Merge branch 'dev' into refactor/webui-function

.eslintrc.js

@@ -88,6 +88,7 @@ module.exports = {
         // imageviewer.js
         modalPrevImage: "readonly",
         modalNextImage: "readonly",
+        updateModalImageIfVisible: "readonly",
         // localStorage.js
         localSet: "readonly",
         localGet: "readonly",

.github/workflows/on_pull_request.yaml

@@ -22,7 +22,7 @@ jobs:
       - name: Install Ruff
         run: pip install ruff==0.3.3
       - name: Run Ruff
-        run: ruff .
+        run: ruff check .
   lint-js:
     name: eslint
     runs-on: ubuntu-latest

CODEOWNERS

@@ -1,12 +1 @@
-*       @AUTOMATIC1111
+*       @AUTOMATIC1111 @w-e-w @catboxanon
-
-# if you were managing a localization and were removed from this file, this is because
-# the intended way to do localizations now is via extensions. See:
-# https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions
-# Make a repo with your localization and since you are still listed as a collaborator
-# you can add it to the wiki page yourself. This change is because some people complained
-# the git commit log is cluttered with things unrelated to almost everyone and
-# because I believe this is the best overall for the project to handle localizations almost
-# entirely without my oversight.
-
-

README.md

@@ -133,7 +133,7 @@ If your system is very new, you need to install python3.11 or python3.10:
 # Ubuntu 24.04
 sudo add-apt-repository ppa:deadsnakes/ppa
 sudo apt update
-sudo apt install python3.11
+sudo apt install python3.11 python3.11-venv
 
 # Manjaro/Arch
 sudo pacman -S yay
@@ -148,6 +148,7 @@ python_cmd="python3.11"
 2. Navigate to the directory you would like the webui to be installed and execute the following command:
 ```bash
 wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh
+chmod +x webui.sh
 ```
 Or just clone the repo wherever you want:
 ```bash

configs/sd_xl_v.yaml

@@ -0,0 +1,98 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        weighting_config:
+          target: sgm.modules.diffusionmodules.denoiser_weighting.VWeighting
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        adm_in_channels: 2816
+        num_classes: sequential
+        use_checkpoint: False
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4]
+        num_head_channels: 64
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: [1, 2, 10]  # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
+        context_dim: 2048
+        spatial_transformer_attn_type: softmax-xformers
+        legacy: False
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          # crossattn cond
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
+            params:
+              layer: hidden
+              layer_idx: 11
+          # crossattn and vector cond
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+            params:
+              arch: ViT-bigG-14
+              version: laion2b_s39b_b160k
+              freeze: True
+              layer: penultimate
+              always_return_pooled: True
+              legacy: False
+          # vector cond
+          - is_trainable: False
+            input_key: original_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: crop_coords_top_left
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: target_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity

extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js

@@ -816,7 +816,7 @@ onUiLoaded(async() => {
                 // Increase or decrease brush size based on scroll direction
                 adjustBrushSize(elemId, e.deltaY);
             }
-        });
+        }, {passive: false});
 
         // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element.
         function handleMoveKeyDown(e) {

extensions-builtin/hypertile/hypertile.py

@@ -1,7 +1,7 @@
 """
 Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE
 Warn: The patch works well only if the input image has a width and height that are multiples of 128
-Original author: @tfernd Github: https://github.com/tfernd/HyperTile
+Original author: @tfernd GitHub: https://github.com/tfernd/HyperTile
 """
 
 from __future__ import annotations

extensions-builtin/postprocessing-for-training/scripts/postprocessing_autosized_crop.py

@@ -34,14 +34,14 @@ class ScriptPostprocessingAutosizedCrop(scripts_postprocessing.ScriptPostprocess
         with ui_components.InputAccordion(False, label="Auto-sized crop") as enable:
             gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
             with gr.Row():
-                mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="postprocess_multicrop_mindim")
+                mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id=self.elem_id_suffix("postprocess_multicrop_mindim"))
-                maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="postprocess_multicrop_maxdim")
+                maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id=self.elem_id_suffix("postprocess_multicrop_maxdim"))
             with gr.Row():
-                minarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area lower bound", value=64 * 64, elem_id="postprocess_multicrop_minarea")
+                minarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area lower bound", value=64 * 64, elem_id=self.elem_id_suffix("postprocess_multicrop_minarea"))
-                maxarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area upper bound", value=640 * 640, elem_id="postprocess_multicrop_maxarea")
+                maxarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area upper bound", value=640 * 640, elem_id=self.elem_id_suffix("postprocess_multicrop_maxarea"))
             with gr.Row():
-                objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="postprocess_multicrop_objective")
+                objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id=self.elem_id_suffix("postprocess_multicrop_objective"))
-                threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="postprocess_multicrop_threshold")
+                threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id=self.elem_id_suffix("postprocess_multicrop_threshold"))
 
         return {
             "enable": enable,

extensions-builtin/postprocessing-for-training/scripts/postprocessing_focal_crop.py

@@ -11,10 +11,10 @@ class ScriptPostprocessingFocalCrop(scripts_postprocessing.ScriptPostprocessing)
 
     def ui(self):
         with ui_components.InputAccordion(False, label="Auto focal point crop") as enable:
-            face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_face_weight")
+            face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id=self.elem_id_suffix("postprocess_focal_crop_face_weight"))
-            entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_entropy_weight")
+            entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id=self.elem_id_suffix("postprocess_focal_crop_entropy_weight"))
-            edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_edges_weight")
+            edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id=self.elem_id_suffix("postprocess_focal_crop_edges_weight"))
-            debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
+            debug = gr.Checkbox(label='Create debug image', elem_id=self.elem_id_suffix("train_process_focal_crop_debug"))
 
         return {
             "enable": enable,

extensions-builtin/postprocessing-for-training/scripts/postprocessing_split_oversized.py

@@ -35,8 +35,8 @@ class ScriptPostprocessingSplitOversized(scripts_postprocessing.ScriptPostproces
     def ui(self):
         with ui_components.InputAccordion(False, label="Split oversized images") as enable:
             with gr.Row():
-                split_threshold = gr.Slider(label='Threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_split_threshold")
+                split_threshold = gr.Slider(label='Threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id=self.elem_id_suffix("postprocess_split_threshold"))
-                overlap_ratio = gr.Slider(label='Overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="postprocess_overlap_ratio")
+                overlap_ratio = gr.Slider(label='Overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id=self.elem_id_suffix("postprocess_overlap_ratio"))
 
         return {
             "enable": enable,

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

@@ -1,36 +1,69 @@
-// Stable Diffusion WebUI - Bracket checker
+// Stable Diffusion WebUI - Bracket Checker
-// By Hingashi no Florin/Bwin4L & @akx
+// By @Bwin4L, @akx, @w-e-w, @Haoming02
 // Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
-// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
+// If there's a mismatch, the keyword counter turns red, and if you hover on it, a tooltip tells you what's wrong.
