation.
+    """
+
+    if len(loaded_loras) == 0:
+        return original_forward(module, input)
+
+    input = devices.cond_cast_unet(input)
+
+    lora_restore_weights_from_backup(module)
+    lora_reset_cached_weight(module)
+
+    res = original_forward(module, input)
+
+    lora_layer_name = getattr(module, 'lora_layer_name', None)
+    for lora in loaded_loras:
+        module = lora.modules.get(lora_layer_name, None)
+        if module is None:
+            continue
+
+        module.up.to(device=devices.device)
+        module.down.to(device=devices.device)
+
+        res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+
+    return res
+
+
 def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
     setattr(self, "lora_current_names", ())
     setattr(self, "lora_weights_backup", None)
 
 
 def lora_Linear_forward(self, input):
+    if shared.opts.lora_functional:
+        return lora_forward(self, input, torch.nn.Linear_forward_before_lora)
+
     lora_apply_weights(self)
 
     return torch.nn.Linear_forward_before_lora(self, input)
@@ -318,6 +364,9 @@ def lora_Linear_load_state_dict(self, *args, **kwargs):
 
 
 def lora_Conv2d_forward(self, input):
+    if shared.opts.lora_functional:
+        return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora)
+
     lora_apply_weights(self)
 
     return torch.nn.Conv2d_forward_before_lora(self, input)
@@ -343,24 +392,59 @@ def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
 
 def list_available_loras():
     available_loras.clear()
+    available_lora_aliases.clear()
 
     os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
 
-    candidates = \
-        glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.pt'), recursive=True) + \
-        glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \
-        glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True)
-
+    candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
     for filename in sorted(candidates, key=str.lower):
         if os.path.isdir(filename):
             continue
 
         name = os.path.splitext(os.path.basename(filename))[0]
+        entry = LoraOnDisk(name, filename)
+
+        available_loras[name] = entry
+
+        available_lora_aliases[name] = entry
+        available_lora_aliases[entry.alias] = entry
+
+
+re_lora_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
+
+
+def infotext_pasted(infotext, params):
+    if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
+        return  # if the other extension is active, it will handle those fields, no need to do anything
+
+    added = []
+
+    for k, v in params.items():
+        if not k.startswith("AddNet Model "):
+            continue
+
+        num = k[13:]
+
+        if params.get("AddNet Module " + num) != "LoRA":
+            continue
+
+        name = params.get("AddNet Model " + num)
+        if name is None:
+            continue
+
+        m = re_lora_name.match(name)
+        if m:
+            name = m.group(1)
+
+        multiplier = params.get("AddNet Weight A " + num, "1.0")
 
-        available_loras[name] = LoraOnDisk(name, filename)
+        added.append(f"<lora:{name}:{multiplier}>")
 
+    if added:
+        params["Prompt"] += "\n" + "".join(added)
 
 available_loras = {}
+available_lora_aliases = {}
 loaded_loras = []
 
 list_available_loras()

extensions-builtin/Lora/scripts/lora_script.py

 import torch
 import gradio as gr
+from fastapi import FastAPI
 
 import lora
 import extra_networks_lora
 import ui_extra_networks_lora
 from modules import script_callbacks, ui_extra_networks, extra_networks, shared
 
-
 def unload():
     torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
     torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
@@ -49,8 +49,33 @@ torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention
 script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
 script_callbacks.on_script_unloaded(unload)
 script_callbacks.on_before_ui(before_ui)
+script_callbacks.on_infotext_pasted(lora.infotext_pasted)
 
 
 shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
     "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),
 }))
+
+
+shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
+    "lora_functional": shared.OptionInfo(False, "Lora: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
+}))
+
+
+def create_lora_json(obj: lora.LoraOnDisk):
+    return {
+        "name": obj.name,
+        "alias": obj.alias,
+        "path": obj.filename,
+        "metadata": obj.metadata,
+    }
+
+
+def api_loras(_: gr.Blocks, app: FastAPI):
+    @app.get("/sdapi/v1/loras")
+    async def get_loras():
+        return [create_lora_json(obj) for obj in lora.available_loras.values()]
+
+
+script_callbacks.on_app_started(api_loras)
+

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -21,7 +21,7 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
                 "preview": self.find_preview(path),
                 "description": self.find_description(path),
                 "search_term": self.search_terms_from_path(lora_on_disk.filename),
-                "prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
+                "prompt": json.dumps(f"<lora:{lora_on_disk.alias}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
                 "local_preview": f"{path}.{shared.opts.samples_format}",
                 "metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
             }

html/extra-networks-card.html

@@ -6,7 +6,7 @@
 			<ul>
 				<a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a>
 			</ul>
-			<span style="display:none" class='search_term'>{search_term}</span>
+			<span style="display:none" class='search_term{serach_only}'>{search_term}</span>
 		</div>
 		<span class='name'>{name}</span>
 		<span class='description'>{description}</span>

javascript/aspectRatioOverlay.js

@@ -45,29 +45,24 @@ function dimensionChange(e, is_width, is_height){
 
 		var viewportOffset = targetElement.getBoundingClientRect();
 
-		viewportscale = Math.min(  targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
+		var viewportscale = Math.min(  targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
 
-		scaledx = targetElement.naturalWidth*viewportscale
-		scaledy = targetElement.naturalHeight*viewportscale
+		var scaledx = targetElement.naturalWidth*viewportscale
+		var scaledy = targetElement.naturalHeight*viewportscale
 
-		cleintRectTop    = (viewportOffset.top+window.scrollY)
-		cleintRectLeft   = (viewportOffset.left+window.scrollX)
-		cleintRectCentreY = cleintRectTop  + (targetElement.clientHeight/2)
-		cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
+		var cleintRectTop    = (viewportOffset.top+window.scrollY)
+		var cleintRectLeft   = (viewportOffset.left+window.scrollX)
+		var cleintRectCentreY = cleintRectTop  + (targetElement.clientHeight/2)
+		var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
 
-		viewRectTop    = cleintRectCentreY-(scaledy/2)
-		viewRectLeft   = cleintRectCentreX-(scaledx/2)
-		arRectWidth  = scaledx
-		arRectHeight = scaledy
+		var arscale = Math.min(  scaledx/currentWidth, scaledy/currentHeight )
+		var arscaledx = currentWidth*arscale
+		var arscaledy = currentHeight*arscale
 
-		arscale = Math.min(  arRectWidth/currentWidth, arRectHeight/currentHeight )
-		arscaledx = currentWidth*arscale
-		arscaledy = currentHeight*arscale
-
-		arRectTop    = cleintRectCentreY-(arscaledy/2)
-		arRectLeft   = cleintRectCentreX-(arscaledx/2)
-		arRectWidth  = arscaledx
-		arRectHeight = arscaledy
+		var arRectTop    = cleintRectCentreY-(arscaledy/2)
+		var arRectLeft   = cleintRectCentreX-(arscaledx/2)
+		var arRectWidth  = arscaledx
+		var arRectHeight = arscaledy
 
 	    arPreviewRect.style.top  = arRectTop+'px';
 	    arPreviewRect.style.left = arRectLeft+'px';

javascript/contextMenus.js

@@ -4,7 +4,7 @@ contextMenuInit = function(){
   let menuSpecs = new Map();
 
   const uid = function(){
-    return Date.now().toString(36) + Math.random().toString(36).substr(2);
+    return Date.now().toString(36) + Math.random().toString(36).substring(2);
   }
 
   function showContextMenu(event,element,menuEntries){
@@ -16,8 +16,7 @@ contextMenuInit = function(){
       oldMenu.remove()
     }
 
-    let tabButton = uiCurrentTab
-    let baseStyle = window.getComputedStyle(tabButton)
+    let baseStyle = window.getComputedStyle(uiCurrentTab)
 
     const contextMenu = document.createElement('nav')
     contextMenu.id = "context-menu"
@@ -36,7 +35,7 @@ contextMenuInit = function(){
     menuEntries.forEach(function(entry){
       let contextMenuEntry = document.createElement('a')
       contextMenuEntry.innerHTML = entry['name']
-      contextMenuEntry.addEventListener("click", function(e) {
+      contextMenuEntry.addEventListener("click", function() {
         entry['func']();
       })
       contextMenuList.append(contextMenuEntry);
@@ -63,7 +62,7 @@ contextMenuInit = function(){
 
   function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
 
-    currentItems = menuSpecs.get(targetElementSelector)
+    var currentItems = menuSpecs.get(targetElementSelector)
 
     if(!currentItems){
       currentItems = []
@@ -79,7 +78,7 @@ contextMenuInit = function(){
   }
 
   function removeContextMenuOption(uid){
-    menuSpecs.forEach(function(v,k) {
+    menuSpecs.forEach(function(v) {
       let index = -1
       v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
       if(index>=0){
@@ -93,8 +92,7 @@ contextMenuInit = function(){
       return;
     }
     gradioApp().addEventListener("click", function(e) {
-      let source = e.composedPath()[0]
-      if(source.id && source.id.indexOf('check_progress')>-1){
+      if(! e.isTrusted){
         return
       }
 
@@ -112,7 +110,6 @@ contextMenuInit = function(){
         if(e.composedPath()[0].matches(k)){
           showContextMenu(e,e.composedPath()[0],v)
           e.preventDefault()
-          return
         }
       })
     });

javascript/edit-attention.js

@@ -69,8 +69,8 @@ function keyupEditAttention(event){
 
 	event.preventDefault();
 
-    closeCharacter = ')'
-    delta = opts.keyedit_precision_attention
+    var closeCharacter = ')'
+    var delta = opts.keyedit_precision_attention
 
     if (selectionStart > 0 && text[selectionStart - 1] == '<'){
         closeCharacter = '>'
@@ -91,8 +91,8 @@ function keyupEditAttention(event){
         selectionEnd += 1;
     }
 
-	end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
-	weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
+	var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
+	var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
 	if (isNaN(weight)) return;
 
 	weight += isPlus ? delta : -delta;

javascript/extensions.js

 
-function extensions_apply(_, _, disable_all){
+function extensions_apply(_disabled_list, _update_list, disable_all){
     var disable = []
     var update = []
 
     gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
         if(x.name.startsWith("enable_") && ! x.checked)
-            disable.push(x.name.substr(7))
+            disable.push(x.name.substring(7))
 
         if(x.name.startsWith("update_") && x.checked)
-            update.push(x.name.substr(7))
+            update.push(x.name.substring(7))
     })
 
     restart_reload()
@@ -16,12 +16,12 @@ function extensions_apply(_, _, disable_all){
     return [JSON.stringify(disable), JSON.stringify(update), disable_all]
 }
 
-function extensions_check(_, _){
+function extensions_check(){
     var disable = []
 
     gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
         if(x.name.startsWith("enable_") && ! x.checked)
-            disable.push(x.name.substr(7))
+            disable.push(x.name.substring(7))
     })
 
     gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
@@ -41,7 +41,7 @@ function install_extension_from_index(button, url){
     button.disabled = "disabled"
     button.value = "Installing..."
 
