Merge pull request #17313 from WhizZest/fix-setuptools-version

Fix the issue of `pip install 'setuptools<70'` failing in cmd

.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

.gitignore

@@ -2,6 +2,7 @@ __pycache__
 *.ckpt
 *.safetensors
 *.pth
+.DS_Store
 /ESRGAN/*
 /SwinIR/*
 /repositories
@@ -40,3 +41,4 @@ notification.mp3
 /test/test_outputs
 /cache
 trace.json
+/sysinfo-????-??-??-??-??.json

CHANGELOG.md

@@ -1,8 +1,14 @@
+## 1.10.1
+
+### Bug Fixes:
+* fix image upscale on cpu ([#16275](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16275))
+
+
 ## 1.10.0
 
 ### Features:
 * A lot of performance improvements (see below in Performance section)
-* Stable Diffusion 3 support ([#16030](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16030))
+* Stable Diffusion 3 support ([#16030](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16030), [#16164](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16164), [#16212](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16212))
   * Recommended Euler sampler; DDIM and other timestamp samplers currently not supported
   * T5 text model is disabled by default, enable it in settings
 * New schedulers:
@@ -11,6 +17,7 @@
   * Normal ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149))
   * DDIM ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149))
   * Simple ([#16142](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16142))
+  * Beta ([#16235](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16235))
 * New sampler: DDIM CFG++ ([#16035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16035))
 
 ### Minor:
@@ -25,6 +32,8 @@
 * Add option to enable clip skip for clip L on SDXL ([#15992](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15992))
 * Option to prevent screen sleep during generation ([#16001](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16001))
 * ToggleLivePriview button in image viewer ([#16065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16065))
+* Remove ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153))
+* option to disable save button log.csv ([#16242](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16242))
 
 ### Extensions and API:
 * Add process_before_every_sampling hook ([#15984](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15984))
@@ -73,6 +82,10 @@
 * Fix SD2 loading ([#16078](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16078), [#16079](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16079))
 * fix infotext Lora hashes for hires fix different lora ([#16062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16062))
 * Fix sampler scheduler autocorrection warning ([#16054](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16054))
+* fix ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153))
+* fix upscale logic ([#16239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16239))
+* [bug] do not break progressbar on non-job actions (add wrap_gradio_call_no_job) ([#16202](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16202))
+* fix OSError: cannot write mode P as JPEG ([#16194](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16194))
 
 ### Other:
 * fix changelog #15883 -> #15882 ([#15907](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15907))
@@ -89,10 +102,17 @@
 * Bump spandrel to 0.3.4 ([#16144](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16144))
 * Defunct --max-batch-count ([#16119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16119))
 * docs: update bug_report.yml ([#16102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16102))
-* Maintaining Project Compatibility for Python 3.9 Users Without Upgrade Requirements. ([#16088](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16088))
+* Maintaining Project Compatibility for Python 3.9 Users Without Upgrade Requirements. ([#16088](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16088), [#16169](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16169), [#16192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16192))
 * Update torch for ARM Macs to 2.3.1 ([#16059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16059))
 * remove deprecated setting dont_fix_second_order_samplers_schedule ([#16061](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16061))
 * chore: fix typos ([#16060](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16060))
+* shlex.join launch args in console log ([#16170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16170))
+* activate venv .bat ([#16231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16231))
+* add ids to the resize tabs in img2img ([#16218](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16218))
+* update installation guide linux ([#16178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16178))
+* Robust sysinfo ([#16173](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16173))
+* do not send image size on paste inpaint ([#16180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16180))
+* Fix noisy DS_Store files for MacOS ([#16166](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16166))
 
 
 ## 1.9.4

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

@@ -128,10 +128,33 @@ sudo zypper install wget git python3 libtcmalloc4 libglvnd
 # Arch-based:
 sudo pacman -S wget git python3
 ```
+If your system is very new, you need to install python3.11 or python3.10:
+```bash
+# Ubuntu 24.04
+sudo add-apt-repository ppa:deadsnakes/ppa
+sudo apt update
+sudo apt install python3.11 python3.11-venv
+
+# Manjaro/Arch
+sudo pacman -S yay
+yay -S python311 # do not confuse with python3.11 package
+
+# Only for 3.11
+# Then set up env variable in launch script
+export python_cmd="python3.11"