 
-function checkBrackets(textArea, counterElt) {
+function checkBrackets(textArea, counterElem) {
-    var counts = {};
+    const pairs = [
-    (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => {
+        ['(', ')', 'round brackets'],
-        counts[bracket] = (counts[bracket] || 0) + 1;
+        ['[', ']', 'square brackets'],
-    });
+        ['{', '}', 'curly brackets']
-    var errors = [];
+    ];
 
-    function checkPair(open, close, kind) {
+    const counts = {};
-        if (counts[open] !== counts[close]) {
+    const errors = new Set();
-            errors.push(
+    let i = 0;
-                `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
+
-            );
+    while (i < textArea.value.length) {
+        let char = textArea.value[i];
+        let escaped = false;
+        while (char === '\\' && i + 1 < textArea.value.length) {
+            escaped = !escaped;
+            i++;
+            char = textArea.value[i];
+        }
+
+        if (escaped) {
+            i++;
+            continue;
+        }
+
+        for (const [open, close, label] of pairs) {
+            if (char === open) {
+                counts[label] = (counts[label] || 0) + 1;
+            } else if (char === close) {
+                counts[label] = (counts[label] || 0) - 1;
+                if (counts[label] < 0) {
+                    errors.add(`Incorrect order of ${label}.`);
+                }
+            }
+        }
+
+        i++;
+    }
+
+    for (const [open, close, label] of pairs) {
+        if (counts[label] == undefined) {
+            continue;
+        }
+
+        if (counts[label] > 0) {
+            errors.add(`${open} ... ${close} - Detected ${counts[label]} more opening than closing ${label}.`);
+        } else if (counts[label] < 0) {
+            errors.add(`${open} ... ${close} - Detected ${-counts[label]} more closing than opening ${label}.`);
         }
     }
 
-    checkPair('(', ')', 'round brackets');
+    counterElem.title = [...errors].join('\n');
-    checkPair('[', ']', 'square brackets');
+    counterElem.classList.toggle('error', errors.size !== 0);
-    checkPair('{', '}', 'curly brackets');
-    counterElt.title = errors.join('\n');
-    counterElt.classList.toggle('error', errors.length !== 0);
 }
 
 function setupBracketChecking(id_prompt, id_counter) {
-    var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
+    const textarea = gradioApp().querySelector(`#${id_prompt} > label > textarea`);
-    var counter = gradioApp().getElementById(id_counter);
+    const counter = gradioApp().getElementById(id_counter);
 
     if (textarea && counter) {
-        textarea.addEventListener("input", () => checkBrackets(textarea, counter));
+        onEdit(`${id_prompt}_BracketChecking`, textarea, 400, () => checkBrackets(textarea, counter));
     }
 }
 

html/footer.html

@@ -1,7 +1,7 @@
 <div>
-        <a href="{api_docs}">API</a>
+        <a href="{api_docs}" target="_blank">API</a>
          • 
-        <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">Github</a>
+        <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">GitHub</a>
          • 
         <a href="https://gradio.app">Gradio</a>
          • 

javascript/contextMenus.js

@@ -104,7 +104,7 @@ var contextMenuInit = function() {
                         e.preventDefault();
                     }
                 });
-            });
+            }, {passive: false});
         });
         eventListenerApplied = true;
 

javascript/extraNetworks.js

@@ -201,7 +201,7 @@ function setupExtraNetworks() {
     setupExtraNetworksForTab('img2img');
 }
 
-var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/;
+var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/s;
 var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g;
 
 var re_extranet_neg = /\(([^:^>]+:[\d.]+)\)/;

javascript/imageviewer.js

@@ -13,6 +13,7 @@ function showModal(event) {
     if (modalImage.style.display === 'none') {
         lb.style.setProperty('background-image', 'url(' + source.src + ')');
     }
+    updateModalImage();
     lb.style.display = "flex";
     lb.focus();
 
@@ -31,24 +32,30 @@ function negmod(n, m) {
     return ((n % m) + m) % m;
 }
 
-function updateOnBackgroundChange() {
+function updateModalImage() {
     const modalImage = gradioApp().getElementById("modalImage");
-    if (modalImage && modalImage.offsetParent) {
+    let currentButton = selected_gallery_button();
-        let currentButton = selected_gallery_button();
+    let preview = gradioApp().querySelectorAll('.livePreview > img');
-        let preview = gradioApp().querySelectorAll('.livePreview > img');
+    if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) {
-        if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) {
+        // show preview image if available
-            // show preview image if available
+        modalImage.src = preview[preview.length - 1].src;
-            modalImage.src = preview[preview.length - 1].src;
+    } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
-        } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
+        modalImage.src = currentButton.children[0].src;
-            modalImage.src = currentButton.children[0].src;
+        if (modalImage.style.display === 'none') {
-            if (modalImage.style.display === 'none') {
+            const modal = gradioApp().getElementById("lightboxModal");
-                const modal = gradioApp().getElementById("lightboxModal");
+            modal.style.setProperty('background-image', `url(${modalImage.src})`);
-                modal.style.setProperty('background-image', `url(${modalImage.src})`);
-            }
         }
     }
 }
 
+function updateOnBackgroundChange() {
+    const modalImage = gradioApp().getElementById("modalImage");
+    if (modalImage && modalImage.offsetParent) {
+        updateModalImage();
+    }
+}
+const updateModalImageIfVisible = updateOnBackgroundChange;
+
 function modalImageSwitch(offset) {
     var galleryButtons = all_gallery_buttons();
 
@@ -158,6 +165,7 @@ function modalLivePreviewToggle(event) {
     const modalToggleLivePreview = gradioApp().getElementById("modal_toggle_live_preview");
     opts.js_live_preview_in_modal_lightbox = !opts.js_live_preview_in_modal_lightbox;
     modalToggleLivePreview.innerHTML = opts.js_live_preview_in_modal_lightbox ? "🗇" : "🗆";
+    updateModalImageIfVisible();
     event.stopPropagation();
 }
 

javascript/progressbar.js

@@ -79,11 +79,12 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
     var wakeLock = null;
 
     var requestWakeLock = async function() {
-        if (!opts.prevent_screen_sleep_during_generation || wakeLock) return;
+        if (!opts.prevent_screen_sleep_during_generation || wakeLock !== null) return;
         try {
             wakeLock = await navigator.wakeLock.request('screen');
         } catch (err) {
             console.error('Wake Lock is not supported.');
+            wakeLock = false;
         }
     };
 
@@ -189,7 +190,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                         livePreview.className = 'livePreview';
                         gallery.insertBefore(livePreview, gallery.firstElementChild);
                     }
-
+                    updateModalImageIfVisible();
                     livePreview.appendChild(img);
                     if (livePreview.childElementCount > 2) {
                         livePreview.removeChild(livePreview.firstElementChild);

javascript/resizeHandle.js

@@ -124,7 +124,7 @@
                 } else {
                     R.screenX = evt.changedTouches[0].screenX;
                 }
-            });
+            }, {passive: false});
         });
 
         resizeHandle.addEventListener('dblclick', onDoubleClick);

launch.py

@@ -6,6 +6,11 @@ git = launch_utils.git
 index_url = launch_utils.index_url
 dir_repos = launch_utils.dir_repos
 
+if args.uv:
+    from modules.uv_hook import patch
+    patch()
+
+
 commit_hash = launch_utils.commit_hash
 git_tag = launch_utils.git_tag
 

modules/api/api.py

@@ -122,7 +122,7 @@ def encode_pil_to_base64(image):
             if opts.samples_format.lower() in ("jpg", "jpeg"):
                 image.save(output_bytes, format="JPEG", exif = exif_bytes, quality=opts.jpeg_quality)
             else:
-                image.save(output_bytes, format="WEBP", exif = exif_bytes, quality=opts.jpeg_quality)
+                image.save(output_bytes, format="WEBP", exif = exif_bytes, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
 
         else:
             raise HTTPException(status_code=500, detail="Invalid image format")

modules/cmd_args.py

@@ -126,3 +126,4 @@ parser.add_argument("--skip-load-model-at-start", action='store_true', help="if
 parser.add_argument("--unix-filenames-sanitization", action='store_true', help="allow any symbols except '/' in filenames. May conflict with your browser and file system")
 parser.add_argument("--filenames-max-length", type=int, default=128, help='maximal length of filenames of saved images. If you override it, it can conflict with your file system')
 parser.add_argument("--no-prompt-history", action='store_true', help="disable read prompt from last generation feature; settings this argument will not create '--data_path/params.txt' file")
+parser.add_argument("--uv", action='store_true', help="use the uv package manager")

modules/dat_model.py

@@ -1,7 +1,7 @@
 import os
 
 from modules import modelloader, errors
-from modules.shared import cmd_opts, opts
+from modules.shared import cmd_opts, opts, hf_endpoint
 from modules.upscaler import Upscaler, UpscalerData
 from modules.upscaler_utils import upscale_with_model
 