-    textarea = gradioApp().querySelector('#extension_to_install textarea')
+    var textarea = gradioApp().querySelector('#extension_to_install textarea')
     textarea.value = url
     updateInput(textarea)
 

javascript/extraNetworks.js

-
 function setupExtraNetworksForTab(tabname){
     gradioApp().querySelector('#'+tabname+'_extra_tabs').classList.add('extra-networks')
 
@@ -10,16 +9,34 @@ function setupExtraNetworksForTab(tabname){
     tabs.appendChild(search)
     tabs.appendChild(refresh)
 
-    search.addEventListener("input", function(evt){
-        searchTerm = search.value.toLowerCase()
+    var applyFilter = function(){
+        var searchTerm = search.value.toLowerCase()
 
         gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){
-            text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
-            elem.style.display = text.indexOf(searchTerm) == -1 ? "none" : ""
+            var searchOnly = elem.querySelector('.search_only')
+            var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
+
+            var visible = text.indexOf(searchTerm) != -1
+
+            if(searchOnly && searchTerm.length < 4){
+                visible = false
+            }
+
+            elem.style.display = visible ? "" : "none"
         })
-    });
+    }
+
+    search.addEventListener("input", applyFilter);
+    applyFilter();
+
+    extraNetworksApplyFilter[tabname] = applyFilter;
+}
+
+function applyExtraNetworkFilter(tabname){
+    setTimeout(extraNetworksApplyFilter[tabname], 1);
 }
 
+var extraNetworksApplyFilter = {}
 var activePromptTextarea = {};
 
 function setupExtraNetworks(){
@@ -55,7 +72,7 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text){
 
     var partToSearch = m[1]
     var replaced = false
-    var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, index){
+    var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found){
         m = found.match(re_extranet);
         if(m[1] == partToSearch){
             replaced = true;
@@ -96,9 +113,9 @@ function saveCardPreview(event, tabname, filename){
 }
 
 function extraNetworksSearchButton(tabs_id, event){
-    searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
-    button = event.target
-    text = button.classList.contains("search-all") ? "" : button.textContent.trim()
+    var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
+    var button = event.target
+    var text = button.classList.contains("search-all") ? "" : button.textContent.trim()
 
     searchTextarea.value = text
     updateInput(searchTextarea)
@@ -133,7 +150,7 @@ function popup(contents){
 }
 
 function extraNetworksShowMetadata(text){
-    elem = document.createElement('pre')
+    var elem = document.createElement('pre')
     elem.classList.add('popup-metadata');
     elem.textContent = text;
 
@@ -165,7 +182,7 @@ function requestGet(url, data, handler, errorHandler){
 }
 
 function extraNetworksRequestMetadata(event, extraPage, cardName){
-    showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
+    var showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
 
     requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
         if(data && data.metadata){

javascript/generationParams.js

@@ -23,7 +23,7 @@ let modalObserver = new MutationObserver(function(mutations) {
 });
 
 function attachGalleryListeners(tab_name) {
-	gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
+	var gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
 	gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
 	gallery?.addEventListener('keydown', (e) => {
 		if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow

javascript/hints.js

@@ -66,8 +66,8 @@ 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], [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.",
+    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [denoising], [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], [denoising], [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.",
@@ -118,16 +118,18 @@ titles = {
 
 onUiUpdate(function(){
 	gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){
-		tooltip = titles[span.textContent];
+		if (span.title) return;  // already has a title
 
-		if(!tooltip){
-		    tooltip = titles[span.value];
+		let tooltip = localization[titles[span.textContent]] || titles[span.textContent];
+
+    if(!tooltip){
+		    tooltip = localization[titles[span.value]] || titles[span.value];
 		}
 
 		if(!tooltip){
 			for (const c of span.classList) {
 				if (c in titles) {
-					tooltip = titles[c];
+					tooltip = localization[titles[c]] || titles[c];
 					break;
 				}
 			}
@@ -142,7 +144,7 @@ onUiUpdate(function(){
 	    if (select.onchange != null) return;
 
 	    select.onchange = function(){
-            select.title = titles[select.value] || "";
+            select.title = localization[titles[select.value]] || titles[select.value] || "";
 	    }
 	})
 })

javascript/hires_fix.js

 
-function setInactive(elem, inactive){
-    if(inactive){
-        elem.classList.add('inactive')
-    } else{
-        elem.classList.remove('inactive')
+function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
+    function setInactive(elem, inactive){
+        elem.classList.toggle('inactive', !!inactive)
     }
-}
 
-function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
-    hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
-    hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
-    hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
+    var hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
+    var hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
+    var hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
 
     gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""
 

javascript/imageMaskFix.js

@@ -2,11 +2,10 @@
  * temporary fix for https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668
  * @see https://github.com/gradio-app/gradio/issues/1721
  */
-window.addEventListener( 'resize', () => imageMaskResize());
 function imageMaskResize() {
     const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
     if ( ! canvases.length ) {
-    canvases_fixed = false;
+    canvases_fixed = false; // TODO: this is unused..?
     window.removeEventListener( 'resize', imageMaskResize );
     return;
     }
@@ -15,7 +14,7 @@ function imageMaskResize() {
     const previewImage = wrapper.previousElementSibling;
 
     if ( ! previewImage.complete ) {
-        previewImage.addEventListener( 'load', () => imageMaskResize());
+        previewImage.addEventListener( 'load', imageMaskResize);
         return;
     }
 
@@ -24,7 +23,6 @@ function imageMaskResize() {
     const nw = previewImage.naturalWidth;
     const nh = previewImage.naturalHeight;
     const portrait = nh > nw;
-    const factor = portrait;
 
     const wW = Math.min(w, portrait ? h/nh*nw : w/nw*nw);
     const wH = Math.min(h, portrait ? h/nh*nh : w/nw*nh);
@@ -40,6 +38,7 @@ function imageMaskResize() {
         c.style.maxHeight = '100%';
         c.style.objectFit = 'contain';
     });
- }
-  
- onUiUpdate(() => imageMaskResize());
+}
+
+onUiUpdate(imageMaskResize);
+window.addEventListener( 'resize', imageMaskResize);

javascript/imageParams.js

 window.onload = (function(){
     window.addEventListener('drop', e => {
         const target = e.composedPath()[0];
-        const idx = selected_gallery_index();
         if (target.placeholder.indexOf("Prompt") == -1) return;
 
         let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";

javascript/imageviewer.js

@@ -57,7 +57,7 @@ function modalImageSwitch(offset) {
         })
 
         if (result != -1) {
-            nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
+            var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
             nextButton.click()
             const modalImage = gradioApp().getElementById("modalImage");
             const modal = gradioApp().getElementById("lightboxModal");
@@ -144,15 +144,11 @@ function setupImageForLightbox(e) {
 }
 
 function modalZoomSet(modalImage, enable) {
-    if (enable) {
-        modalImage.classList.add('modalImageFullscreen');
-    } else {
-        modalImage.classList.remove('modalImageFullscreen');
-    }
+    if(modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable);
 }
 
 function modalZoomToggle(event) {
-    modalImage = gradioApp().getElementById("modalImage");
+    var modalImage = gradioApp().getElementById("modalImage");
     modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'))
     event.stopPropagation()
 }
@@ -179,7 +175,7 @@ function galleryImageHandler(e) {
 }
 
 onUiUpdate(function() {
-    fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
+    var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
     if (fullImg_preview != null) {
         fullImg_preview.forEach(setupImageForLightbox);
     }

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) {
+window.addEventListener('gamepadconnected', (e) => {
+    const index = e.gamepad.index;
+    let isWaiting = false;
+    setInterval(async () => {
+        if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
+        const gamepad = navigator.getGamepads()[index];
+        const xValue = gamepad.axes[0];
+        if (xValue <= -0.3) {
             modalPrevImage(e);
-        } else if (e.deltaX >= 0.6) {
+            isWaiting = true;
+        } else if (xValue >= 0.3) {
             modalNextImage(e);
+            isWaiting = true;
         }
+        if (isWaiting) {
+            await sleepUntil(() => {
+                const xValue = navigator.getGamepads()[index].axes[0]
+                if (xValue < 0.3 && xValue > -0.3) {
+                    return true;
+                }
+            }, opts.js_modal_lightbox_gamepad_repeat);
+            isWaiting = false;
+        }
+    }, 10);
+});
+
+/*
+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 (!opts.js_modal_lightbox_gamepad || 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
+    setTimeout(() => {
+        isScrolling = false;
+    }, opts.js_modal_lightbox_gamepad_repeat);
+});
+
+function sleepUntil(f, timeout) {
+    return new Promise((resolve) => {
+        const timeStart = new Date();
+        const wait = setInterval(function() {
+            if (f() || new Date() - timeStart > timeout) {
+                clearInterval(wait);
+                resolve();
+            }
+        }, 20);
+    });
+}

javascript/localization.js

@@ -25,6 +25,10 @@ re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u
 original_lines = {}
 translated_lines = {}
 