+# or in webui-user.sh
+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
+git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui
+```
+
 3. Run `webui.sh`.
 4. Check `webui-user.sh` for options.
 ### Installation on Apple Silicon

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/Lora/network.py

@@ -7,6 +7,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from modules import sd_models, cache, errors, hashes, shared
+import modules.models.sd3.mmdit
 
 NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
 
@@ -114,7 +115,10 @@ class NetworkModule:
         self.sd_key = weights.sd_key
         self.sd_module = weights.sd_module
 
-        if hasattr(self.sd_module, 'weight'):
+        if isinstance(self.sd_module, modules.models.sd3.mmdit.QkvLinear):
+            s = self.sd_module.weight.shape
+            self.shape = (s[0] // 3, s[1])
+        elif hasattr(self.sd_module, 'weight'):
             self.shape = self.sd_module.weight.shape
         elif isinstance(self.sd_module, nn.MultiheadAttention):
             # For now, only self-attn use Pytorch's MHA

extensions-builtin/Lora/network_lora.py

@@ -1,6 +1,7 @@
 import torch
 
 import lyco_helpers
+import modules.models.sd3.mmdit
 import network
 from modules import devices
 
@@ -10,6 +11,13 @@ class ModuleTypeLora(network.ModuleType):
         if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]):
             return NetworkModuleLora(net, weights)
 
+        if all(x in weights.w for x in ["lora_A.weight", "lora_B.weight"]):
+            w = weights.w.copy()
+            weights.w.clear()
+            weights.w.update({"lora_up.weight": w["lora_B.weight"], "lora_down.weight": w["lora_A.weight"]})
+
+            return NetworkModuleLora(net, weights)
+
         return None
 
 
@@ -29,7 +37,7 @@ class NetworkModuleLora(network.NetworkModule):
         if weight is None and none_ok:
             return None
 
-        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention]
+        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention, modules.models.sd3.mmdit.QkvLinear]
         is_conv = type(self.sd_module) in [torch.nn.Conv2d]
 
         if is_linear:

extensions-builtin/Lora/networks.py

@@ -1,3 +1,4 @@
+from __future__ import annotations
 import gradio as gr
 import logging
 import os
@@ -19,6 +20,7 @@ from typing import Union
 
 from modules import shared, devices, sd_models, errors, scripts, sd_hijack
 import modules.textual_inversion.textual_inversion as textual_inversion
+import modules.models.sd3.mmdit
 
 from lora_logger import logger
 
@@ -165,11 +167,25 @@ def load_network(name, network_on_disk):
 
     keys_failed_to_match = {}
     is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
+    if hasattr(shared.sd_model, 'diffusers_weight_map'):
+        diffusers_weight_map = shared.sd_model.diffusers_weight_map
+    elif hasattr(shared.sd_model, 'diffusers_weight_mapping'):
+        diffusers_weight_map = {}
+        for k, v in shared.sd_model.diffusers_weight_mapping():
+            diffusers_weight_map[k] = v
+        shared.sd_model.diffusers_weight_map = diffusers_weight_map
+    else:
+        diffusers_weight_map = None
 
     matched_networks = {}
     bundle_embeddings = {}
 
     for key_network, weight in sd.items():
+
+        if diffusers_weight_map:
+            key_network_without_network_parts, network_name, network_weight = key_network.rsplit(".", 2)
+            network_part = network_name + '.' + network_weight
+        else:
             key_network_without_network_parts, _, network_part = key_network.partition(".")
 
         if key_network_without_network_parts == "bundle_emb":
@@ -182,7 +198,11 @@ def load_network(name, network_on_disk):
                 emb_dict[vec_name] = weight
             bundle_embeddings[emb_name] = emb_dict
 
+        if diffusers_weight_map:
+            key = diffusers_weight_map.get(key_network_without_network_parts, key_network_without_network_parts)
+        else:
             key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
+
         sd_module = shared.sd_model.network_layer_mapping.get(key, None)
 
         if sd_module is None:
@@ -346,6 +366,28 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
     purge_networks_from_memory()
 
 
+def allowed_layer_without_weight(layer):
+    if isinstance(layer, torch.nn.LayerNorm) and not layer.elementwise_affine:
+        return True
+
+    return False
+
+
+def store_weights_backup(weight):
+    if weight is None:
+        return None
+
+    return weight.to(devices.cpu, copy=True)
+
+
+def restore_weights_backup(obj, field, weight):
+    if weight is None:
+        setattr(obj, field, None)
+        return
+
+    getattr(obj, field).copy_(weight)
+
+
 def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
     weights_backup = getattr(self, "network_weights_backup", None)
     bias_backup = getattr(self, "network_bias_backup", None)
@@ -355,21 +397,15 @@ def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Li
 
     if weights_backup is not None:
         if isinstance(self, torch.nn.MultiheadAttention):
-            self.in_proj_weight.copy_(weights_backup[0])
+            restore_weights_backup(self, 'in_proj_weight', weights_backup[0])
-            self.out_proj.weight.copy_(weights_backup[1])
+            restore_weights_backup(self.out_proj, 'weight', weights_backup[1])
         else:
-            self.weight.copy_(weights_backup)
+            restore_weights_backup(self, 'weight', weights_backup)
 
-    if bias_backup is not None:
     if isinstance(self, torch.nn.MultiheadAttention):
-            self.out_proj.bias.copy_(bias_backup)
+        restore_weights_backup(self.out_proj, 'bias', bias_backup)
     else:
-            self.bias.copy_(bias_backup)
+        restore_weights_backup(self, 'bias', bias_backup)
-    else:
-        if isinstance(self, torch.nn.MultiheadAttention):
-            self.out_proj.bias = None
-        else:
-            self.bias = None
 
 
 def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
@@ -388,22 +424,22 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
     weights_backup = getattr(self, "network_weights_backup", None)
     if weights_backup is None and wanted_names != ():
-        if current_names != ():
+        if current_names != () and not allowed_layer_without_weight(self):
-            raise RuntimeError("no backup weights found and current weights are not unchanged")
+            raise RuntimeError(f"{network_layer_name} - no backup weights found and current weights are not unchanged")
 
         if isinstance(self, torch.nn.MultiheadAttention):
-            weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
+            weights_backup = (store_weights_backup(self.in_proj_weight), store_weights_backup(self.out_proj.weight))
         else:
-            weights_backup = self.weight.to(devices.cpu, copy=True)
+            weights_backup = store_weights_backup(self.weight)
 
         self.network_weights_backup = weights_backup
 
     bias_backup = getattr(self, "network_bias_backup", None)
     if bias_backup is None and wanted_names != ():
         if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
-            bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
+            bias_backup = store_weights_backup(self.out_proj.bias)
         elif getattr(self, 'bias', None) is not None:
-            bias_backup = self.bias.to(devices.cpu, copy=True)
+            bias_backup = store_weights_backup(self.bias)
         else:
             bias_backup = None
 