@@ -49,7 +49,18 @@ class UpscalerDAT(Upscaler):
                     scaler.local_data_path = modelloader.load_file_from_url(
                         scaler.data_path,
                         model_dir=self.model_download_path,
+                        hash_prefix=scaler.sha256,
                     )
+
+                    if os.path.getsize(scaler.local_data_path) < 200:
+                        # Re-download if the file is too small, probably an LFS pointer
+                        scaler.local_data_path = modelloader.load_file_from_url(
+                            scaler.data_path,
+                            model_dir=self.model_download_path,
+                            hash_prefix=scaler.sha256,
+                            re_download=True,
+                        )
+
                 if not os.path.exists(scaler.local_data_path):
                     raise FileNotFoundError(f"DAT data missing: {scaler.local_data_path}")
                 return scaler
@@ -60,20 +71,23 @@ def get_dat_models(scaler):
     return [
         UpscalerData(
             name="DAT x2",
-            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x2.pth",
+            path=f"{hf_endpoint}/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x2.pth",
             scale=2,
             upscaler=scaler,
+            sha256='7760aa96e4ee77e29d4f89c3a4486200042e019461fdb8aa286f49aa00b89b51',
         ),
         UpscalerData(
             name="DAT x3",
-            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x3.pth",
+            path=f"{hf_endpoint}/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x3.pth",
             scale=3,
             upscaler=scaler,
+            sha256='581973e02c06f90d4eb90acf743ec9604f56f3c2c6f9e1e2c2b38ded1f80d197',
         ),
         UpscalerData(
             name="DAT x4",
-            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x4.pth",
+            path=f"{hf_endpoint}/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x4.pth",
             scale=4,
             upscaler=scaler,
+            sha256='391a6ce69899dff5ea3214557e9d585608254579217169faf3d4c353caff049e',
         ),
     ]

modules/extras.py

@@ -23,7 +23,7 @@ def run_pnginfo(image):
     info = ''
     for key, text in items.items():
         info += f"""
-<div>
+<div class="infotext">
 <p><b>{plaintext_to_html(str(key))}</b></p>
 <p>{plaintext_to_html(str(text))}</p>
 </div>

modules/hashes.py

@@ -1,7 +1,7 @@
 import hashlib
 import os.path
 
-from modules import shared
+from modules import shared, errors
 import modules.cache
 
 dump_cache = modules.cache.dump_cache
@@ -32,7 +32,7 @@ def sha256_from_cache(filename, title, use_addnet_hash=False):
     cached_sha256 = hashes[title].get("sha256", None)
     cached_mtime = hashes[title].get("mtime", 0)
 
-    if ondisk_mtime > cached_mtime or cached_sha256 is None:
+    if ondisk_mtime != cached_mtime or cached_sha256 is None:
         return None
 
     return cached_sha256
@@ -82,3 +82,31 @@ def addnet_hash_safetensors(b):
 
     return hash_sha256.hexdigest()
 
+
+def partial_hash_from_cache(filename, *, ignore_cache: bool = False, digits: int = 8):
+    """old hash that only looks at a small part of the file and is prone to collisions
+    kept for compatibility, don't use this for new things
+    """
+    try:
+        filename = str(filename)
+        mtime = os.path.getmtime(filename)
+        hashes = cache('partial-hash')
+        cache_entry = hashes.get(filename, {})
+        cache_mtime = cache_entry.get("mtime", 0)
+        cache_hash = cache_entry.get("hash", None)
+        if mtime == cache_mtime and cache_hash and not ignore_cache:
+            return cache_hash[0:digits]
+
+        with open(filename, 'rb') as file:
+            m = hashlib.sha256()
+            file.seek(0x100000)
+            m.update(file.read(0x10000))
+            partial_hash = m.hexdigest()
+            hashes[filename] = {'mtime': mtime, 'hash': partial_hash}
+            return partial_hash[0:digits]
+
+    except FileNotFoundError:
+        pass
+    except Exception:
+        errors.report(f'Error calculating partial hash for {filename}', exc_info=True)
+    return 'NOFILE'

modules/images.py

@@ -409,6 +409,7 @@ class FilenameGenerator:
         'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if (self.p.n_iter == 1 and self.p.batch_size == 1) or self.zip 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"],
+        'randn_source': lambda self: opts.data["randn_source"],
         'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
         'user': lambda self: self.p.user,
         'vae_filename': lambda self: self.get_vae_filename(),

modules/launch_utils.py

@@ -43,9 +43,7 @@ def check_python_version():
         supported_minors = [7, 8, 9, 10, 11]
 
     if not (major == 3 and minor in supported_minors):
-        import modules.errors
+        errors.print_error_explanation(f"""
-
-        modules.errors.print_error_explanation(f"""
 INCOMPATIBLE PYTHON VERSION
 
 This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
@@ -315,9 +313,43 @@ def requirements_met(requirements_file):
     return True
 
 
+def get_cuda_comp_cap():
+    """
+    Returns float of CUDA Compute Capability using nvidia-smi
+    Returns 0.0 on error
+    CUDA Compute Capability
+    ref https://developer.nvidia.com/cuda-gpus
+    ref https://en.wikipedia.org/wiki/CUDA
+    Blackwell consumer GPUs should return 12.0 data-center GPUs should return 10.0
+    """
+    try:
+        return max(map(float, subprocess.check_output(['nvidia-smi', '--query-gpu=compute_cap', '--format=noheader,csv'], text=True).splitlines()))
+    except Exception as _:
+        return 0.0
+
+
+def early_access_blackwell_wheels():
+    """For Blackwell GPUs, use Early Access PyTorch Wheels provided by Nvidia"""
+    print('deprecated early_access_blackwell_wheels')
+    if all([
+            os.environ.get('TORCH_INDEX_URL') is None,
+            sys.version_info.major == 3,
+            sys.version_info.minor in (10, 11, 12),
+            platform.system() == "Windows",
+            get_cuda_comp_cap() >= 10,  # Blackwell
+    ]):
+        base_repo = 'https://huggingface.co/w-e-w/torch-2.6.0-cu128.nv/resolve/main/'
+        ea_whl = {
+            10: f'{base_repo}torch-2.6.0+cu128.nv-cp310-cp310-win_amd64.whl#sha256=fef3de7ce8f4642e405576008f384304ad0e44f7b06cc1aa45e0ab4b6e70490d {base_repo}torchvision-0.20.0a0+cu128.nv-cp310-cp310-win_amd64.whl#sha256=50841254f59f1db750e7348b90a8f4cd6befec217ab53cbb03780490b225abef',
+            11: f'{base_repo}torch-2.6.0+cu128.nv-cp311-cp311-win_amd64.whl#sha256=6665c36e6a7e79e7a2cb42bec190d376be9ca2859732ed29dd5b7b5a612d0d26 {base_repo}torchvision-0.20.0a0+cu128.nv-cp311-cp311-win_amd64.whl#sha256=bbc0ee4938e35fe5a30de3613bfcd2d8ef4eae334cf8d49db860668f0bb47083',
+            12: f'{base_repo}torch-2.6.0+cu128.nv-cp312-cp312-win_amd64.whl#sha256=a3197f72379d34b08c4a4bcf49ea262544a484e8702b8c46cbcd66356c89def6 {base_repo}torchvision-0.20.0a0+cu128.nv-cp312-cp312-win_amd64.whl#sha256=235e7be71ac4e75b0f8e817bae4796d7bac8a67146d2037ab96394f2bdc63e6c'
+        }
+        return f'pip install {ea_whl.get(sys.version_info.minor)}'
+
+
 def prepare_environment():
-    torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu121")
+    torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu128")
-    torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.1.2 torchvision==0.16.2 --extra-index-url {torch_index_url}")
+    torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.7.0 torchvision==0.22.0 --extra-index-url {torch_index_url}")
     if args.use_ipex:
         if platform.system() == "Windows":
             # The "Nuullll/intel-extension-for-pytorch" wheels were built from IPEX source for Intel Arc GPU: https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main
@@ -341,7 +373,7 @@ def prepare_environment():
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
     requirements_file_for_npu = os.environ.get('REQS_FILE_FOR_NPU', "requirements_npu.txt")
 
-    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23.post1')
+    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.30')
     clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
     openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
 

modules/modelloader.py

@@ -10,6 +10,7 @@ import torch
 
 from modules import shared
 from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
+from modules.util import load_file_from_url  # noqa, backwards compatibility
 
 if TYPE_CHECKING:
     import spandrel
@@ -17,30 +18,6 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
-def load_file_from_url(
-    url: str,
-    *,
-    model_dir: str,
-    progress: bool = True,
-    file_name: str | None = None,
-    hash_prefix: str | None = None,
-) -> str:
-    """Download a file from `url` into `model_dir`, using the file present if possible.
-
-    Returns the path to the downloaded file.
-    """
-    os.makedirs(model_dir, exist_ok=True)
-    if not file_name:
-        parts = urlparse(url)
-        file_name = os.path.basename(parts.path)
-    cached_file = os.path.abspath(os.path.join(model_dir, file_name))
-    if not os.path.exists(cached_file):
-        print(f'Downloading: "{url}" to {cached_file}\n')
-        from torch.hub import download_url_to_file
-        download_url_to_file(url, cached_file, progress=progress, hash_prefix=hash_prefix)
-    return cached_file
-
-
 def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None, hash_prefix=None) -> list:
     """
     A one-and done loader to try finding the desired models in specified directories.