+function hasLocalization() {
+    return window.localization && Object.keys(window.localization).length > 0;
+}
+
 function textNodesUnder(el){
     var n, a=[], walk=document.createTreeWalker(el,NodeFilter.SHOW_TEXT,null,false);
     while(n=walk.nextNode()) a.push(n);
@@ -35,11 +39,11 @@ function canBeTranslated(node, text){
     if(! text) return false;
     if(! node.parentElement) return false;
 
-    parentType = node.parentElement.nodeName
+    var parentType = node.parentElement.nodeName
     if(parentType=='SCRIPT' || parentType=='STYLE' || parentType=='TEXTAREA') return false;
 
     if (parentType=='OPTION' || parentType=='SPAN'){
-        pnode = node
+        var pnode = node
         for(var level=0; level<4; level++){
             pnode = pnode.parentElement
             if(! pnode) break;
@@ -69,7 +73,7 @@ function getTranslation(text){
 }
 
 function processTextNode(node){
-    text = node.textContent.trim()
+    var text = node.textContent.trim()
 
     if(! canBeTranslated(node, text)) return
 
@@ -105,7 +109,7 @@ function processNode(node){
 }
 
 function dumpTranslations(){
-    dumped = {}
+    var dumped = {}
     if (localization.rtl) {
         dumped.rtl = true
     }
@@ -119,39 +123,8 @@ function dumpTranslations(){
     return dumped
 }
 
-onUiUpdate(function(m){
-    m.forEach(function(mutation){
-        mutation.addedNodes.forEach(function(node){
-            processNode(node)
-        })
-    });
-})
-
-
-document.addEventListener("DOMContentLoaded", function() {
-    processNode(gradioApp())
-
-    if (localization.rtl) {  // if the language is from right to left,
-        (new MutationObserver((mutations, observer) => { // wait for the style to load
-            mutations.forEach(mutation => {
-                mutation.addedNodes.forEach(node => {
-                    if (node.tagName === 'STYLE') {
-                        observer.disconnect();
-
-                        for (const x of node.sheet.rules) {  // find all rtl media rules
-                            if (Array.from(x.media || []).includes('rtl')) {
-                                x.media.appendMedium('all');  // enable them
-                            }
-                        }
-                    }
-                })
-            });
-        })).observe(gradioApp(), { childList: true });
-    }
-})
-
 function download_localization() {
-    text = JSON.stringify(dumpTranslations(), null, 4)
+    var text = JSON.stringify(dumpTranslations(), null, 4)
 
     var element = document.createElement('a');
     element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
@@ -163,3 +136,36 @@ function download_localization() {
 
     document.body.removeChild(element);
 }
+
+if(hasLocalization()) {
+    onUiUpdate(function (m) {
+        m.forEach(function (mutation) {
+            mutation.addedNodes.forEach(function (node) {
+                processNode(node)
+            })
+        });
+    })
+
+
+    document.addEventListener("DOMContentLoaded", function () {
+        processNode(gradioApp())
+
+        if (localization.rtl) {  // if the language is from right to left,
+            (new MutationObserver((mutations, observer) => { // wait for the style to load
+                mutations.forEach(mutation => {
+                    mutation.addedNodes.forEach(node => {
+                        if (node.tagName === 'STYLE') {
+                            observer.disconnect();
+
+                            for (const x of node.sheet.rules) {  // find all rtl media rules
+                                if (Array.from(x.media || []).includes('rtl')) {
+                                    x.media.appendMedium('all');  // enable them
+                                }
+                            }
+                        }
+                    })
+                });
+            })).observe(gradioApp(), { childList: true });
+        }
+    })
+}

javascript/notification.js

@@ -2,15 +2,15 @@
 
 let lastHeadImg = null;
 
-notificationButton = null
+let notificationButton = null;
 
 onUiUpdate(function(){
     if(notificationButton == null){
         notificationButton = gradioApp().getElementById('request_notifications')
 
         if(notificationButton != null){
-            notificationButton.addEventListener('click', function (evt) {
-                Notification.requestPermission();
+            notificationButton.addEventListener('click', () => {
+                void Notification.requestPermission();
             },true);
         }
     }

javascript/progressbar.js

 // code related to showing and updating progressbar shown as the image is being made
 
-function rememberGallerySelection(id_gallery){
+function rememberGallerySelection(){
 
 }
 
-function getGallerySelectedIndex(id_gallery){
+function getGallerySelectedIndex(){
 
 }
 
 function request(url, data, handler, errorHandler){
     var xhr = new XMLHttpRequest();
-    var url = url;
     xhr.open("POST", url, true);
     xhr.setRequestHeader("Content-Type", "application/json");
     xhr.onreadystatechange = function () {
@@ -107,7 +106,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                 divProgress.style.width = rect.width + "px";
             }
 
-            progressText = ""
+            let progressText = ""
 
             divInner.style.width = ((res.progress || 0) * 100.0) + '%'
             divInner.style.background = res.progress ? "" : "transparent"

javascript/ui.js

 // various functions for interaction with ui.py not large enough to warrant putting them in separate files
 
 function set_theme(theme){
-    gradioURL = window.location.href
+    var gradioURL = window.location.href
     if (!gradioURL.includes('?__theme=')) {
       window.location.replace(gradioURL + '?__theme=' + theme);
     }
@@ -47,7 +47,7 @@ function extract_image_from_gallery(gallery){
         return [gallery[0]];
     }
 
-    index = selected_gallery_index()
+    var index = selected_gallery_index()
 
     if (index < 0 || index >= gallery.length){
         // Use the first image in the gallery as the default
@@ -58,7 +58,7 @@ function extract_image_from_gallery(gallery){
 }
 
 function args_to_array(args){
-    res = []
+    var res = []
     for(var i=0;i<args.length;i++){
         res.push(args[i])
     }
@@ -138,7 +138,7 @@ function get_img2img_tab_index() {
 }
 
 function create_submit_args(args){
-    res = []
+    var res = []
     for(var i=0;i<args.length;i++){
         res.push(args[i])
     }
@@ -160,7 +160,7 @@ function showSubmitButtons(tabname, show){
 }
 
 function showRestoreProgressButton(tabname, show){
-    button = gradioApp().getElementById(tabname + "_restore_progress")
+    var button = gradioApp().getElementById(tabname + "_restore_progress")
     if(! button) return
 
     button.style.display = show ? "flex" : "none"
@@ -207,8 +207,9 @@ function submit_img2img(){
     return res
 }
 
-function restoreProgressTxt2img(x){
+function restoreProgressTxt2img(){
     showRestoreProgressButton("txt2img", false)
+    var id = localStorage.getItem("txt2img_task_id")
 
     id = localStorage.getItem("txt2img_task_id")
 
@@ -220,10 +221,11 @@ function restoreProgressTxt2img(x){
 
     return id
 }
-function restoreProgressImg2img(x){
-    showRestoreProgressButton("img2img", false)
 
-    id = localStorage.getItem("img2img_task_id")
+function restoreProgressImg2img(){
+    showRestoreProgressButton("img2img", false)
+    
+    var id = localStorage.getItem("img2img_task_id")
 
     if(id) {
         requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function(){
@@ -252,7 +254,7 @@ function modelmerger(){
 
 
 function ask_for_style_name(_, prompt_text, negative_prompt_text) {
-    name_ = prompt('Style name:')
+    var name_ = prompt('Style name:')
     return [name_, prompt_text, negative_prompt_text]
 }
 
@@ -287,11 +289,11 @@ function recalculate_prompts_img2img(){
 }
 
 
-opts = {}
+var opts = {}
 onUiUpdate(function(){
 	if(Object.keys(opts).length != 0) return;
 
-	json_elem = gradioApp().getElementById('settings_json')
+	var json_elem = gradioApp().getElementById('settings_json')
 	if(json_elem == null) return;
 
     var textarea = json_elem.querySelector('textarea')
@@ -340,12 +342,15 @@ onUiUpdate(function(){
     registerTextarea('img2img_prompt', 'img2img_token_counter', 'img2img_token_button')
     registerTextarea('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button')
 