@@ -411,6 +447,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
         # Only report if bias is not None and current bias are not unchanged.
         if bias_backup is not None and current_names != ():
             raise RuntimeError("no backup bias found and current bias are not unchanged")
+
         self.network_bias_backup = bias_backup
 
     if current_names != wanted_names:
@@ -418,7 +455,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
         for net in loaded_networks:
             module = net.modules.get(network_layer_name, None)
-            if module is not None and hasattr(self, 'weight'):
+            if module is not None and hasattr(self, 'weight') and not isinstance(module, modules.models.sd3.mmdit.QkvLinear):
                 try:
                     with torch.no_grad():
                         if getattr(self, 'fp16_weight', None) is None:
@@ -478,6 +515,24 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
                 continue
 
+            if isinstance(self, modules.models.sd3.mmdit.QkvLinear) and module_q and module_k and module_v:
+                try:
+                    with torch.no_grad():
+                        # Send "real" orig_weight into MHA's lora module
+                        qw, kw, vw = self.weight.chunk(3, 0)
+                        updown_q, _ = module_q.calc_updown(qw)
+                        updown_k, _ = module_k.calc_updown(kw)
+                        updown_v, _ = module_v.calc_updown(vw)
+                        del qw, kw, vw
+                        updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+                        self.weight += updown_qkv
+
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+                continue
+
             if module is None:
                 continue
 

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}.`);
+                }
             }
         }
 
-    checkPair('(', ')', 'round brackets');
+        i++;
-    checkPair('[', ']', 'square brackets');
+    }
-    checkPair('{', '}', 'curly brackets');
+
-    counterElt.title = errors.join('\n');
+    for (const [open, close, label] of pairs) {
-    counterElt.classList.toggle('error', errors.length !== 0);
+        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}.`);
+        }
+    }
+
+    counterElem.title = [...errors].join('\n');
+    counterElem.classList.toggle('error', errors.size !== 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();
 
@@ -31,9 +32,8 @@ 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 preview = gradioApp().querySelectorAll('.livePreview > img');
     if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) {
@@ -46,8 +46,15 @@ function updateOnBackgroundChange() {
             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

@@ -113,7 +113,7 @@ def encode_pil_to_base64(image):
             image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
 
         elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
-            if image.mode == "RGBA":
+            if image.mode in ("RGBA", "P"):
                 image = image.convert("RGB")
             parameters = image.info.get('parameters', None)
             exif_bytes = piexif.dump({
@@ -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/call_queue.py

@@ -47,6 +47,22 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
 
 def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+    @wraps(func)
+    def f(*args, **kwargs):
+        try:
+            res = func(*args, **kwargs)
+        finally:
+            shared.state.skipped = False
+            shared.state.interrupted = False
+            shared.state.stopping_generation = False
+            shared.state.job_count = 0
+            shared.state.job = ""
+        return res
+
+    return wrap_gradio_call_no_job(f, extra_outputs, add_stats)
+
+
+def wrap_gradio_call_no_job(func, extra_outputs=None, add_stats=False):
     @wraps(func)
     def f(*args, extra_outputs_array=extra_outputs, **kwargs):
         run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
@@ -66,9 +82,6 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
                 arg_str += f" (Argument list truncated at {max_debug_str_len}/{len(arg_str)} characters)"
             errors.report(f"{message}\n{arg_str}", exc_info=True)
 
-            shared.state.job = ""
-            shared.state.job_count = 0
-
             if extra_outputs_array is None:
                 extra_outputs_array = [None, '']
 
@@ -77,11 +90,6 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
 
         devices.torch_gc()
 
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.stopping_generation = False
-        shared.state.job_count = 0
-
         if not add_stats:
             return tuple(res)
 
@@ -123,3 +131,4 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
         return tuple(res)
 
     return f
+

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/infotext_utils.py

@@ -146,18 +146,19 @@ def connect_paste_params_buttons():
         destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
 
         if binding.source_image_component and destination_image_component:
+            need_send_dementions = destination_width_component and binding.tabname != 'inpaint'
             if isinstance(binding.source_image_component, gr.Gallery):
-                func = send_image_and_dimensions if destination_width_component else image_from_url_text
+                func = send_image_and_dimensions if need_send_dementions else image_from_url_text
                 jsfunc = "extract_image_from_gallery"
             else:
-                func = send_image_and_dimensions if destination_width_component else lambda x: x
+                func = send_image_and_dimensions if need_send_dementions else lambda x: x
                 jsfunc = None
 
             binding.paste_button.click(
                 fn=func,
                 _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],
+                outputs=[destination_image_component, destination_width_component, destination_height_component] if need_send_dementions else [destination_image_component],
                 show_progress=False,
             )
 

modules/launch_utils.py

@@ -9,6 +9,7 @@ import importlib.util
 import importlib.metadata
 import platform
 import json
+import shlex
 from functools import lru_cache
 
 from modules import cmd_args, errors
@@ -42,9 +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}.