modules/models/sd3/sd3_cond.py

@@ -24,7 +24,7 @@ class SafetensorsMapping(typing.Mapping):
         return self.file.get_tensor(key)
 
 
-CLIPL_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_l.safetensors"
+CLIPL_URL = f"{shared.hf_endpoint}/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_l.safetensors"
 CLIPL_CONFIG = {
     "hidden_act": "quick_gelu",
     "hidden_size": 768,
@@ -33,7 +33,7 @@ CLIPL_CONFIG = {
     "num_hidden_layers": 12,
 }
 
-CLIPG_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_g.safetensors"
+CLIPG_URL = f"{shared.hf_endpoint}/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_g.safetensors"
 CLIPG_CONFIG = {
     "hidden_act": "gelu",
     "hidden_size": 1280,
@@ -43,7 +43,7 @@ CLIPG_CONFIG = {
     "textual_inversion_key": "clip_g",
 }
 
-T5_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
+T5_URL = f"{shared.hf_endpoint}/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
 T5_CONFIG = {
     "d_ff": 10240,
     "d_model": 4096,

modules/processing.py

@@ -1259,7 +1259,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 if self.hr_checkpoint_info is None:
                     raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}')
 
-                self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title
+                if shared.sd_model.sd_checkpoint_info == self.hr_checkpoint_info:
+                    self.hr_checkpoint_info = None
+                else:
+                    self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title
 
             if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
                 self.extra_generation_params["Hires sampler"] = self.hr_sampler_name

modules/scripts_auto_postprocessing.py

@@ -13,6 +13,7 @@ class ScriptPostprocessingForMainUI(scripts.Script):
         return scripts.AlwaysVisible
 
     def ui(self, is_img2img):
+        self.script.tab_name = '_img2img' if is_img2img else '_txt2img'
         self.postprocessing_controls = self.script.ui()
         return self.postprocessing_controls.values()
 
@@ -33,7 +34,7 @@ def create_auto_preprocessing_script_data():
 
     for name in shared.opts.postprocessing_enable_in_main_ui:
         script = next(iter([x for x in scripts.postprocessing_scripts_data if x.script_class.name == name]), None)
-        if script is None:
+        if script is None or script.script_class.extra_only:
             continue
 
         constructor = lambda s=script: ScriptPostprocessingForMainUI(s.script_class())

modules/scripts_postprocessing.py

@@ -1,3 +1,4 @@
+import re
 import dataclasses
 import os
 import gradio as gr
@@ -59,6 +60,10 @@ class ScriptPostprocessing:
     args_from = None
     args_to = None
 
+    # define if the script should be used only in extras or main UI
+    extra_only = None
+    main_ui_only = None
+
     order = 1000
     """scripts will be ordred by this value in postprocessing UI"""
 
@@ -97,6 +102,31 @@ class ScriptPostprocessing:
     def image_changed(self):
         pass
 
+    tab_name = ''  # used by ScriptPostprocessingForMainUI
+    replace_pattern = re.compile(r'\s')
+    rm_pattern = re.compile(r'[^a-z_0-9]')
+
+    def elem_id(self, item_id):
+        """
+        Helper function to generate id for a HTML element
+        constructs final id out of script name and user-supplied item_id
+        'script_extras_{self.name.lower()}_{item_id}'
+        {tab_name} will append to the end of the id if set
+        tab_name will be set to '_img2img' or '_txt2img' if use by ScriptPostprocessingForMainUI
+
+        Extensions should use this function to generate element IDs
+        """
+        return self.elem_id_suffix(f'extras_{self.name.lower()}_{item_id}')
+
+    def elem_id_suffix(self, base_id):
+        """
+        Append tab_name to the base_id
+
+        Extensions that already have specific there element IDs and wish to keep their IDs the same when possible should use this function
+        """
+        base_id = self.rm_pattern.sub('', self.replace_pattern.sub('_', base_id))
+        return f'{base_id}{self.tab_name}'
+
 
 def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
     try:
@@ -119,10 +149,6 @@ class ScriptPostprocessingRunner:
         for script_data in scripts_data:
             script: ScriptPostprocessing = script_data.script_class()
             script.filename = script_data.path
-
-            if script.name == "Simple Upscale":
-                continue
-
             self.scripts.append(script)
 
     def create_script_ui(self, script, inputs):
@@ -152,7 +178,7 @@ class ScriptPostprocessingRunner:
 
             return len(self.scripts)
 
-        filtered_scripts = [script for script in self.scripts if script.name not in scripts_filter_out]
+        filtered_scripts = [script for script in self.scripts if script.name not in scripts_filter_out and not script.main_ui_only]
         script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(filtered_scripts)}
 
         return sorted(filtered_scripts, key=lambda x: script_scores[x.name])

modules/sd_disable_initialization.py

@@ -76,7 +76,7 @@ class DisableInitialization(ReplaceHelper):
         def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
 
             # this file is always 404, prevent making request
-            if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
+            if url == f'{shared.hf_endpoint}/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
                 return None
 
             try:

modules/sd_models.py

@@ -13,6 +13,7 @@ from urllib import request
 import ldm.modules.midas as midas
 
 from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
+from modules.hashes import partial_hash_from_cache as model_hash  # noqa: F401 for backwards compatibility
 from modules.timer import Timer
 from modules.shared import opts
 import tomesd
@@ -87,7 +88,7 @@ class CheckpointInfo:
         self.name = name
         self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
         self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
-        self.hash = model_hash(filename)
+        self.hash = hashes.partial_hash_from_cache(filename)
 
         self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
         self.shorthash = self.sha256[0:10] if self.sha256 else None
@@ -159,7 +160,7 @@ def list_models():
         model_url = None
         expected_sha256 = None
     else:
-        model_url = f"{shared.hf_endpoint}/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
+        model_url = f"{shared.hf_endpoint}/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
         expected_sha256 = '6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa'
 
     model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"], hash_prefix=expected_sha256)
@@ -200,21 +201,6 @@ def get_closet_checkpoint_match(search_string):
     return None
 
 
-def model_hash(filename):
-    """old hash that only looks at a small part of the file and is prone to collisions"""
-
-    try:
-        with open(filename, "rb") as file:
-            import hashlib
-            m = hashlib.sha256()
-
-            file.seek(0x100000)
-            m.update(file.read(0x10000))
-            return m.hexdigest()[0:8]
-    except FileNotFoundError:
-        return 'NOFILE'
-
-
 def select_checkpoint():
     """Raises `FileNotFoundError` if no checkpoints are found."""
     model_checkpoint = shared.opts.sd_model_checkpoint
@@ -423,6 +409,10 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
 
     set_model_type(model, state_dict)
     set_model_fields(model)
+    if 'ztsnr' in state_dict:
+        model.ztsnr = True
+    else:
+        model.ztsnr = False
 
     if model.is_sdxl:
         sd_models_xl.extend_sdxl(model)
@@ -661,7 +651,7 @@ def apply_alpha_schedule_override(sd_model, p=None):
             p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
         sd_model.alphas_cumprod = sd_model.alphas_cumprod.half().to(shared.device)
 
-    if opts.sd_noise_schedule == "Zero Terminal SNR":
+    if opts.sd_noise_schedule == "Zero Terminal SNR" or (hasattr(sd_model, 'ztsnr') and sd_model.ztsnr):
         if p is not None:
             p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
         sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(sd_model.alphas_cumprod).to(shared.device)
@@ -783,7 +773,7 @@ def get_obj_from_str(string, reload=False):
     return getattr(importlib.import_module(module, package=None), cls)
 
 
-def load_model(checkpoint_info=None, already_loaded_state_dict=None):
+def load_model(checkpoint_info=None, already_loaded_state_dict=None, checkpoint_config=None):
     from modules import sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
 
@@ -801,7 +791,8 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     else:
         state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
 
-    checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
+    if not checkpoint_config:
+        checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
     clip_is_included_into_sd = any(x for x in [sd1_clip_weight, sd2_clip_weight, sdxl_clip_weight, sdxl_refiner_clip_weight] if x in state_dict)
 
     timer.record("find config")
@@ -974,7 +965,7 @@ def reload_model_weights(sd_model=None, info=None, forced_reload=False):
         if sd_model is not None:
             send_model_to_trash(sd_model)
 
-        load_model(checkpoint_info, already_loaded_state_dict=state_dict)
+        load_model(checkpoint_info, already_loaded_state_dict=state_dict, checkpoint_config=checkpoint_config)
         return model_data.sd_model
 
     try:

modules/sd_models_config.py

@@ -14,6 +14,7 @@ config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
 config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
 config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
 config_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml")
+config_sdxlv = os.path.join(sd_configs_path, "sd_xl_v.yaml")
 config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml")
 config_sdxl_inpainting = os.path.join(sd_configs_path, "sd_xl_inpaint.yaml")
 config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
@@ -81,6 +82,9 @@ def guess_model_config_from_state_dict(sd, filename):
         if diffusion_model_input.shape[1] == 9:
             return config_sdxl_inpainting
         else:
+            if ('v_pred' in sd):
+                del sd['v_pred']
+                return config_sdxlv
             return config_sdxl
 
     if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:

modules/sd_schedulers.py

@@ -117,12 +117,15 @@ def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):
 
 
 def beta_scheduler(n, sigma_min, sigma_max, inner_model, device):
-    # From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024) """
+    # From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024)
     alpha = shared.opts.beta_dist_alpha
     beta = shared.opts.beta_dist_beta
-    timesteps = 1 - np.linspace(0, 1, n)
+    curve = [stats.beta.ppf(x, alpha, beta) for x in np.linspace(1, 0, n)]
-    timesteps = [stats.beta.ppf(x, alpha, beta) for x in timesteps]
+
-    sigmas = [sigma_min + (x * (sigma_max-sigma_min)) for x in timesteps]
+    start = inner_model.sigma_to_t(torch.tensor(sigma_max))
+    end = inner_model.sigma_to_t(torch.tensor(sigma_min))
+    timesteps = [end + x * (start - end) for x in curve]
+    sigmas = [inner_model.t_to_sigma(ts) for ts in timesteps]
     sigmas += [0.0]
     return torch.FloatTensor(sigmas).to(device)
 

modules/shared_items.py

@@ -16,10 +16,12 @@ def dat_models_names():
     return [x.name for x in modules.dat_model.get_dat_models(None)]
 
 
-def postprocessing_scripts():
+def postprocessing_scripts(filter_out_extra_only=False, filter_out_main_ui_only=False):
     import modules.scripts
-
+    return list(filter(
-    return modules.scripts.scripts_postproc.scripts
+        lambda s: (not filter_out_extra_only or not s.extra_only) and (not filter_out_main_ui_only or not s.main_ui_only),
+        modules.scripts.scripts_postproc.scripts,
+    ))
 
 
 def sd_vae_items():
@@ -123,7 +125,7 @@ def ui_reorder_categories():
 
 def callbacks_order_settings():
     options = {
-        "sd_vae_explanation": OptionHTML("""
+        "callbacks_order_explanation": OptionHTML("""
     For categories below, callbacks added to dropdowns happen before others, in order listed.