-    show_all_pages = gradioApp().getElementById('settings_show_all_pages')
-    settings_tabs = gradioApp().querySelector('#settings div')
+    var show_all_pages = gradioApp().getElementById('settings_show_all_pages')
+    var settings_tabs = gradioApp().querySelector('#settings div')
     if(show_all_pages && settings_tabs){
         settings_tabs.appendChild(show_all_pages)
         show_all_pages.onclick = function(){
             gradioApp().querySelectorAll('#settings > div').forEach(function(elem){
+                if(elem.id == "settings_tab_licenses")
+                    return;
+
                 elem.style.display = "block";
             })
         }
@@ -353,9 +358,9 @@ onUiUpdate(function(){
 })
 
 onOptionsChanged(function(){
-    elem = gradioApp().getElementById('sd_checkpoint_hash')
-    sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
-    shorthash = sd_checkpoint_hash.substr(0,10)
+    var elem = gradioApp().getElementById('sd_checkpoint_hash')
+    var sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
+    var shorthash = sd_checkpoint_hash.substring(0,10)
 
 	if(elem && elem.textContent != shorthash){
 	    elem.textContent = shorthash
@@ -390,7 +395,16 @@ function update_token_counter(button_id) {
 
 function restart_reload(){
     document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
-    setTimeout(function(){location.reload()},2000)
+
+    var requestPing = function(){
+        requestGet("./internal/ping", {}, function(data){
+            location.reload();
+        }, function(){
+            setTimeout(requestPing, 500);
+        })
+    }
+
+    setTimeout(requestPing, 2000);
 
     return []
 }

javascript/ui_settings_hints.js

+// various hints and extra info for the settings tab
+
+onUiLoaded(function(){
+    createLink = function(elem_id, text, href){
+        var a = document.createElement('A')
+        a.textContent = text
+        a.target = '_blank';
+
+        elem = gradioApp().querySelector('#'+elem_id)
+        elem.insertBefore(a, elem.querySelector('label'))
+
+        return a
+    }
+
+    createLink("setting_samples_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
+    createLink("setting_directories_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
+
+    createLink("setting_quicksettings_list", "[info] ").addEventListener("click", function(event){
+        requestGet("./internal/quicksettings-hint", {}, function(data){
+            var table = document.createElement('table')
+            table.className = 'settings-value-table'
+
+            data.forEach(function(obj){
+                var tr = document.createElement('tr')
+                var td = document.createElement('td')
+                td.textContent = obj.name
+                tr.appendChild(td)
+
+                var td = document.createElement('td')
+                td.textContent = obj.label
+                tr.appendChild(td)
+
+                table.appendChild(tr)
+            })
+
+            popup(table);
+        })
+    });
+})
+
+

launch.py

@@ -19,6 +19,7 @@ python = sys.executable
 git = os.environ.get('GIT', "git")
 index_url = os.environ.get('INDEX_URL', "")
 stored_commit_hash = None
+stored_git_tag = None
 dir_repos = "repositories"
 
 if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
@@ -70,6 +71,20 @@ def commit_hash():
     return stored_commit_hash
 
 
+def git_tag():
+    global stored_git_tag
+
+    if stored_git_tag is not None:
+        return stored_git_tag
+
+    try:
+        stored_git_tag = run(f"{git} describe --tags").strip()
+    except Exception:
+        stored_git_tag = "<none>"
+
+    return stored_git_tag
+
+
 def run(command, desc=None, errdesc=None, custom_env=None, live=False):
     if desc is not None:
         print(desc)
@@ -246,8 +261,10 @@ def prepare_environment():
         check_python_version()
 
     commit = commit_hash()
+    tag = git_tag()
 
     print(f"Python {sys.version}")
+    print(f"Version: {tag}")
     print(f"Commit hash: {commit}")
 
     if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):

modules/api/api.py

@@ -570,20 +570,20 @@ class Api:
             filename = create_embedding(**args) # create empty embedding
             sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
             shared.state.end()
-            return CreateResponse(info = "create embedding filename: {filename}".format(filename = filename))
+            return CreateResponse(info=f"create embedding filename: {filename}")
         except AssertionError as e:
             shared.state.end()
-            return TrainResponse(info = "create embedding error: {error}".format(error = e))
+            return TrainResponse(info=f"create embedding error: {e}")
 
     def create_hypernetwork(self, args: dict):
         try:
             shared.state.begin()
             filename = create_hypernetwork(**args) # create empty embedding
             shared.state.end()
-            return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename))
+            return CreateResponse(info=f"create hypernetwork filename: {filename}")
         except AssertionError as e:
             shared.state.end()
-            return TrainResponse(info = "create hypernetwork error: {error}".format(error = e))
+            return TrainResponse(info=f"create hypernetwork error: {e}")
 
     def preprocess(self, args: dict):
         try:
@@ -593,13 +593,13 @@ class Api:
             return PreprocessResponse(info = 'preprocess complete')
         except KeyError as e:
             shared.state.end()
-            return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e))
+            return PreprocessResponse(info=f"preprocess error: invalid token: {e}")
         except AssertionError as e:
             shared.state.end()
-            return PreprocessResponse(info = "preprocess error: {error}".format(error = e))
+            return PreprocessResponse(info=f"preprocess error: {e}")
         except FileNotFoundError as e:
             shared.state.end()
-            return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e))
+            return PreprocessResponse(info=f'preprocess error: {e}')
 
     def train_embedding(self, args: dict):
         try:
@@ -617,10 +617,10 @@ class Api:
                 if not apply_optimizations:
                     sd_hijack.apply_optimizations()
                 shared.state.end()
-            return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
+            return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
         except AssertionError as msg:
             shared.state.end()
-            return TrainResponse(info = "train embedding error: {msg}".format(msg = msg))
+            return TrainResponse(info=f"train embedding error: {msg}")
 
     def train_hypernetwork(self, args: dict):
         try:
@@ -641,10 +641,10 @@ class Api:
                 if not apply_optimizations:
                     sd_hijack.apply_optimizations()
                 shared.state.end()
-            return TrainResponse(info="train embedding complete: filename: {filename} error: {error}".format(filename=filename, error=error))
+            return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
         except AssertionError as msg:
             shared.state.end()
-            return TrainResponse(info="train embedding error: {error}".format(error=error))
+            return TrainResponse(info=f"train embedding error: {error}")
 
     def get_memory(self):
         try:

modules/call_queue.py

@@ -60,7 +60,7 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
             max_debug_str_len = 131072 # (1024*1024)/8
 
             print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
+            argStr = f"Arguments: {args} {kwargs}"
             print(argStr[:max_debug_str_len], file=sys.stderr)
             if len(argStr) > max_debug_str_len:
                 print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
@@ -73,7 +73,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
             if extra_outputs_array is None:
                 extra_outputs_array = [None, '']
 
-            res = extra_outputs_array + [f"<div class='error'>{html.escape(type(e).__name__+': '+str(e))}</div>"]
+            error_message = f'{type(e).__name__}: {e}'
+            res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
 
         shared.state.skipped = False
         shared.state.interrupted = False

modules/cmd_args.py

@@ -102,3 +102,4 @@ parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gra
 parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
 parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
 parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
+parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
\ No newline at end of file

modules/esrgan_model.py

@@ -156,13 +156,16 @@ class UpscalerESRGAN(Upscaler):
 
     def load_model(self, path: str):
         if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
-                                          file_name="%s.pth" % self.model_name,
-                                          progress=True)
+            filename = load_file_from_url(
+                url=self.model_url,
+                model_dir=self.model_path,
+                file_name=f"{self.model_name}.pth",
+                progress=True,
+            )
         else:
             filename = path
         if not os.path.exists(filename) or filename is None:
-            print("Unable to load %s from %s" % (self.model_path, filename))
+            print(f"Unable to load {self.model_path} from {filename}")
             return None
 
         state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)

modules/esrgan_model_arch.py

@@ -38,7 +38,7 @@ class RRDBNet(nn.Module):
         elif upsample_mode == 'pixelshuffle':
             upsample_block = pixelshuffle_block
         else:
-            raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
+            raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
         if upscale == 3:
             upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
         else:
@@ -261,10 +261,10 @@ class Upsample(nn.Module):
 
     def extra_repr(self):
         if self.scale_factor is not None:
-            info = 'scale_factor=' + str(self.scale_factor)
+            info = f'scale_factor={self.scale_factor}'
         else:
-            info = 'size=' + str(self.size)
-        info += ', mode=' + self.mode
+            info = f'size={self.size}'
+        info += f', mode={self.mode}'
         return info
 
 
@@ -350,7 +350,7 @@ def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
     elif act_type == 'sigmoid':  # [0, 1] range output
         layer = nn.Sigmoid()
     else:
-        raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
+        raise NotImplementedError(f'activation layer [{act_type}] is not found')
     return layer
 
 
@@ -372,7 +372,7 @@ def norm(norm_type, nc):
     elif norm_type == 'none':
         def norm_layer(x): return Identity()
     else:
-        raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
+        raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
     return layer
 
 
@@ -388,7 +388,7 @@ def pad(pad_type, padding):
     elif pad_type == 'zero':
         layer = nn.ZeroPad2d(padding)
     else:
-        raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
+        raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
     return layer
 
 
@@ -432,7 +432,7 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
                pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
                spectral_norm=False):
     """ Conv layer with padding, normalization, activation """
-    assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)
+    assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
     padding = get_valid_padding(kernel_size, dilation)
     p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
     padding = padding if pad_type == 'zero' else 0

modules/extra_networks_hypernet.py

@@ -10,7 +10,8 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
         additional = shared.opts.sd_hypernetwork
 
         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]
+            hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
+            p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
 
         names = []

modules/generation_parameters_copypaste.py

@@ -59,6 +59,7 @@ def image_from_url_text(filedata):
         is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
         assert is_in_right_dir, 'trying to open image file outside of allowed directories'
 
+        filename = filename.rsplit('?', 1)[0]
         return Image.open(filename)
 
     if type(filedata) == list:
@@ -129,6 +130,7 @@ def connect_paste_params_buttons():
                 _js=jsfunc,
                 inputs=[binding.source_image_component],
                 outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
+                show_progress=False,
             )
 
         if binding.source_text_component is not None and fields is not None:
@@ -140,6 +142,7 @@ def connect_paste_params_buttons():
                 fn=lambda *x: x,
                 inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
                 outputs=[field for field, name in fields if name in paste_field_names],
+                show_progress=False,
             )
 
         binding.paste_button.click(
@@ -147,6 +150,7 @@ def connect_paste_params_buttons():
             _js=f"switch_to_{binding.tabname}",
             inputs=None,
             outputs=None,
+            show_progress=False,
         )
 