@@ -314,9 +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
@@ -340,12 +373,12 @@ 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")
 
     assets_repo = os.environ.get('ASSETS_REPO', "https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets.git")
-    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
+    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/w-e-w/stablediffusion.git")
     stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.git")
     k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
     blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
@@ -389,8 +422,24 @@ def prepare_environment():
         )
     startup_timer.record("torch GPU test")
 
+    # Ensure build dependencies are installed before any package that might need them
+    def ensure_build_dependencies():
+        """Ensure essential build tools are available"""
+        if not is_installed("wheel"):
+            run_pip("install wheel", "wheel")
+        # Check setuptools version compatibility
+        try:
+            setuptools_version = run(f'"{python}" -c "import setuptools; print(setuptools.__version__)"', None, None).strip()
+            if setuptools_version >= "70":
+                run_pip("install setuptools==69.5.1", "setuptools")
+        except Exception:
+            # If setuptools check fails, install compatible version
+            run_pip("install setuptools==69.5.1", "setuptools")
+    # Install build dependencies early
+    ensure_build_dependencies()
+
     if not is_installed("clip"):
-        run_pip(f"install {clip_package}", "clip")
+        run_pip(f"install --no-build-isolation {clip_package}", "clip")
         startup_timer.record("install clip")
 
     if not is_installed("open_clip"):
@@ -445,7 +494,6 @@ def prepare_environment():
         exit(0)
 
 
-
 def configure_for_tests():
     if "--api" not in sys.argv:
         sys.argv.append("--api")
@@ -461,7 +509,7 @@ def configure_for_tests():
 
 
 def start():
-    print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
+    print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {shlex.join(sys.argv[1:])}")
     import webui
     if '--nowebui' in sys.argv:
         webui.api_only()

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/mmdit.py

@@ -175,6 +175,9 @@ class VectorEmbedder(nn.Module):
 #################################################################################
 
 
+class QkvLinear(torch.nn.Linear):
+    pass
+
 def split_qkv(qkv, head_dim):
     qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
     return qkv[0], qkv[1], qkv[2]
@@ -202,7 +205,7 @@ class SelfAttention(nn.Module):
         self.num_heads = num_heads
         self.head_dim = dim // num_heads
 
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        self.qkv = QkvLinear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
         if not pre_only:
             self.proj = nn.Linear(dim, dim, dtype=dtype, device=device)
         assert attn_mode in self.ATTENTION_MODES

modules/models/sd3/other_impls.py

@@ -5,6 +5,8 @@ import math
 from torch import nn
 from transformers import CLIPTokenizer, T5TokenizerFast
 
+from modules import sd_hijack
+
 
 #################################################################################################
 ### Core/Utility
@@ -110,9 +112,9 @@ class CLIPEncoder(torch.nn.Module):
 
 
 class CLIPEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None):
+    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, textual_inversion_key="clip_l"):
         super().__init__()
-        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
+        self.token_embedding = sd_hijack.TextualInversionEmbeddings(vocab_size, embed_dim, dtype=dtype, device=device, textual_inversion_key=textual_inversion_key)
         self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
 
     def forward(self, input_tokens):
@@ -127,7 +129,7 @@ class CLIPTextModel_(torch.nn.Module):
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
         super().__init__()
-        self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)
+        self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device, textual_inversion_key=config_dict.get('textual_inversion_key', 'clip_l'))
         self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device)
         self.final_layer_norm = nn.LayerNorm(embed_dim, dtype=dtype, device=device)
 

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,
@@ -33,16 +33,17 @@ 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,
     "intermediate_size": 5120,
     "num_attention_heads": 20,
     "num_hidden_layers": 32,
+    "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,
@@ -204,7 +205,10 @@ class SD3Cond(torch.nn.Module):
                 self.t5xxl.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
 
     def encode_embedding_init_text(self, init_text, nvpt):
-        return torch.tensor([[0]], device=devices.device) # XXX
+        return self.model_lg.encode_embedding_init_text(init_text, nvpt)
+
+    def tokenize(self, texts):
+        return self.model_lg.tokenize(texts)
 
     def medvram_modules(self):
         return [self.clip_g, self.clip_l, self.t5xxl]

modules/models/sd3/sd3_impls.py

@@ -67,6 +67,7 @@ class BaseModel(torch.nn.Module):
         }
         self.diffusion_model = MMDiT(input_size=None, pos_embed_scaling_factor=None, pos_embed_offset=None, pos_embed_max_size=pos_embed_max_size, patch_size=patch_size, in_channels=16, depth=depth, num_patches=num_patches, adm_in_channels=adm_in_channels, context_embedder_config=context_embedder_config, device=device, dtype=dtype)
         self.model_sampling = ModelSamplingDiscreteFlow(shift=shift)
+        self.depth = depth
 
     def apply_model(self, x, sigma, c_crossattn=None, y=None):
         dtype = self.get_dtype()

modules/models/sd3/sd3_model.py

@@ -82,3 +82,15 @@ class SD3Inferencer(torch.nn.Module):
 
     def fix_dimensions(self, width, height):
         return width // 16 * 16, height // 16 * 16
+
+    def diffusers_weight_mapping(self):
+        for i in range(self.model.depth):
+            yield f"transformer.transformer_blocks.{i}.attn.to_q", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_q_proj"
+            yield f"transformer.transformer_blocks.{i}.attn.to_k", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_k_proj"
+            yield f"transformer.transformer_blocks.{i}.attn.to_v", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_v_proj"
+            yield f"transformer.transformer_blocks.{i}.attn.to_out.0", f"diffusion_model_joint_blocks_{i}_x_block_attn_proj"
+
+            yield f"transformer.transformer_blocks.{i}.attn.add_q_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_q_proj"