     """),
 

modules/shared_options.py

@@ -33,12 +33,12 @@ categories.register_category("training", "Training")
 
 options_templates.update(options_section(('saving-images', "Saving images/grids", "saving"), {
     "samples_save": OptionInfo(True, "Always save all generated images"),
-    "samples_format": OptionInfo('png', 'File format for images'),
+    "samples_format": OptionInfo('png', 'File format for images', ui_components.DropdownEditable, {"choices": ("png", "jpg", "jpeg", "webp", "avif")}).info("manual input of <a href='https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html' target='_blank'>other formats</a> is possible, but compatibility is not guaranteed"),
     "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
     "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
     "save_images_replace_action": OptionInfo("Replace", "Saving the image to an existing file", gr.Radio, {"choices": ["Replace", "Add number suffix"], **hide_dirs}),
     "grid_save": OptionInfo(True, "Always save all generated image grids"),
-    "grid_format": OptionInfo('png', 'File format for grids'),
+    "grid_format": OptionInfo('png', 'File format for grids', ui_components.DropdownEditable, {"choices": ("png", "jpg", "jpeg", "webp", "avif")}).info("manual input of <a href='https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html' target='_blank'>other formats</a> is possible, but compatibility is not guaranteed"),
     "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
     "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
     "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
@@ -128,6 +128,7 @@ options_templates.update(options_section(('system', "System", "system"), {
     "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
     "hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
     "dump_stacks_on_signal": OptionInfo(False, "Print stack traces before exiting the program with ctrl+c."),
+    "concurrent_git_fetch_limit": OptionInfo(16, "Number of simultaneous extension update checks ", gr.Slider, {"step": 1, "minimum": 1, "maximum": 100}).info("reduce extension update check time"),
 }))
 
 options_templates.update(options_section(('profiler', "Profiler", "system"), {
@@ -231,7 +232,7 @@ options_templates.update(options_section(('img2img', "img2img", "sd"), {
 
 options_templates.update(options_section(('optimizations', "Optimizations", "sd"), {
     "cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
-    "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}, infotext='NGMS').link("PR", "https://github.com/AUTOMATIC1111/stablediffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+    "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}, infotext='NGMS').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
     "s_min_uncond_all": OptionInfo(False, "Negative Guidance minimum sigma all steps", infotext='NGMS all steps').info("By default, NGMS above skips every other step; this makes it skip all steps"),
     "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
     "token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
@@ -291,6 +292,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks", "s
     "textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
     "textual_inversion_add_hashes_to_infotext": OptionInfo(True, "Add Textual Inversion hashes to infotext"),
     "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *shared.hypernetworks]}, refresh=shared_items.reload_hypernetworks),
+    "textual_inversion_image_embedding_data_cache": OptionInfo(False, 'Cache the data of image embeddings').info('potentially increase TI load time at the cost some disk space'),
 }))
 
 options_templates.update(options_section(('ui_prompt_editing', "Prompt editing", "ui"), {
@@ -404,15 +406,15 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}, infotext='UniPC order').info("must be < sampling steps"),
     'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
     'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models"),
-    'skip_early_cond': OptionInfo(0.0, "Ignore negative prompt during early sampling", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext="Skip Early CFG").info("disables CFG on a proportion of steps at the beginning of generation; 0=skip none; 1=skip all; can both improve sample diversity/quality and speed up sampling"),
+    'skip_early_cond': OptionInfo(0.0, "Ignore negative prompt during early sampling", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext="Skip Early CFG").info("disables CFG on a proportion of steps at the beginning of generation; 0=skip none; 1=skip all; can both improve sample diversity/quality and speed up sampling; XYZ plot: Skip Early CFG"),
-    'beta_dist_alpha': OptionInfo(0.6, "Beta scheduler - alpha", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler alpha').info('Default = 0.6; the alpha parameter of the beta distribution used in Beta sampling'),
+    'beta_dist_alpha': OptionInfo(0.6, "Beta scheduler - alpha", gr.Slider, {"minimum": 0.01, "maximum": 5.0, "step": 0.01}, infotext='Beta scheduler alpha').info('Default = 0.6; the alpha parameter of the beta distribution used in Beta sampling'),
-    'beta_dist_beta': OptionInfo(0.6, "Beta scheduler - beta", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler beta').info('Default = 0.6; the beta parameter of the beta distribution used in Beta sampling'),
+    'beta_dist_beta': OptionInfo(0.6, "Beta scheduler - beta", gr.Slider, {"minimum": 0.01, "maximum": 5.0, "step": 0.01}, infotext='Beta scheduler beta').info('Default = 0.6; the beta parameter of the beta distribution used in Beta sampling'),
 }))
 
 options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {
-    'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
+    'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts(filter_out_extra_only=True)]}),
-    'postprocessing_disable_in_extras': OptionInfo([], "Disable postprocessing operations in extras tab", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
+    'postprocessing_disable_in_extras': OptionInfo([], "Disable postprocessing operations in extras tab", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts(filter_out_main_ui_only=True)]}),
-    'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
+    'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts(filter_out_main_ui_only=True)]}),
     'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     'postprocessing_existing_caption_action': OptionInfo("Ignore", "Action for existing captions", gr.Radio, {"choices": ["Ignore", "Keep", "Prepend", "Append"]}).info("when generating captions using postprocessing; Ignore = use generated; Keep = use original; Prepend/Append = combine both"),
 }))

modules/textual_inversion/textual_inversion.py

@@ -12,7 +12,7 @@ import safetensors.torch
 import numpy as np
 from PIL import Image, PngImagePlugin
 
-from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
+from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes, cache
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -116,6 +116,7 @@ class EmbeddingDatabase:
         self.expected_shape = -1
         self.embedding_dirs = {}
         self.previously_displayed_embeddings = ()
+        self.image_embedding_cache = cache.cache('image-embedding')
 
     def add_embedding_dir(self, path):
         self.embedding_dirs[path] = DirWithTextualInversionEmbeddings(path)
@@ -154,6 +155,31 @@ class EmbeddingDatabase:
         vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
         return vec.shape[1]
 
+    def read_embedding_from_image(self, path, name):
+        try:
+            ondisk_mtime = os.path.getmtime(path)
+
+            if (cache_embedding := self.image_embedding_cache.get(path)) and ondisk_mtime == cache_embedding.get('mtime', 0):
+                # cache will only be used if the file has not been modified time matches
+                return cache_embedding.get('data', None), cache_embedding.get('name', None)
+
+            embed_image = Image.open(path)
+            if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
+                data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
+                name = data.get('name', name)
+            elif data := extract_image_data_embed(embed_image):
+                name = data.get('name', name)
+
+            if data is None or shared.opts.textual_inversion_image_embedding_data_cache:
+                # data of image embeddings only will be cached if the option textual_inversion_image_embedding_data_cache is enabled
+                # results of images that are not embeddings will allways be cached to reduce unnecessary future disk reads
+                self.image_embedding_cache[path] = {'data': data, 'name': None if data is None else name, 'mtime': ondisk_mtime}
+
+            return data, name
+        except Exception:
+            errors.report(f"Error loading embedding {path}", exc_info=True)
+        return None, None
+
     def load_from_file(self, path, filename):
         name, ext = os.path.splitext(filename)
         ext = ext.upper()
@@ -163,17 +189,10 @@ class EmbeddingDatabase:
             if second_ext.upper() == '.PREVIEW':
                 return
 
-            embed_image = Image.open(path)
+            data, name = self.read_embedding_from_image(path, name)
-            if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
+            if data is None:
-                data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
+                return
-                name = data.get('name', name)
+
-            else:
-                data = extract_image_data_embed(embed_image)
-                if data:
-                    name = data.get('name', name)
-                else:
-                    # if data is None, means this is not an embedding, just a preview image
-                    return
         elif ext in ['.BIN', '.PT']:
             data = torch.load(path, map_location="cpu")
         elif ext in ['.SAFETENSORS']:
@@ -191,7 +210,6 @@ class EmbeddingDatabase:
         else:
             print(f"Unable to load Textual inversion embedding due to data issue: '{name}'.")