 
@@ -265,8 +269,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
         m = re_imagesize.match(v)
         if m is not None:
-            res[k+"-1"] = m.group(1)
-            res[k+"-2"] = m.group(2)
+            res[f"{k}-1"] = m.group(1)
+            res[f"{k}-2"] = m.group(2)
         else:
             res[k] = v
 
@@ -409,12 +413,14 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
         fn=paste_func,
         inputs=[input_comp],
         outputs=[x[0] for x in paste_fields],
+        show_progress=False,
     )
     button.click(
         fn=None,
         _js=f"recalculate_prompts_{tabname}",
         inputs=[],
         outputs=[],
+        show_progress=False,
     )
 
 

modules/hashes.py

@@ -13,7 +13,7 @@ cache_data = None
 
 
 def dump_cache():
-    with filelock.FileLock(cache_filename+".lock"):
+    with filelock.FileLock(f"{cache_filename}.lock"):
         with open(cache_filename, "w", encoding="utf8") as file:
             json.dump(cache_data, file, indent=4)
 
@@ -22,7 +22,7 @@ def cache(subsection):
     global cache_data
 
     if cache_data is None:
-        with filelock.FileLock(cache_filename+".lock"):
+        with filelock.FileLock(f"{cache_filename}.lock"):
             if not os.path.isfile(cache_filename):
                 cache_data = {}
             else:

modules/images.py

@@ -357,6 +357,7 @@ class FilenameGenerator:
         '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"],
+        'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
     }
     default_time_format = '%Y%m%d%H%M%S'
 
@@ -466,7 +467,7 @@ def get_next_sequence_number(path, basename):
     """
     result = -1
     if basename != '':
-        basename = basename + "-"
+        basename = f"{basename}-"
 
     prefix_length = len(basename)
     for p in os.listdir(path):
@@ -535,7 +536,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         add_number = opts.save_images_add_number or file_decoration == ''
 
         if file_decoration != "" and add_number:
-            file_decoration = "-" + file_decoration
+            file_decoration = f"-{file_decoration}"
 
         file_decoration = namegen.apply(file_decoration) + suffix
 
@@ -565,7 +566,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
     def _atomically_save_image(image_to_save, filename_without_extension, extension):
         # save image with .tmp extension to avoid race condition when another process detects new image in the directory
-        temp_file_path = filename_without_extension + ".tmp"
+        temp_file_path = f"{filename_without_extension}.tmp"
         image_format = Image.registered_extensions()[extension]
 
         if extension.lower() == '.png':
@@ -625,7 +626,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     if opts.save_txt and info is not None:
         txt_fullfn = f"{fullfn_without_extension}.txt"
         with open(txt_fullfn, "w", encoding="utf8") as file:
-            file.write(info + "\n")
+            file.write(f"{info}\n")
     else:
         txt_fullfn = None
 

modules/img2img.py

@@ -48,7 +48,8 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
 
         try:
             img = Image.open(image)
-        except UnidentifiedImageError:
+        except UnidentifiedImageError as e:
+            print(e)
             continue
         # Use the EXIF orientation of photos taken by smartphones.
         img = ImageOps.exif_transpose(img)

modules/interrogate.py

@@ -28,7 +28,7 @@ def category_types():
 def download_default_clip_interrogate_categories(content_dir):
     print("Downloading CLIP categories...")
 
-    tmpdir = content_dir + "_tmp"
+    tmpdir = f"{content_dir}_tmp"
     category_types = ["artists", "flavors", "mediums", "movements"]
 
     try:
@@ -214,7 +214,7 @@ class InterrogateModels:
                         if shared.opts.interrogate_return_ranks:
                             res += f", ({match}:{score/100:.3f})"
                         else:
-                            res += ", " + match
+                            res += f", {match}"
 
         except Exception:
             print("Error interrogating", file=sys.stderr)

modules/mac_specific.py

@@ -54,6 +54,11 @@ if has_mps:
         CondFunc('torch.cumsum', cumsum_fix_func, None)
         CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
         CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
-    if version.parse(torch.__version__) == version.parse("2.0"):
+
         # MPS workaround for https://github.com/pytorch/pytorch/issues/96113
-        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda *args, **kwargs: len(args) == 6)
+        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps')
+
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/92311
+        if platform.processor() == 'i386':
+            for funcName in ['torch.argmax', 'torch.Tensor.argmax']:
+                CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps')
\ No newline at end of file

modules/modelloader.py

@@ -22,9 +22,6 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
     """
     output = []
 
-    if ext_filter is None:
-        ext_filter = []
-
     try:
         places = []
 
@@ -39,22 +36,14 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
         places.append(model_path)
 
         for place in places:
-            if os.path.exists(place):
-                for file in glob.iglob(place + '**/**', recursive=True):
-                    full_path = file
-                    if os.path.isdir(full_path):
-                        continue
-                    if os.path.islink(full_path) and not os.path.exists(full_path):
-                        print(f"Skipping broken symlink: {full_path}")
-                        continue
-                    if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
-                        continue
-                    if len(ext_filter) != 0:
-                        model_name, extension = os.path.splitext(file)
-                        if extension not in ext_filter:
-                            continue
-                    if file not in output:
-                        output.append(full_path)
+            for full_path in shared.walk_files(place, allowed_extensions=ext_filter):
+                if os.path.islink(full_path) and not os.path.exists(full_path):
+                    print(f"Skipping broken symlink: {full_path}")
+                    continue
+                if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
+                    continue
+                if full_path not in output:
+                    output.append(full_path)
 
         if model_url is not None and len(output) == 0:
             if download_name is not None:
@@ -133,12 +122,9 @@ forbidden_upscaler_classes = set()
 
 
 def list_builtin_upscalers():
-    load_upscalers()
-
     builtin_upscaler_classes.clear()
     builtin_upscaler_classes.extend(Upscaler.__subclasses__())
 
-
 def forbid_loaded_nonbuiltin_upscalers():
     for cls in Upscaler.__subclasses__():
         if cls not in builtin_upscaler_classes:

modules/models/diffusion/ddpm_edit.py

@@ -223,7 +223,7 @@ class DDPM(pl.LightningModule):
         for k in keys:
             for ik in ignore_keys:
                 if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
+                    print(f"Deleting key {k} from state_dict.")
                     del sd[k]
         missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
             sd, strict=False)
@@ -386,7 +386,7 @@ class DDPM(pl.LightningModule):
         _, loss_dict_no_ema = self.shared_step(batch)
         with self.ema_scope():
             _, loss_dict_ema = self.shared_step(batch)
-            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+            loss_dict_ema = {f"{key}_ema": loss_dict_ema[key] for key in loss_dict_ema}
         self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
         self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
 

modules/models/diffusion/uni_pc/uni_pc.py

@@ -94,7 +94,7 @@ class NoiseScheduleVP:
         """
 
         if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
+            raise ValueError(f"Unsupported noise schedule {schedule}. The schedule needs to be 'discrete' or 'linear' or 'cosine'")
 
         self.schedule = schedule
         if schedule == 'discrete':
@@ -469,7 +469,7 @@ class UniPC:
             t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
             return t
         else:
-            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
+            raise ValueError(f"Unsupported skip_type {skip_type}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'")
 
     def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
         """

modules/ngrok.py

@@ -7,8 +7,8 @@ def connect(token, port, region):
     else:
         if ':' in token:
             # token = authtoken:username:password
-            account = token.split(':')[1] + ':' + token.split(':')[-1]
-            token = token.split(':')[0]
+            token, username, password = token.split(':', 2)
+            account = f"{username}:{password}"
 
     config = conf.PyngrokConfig(
         auth_token=token, region=region

modules/paths.py

@@ -16,7 +16,7 @@ for possible_sd_path in possible_sd_paths:
         sd_path = os.path.abspath(possible_sd_path)
         break
 
-assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)
+assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possible_sd_paths}"
 
 path_dirs = [
     (sd_path, 'ldm', 'Stable Diffusion', []),

modules/processing.py

@@ -458,6 +458,16 @@ def fix_seed(p):
     p.subseed = get_fixed_seed(p.subseed)
 
 
+def program_version():
+    import launch
+
+    res = launch.git_tag()
+    if res == "<none>":
+        res = None
+
+    return res
+
+
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
@@ -483,13 +493,14 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "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,
+        "Version": program_version() if opts.add_version_to_infotext else None,
     }
 
     generation_params.update(p.extra_generation_params)
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
+    negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
 
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
@@ -769,7 +780,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     devices.torch_gc()
 