+            yield f"transformer.transformer_blocks.{i}.attn.add_k_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_k_proj"
+            yield f"transformer.transformer_blocks.{i}.attn.add_v_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_v_proj"
+            yield f"transformer.transformer_blocks.{i}.attn.add_out_proj.0", f"diffusion_model_joint_blocks_{i}_context_block_attn_proj"

modules/processing.py

@@ -1259,6 +1259,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 if self.hr_checkpoint_info is None:
                     raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}')
 
+                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:

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_hijack.py

@@ -359,13 +359,28 @@ class EmbeddingsWithFixes(torch.nn.Module):
                 vec = embedding.vec[self.textual_inversion_key] if isinstance(embedding.vec, dict) else embedding.vec
                 emb = devices.cond_cast_unet(vec)
                 emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
-                tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
+                tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]]).to(dtype=inputs_embeds.dtype)
 
             vecs.append(tensor)
 
         return torch.stack(vecs)
 
 
+class TextualInversionEmbeddings(torch.nn.Embedding):
+    def __init__(self, num_embeddings: int, embedding_dim: int, textual_inversion_key='clip_l', **kwargs):
+        super().__init__(num_embeddings, embedding_dim, **kwargs)
+
+        self.embeddings = model_hijack
+        self.textual_inversion_key = textual_inversion_key
+
+    @property
+    def wrapped(self):
+        return super().forward
+
+    def forward(self, input_ids):
+        return EmbeddingsWithFixes.forward(self, input_ids)
+
+
 def add_circular_option_to_conv_2d():
     conv2d_constructor = torch.nn.Conv2d.__init__
 

modules/sd_hijack_optimizations.py

@@ -54,7 +54,7 @@ class SdOptimizationXformers(SdOptimization):
     priority = 100
 
     def is_available(self):
-        return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.cuda.is_available() and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0))
+        return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.cuda.is_available() and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (12, 0))
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = xformers_attention_forward

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()
 
@@ -801,6 +791,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     else:
         state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
 
+    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)
 
@@ -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_samplers_kdiffusion.py

@@ -120,6 +120,10 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
             if scheduler.need_inner_model:
                 sigmas_kwargs['inner_model'] = self.model_wrap
 
+            if scheduler.label == 'Beta':
+                p.extra_generation_params["Beta schedule alpha"] = opts.beta_dist_alpha
+                p.extra_generation_params["Beta schedule beta"] = opts.beta_dist_beta
+
             sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=devices.cpu)
 
         if discard_next_to_last_sigma:

modules/sd_schedulers.py

@@ -2,6 +2,7 @@ import dataclasses
 import torch
 import k_diffusion
 import numpy as np
+from scipy import stats
 
 from modules import shared
 
@@ -115,6 +116,20 @@ def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):
     return torch.FloatTensor(sigs).to(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)
+    alpha = shared.opts.beta_dist_alpha
+    beta = shared.opts.beta_dist_beta
+    curve = [stats.beta.ppf(x, alpha, beta) for x in np.linspace(1, 0, n)]
+
+    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)
+
+
 schedulers = [
     Scheduler('automatic', 'Automatic', None),
     Scheduler('uniform', 'Uniform', uniform, need_inner_model=True),
@@ -127,6 +142,7 @@ schedulers = [
     Scheduler('simple', 'Simple', simple_scheduler, need_inner_model=True),
     Scheduler('normal', 'Normal', normal_scheduler, need_inner_model=True),
     Scheduler('ddim', 'DDIM', ddim_scheduler, need_inner_model=True),
+    Scheduler('beta', 'Beta', beta_scheduler, need_inner_model=True),
 ]
 
 schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}

modules/shared_gradio_themes.py

@@ -69,3 +69,44 @@ def reload_gradio_theme(theme_name=None):
     # append additional values gradio_theme
     shared.gradio_theme.sd_webui_modal_lightbox_toolbar_opacity = shared.opts.sd_webui_modal_lightbox_toolbar_opacity
     shared.gradio_theme.sd_webui_modal_lightbox_icon_opacity = shared.opts.sd_webui_modal_lightbox_icon_opacity
+
+
+def resolve_var(name: str, gradio_theme=None, history=None):
+    """
+    Attempt to resolve a theme variable name to its value
+
+    Parameters:
+        name (str): The name of the theme variable
+            ie "background_fill_primary", "background_fill_primary_dark"
+            spaces and asterisk (*) prefix is removed from name before lookup
+        gradio_theme (gradio.themes.ThemeClass): The theme object to resolve the variable from
+            blank to use the webui default shared.gradio_theme
+        history (list): A list of previously resolved variables to prevent circular references
+            for regular use leave blank
+    Returns:
+        str: The resolved value
+
+    Error handling:
+        return either #000000 or #ffffff depending on initial name ending with "_dark"
+    """
+    try:
+        if history is None:
+            history = []
+        if gradio_theme is None:
+            gradio_theme = shared.gradio_theme
+
+        name = name.strip()
+        name = name[1:] if name.startswith("*") else name
+
+        if name in history:
+            raise ValueError(f'Circular references: name "{name}" in {history}')
+
+        if value := getattr(gradio_theme, name, None):
+            return resolve_var(value, gradio_theme, history + [name])
+        else:
+            return name
+
+    except Exception:
+        name = history[0] if history else name
+        errors.report(f'resolve_color({name})', exc_info=True)
+        return '#000000' if name.endswith("_dark") else '#ffffff'

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)"),
@@ -64,6 +64,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
     "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
+    "save_write_log_csv": OptionInfo(True, "Write log.csv when saving images using 'Save' button"),
     "save_init_img": OptionInfo(False, "Save init images when using img2img"),
 