 
-
     def load_from_dir(self, embdir):
         if not os.path.isdir(embdir.path):
             return

modules/ui.py

@@ -44,6 +44,9 @@ mimetypes.add_type('application/javascript', '.mjs')
 mimetypes.add_type('image/webp', '.webp')
 mimetypes.add_type('image/avif', '.avif')
 
+# override potentially incorrect mimetypes
+mimetypes.add_type('text/css', '.css')
+
 if not cmd_opts.share and not cmd_opts.listen:
     # fix gradio phoning home
     gradio.utils.version_check = lambda: None

modules/ui_components.py

@@ -91,6 +91,7 @@ class InputAccordion(gr.Checkbox):
     Actually just a hidden checkbox, but creates an accordion that follows and is followed by the state of the checkbox.
     """
 
+    accordion_id_set = set()
     global_index = 0
 
     def __init__(self, value, **kwargs):
@@ -99,6 +100,18 @@ class InputAccordion(gr.Checkbox):
             self.accordion_id = f"input-accordion-{InputAccordion.global_index}"
             InputAccordion.global_index += 1
 
+        if not InputAccordion.accordion_id_set:
+            from modules import script_callbacks
+            script_callbacks.on_script_unloaded(InputAccordion.reset)
+
+        if self.accordion_id in InputAccordion.accordion_id_set:
+            count = 1
+            while (unique_id := f'{self.accordion_id}-{count}') in InputAccordion.accordion_id_set:
+                count += 1
+            self.accordion_id = unique_id
+
+        InputAccordion.accordion_id_set.add(self.accordion_id)
+
         kwargs_checkbox = {
             **kwargs,
             "elem_id": f"{self.accordion_id}-checkbox",
@@ -143,3 +156,7 @@ class InputAccordion(gr.Checkbox):
     def get_block_name(self):
         return "checkbox"
 
+    @classmethod
+    def reset(cls):
+        cls.global_index = 0
+        cls.accordion_id_set.clear()

modules/ui_extensions.py

@@ -1,5 +1,6 @@
 import json
 import os
+from concurrent.futures import ThreadPoolExecutor
 import threading
 import time
 from datetime import datetime, timezone
@@ -106,18 +107,24 @@ def check_updates(id_task, disable_list):
     exts = [ext for ext in extensions.extensions if ext.remote is not None and ext.name not in disabled]
     shared.state.job_count = len(exts)
 
-    for ext in exts:
+    lock = threading.Lock()
-        shared.state.textinfo = ext.name
 
+    def _check_update(ext):
         try:
             ext.check_updates()
         except FileNotFoundError as e:
             if 'FETCH_HEAD' not in str(e):
                 raise
         except Exception:
-            errors.report(f"Error checking updates for {ext.name}", exc_info=True)
+            with lock:
+                errors.report(f"Error checking updates for {ext.name}", exc_info=True)
+        with lock:
+            shared.state.textinfo = ext.name
+            shared.state.nextjob()
 
-        shared.state.nextjob()
+    with ThreadPoolExecutor(max_workers=max(1, int(shared.opts.concurrent_git_fetch_limit))) as executor:
+        for ext in exts:
+            executor.submit(_check_update, ext)
 
     return extension_table(), ""
 

modules/ui_extra_networks.py

@@ -177,10 +177,8 @@ def add_pages_to_demo(app):
     app.add_api_route("/sd_extra_networks/get-single-card", get_single_card, methods=["GET"])
 
 
-def quote_js(s):
+def quote_js(s: str):
-    s = s.replace('\\', '\\\\')
+    return json.dumps(s, ensure_ascii=False)
-    s = s.replace('"', '\\"')
-    return f'"{s}"'
 
 
 class ExtraNetworksPage:

modules/ui_loadsave.py

@@ -176,7 +176,7 @@ class UiLoadsave:
             if new_value == old_value:
                 continue
 
-            if old_value is None and new_value == '' or new_value == []:
+            if old_value is None and (new_value == '' or new_value == []):
                 continue
 
             yield path, old_value, new_value

modules/upscaler.py

@@ -93,13 +93,14 @@ class UpscalerData:
     scaler: Upscaler = None
     model: None
 
-    def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None):
+    def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None, sha256: str = None):
         self.name = name
         self.data_path = path
         self.local_data_path = path
         self.scaler = upscaler
         self.scale = scale
         self.model = model
+        self.sha256 = sha256
 
     def __repr__(self):
         return f"<UpscalerData name={self.name} path={self.data_path} scale={self.scale}>"

modules/util.py

@@ -1,3 +1,4 @@
+from __future__ import annotations
 import os
 import re
 
@@ -211,3 +212,80 @@ Requested path was: {path}
         subprocess.Popen(["explorer.exe", subprocess.check_output(["wslpath", "-w", path])])
     else:
         subprocess.Popen(["xdg-open", path])
+
+
+def load_file_from_url(
+    url: str,
+    *,
+    model_dir: str,
+    progress: bool = True,
+    file_name: str | None = None,
+    hash_prefix: str | None = None,
+    re_download: bool = False,
+) -> str:
+    """Download a file from `url` into `model_dir`, using the file present if possible.
+    Returns the path to the downloaded file.
+
+    file_name: if specified, it will be used as the filename, otherwise the filename will be extracted from the url.
+        file is downloaded to {file_name}.tmp then moved to the final location after download is complete.
+    hash_prefix: sha256 hex string, if provided, the hash of the downloaded file will be checked against this prefix.
+        if the hash does not match, the temporary file is deleted and a ValueError is raised.
+    re_download: forcibly re-download the file even if it already exists.
+    """
+    from urllib.parse import urlparse
+    import requests
+    try:
+        from tqdm import tqdm
+    except ImportError:
+        class tqdm:
+            def __init__(self, *args, **kwargs):
+                pass
+
+            def update(self, n=1, *args, **kwargs):
+                pass
+
+            def __enter__(self):
+                return self
+
+            def __exit__(self, exc_type, exc_val, exc_tb):
+                pass
+
+    if not file_name:
+        parts = urlparse(url)
+        file_name = os.path.basename(parts.path)
+
+    cached_file = os.path.abspath(os.path.join(model_dir, file_name))
+
+    if re_download or not os.path.exists(cached_file):
+        os.makedirs(model_dir, exist_ok=True)
+        temp_file = os.path.join(model_dir, f"{file_name}.tmp")
+        print(f'\nDownloading: "{url}" to {cached_file}')
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+        total_size = int(response.headers.get('content-length', 0))
+        with tqdm(total=total_size, unit='B', unit_scale=True, desc=file_name, disable=not progress) as progress_bar:
+            with open(temp_file, 'wb') as file:
+                for chunk in response.iter_content(chunk_size=1024):
+                    if chunk:
+                        file.write(chunk)
+                        progress_bar.update(len(chunk))
+
+        if hash_prefix and not compare_sha256(temp_file, hash_prefix):
+            print(f"Hash mismatch for {temp_file}. Deleting the temporary file.")
+            os.remove(temp_file)
+            raise ValueError(f"File hash does not match the expected hash prefix {hash_prefix}!")
+
+        os.rename(temp_file, cached_file)
+    return cached_file
+
+
+def compare_sha256(file_path: str, hash_prefix: str) -> bool:
+    """Check if the SHA256 hash of the file matches the given prefix."""