-    res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
+    res = Processed(
+        p,
+        images_list=output_images,
+        seed=p.all_seeds[0],
+        info=infotext(),
+        comments="".join(f"\n\n{comment}" for comment in comments),
+        subseed=p.all_subseeds[0],
+        index_of_first_image=index_of_first_image,
+        infotexts=infotexts,
+    )
 
     if p.scripts is not None:
         p.scripts.postprocess(p, res)

modules/progress.py

@@ -96,7 +96,8 @@ def progressapi(req: ProgressRequest):
         if image is not None:
             buffered = io.BytesIO()
             image.save(buffered, format="png")
-            live_preview = 'data:image/png;base64,' + base64.b64encode(buffered.getvalue()).decode("ascii")
+            base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
+            live_preview = f"data:image/png;base64,{base64_image}"
             id_live_preview = shared.state.id_live_preview
         else:
             live_preview = None

modules/realesrgan_model.py

@@ -28,9 +28,9 @@ class UpscalerRealESRGAN(Upscaler):
             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
+                    local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
+                    if local_model_candidates:
+                        scaler.local_data_path = local_model_candidates[0]
 
                 if scaler.name in opts.realesrgan_enabled_models:
                     self.scalers.append(scaler)
@@ -47,7 +47,7 @@ class UpscalerRealESRGAN(Upscaler):
 
         info = self.load_model(path)
         if not os.path.exists(info.local_data_path):
-            print("Unable to load RealESRGAN model: %s" % info.name)
+            print(f"Unable to load RealESRGAN model: {info.name}")
             return img
 
         upsampler = RealESRGANer(

modules/safe.py

@@ -24,7 +24,11 @@ class RestrictedUnpickler(pickle.Unpickler):
 
     def persistent_load(self, saved_id):
         assert saved_id[0] == 'storage'
-        return TypedStorage(_internal=True)
+
+        try:
+            return TypedStorage(_internal=True)
+        except TypeError:
+            return TypedStorage()  # PyTorch before 2.0 does not have the _internal argument
 
     def find_class(self, module, name):
         if self.extra_handler is not None:

modules/scripts.py

@@ -163,7 +163,8 @@ class Script:
         """helper function to generate id for a HTML element, constructs final id out of script name, tab and user-supplied item_id"""
 
         need_tabname = self.show(True) == self.show(False)
-        tabname = ('img2img' if self.is_img2img else 'txt2txt') + "_" if need_tabname else ""
+        tabkind = 'img2img' if self.is_img2img else 'txt2txt'
+        tabname = f"{tabkind}_" if need_tabname else ""
         title = re.sub(r'[^a-z_0-9]', '', re.sub(r'\s', '_', self.title().lower()))
 
         return f'script_{tabname}{title}_{item_id}'
@@ -526,7 +527,7 @@ def add_classes_to_gradio_component(comp):
     this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
     """
 
-    comp.elem_classes = ["gradio-" + comp.get_block_name(), *(comp.elem_classes or [])]
+    comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
 
     if getattr(comp, 'multiselect', False):
         comp.elem_classes.append('multiselect')

modules/sd_hijack_clip_old.py

@@ -75,7 +75,8 @@ def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, text
     self.hijack.comments += hijack_comments
 
     if len(used_custom_terms) > 0:
-        self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
+        embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
+        self.hijack.comments.append(f"Used embeddings: {embedding_names}")
 
     self.hijack.fixes = hijack_fixes
     return self.process_tokens(remade_batch_tokens, batch_multipliers)

modules/sd_hijack_optimizations.py

@@ -256,6 +256,9 @@ def sub_quad_attention_forward(self, x, context=None, mask=None):
     k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
     v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
 
+    if q.device.type == 'mps':
+        q, k, v = q.contiguous(), k.contiguous(), v.contiguous()
+
     dtype = q.dtype
     if shared.opts.upcast_attn:
         q, k = q.float(), k.float()

modules/sd_hijack_unet.py

@@ -18,7 +18,7 @@ class TorchHijackForUnet:
         if hasattr(torch, item):
             return getattr(torch, item)
 
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
 
     def cat(self, tensors, *args, **kwargs):
         if len(tensors) == 2:

modules/sd_models.py

@@ -2,6 +2,8 @@ import collections
 import os.path
 import sys
 import gc
+import threading
+
 import torch
 import re
 import safetensors.torch
@@ -45,7 +47,7 @@ class CheckpointInfo:
         self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
         self.hash = model_hash(filename)
 
-        self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + name)
+        self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
         self.shorthash = self.sha256[0:10] if self.sha256 else None
 
         self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
@@ -67,7 +69,7 @@ class CheckpointInfo:
             checkpoint_alisases[id] = self
 
     def calculate_shorthash(self):
-        self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name)
+        self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
         if self.sha256 is None:
             return
 
@@ -404,13 +406,39 @@ def repair_config(sd_config):
 sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
 sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
 
-def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_to_load_state_dict=None):
+
+class SdModelData:
+    def __init__(self):
+        self.sd_model = None
+        self.lock = threading.Lock()
+
+    def get_sd_model(self):
+        if self.sd_model is None:
+            with self.lock:
+                try:
+                    load_model()
+                except Exception as e:
+                    errors.display(e, "loading stable diffusion model")
+                    print("", file=sys.stderr)
+                    print("Stable diffusion model failed to load", file=sys.stderr)
+                    self.sd_model = None
+
+        return self.sd_model
+
+    def set_sd_model(self, v):
+        self.sd_model = v
+
+
+model_data = SdModelData()
+
+
+def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
 
-    if shared.sd_model:
-        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
-        shared.sd_model = None
+    if model_data.sd_model:
+        sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
+        model_data.sd_model = None
         gc.collect()
         devices.torch_gc()
 
@@ -464,7 +492,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_
     timer.record("hijack")
 
     sd_model.eval()
-    shared.sd_model = sd_model
+    model_data.sd_model = sd_model
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
 
@@ -484,7 +512,7 @@ def reload_model_weights(sd_model=None, info=None):
     checkpoint_info = info or select_checkpoint()
 
     if not sd_model:
-        sd_model = shared.sd_model
+        sd_model = model_data.sd_model
 
     if sd_model is None:  # previous model load failed
         current_checkpoint_info = None
@@ -512,7 +540,7 @@ def reload_model_weights(sd_model=None, info=None):
         del sd_model
         checkpoints_loaded.clear()
         load_model(checkpoint_info, already_loaded_state_dict=state_dict)
-        return shared.sd_model
+        return model_data.sd_model
 
     try:
         load_model_weights(sd_model, checkpoint_info, state_dict, timer)
@@ -535,17 +563,15 @@ def reload_model_weights(sd_model=None, info=None):
 
     return sd_model
 
+
 def unload_model_weights(sd_model=None, info=None):
     from modules import lowvram, devices, sd_hijack
     timer = Timer()
 
-    if shared.sd_model:
-
-        # shared.sd_model.cond_stage_model.to(devices.cpu)
-        # shared.sd_model.first_stage_model.to(devices.cpu)
-        shared.sd_model.to(devices.cpu)
-        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
-        shared.sd_model = None
+    if model_data.sd_model:
+        model_data.sd_model.to(devices.cpu)
+        sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
+        model_data.sd_model = None
         sd_model = None
         gc.collect()
         devices.torch_gc()

modules/sd_models_config.py

@@ -111,7 +111,7 @@ def find_checkpoint_config_near_filename(info):
     if info is None:
         return None
 
-    config = os.path.splitext(info.filename)[0] + ".yaml"
+    config = f"{os.path.splitext(info.filename)[0]}.yaml"
     if os.path.exists(config):
         return config
 

modules/sd_samplers_kdiffusion.py

@@ -198,7 +198,7 @@ class TorchHijack:
         if hasattr(torch, item):
             return getattr(torch, item)
 
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
 
     def randn_like(self, x):
         if self.sampler_noises:

modules/sd_vae.py

@@ -89,7 +89,7 @@ def refresh_vae_list():
 
 def find_vae_near_checkpoint(checkpoint_file):
     checkpoint_path = os.path.splitext(checkpoint_file)[0]
-    for vae_location in [checkpoint_path + ".vae.pt", checkpoint_path + ".vae.ckpt", checkpoint_path + ".vae.safetensors"]:
+    for vae_location in [f"{checkpoint_path}.vae.pt", f"{checkpoint_path}.vae.ckpt", f"{checkpoint_path}.vae.safetensors"]:
         if os.path.isfile(vae_location):
             return vae_location
 

modules/shared.py

@@ -16,6 +16,7 @@ import modules.styles
 import modules.devices as devices
 from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
 from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir
+from ldm.models.diffusion.ddpm import LatentDiffusion
 
 demo = None
 
@@ -391,21 +392,20 @@ options_templates.update(options_section(('ui', "User interface"), {
     "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
     "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
-    "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
-    "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
     "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+    "js_modal_lightbox_gamepad": OptionInfo(True, "Navigate image viewer with gamepad"),
+    "js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
     "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
     "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"),
+    "keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
+    "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
     "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"),
@@ -413,6 +413,13 @@ options_templates.update(options_section(('ui', "User interface"), {
     "gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
 }))
 
+options_templates.update(options_section(('infotext', "Infotext"), {
+    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
+    "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
+    "add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
+    "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
+}))
+
 options_templates.update(options_section(('ui', "Live previews"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
@@ -542,6 +549,10 @@ class Options:
         with open(filename, "r", encoding="utf8") as file:
             self.data = json.load(file)
 
+        # 1.1.1 quicksettings list migration
+        if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
+            self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
+
         bad_settings = 0
         for k, v in self.data.items():
             info = self.data_labels.get(k, None)
@@ -600,13 +611,37 @@ class Options:
         return value
 
 
-
 opts = Options()
 if os.path.exists(config_filename):
     opts.load(config_filename)
 
+
+class Shared(sys.modules[__name__].__class__):
+    """
+    this class is here to provide sd_model field as a property, so that it can be created and loaded on demand rather than
+    at program startup.
+    """
+
+    sd_model_val = None
+
+    @property
+    def sd_model(self):
+        import modules.sd_models
+
+        return modules.sd_models.model_data.get_sd_model()
+
+    @sd_model.setter
+    def sd_model(self, value):
+        import modules.sd_models
+
+        modules.sd_models.model_data.set_sd_model(value)
+
+
+sd_model: LatentDiffusion = None  # this var is here just for IDE's type checking; it cannot be accessed because the class field above will be accessed instead
+sys.modules[__name__].__class__ = Shared
+
 settings_components = None
-"""assinged from ui.py, a mapping on setting anmes to gradio components repsponsible for those settings"""
+"""assinged from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
 
 latent_upscale_default_mode = "Latent"
 latent_upscale_modes = {
@@ -620,8 +655,6 @@ latent_upscale_modes = {
 
 sd_upscalers = []
 