     "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
@@ -127,6 +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"), {
@@ -230,7 +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"),
@@ -290,6 +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"), {
@@ -403,13 +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": 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": 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/shared_state.py

@@ -162,7 +162,7 @@ class State:
             errors.record_exception()
 
     def assign_current_image(self, image):
-        if shared.opts.live_previews_image_format == 'jpeg' and image.mode == 'RGBA':
+        if shared.opts.live_previews_image_format == 'jpeg' and image.mode in ('RGBA', 'P'):
             image = image.convert('RGB')
         self.current_image = image
         self.id_live_preview += 1

modules/sysinfo.py

@@ -1,15 +1,13 @@
 import json
 import os
 import sys
-
+import subprocess
 import platform
 import hashlib
-import pkg_resources
-import psutil
 import re
+from pathlib import Path
 
-import launch
+from modules import paths_internal, timer, shared_cmd_options, errors, launch_utils
-from modules import paths_internal, timer, shared, extensions, errors
 
 checksum_token = "DontStealMyGamePlz__WINNERS_DONT_USE_DRUGS__DONT_COPY_THAT_FLOPPY"
 environment_whitelist = {
@@ -69,14 +67,46 @@ def check(x):
     return h.hexdigest() == m.group(1)
 
 
-def get_dict():
+def get_cpu_info():
-    ram = psutil.virtual_memory()
+    cpu_info = {"model": platform.processor()}
+    try:
+        import psutil
+        cpu_info["count logical"] = psutil.cpu_count(logical=True)
+        cpu_info["count physical"] = psutil.cpu_count(logical=False)
+    except Exception as e:
+        cpu_info["error"] = str(e)
+    return cpu_info
 
+
+def get_ram_info():
+    try:
+        import psutil
+        ram = psutil.virtual_memory()
+        return {x: pretty_bytes(getattr(ram, x, 0)) for x in ["total", "used", "free", "active", "inactive", "buffers", "cached", "shared"] if getattr(ram, x, 0) != 0}
+    except Exception as e:
+        return str(e)
+
+
+def get_packages():
+    try:
+        return subprocess.check_output([sys.executable, '-m', 'pip', 'freeze', '--all']).decode("utf8").splitlines()
+    except Exception as pip_error:
+        try:
+            import importlib.metadata
+            packages = importlib.metadata.distributions()
+            return sorted([f"{package.metadata['Name']}=={package.version}" for package in packages])
+        except Exception as e2:
+            return {'error pip': pip_error, 'error importlib': str(e2)}
+
+
+def get_dict():
+    config = get_config()
     res = {
         "Platform": platform.platform(),
         "Python": platform.python_version(),
-        "Version": launch.git_tag(),
+        "Version": launch_utils.git_tag(),
-        "Commit": launch.commit_hash(),
+        "Commit": launch_utils.commit_hash(),
+        "Git status": git_status(paths_internal.script_path),
         "Script path": paths_internal.script_path,
         "Data path": paths_internal.data_path,
         "Extensions dir": paths_internal.extensions_dir,
@@ -84,20 +114,14 @@ def get_dict():
         "Commandline": get_argv(),
         "Torch env info": get_torch_sysinfo(),
         "Exceptions": errors.get_exceptions(),
-        "CPU": {
+        "CPU": get_cpu_info(),
-            "model": platform.processor(),
+        "RAM": get_ram_info(),
-            "count logical": psutil.cpu_count(logical=True),
+        "Extensions": get_extensions(enabled=True, fallback_disabled_extensions=config.get('disabled_extensions', [])),
-            "count physical": psutil.cpu_count(logical=False),
+        "Inactive extensions": get_extensions(enabled=False, fallback_disabled_extensions=config.get('disabled_extensions', [])),
-        },
-        "RAM": {
-            x: pretty_bytes(getattr(ram, x, 0)) for x in ["total", "used", "free", "active", "inactive", "buffers", "cached", "shared"] if getattr(ram, x, 0) != 0
-        },
-        "Extensions": get_extensions(enabled=True),
-        "Inactive extensions": get_extensions(enabled=False),
         "Environment": get_environment(),
-        "Config": get_config(),
+        "Config": config,
         "Startup": timer.startup_record,
-        "Packages": sorted([f"{pkg.key}=={pkg.version}" for pkg in pkg_resources.working_set]),
+        "Packages": get_packages(),
     }
 
     return res
@@ -111,11 +135,11 @@ def get_argv():
     res = []
 
     for v in sys.argv:
-        if shared.cmd_opts.gradio_auth and shared.cmd_opts.gradio_auth == v:
+        if shared_cmd_options.cmd_opts.gradio_auth and shared_cmd_options.cmd_opts.gradio_auth == v:
             res.append("<hidden>")
             continue
 
-        if shared.cmd_opts.api_auth and shared.cmd_opts.api_auth == v:
+        if shared_cmd_options.cmd_opts.api_auth and shared_cmd_options.cmd_opts.api_auth == v:
             res.append("<hidden>")
             continue
 
@@ -123,6 +147,7 @@ def get_argv():
 
     return res
 
+
 re_newline = re.compile(r"\r*\n")
 
 
@@ -136,25 +161,55 @@ def get_torch_sysinfo():
         return str(e)
 
 
-def get_extensions(*, enabled):
+def run_git(path, *args):
-
     try:
+        return subprocess.check_output([launch_utils.git, '-C', path, *args], shell=False, encoding='utf8').strip()
+    except Exception as e:
+        return str(e)
+
+
+def git_status(path):
+    if (Path(path) / '.git').is_dir():
+        return run_git(paths_internal.script_path, 'status')
+
+
+def get_info_from_repo_path(path: Path):
+    is_repo = (path / '.git').is_dir()
+    return {
+        'name': path.name,
+        'path': str(path),
+        'commit': run_git(path, 'rev-parse', 'HEAD') if is_repo else None,
+        'branch': run_git(path, 'branch', '--show-current') if is_repo else None,
+        'remote': run_git(path, 'remote', 'get-url', 'origin') if is_repo else None,
+    }
+
+
+def get_extensions(*, enabled, fallback_disabled_extensions=None):
+    try:
+        from modules import extensions
+        if extensions.extensions:
             def to_json(x: extensions.Extension):
                 return {
                     "name": x.name,
                     "path": x.path,
-                "version": x.version,
+                    "commit": x.commit_hash,
                     "branch": x.branch,
                     "remote": x.remote,
                 }
-
             return [to_json(x) for x in extensions.extensions if not x.is_builtin and x.enabled == enabled]
+        else:
+            return [get_info_from_repo_path(d) for d in Path(paths_internal.extensions_dir).iterdir() if d.is_dir() and enabled != (str(d.name) in fallback_disabled_extensions)]