+    import hashlib
+    hash_sha256 = hashlib.sha256()
+    blksize = 1024 * 1024
+
+    with open(file_path, "rb") as f:
+        for chunk in iter(lambda: f.read(blksize), b""):
+            hash_sha256.update(chunk)
+    return hash_sha256.hexdigest().startswith(hash_prefix.strip().lower())

modules/uv_hook.py

@@ -0,0 +1,49 @@
+import sys
+import copy
+import shlex
+import subprocess
+from functools import wraps
+
+BAD_FLAGS = ("--prefer-binary", '-I', '--ignore-installed')
+
+
+def patch():
+    if hasattr(subprocess, "__original_run"):
+        return
+
+    print("using uv")
+    try:
+        subprocess.run(['uv', '-V'])
+    except FileNotFoundError:
+        subprocess.run([sys.executable, '-m', 'pip', 'install', 'uv'])
+
+    subprocess.__original_run = subprocess.run
+
+    @wraps(subprocess.__original_run)
+    def patched_run(*args, **kwargs):
+        _kwargs = copy.copy(kwargs)
+        if args:
+            command, *_args = args
+        else:
+            command, _args = _kwargs.pop("args", ""), ()
+
+        if isinstance(command, str):
+            command = shlex.split(command)
+        else:
+            command = [arg.strip() for arg in command]
+
+        if not isinstance(command, list) or "pip" not in command:
+            return subprocess.__original_run(*args, **kwargs)
+
+        cmd = command[command.index("pip") + 1:]
+
+        cmd = [arg for arg in cmd if arg not in BAD_FLAGS]
+
+        modified_command = ["uv", "pip", *cmd]
+
+        result = subprocess.__original_run([*modified_command, *_args], **_kwargs)
+        if result.returncode != 0:
+            return subprocess.__original_run(*args, **kwargs)
+        return result
+
+    subprocess.run = patched_run

requirements_versions.txt

@@ -22,7 +22,7 @@ protobuf==3.20.0
 psutil==5.9.5
 pytorch_lightning==1.9.4
 resize-right==0.0.2
-safetensors==0.4.2
+safetensors==0.4.5
 scikit-image==0.21.0
 spandrel==0.3.4
 spandrel-extra-arches==0.1.1

script.js

@@ -182,7 +182,7 @@ document.addEventListener('keydown', function(e) {
         const lightboxModal = document.querySelector('#lightboxModal');
         if (!globalPopup || globalPopup.style.display === 'none') {
             if (document.activeElement === lightboxModal) return;
-            if (interruptButton.style.display === 'block') {
+            if (interruptButton?.style.display === 'block') {
                 interruptButton.click();
                 e.preventDefault();
             }

scripts/postprocessing_codeformer.py

@@ -12,8 +12,8 @@ class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing
     def ui(self):
         with ui_components.InputAccordion(False, label="CodeFormer") as enable:
             with gr.Row():
-                codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id="extras_codeformer_visibility")
+                codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id=self.elem_id_suffix("extras_codeformer_visibility"))
-                codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Weight (0 = maximum effect, 1 = minimum effect)", value=0, elem_id="extras_codeformer_weight")
+                codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Weight (0 = maximum effect, 1 = minimum effect)", value=0, elem_id=self.elem_id_suffix("extras_codeformer_weight"))
 
         return {
             "enable": enable,
@@ -29,6 +29,10 @@ class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing
         res = Image.fromarray(restored_img)
 
         if codeformer_visibility < 1.0:
+            if pp.image.size != res.size:
+                res = res.resize(pp.image.size)
+            if pp.image.mode != res.mode:
+                res = res.convert(pp.image.mode)
             res = Image.blend(pp.image, res, codeformer_visibility)
 
         pp.image = res

scripts/postprocessing_gfpgan.py

@@ -11,7 +11,7 @@ class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
 
     def ui(self):
         with ui_components.InputAccordion(False, label="GFPGAN") as enable:
-            gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id="extras_gfpgan_visibility")
+            gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id=self.elem_id_suffix("extras_gfpgan_visibility"))
 
         return {
             "enable": enable,
@@ -26,6 +26,10 @@ class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
         res = Image.fromarray(restored_img)
 
         if gfpgan_visibility < 1.0:
+            if pp.image.size != res.size:
+                res = res.resize(pp.image.size)
+            if pp.image.mode != res.mode:
+                res = res.convert(pp.image.mode)
             res = Image.blend(pp.image, res, gfpgan_visibility)
 
         pp.image = res

scripts/postprocessing_upscale.py

@@ -30,31 +30,31 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
     def ui(self):
         selected_tab = gr.Number(value=0, visible=False)
 
-        with InputAccordion(True, label="Upscale", elem_id="extras_upscale") as upscale_enabled:
+        with InputAccordion(True, label="Upscale", elem_id=self.elem_id_suffix("extras_upscale")) as upscale_enabled:
             with FormRow():
-                extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+                extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id=self.elem_id_suffix("extras_upscaler_1"), choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
 
             with FormRow():
-                extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+                extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id=self.elem_id_suffix("extras_upscaler_2"), choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
-                extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
+                extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id=self.elem_id_suffix("extras_upscaler_2_visibility"))
 
             with FormRow():
-                with gr.Tabs(elem_id="extras_resize_mode"):
+                with gr.Tabs(elem_id=self.elem_id_suffix("extras_resize_mode")):
-                    with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
+                    with gr.TabItem('Scale by', elem_id=self.elem_id_suffix("extras_scale_by_tab")) as tab_scale_by:
                         with gr.Row():
                             with gr.Column(scale=4):
-                                upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
+                                upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id=self.elem_id_suffix("extras_upscaling_resize"))
                             with gr.Column(scale=1, min_width=160):
-                                max_side_length = gr.Number(label="Max side length", value=0, elem_id="extras_upscale_max_side_length", tooltip="If any of two sides of the image ends up larger than specified, will downscale it to fit. 0 = no limit.", min_width=160, step=8, minimum=0)
+                                max_side_length = gr.Number(label="Max side length", value=0, elem_id=self.elem_id_suffix("extras_upscale_max_side_length"), tooltip="If any of two sides of the image ends up larger than specified, will downscale it to fit. 0 = no limit.", min_width=160, step=8, minimum=0)
 
-                    with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
+                    with gr.TabItem('Scale to', elem_id=self.elem_id_suffix("extras_scale_to_tab")) as tab_scale_to:
                         with FormRow():
-                            with gr.Column(elem_id="upscaling_column_size", scale=4):
+                            with gr.Column(elem_id=self.elem_id_suffix("upscaling_column_size"), scale=4):
-                                upscaling_resize_w = gr.Slider(minimum=64, maximum=8192, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
+                                upscaling_resize_w = gr.Slider(minimum=64, maximum=8192, step=8, label="Width", value=512, elem_id=self.elem_id_suffix("extras_upscaling_resize_w"))
-                                upscaling_resize_h = gr.Slider(minimum=64, maximum=8192, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h")
+                                upscaling_resize_h = gr.Slider(minimum=64, maximum=8192, step=8, label="Height", value=512, elem_id=self.elem_id_suffix("extras_upscaling_resize_h"))
-                            with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                            with gr.Column(elem_id=self.elem_id_suffix("upscaling_dimensions_row"), scale=1, elem_classes="dimensions-tools"):
-                                upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn", tooltip="Switch width/height")
+                                upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id=self.elem_id_suffix("upscaling_res_switch_btn"), tooltip="Switch width/height")
-                                upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+                                upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id=self.elem_id_suffix("extras_upscaling_crop"))
 
         def on_selected_upscale_method(upscale_method):
             if not shared.opts.set_scale_by_when_changing_upscaler:
@@ -169,6 +169,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
 class ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale):
     name = "Simple Upscale"
     order = 900
+    main_ui_only = True
 
     def ui(self):
         with FormRow():

scripts/xyz_grid.py

@@ -20,7 +20,7 @@ import modules.sd_models
 import modules.sd_vae
 import re
 
-from modules.ui_components import ToolButton
+from modules.ui_components import ToolButton, InputAccordion
 
 fill_values_symbol = "\U0001f4d2"  # 📒
 
@@ -259,6 +259,7 @@ axis_options = [
     AxisOption("Schedule min sigma", float, apply_override("sigma_min")),
     AxisOption("Schedule max sigma", float, apply_override("sigma_max")),
     AxisOption("Schedule rho", float, apply_override("rho")),
+    AxisOption("Skip Early CFG", float, apply_override('skip_early_cond')),
     AxisOption("Beta schedule alpha", float, apply_override("beta_dist_alpha")),
     AxisOption("Beta schedule beta", float, apply_override("beta_dist_beta")),
     AxisOption("Eta", float, apply_field("eta")),
@@ -284,7 +285,7 @@ axis_options = [
 ]
 
 
-def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size):
+def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size, draw_grid):
     hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
     ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
     title_texts = [[images.GridAnnotation(z)] for z in z_labels]
@@ -369,29 +370,30 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
         print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
         return Processed(p, [])
 
-    z_count = len(zs)
+    if draw_grid:
+        z_count = len(zs)
 
-    for i in range(z_count):
+        for i in range(z_count):
-        start_index = (i * len(xs) * len(ys)) + i
+            start_index = (i * len(xs) * len(ys)) + i
-        end_index = start_index + len(xs) * len(ys)
+            end_index = start_index + len(xs) * len(ys)
-        grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
+            grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
+            if draw_legend:
+                grid_max_w, grid_max_h = map(max, zip(*(img.size for img in processed_result.images[start_index:end_index])))
+                grid = images.draw_grid_annotations(grid, grid_max_w, grid_max_h, hor_texts, ver_texts, margin_size)
+            processed_result.images.insert(i, grid)
+            processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
+            processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
+            processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
+
+        z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
+        z_sub_grid_max_w, z_sub_grid_max_h = map(max, zip(*(img.size for img in processed_result.images[:z_count])))
         if draw_legend:
-            grid_max_w, grid_max_h = map(max, zip(*(img.size for img in processed_result.images[start_index:end_index])))
+            z_grid = images.draw_grid_annotations(z_grid, z_sub_grid_max_w, z_sub_grid_max_h, title_texts, [[images.GridAnnotation()]])
-            grid = images.draw_grid_annotations(grid, grid_max_w, grid_max_h, hor_texts, ver_texts, margin_size)
+        processed_result.images.insert(0, z_grid)
-        processed_result.images.insert(i, grid)
+        # TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
-        processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
+        # processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
-        processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
+        # processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
-        processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
+        processed_result.infotexts.insert(0, processed_result.infotexts[0])
-
-    z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
-    z_sub_grid_max_w, z_sub_grid_max_h = map(max, zip(*(img.size for img in processed_result.images[:z_count])))
-    if draw_legend:
-        z_grid = images.draw_grid_annotations(z_grid, z_sub_grid_max_w, z_sub_grid_max_h, title_texts, [[images.GridAnnotation()]])
-    processed_result.images.insert(0, z_grid)
-    # TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
-    # processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
-    # processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
-    processed_result.infotexts.insert(0, processed_result.infotexts[0])
 
     return processed_result
 
@@ -441,7 +443,6 @@ class Script(scripts.Script):
 
         with gr.Row(variant="compact", elem_id="axis_options"):
             with gr.Column():
-                draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
                 no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
                 with gr.Row():
                     vary_seeds_x = gr.Checkbox(label='Vary seeds for X', value=False, min_width=80, elem_id=self.elem_id("vary_seeds_x"), tooltip="Use different seeds for images along X axis.")