-sd_model = None
-
 clip_model = None
 
 progress_print_out = sys.stdout
@@ -701,3 +734,20 @@ def html(filename):
             return file.read()
 
     return ""
+
+
+def walk_files(path, allowed_extensions=None):
+    if not os.path.exists(path):
+        return
+
+    if allowed_extensions is not None:
+        allowed_extensions = set(allowed_extensions)
+
+    for root, dirs, files in os.walk(path):
+        for filename in files:
+            if allowed_extensions is not None:
+                _, ext = os.path.splitext(filename)
+                if ext not in allowed_extensions:
+                    continue
+
+            yield os.path.join(root, filename)

modules/styles.py

@@ -74,7 +74,7 @@ class StyleDatabase:
     def save_styles(self, path: str) -> None:
         # Always keep a backup file around
         if os.path.exists(path):
-            shutil.copy(path, path + ".bak")
+            shutil.copy(path, f"{path}.bak")
 
         fd = os.open(path, os.O_RDWR|os.O_CREAT)
         with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:

modules/textual_inversion/autocrop.py

@@ -111,7 +111,7 @@ def focal_point(im, settings):
       if corner_centroid is not None:
         color = BLUE
         box = corner_centroid.bounding(max_size * corner_centroid.weight)
-        d.text((box[0], box[1]-15), "Edge: %.02f" % corner_centroid.weight, fill=color)
+        d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
         d.ellipse(box, outline=color)
         if len(corner_points) > 1:
           for f in corner_points:
@@ -119,7 +119,7 @@ def focal_point(im, settings):
       if entropy_centroid is not None:
         color = "#ff0"
         box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
-        d.text((box[0], box[1]-15), "Entropy: %.02f" % entropy_centroid.weight, fill=color)
+        d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
         d.ellipse(box, outline=color)
         if len(entropy_points) > 1:
           for f in entropy_points:
@@ -127,7 +127,7 @@ def focal_point(im, settings):
       if face_centroid is not None:
         color = RED
         box = face_centroid.bounding(max_size * face_centroid.weight)
-        d.text((box[0], box[1]-15), "Face: %.02f" % face_centroid.weight, fill=color)
+        d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
         d.ellipse(box, outline=color)
         if len(face_points) > 1:
           for f in face_points:

modules/textual_inversion/dataset.py

@@ -72,7 +72,7 @@ class PersonalizedBase(Dataset):
             except Exception:
                 continue
 
-            text_filename = os.path.splitext(path)[0] + ".txt"
+            text_filename = f"{os.path.splitext(path)[0]}.txt"
             filename = os.path.basename(path)
 
             if os.path.exists(text_filename):

modules/textual_inversion/preprocess.py

@@ -63,9 +63,9 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
     image.save(os.path.join(params.dstdir, f"{basename}.png"))
 
     if params.preprocess_txt_action == 'prepend' and existing_caption:
-        caption = existing_caption + ' ' + caption
+        caption = f"{existing_caption} {caption}"
     elif params.preprocess_txt_action == 'append' and existing_caption:
-        caption = caption + ' ' + existing_caption
+        caption = f"{caption} {existing_caption}"
     elif params.preprocess_txt_action == 'copy' and existing_caption:
         caption = existing_caption
 
@@ -174,7 +174,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
         params.src = filename
 
         existing_caption = None
-        existing_caption_filename = os.path.splitext(filename)[0] + '.txt'
+        existing_caption_filename = f"{os.path.splitext(filename)[0]}.txt"
         if os.path.exists(existing_caption_filename):
             with open(existing_caption_filename, 'r', encoding="utf8") as file:
                 existing_caption = file.read()

modules/textual_inversion/textual_inversion.py

@@ -69,7 +69,7 @@ class Embedding:
                 'hash': self.checksum(),
                 'optimizer_state_dict': self.optimizer_state_dict,
             }
-            torch.save(optimizer_saved_dict, filename + '.optim')
+            torch.save(optimizer_saved_dict, f"{filename}.optim")
 
     def checksum(self):
         if self.cached_checksum is not None:
@@ -437,8 +437,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
     if shared.opts.save_optimizer_state:
         optimizer_state_dict = None
-        if os.path.exists(filename + '.optim'):
-            optimizer_saved_dict = torch.load(filename + '.optim', map_location='cpu')
+        if os.path.exists(f"{filename}.optim"):
+            optimizer_saved_dict = torch.load(f"{filename}.optim", map_location='cpu')
             if embedding.checksum() == optimizer_saved_dict.get('hash', None):
                 optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
     
@@ -599,7 +599,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                         data = torch.load(last_saved_file)
                         info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
-                        title = "<{}>".format(data.get('name', '???'))
+                        title = f"<{data.get('name', '???')}>"
 
                         try:
                             vectorSize = list(data['string_to_param'].values())[0].shape[0]
@@ -608,8 +608,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
 
                         checkpoint = sd_models.select_checkpoint()
                         footer_left = checkpoint.model_name
-                        footer_mid = '[{}]'.format(checkpoint.shorthash)
-                        footer_right = '{}v {}s'.format(vectorSize, steps_done)
+                        footer_mid = f'[{checkpoint.shorthash}]'
+                        footer_right = f'{vectorSize}v {steps_done}s'
 
                         captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                         captioned_image = insert_image_data_embed(captioned_image, data)

modules/ui_extensions.py

@@ -61,7 +61,8 @@ def save_config_state(name):
     if not name:
         name = "Config"
     current_config_state["name"] = name
-    filename = os.path.join(config_states_dir, datetime.now().strftime("%Y_%m_%d-%H_%M_%S") + "_" + name + ".json")
+    timestamp = datetime.now().strftime('%Y_%m_%d-%H_%M_%S')
+    filename = os.path.join(config_states_dir, f"{timestamp}_{name}.json")
     print(f"Saving backup of webui/extension state to {filename}.")
     with open(filename, "w", encoding="utf-8") as f:
         json.dump(current_config_state, f)

modules/ui_extra_networks.py

@@ -69,7 +69,9 @@ class ExtraNetworksPage:
         pass
 
     def link_preview(self, filename):
-        return "./sd_extra_networks/thumb?filename=" + urllib.parse.quote(filename.replace('\\', '/')) + "&mtime=" + str(os.path.getmtime(filename))
+        quoted_filename = urllib.parse.quote(filename.replace('\\', '/'))
+        mtime = os.path.getmtime(filename)
+        return f"./sd_extra_networks/thumb?filename={quoted_filename}&mtime={mtime}"
 
     def search_terms_from_path(self, filename, possible_directories=None):
         abspath = os.path.abspath(filename)
@@ -89,19 +91,22 @@ class ExtraNetworksPage:
 
         subdirs = {}
         for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
-            for x in glob.glob(os.path.join(parentdir, '**/*'), recursive=True):
-                if not os.path.isdir(x):
-                    continue
+            for root, dirs, files in os.walk(parentdir):
+                for dirname in dirs:
+                    x = os.path.join(root, dirname)
 
-                subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
-                while subdir.startswith("/"):
-                    subdir = subdir[1:]
+                    if not os.path.isdir(x):
+                        continue
 
-                is_empty = len(os.listdir(x)) == 0
-                if not is_empty and not subdir.endswith("/"):
-                    subdir = subdir + "/"
+                    subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
+                    while subdir.startswith("/"):
+                        subdir = subdir[1:]
 
-                subdirs[subdir] = 1
+                    is_empty = len(os.listdir(x)) == 0
+                    if not is_empty and not subdir.endswith("/"):
+                        subdir = subdir + "/"
+
+                    subdirs[subdir] = 1
 
         if subdirs:
             subdirs = {"": 1, **subdirs}
@@ -157,8 +162,20 @@ class ExtraNetworksPage:
         if metadata:
             metadata_button = f"<div class='metadata-button' title='Show metadata' onclick='extraNetworksRequestMetadata(event, {json.dumps(self.name)}, {json.dumps(item['name'])})'></div>"
 
+        local_path = ""
+        filename = item.get("filename", "")
+        for reldir in self.allowed_directories_for_previews():
+            absdir = os.path.abspath(reldir)
+
+            if filename.startswith(absdir):
+                local_path = filename[len(absdir):]
+
+        # if this is true, the item must not be show in the default view, and must instead only be
+        # shown when searching for it
+        serach_only = "/." in local_path or "\\." in local_path
+
         args = {
-            "style": f"'{height}{width}{background_image}'",
+            "style": f"'display: none; {height}{width}{background_image}'",
             "prompt": item.get("prompt", None),
             "tabname": json.dumps(tabname),
             "local_preview": json.dumps(item["local_preview"]),
@@ -168,6 +185,7 @@ class ExtraNetworksPage:
             "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
             "search_term": item.get("search_term", ""),
             "metadata_button": metadata_button,
+            "serach_only": " search_only" if serach_only else "",
         }
 
         return self.card_page.format(**args)
@@ -209,6 +227,11 @@ def intialize():
 class ExtraNetworksUi:
     def __init__(self):
         self.pages = None
+        """gradio HTML components related to extra networks' pages"""
+
+        self.page_contents = None
+        """HTML content of the above; empty initially, filled when extra pages have to be shown"""
+
         self.stored_extra_pages = None
 
         self.button_save_preview = None
@@ -236,17 +259,22 @@ def pages_in_preferred_order(pages):
 def create_ui(container, button, tabname):
     ui = ExtraNetworksUi()
     ui.pages = []
+    ui.pages_contents = []
     ui.stored_extra_pages = pages_in_preferred_order(extra_pages.copy())
     ui.tabname = tabname
 
     with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
         for page in ui.stored_extra_pages:
-            with gr.Tab(page.title, id=page.title.lower().replace(" ", "_")):
+            page_id = page.title.lower().replace(" ", "_")
 