     except Exception as e:
         return str(e)
 
 
 def get_config():
     try:
+        from modules import shared
         return shared.opts.data
+    except Exception as _:
+        try:
+            with open(shared_cmd_options.cmd_opts.ui_settings_file, 'r') as f:
+                return json.load(f)
         except Exception as e:
             return str(e)

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'])
-                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

@@ -10,7 +10,7 @@ import gradio as gr
 import gradio.utils
 import numpy as np
 from PIL import Image, PngImagePlugin  # noqa: F401
-from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
+from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call, wrap_gradio_call_no_job # noqa: F401
 
 from modules import gradio_extensons, sd_schedulers  # noqa: F401
 from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow, launch_utils
@@ -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
@@ -622,8 +625,8 @@ def create_ui():
                             with gr.Column(elem_id="img2img_column_size", scale=4):
                                 selected_scale_tab = gr.Number(value=0, visible=False)
 
-                                with gr.Tabs():
+                                with gr.Tabs(elem_id="img2img_tabs_resize"):
-                                    with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
+                                    with gr.Tab(label="Resize to", id="to", elem_id="img2img_tab_resize_to") as tab_scale_to:
                                         with FormRow():
                                             with gr.Column(elem_id="img2img_column_size", scale=4):
                                                 width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
@@ -632,7 +635,7 @@ def create_ui():
                                                 res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn", tooltip="Switch width/height")
                                                 detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn", tooltip="Auto detect size from img2img")
 
-                                    with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
+                                    with gr.Tab(label="Resize by", id="by", elem_id="img2img_tab_resize_by") as tab_scale_by:
                                         scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
 
                                         with FormRow():
@@ -889,7 +892,7 @@ def create_ui():
                     ))
 
         image.change(
-            fn=wrap_gradio_call(modules.extras.run_pnginfo),
+            fn=wrap_gradio_call_no_job(modules.extras.run_pnginfo),
             inputs=[image],
             outputs=[html, generation_info, html2],
         )

modules/ui_common.py

@@ -3,6 +3,7 @@ import dataclasses
 import json
 import html
 import os
+from contextlib import nullcontext
 
 import gradio as gr
 
@@ -103,10 +104,11 @@ def save_files(js_data, images, do_make_zip, index):
 
     # NOTE: ensure csv integrity when fields are added by
     # updating headers and padding with delimiters where needed
-    if os.path.exists(logfile_path):
+    if shared.opts.save_write_log_csv and os.path.exists(logfile_path):
         update_logfile(logfile_path, fields)
 
-    with open(logfile_path, "a", encoding="utf8", newline='') as file:
+    with (open(logfile_path, "a", encoding="utf8", newline='') if shared.opts.save_write_log_csv else nullcontext()) as file:
+        if file:
             at_start = file.tell() == 0
             writer = csv.writer(file)
             if at_start:
@@ -130,6 +132,7 @@ def save_files(js_data, images, do_make_zip, index):
                 filenames.append(os.path.basename(txt_fullfn))
                 fullfns.append(txt_fullfn)
 
+        if file:
             writer.writerow([parsed_infotexts[0]['Prompt'], parsed_infotexts[0]['Seed'], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], parsed_infotexts[0]['Negative prompt'], data["sd_model_name"], data["sd_model_hash"]])
 
     # Make Zip
@@ -228,7 +231,7 @@ def create_output_panel(tabname, outdir, toprow=None):
                         )
 
                     save.click(
-                        fn=call_queue.wrap_gradio_call(save_files),
+                        fn=call_queue.wrap_gradio_call_no_job(save_files),
                         _js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
                         inputs=[
                             res.generation_info,
@@ -244,7 +247,7 @@ def create_output_panel(tabname, outdir, toprow=None):
                     )
 
                     save_zip.click(
-                        fn=call_queue.wrap_gradio_call(save_files),
+                        fn=call_queue.wrap_gradio_call_no_job(save_files),
                         _js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
                         inputs=[
                             res.generation_info,

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
@@ -106,19 +107,25 @@ 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:
+            with lock:
                 errors.report(f"Error checking updates for {ext.name}", exc_info=True)
-
+        with lock:
+            shared.state.textinfo = ext.name
             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(), ""
 
 
@@ -624,37 +631,37 @@ def create_ui():
                 )
 
                 install_extension_button.click(
-                    fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
+                    fn=modules.ui.wrap_gradio_call_no_job(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
                     inputs=[extension_to_install, selected_tags, showing_type, filtering_type, sort_column, search_extensions_text],
                     outputs=[available_extensions_table, extensions_table, install_result],
                 )
 
                 search_extensions_text.change(
-                    fn=modules.ui.wrap_gradio_call(search_extensions, extra_outputs=[gr.update()]),
+                    fn=modules.ui.wrap_gradio_call_no_job(search_extensions, extra_outputs=[gr.update()]),
                     inputs=[search_extensions_text, selected_tags, showing_type, filtering_type, sort_column],
                     outputs=[available_extensions_table, install_result],
                 )
 
                 selected_tags.change(
-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
+                    fn=modules.ui.wrap_gradio_call_no_job(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
                     inputs=[selected_tags, showing_type, filtering_type, sort_column, search_extensions_text],