@@ -449,9 +450,12 @@ class Script(scripts.Script):
                     vary_seeds_z = gr.Checkbox(label='Vary seeds for Z', value=False, min_width=80, elem_id=self.elem_id("vary_seeds_z"), tooltip="Use different seeds for images along Z axis.")
             with gr.Column():
                 include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id("include_lone_images"))
-                include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
                 csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode"))
-            with gr.Column():
+
+        with InputAccordion(True, label='Draw grid', elem_id=self.elem_id('draw_grid')) as draw_grid:
+            with gr.Row():
+                include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
+                draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
                 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"):
@@ -533,9 +537,9 @@ class Script(scripts.Script):
             (z_values_dropdown, lambda params: get_dropdown_update_from_params("Z", params)),
         )
 
-        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode]
+        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode, draw_grid]
 
-    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode):
+    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode, draw_grid):
         x_type, y_type, z_type = x_type or 0, y_type or 0, z_type or 0  # if axle type is None set to 0
 
         if not no_fixed_seeds:
@@ -780,7 +784,8 @@ class Script(scripts.Script):
                 include_sub_grids=include_sub_grids,
                 first_axes_processed=first_axes_processed,
                 second_axes_processed=second_axes_processed,
-                margin_size=margin_size
+                margin_size=margin_size,
+                draw_grid=draw_grid,
             )
 
         if not processed.images:
@@ -789,14 +794,15 @@ class Script(scripts.Script):
 
         z_count = len(zs)
 
-        # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
+        if draw_grid:
-        processed.infotexts[:1 + z_count] = grid_infotext[:1 + z_count]
+            # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
+            processed.infotexts[:1 + z_count] = grid_infotext[:1 + z_count]
 
         if not include_lone_images:
             # Don't need sub-images anymore, drop from list:
-            processed.images = processed.images[:z_count + 1]
+            processed.images = processed.images[:z_count + 1] if draw_grid else []
 
-        if opts.grid_save:
+        if draw_grid and opts.grid_save:
             # Auto-save main and sub-grids:
             grid_count = z_count + 1 if z_count > 1 else 1
             for g in range(grid_count):
@@ -806,7 +812,7 @@ class Script(scripts.Script):
                 if not include_sub_grids:  # if not include_sub_grids then skip saving after the first grid
                     break
 
-        if not include_sub_grids:
+        if draw_grid and not include_sub_grids:
             # Done with sub-grids, drop all related information:
             for _ in range(z_count):
                 del processed.images[1]

style.css

@@ -480,8 +480,10 @@ div.toprow-compact-tools{
 }
 
 #settings_result{
-    height: 1.4em;
+    min-height: 1.4em;
     margin: 0 1.2em;
+    max-height: calc(var(--text-md) * var(--line-sm) * 5);
+    overflow-y: auto;
 }
 
 table.popup-table{
@@ -600,6 +602,7 @@ table.popup-table .link{
     background: var(--background-fill-primary);
     width: 100%;
     height: 100%;
+    pointer-events: none;
 }
 
 .livePreview img{

webui.bat

@@ -4,7 +4,16 @@ if exist webui.settings.bat (
     call webui.settings.bat
 )
 
-if not defined PYTHON (set PYTHON=python)
+if not defined PYTHON (
+  for /f "delims=" %%A in ('where python ^| findstr /n . ^| findstr ^^1:') do (
+    if /i "%%~xA" == ".exe" (
+      set PYTHON=python
+    ) else (
+      set PYTHON=call python
+    )
+  )
+)
+
 if defined GIT (set "GIT_PYTHON_GIT_EXECUTABLE=%GIT%")
 if not defined VENV_DIR (set "VENV_DIR=%~dp0%venv")
 

webui.py

@@ -66,12 +66,53 @@ def monitorar_comandos():
                 print(f"Unknown server command: {server_command}")
 
 
+def warning_if_invalid_install_dir():
+    """
+    Shows a warning if the webui is installed under a path that contains a leading dot in any of its parent directories.
+
+    Gradio '/file=' route will block access to files that have a leading dot in the path segments.
+    We use this route to serve files such as JavaScript and CSS to the webpage,
+    if those files are blocked, the webpage will not function properly.
+    See https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13292
+
+    This is a security feature was added to Gradio 3.32.0 and is removed in later versions,
+    this function replicates Gradio file access blocking logic.
+
+    This check should be removed when it's no longer applicable.
+    """
+    from packaging.version import parse
+    from pathlib import Path
+    import gradio
+
+    if parse('3.32.0') <= parse(gradio.__version__) < parse('4'):
+
+        def abspath(path):
+            """modified from Gradio 3.41.2 gradio.utils.abspath()"""
+            if path.is_absolute():
+                return path
+            is_symlink = path.is_symlink() or any(parent.is_symlink() for parent in path.parents)
+            return Path.cwd() / path if (is_symlink or path == path.resolve()) else path.resolve()
+
+        webui_root = Path(__file__).parent
+        if any(part.startswith(".") for part in abspath(webui_root).parts):
+            print(f'''{"!"*25} Warning {"!"*25}
+WebUI is installed in a directory that has a leading dot (.) in one of its parent directories.
+This will prevent WebUI from functioning properly.
+Please move the installation to a different directory.
+Current path: "{webui_root}"
+For more information see: https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13292
+{"!"*25} Warning {"!"*25}''')
+
+
 def webui():
     initialize.initialize()
     from modules import shared, ui_tempdir, script_callbacks, ui, progress, ui_extra_networks
 
-    while True:
+warning_if_invalid_install_dir()
-        limpar_temp_dir()
+
+while True:
+    limpar_temp_dir()
+    
         script_callbacks.before_ui_callback()
         startup_timer.record("scripts before_ui_callback")
 

webui.sh

@@ -127,7 +127,7 @@ then
 fi
 
 # Check prerequisites
-gpu_info=$(lspci 2>/dev/null | grep -E "VGA|Display")
+gpu_info=$(lspci 2>/dev/null | grep -E "VGA|Display|CMP")
 case "$gpu_info" in
     *"Navi 1"*)
         export HSA_OVERRIDE_GFX_VERSION=10.3.0