-                page_elem = gr.HTML(page.create_html(ui.tabname))
+            with gr.Tab(page.title, id=page_id):
+                elem_id = f"{tabname}_{page_id}_cards_html"
+                page_elem = gr.HTML('', elem_id=elem_id)
                 ui.pages.append(page_elem)
 
-    filter = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
+                page_elem.change(fn=lambda: None, _js='function(){applyExtraNetworkFilter(' + json.dumps(tabname) + '); return []}', inputs=[], outputs=[])
+
+    gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
     button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh")
 
     ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
@@ -254,19 +282,22 @@ def create_ui(container, button, tabname):
 
     def toggle_visibility(is_visible):
         is_visible = not is_visible
-        return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary"))
+
+        if is_visible and not ui.pages_contents:
+            refresh()
+
+        return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary")), *ui.pages_contents
 
     state_visible = gr.State(value=False)
-    button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button])
+    button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button, *ui.pages])
 
     def refresh():
-        res = []
-
         for pg in ui.stored_extra_pages:
             pg.refresh()
-            res.append(pg.create_html(ui.tabname))
 
-        return res
+        ui.pages_contents = [pg.create_html(ui.tabname) for pg in ui.stored_extra_pages]
+
+        return ui.pages_contents
 
     button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages)
 

modules/ui_tempdir.py

@@ -36,7 +36,7 @@ def save_pil_to_file(pil_image, dir=None):
     if already_saved_as and os.path.isfile(already_saved_as):
         register_tmp_file(shared.demo, already_saved_as)
 
-        file_obj = Savedfile(already_saved_as)
+        file_obj = Savedfile(f'{already_saved_as}?{os.path.getmtime(already_saved_as)}')
         return file_obj
 
     if shared.opts.temp_dir != "":

scripts/custom_code.py

@@ -77,7 +77,7 @@ return process_images(p)
         module.display = display
 
         indent = " " * indent_level
-        indented = code.replace('\n', '\n' + indent)
+        indented = code.replace('\n', f"\n{indent}")
         body = f"""def __webuitemp__():
 {indent}{indented}
 __webuitemp__()"""

scripts/loopback.py

@@ -84,7 +84,7 @@ class Script(scripts.Script):
                     p.color_corrections = initial_color_corrections
 
                 if append_interrogation != "None":
-                    p.prompt = original_prompt + ", " if original_prompt != "" else ""
+                    p.prompt = f"{original_prompt}, " if original_prompt else ""
                     if append_interrogation == "CLIP":
                         p.prompt += shared.interrogator.interrogate(p.init_images[0])
                     elif append_interrogation == "DeepBooru":

scripts/xyz_grid.py

@@ -222,7 +222,7 @@ axis_options = [
     AxisOption("Denoising", float, apply_field("denoising_strength")),
     AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
     AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
-    AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),
+    AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
     AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
     AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
     AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
@@ -346,7 +346,7 @@ class SharedSettingsStackHelper(object):
         self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
         self.vae = opts.sd_vae
         self.uni_pc_order = opts.uni_pc_order
-  
+
     def __exit__(self, exc_type, exc_value, tb):
         opts.data["sd_vae"] = self.vae
         opts.data["uni_pc_order"] = self.uni_pc_order
@@ -399,7 +399,7 @@ class Script(scripts.Script):
                 include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
             with gr.Column():
                 margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
-        
+
         with gr.Row(variant="compact", elem_id="swap_axes"):
             swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
             swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
@@ -439,7 +439,7 @@ class Script(scripts.Script):
         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"
+            val_key = f"{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)
@@ -490,7 +490,7 @@ class Script(scripts.Script):
                         start = int(mc.group(1))
                         end   = int(mc.group(2))
                         num   = int(mc.group(3)) if mc.group(3) is not None else 1
-                        
+
                         valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
                     else:
                         valslist_ext.append(val)
@@ -512,7 +512,7 @@ class Script(scripts.Script):
                         start = float(mc.group(1))
                         end   = float(mc.group(2))
                         num   = int(mc.group(3)) if mc.group(3) is not None else 1
-                        
+
                         valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
                     else:
                         valslist_ext.append(val)

style.css

@@ -125,6 +125,10 @@ div.gradio-html.min{
     text-decoration: none;
 }
 
+a{
+    font-weight: bold;
+    cursor: pointer;
+}
 
 
 /* general styled components */
@@ -246,7 +250,7 @@ button.custom-button{
     }
 }
 
-#txt2img_gallery img, #img2img_gallery img{
+#txt2img_gallery img, #img2img_gallery img, #extras_gallery img{
     object-fit: scale-down;
 }
 #txt2img_actions_column, #img2img_actions_column {
@@ -397,6 +401,18 @@ div#extras_scale_to_tab div.form{
     margin: 0 1.2em;
 }
 
+table.settings-value-table{
+    background: white;
+    border-collapse: collapse;
+    margin: 1em;
+    border: 4px solid white;
+}
+
+table.settings-value-table td{
+    padding: 0.4em;
+    border: 1px solid #ccc;
+    max-width: 36em;
+}
 
 /* live preview */
 .progressDiv{
@@ -534,6 +550,8 @@ div#extras_scale_to_tab div.form{
 #lightboxModal > img.modalImageFullscreen{
     object-fit: contain;
     height: 100%;
+    width: 100%;
+    min-height: 0;
 }
 
 .modalPrev,

webui.py

@@ -6,6 +6,8 @@ import signal
 import re
 import warnings
 import json
+from threading import Thread
+
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
@@ -185,24 +187,19 @@ def initialize():
     modules.scripts.load_scripts()
     startup_timer.record("load scripts")
 
+    modelloader.load_upscalers()
+    #startup_timer.record("load upscalers") #Is this necessary? I don't know.
+
     modules.sd_vae.refresh_vae_list()
     startup_timer.record("refresh VAE")
 
     modules.textual_inversion.textual_inversion.list_textual_inversion_templates()
     startup_timer.record("refresh textual inversion templates")
 
-    try:
-        modules.sd_models.load_model()
-    except Exception as e:
-        errors.display(e, "loading stable diffusion model")
-        print("", file=sys.stderr)
-        print("Stable diffusion model failed to load, exiting", file=sys.stderr)
-        exit(1)
-    startup_timer.record("load SD checkpoint")
+    # load model in parallel to other startup stuff
+    Thread(target=lambda: shared.sd_model).start()
 
-    shared.opts.data["sd_model_checkpoint"] = shared.sd_model.sd_checkpoint_info.title
-
-    shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
+    shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()), call=False)
     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)
@@ -286,7 +283,6 @@ def api_only():
     print(f"Startup time: {startup_timer.summary()}.")
     api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
 
-
 def webui():
     launch_api = cmd_opts.api
     initialize()
@@ -313,6 +309,16 @@ def webui():
                 for line in file.readlines():
                     gradio_auth_creds += [x.strip() for x in line.split(',') if x.strip()]
 
+        # this restores the missing /docs endpoint
+        if launch_api and not hasattr(FastAPI, 'original_setup'):
+            def fastapi_setup(self):
+                self.docs_url = "/docs"
+                self.redoc_url = "/redoc"
+                self.original_setup()
+
+            FastAPI.original_setup = FastAPI.setup
+            FastAPI.setup = fastapi_setup
+
         app, local_url, share_url = shared.demo.launch(
             share=cmd_opts.share,
             server_name=server_name,
@@ -339,6 +345,7 @@ def webui():
         setup_middleware(app)
 
         modules.progress.setup_progress_api(app)
+        modules.ui.setup_ui_api(app)
 
         if launch_api:
             create_api(app)
@@ -350,6 +357,11 @@ def webui():
 
         print(f"Startup time: {startup_timer.summary()}.")
 
+        if cmd_opts.subpath:
+            redirector = FastAPI()
+            redirector.get("/")
+            mounted_app = gradio.mount_gradio_app(redirector, shared.demo, path=f"/{cmd_opts.subpath}")
+
         wait_on_server(shared.demo)
         print('Restarting UI...')
 

webui.sh

@@ -153,24 +153,31 @@ else
     cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
 fi
 
-printf "\n%s\n" "${delimiter}"
-printf "Create and activate python venv"
-printf "\n%s\n" "${delimiter}"
-cd "${install_dir}"/"${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
-if [[ ! -d "${venv_dir}" ]]
-then
-    "${python_cmd}" -m venv "${venv_dir}"
-    first_launch=1
-fi
-# shellcheck source=/dev/null
-if [[ -f "${venv_dir}"/bin/activate ]]
+if [[ -z "${VIRTUAL_ENV}" ]];
 then
-    source "${venv_dir}"/bin/activate
+    printf "\n%s\n" "${delimiter}"
+    printf "Create and activate python venv"
+    printf "\n%s\n" "${delimiter}"
+    cd "${install_dir}"/"${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
+    if [[ ! -d "${venv_dir}" ]]
+    then
+        "${python_cmd}" -m venv "${venv_dir}"
+        first_launch=1
+    fi
+    # shellcheck source=/dev/null
+    if [[ -f "${venv_dir}"/bin/activate ]]
+    then
+        source "${venv_dir}"/bin/activate
+    else
+        printf "\n%s\n" "${delimiter}"
+        printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m"
+        printf "\n%s\n" "${delimiter}"
+        exit 1
+    fi
 else
     printf "\n%s\n" "${delimiter}"
-    printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m"
+    printf "python venv already activate: ${VIRTUAL_ENV}"
     printf "\n%s\n" "${delimiter}"
-    exit 1
 fi
 
 # Try using TCMalloc on Linux