                     outputs=[available_extensions_table, install_result]
                 )
 
                 showing_type.change(
-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
+                    fn=modules.ui.wrap_gradio_call_no_job(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
                     inputs=[selected_tags, showing_type, filtering_type, sort_column, search_extensions_text],
                     outputs=[available_extensions_table, install_result]
                 )
 
                 filtering_type.change(
-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
+                    fn=modules.ui.wrap_gradio_call_no_job(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
                     inputs=[selected_tags, showing_type, filtering_type, sort_column, search_extensions_text],
                     outputs=[available_extensions_table, install_result]
                 )
 
                 sort_column.change(
-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
+                    fn=modules.ui.wrap_gradio_call_no_job(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
                     inputs=[selected_tags, showing_type, filtering_type, sort_column, search_extensions_text],
                     outputs=[available_extensions_table, install_result]
                 )
@@ -667,7 +674,7 @@ def create_ui():
                 install_result = gr.HTML(elem_id="extension_install_result")
 
                 install_button.click(
-                    fn=modules.ui.wrap_gradio_call(lambda *args: [gr.update(), *install_extension_from_url(*args)], extra_outputs=[gr.update(), gr.update()]),
+                    fn=modules.ui.wrap_gradio_call_no_job(lambda *args: [gr.update(), *install_extension_from_url(*args)], extra_outputs=[gr.update(), gr.update()]),
                     inputs=[install_dirname, install_url, install_branch],
                     outputs=[install_url, extensions_table, install_result],
                 )

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_gradio_extensions.py

@@ -41,6 +41,11 @@ def css_html():
     if os.path.exists(user_css):
         head += stylesheet(user_css)
 
+    from modules.shared_gradio_themes import resolve_var
+    light = resolve_var('background_fill_primary')
+    dark = resolve_var('background_fill_primary_dark')
+    head += f'<style>html {{ background-color: {light}; }} @media (prefers-color-scheme: dark) {{ html {{background-color:  {dark}; }} }}</style>'
+
     return head
 
 

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/ui_settings.py

@@ -1,7 +1,7 @@
 import gradio as gr
 
 from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo, timer, shared_items
-from modules.call_queue import wrap_gradio_call
+from modules.call_queue import wrap_gradio_call_no_job
 from modules.options import options_section
 from modules.shared import opts
 from modules.ui_components import FormRow
@@ -295,7 +295,7 @@ class UiSettings:
 
     def add_functionality(self, demo):
         self.submit.click(
-            fn=wrap_gradio_call(lambda *args: self.run_settings(*args), extra_outputs=[gr.update()]),
+            fn=wrap_gradio_call_no_job(lambda *args: self.run_settings(*args), extra_outputs=[gr.update()]),
             inputs=self.components,
             outputs=[self.text_settings, self.result],
         )

modules/upscaler.py

@@ -56,8 +56,8 @@ class Upscaler:
         dest_w = int((img.width * scale) // 8 * 8)
         dest_h = int((img.height * scale) // 8 * 8)
 
-        for _ in range(3):
+        for i in range(3):
-            if img.width >= dest_w and img.height >= dest_h and scale != 1:
+            if img.width >= dest_w and img.height >= dest_h and (i > 0 or scale != 1):
                 break
 
             if shared.state.interrupted:
@@ -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/upscaler_utils.py

@@ -41,7 +41,7 @@ def upscale_pil_patch(model, img: Image.Image) -> Image.Image:
     """
     param = torch_utils.get_param(model)
 
-    with torch.no_grad():
+    with torch.inference_mode():
         tensor = pil_image_to_torch_bgr(img).unsqueeze(0)  # add batch dimension
         tensor = tensor.to(device=param.device, dtype=param.dtype)
         with devices.without_autocast():

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 +1,50 @@
+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]
+
+        cmd_str = shlex.join([*modified_command, *_args])
+        result = subprocess.__original_run(cmd_str, **_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"  # 📒
 
@@ -118,10 +118,9 @@ def apply_size(p, x: str, xs) -> None:
 
 
 def find_vae(name: str):
-    match name := name.lower().strip():
+    if (name := name.strip().lower()) in ('auto', 'automatic'):
-        case 'auto', 'automatic':
         return 'Automatic'
-        case 'none':
+    elif name == 'none':
         return 'None'
     return next((k for k in modules.sd_vae.vae_dict if k.lower() == name), print(f'No VAE found for {name}; using Automatic') or 'Automatic')
 
@@ -260,6 +259,9 @@ 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")),
     AxisOption("Clip skip", int, apply_override('CLIP_stop_at_last_layers')),
     AxisOption("Denoising", float, apply_field("denoising_strength")),
@@ -283,7 +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]
@@ -368,6 +370,7 @@ 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, [])
 
+    if draw_grid:
         z_count = len(zs)
 
         for i in range(z_count):
@@ -440,7 +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.")
@@ -448,9 +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"):
@@ -532,9 +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:
@@ -779,7 +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:
@@ -788,14 +794,15 @@ class Script(scripts.Script):
 
         z_count = len(zs)
 
+        if draw_grid:
             # 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):
@@ -805,7 +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")
 
@@ -48,6 +57,7 @@ echo Warning: Failed to upgrade PIP version
 
 :activate_venv
 set PYTHON="%VENV_DIR%\Scripts\Python.exe"
+call "%VENV_DIR%\Scripts\activate.bat"
 echo venv %PYTHON%
 
 :skip_venv

webui.py

@@ -45,6 +45,44 @@ def api_only():
     )
 
 
+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():
     from modules.shared_cmd_options import cmd_opts
 
@@ -53,6 +91,8 @@ def webui():
 
     from modules import shared, ui_tempdir, script_callbacks, ui, progress, ui_extra_networks
 
+    warning_if_invalid_install_dir()
+
     while 1:
         if shared.opts.clean_temp_dir_at_start:
             ui_tempdir.cleanup_tmpdr()

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