clear GenerationParametersList before batch

clears any generation parameters that are with the attribute to_be_clear_before_batch = True prevent buildup of some parameters
fix missing infotext cased by conda cache
2024-11-24 20:16:54 +09:00 · 2024-11-24 17:58:14 +09:00 · 2024-11-20 21:33:59 -05:00 · 2024-11-20 12:29:41 -05:00 · 2024-11-19 14:41:37 +09:00 · 2024-11-18 20:48:36 -05:00
46 changed files with 707 additions and 188 deletions
@@ -1,3 +1,120 @@
+## 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), [#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:
+  * Align Your Steps ([#15751](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15751))
+  * KL Optimal ([#15608](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608))
+  * 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:
+* Option to skip CFG on early steps ([#15607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15607))
+* Add --models-dir option ([#15742](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15742))
+* Allow mobile users to open context menu by using two fingers press ([#15682](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15682))
+* Infotext: add Lora name as TI hashes for bundled Textual Inversion ([#15679](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15679))
+* Check model's hash after downloading it to prevent corruped downloads ([#15602](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15602))
+* More extension tag filtering options ([#15627](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15627))
+* When saving AVIF, use JPEG's quality setting ([#15610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15610))
+* Add filename pattern: `[basename]` ([#15978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15978))
+* 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))
+* Return HTTP 400 instead of 404 on invalid sampler error ([#16140](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16140))
+
+### Performance:
+* [Performance 1/6] use_checkpoint = False ([#15803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15803))
+* [Performance 2/6] Replace einops.rearrange with torch native ops ([#15804](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15804))
+* [Performance 4/6] Precompute is_sdxl_inpaint flag ([#15806](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15806))
+* [Performance 5/6] Prevent unnecessary extra networks bias backup ([#15816](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15816))
+* [Performance 6/6] Add --precision half option to avoid casting during inference ([#15820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15820))
+* [Performance] LDM optimization patches ([#15824](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15824))
+* [Performance] Keep sigmas on CPU ([#15823](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15823))
+* Check for nans in unet only once, after all steps have been completed
+* Added pption to run torch profiler for image generation
+
+### Bug Fixes:
+* Fix for grids without comprehensive infotexts ([#15958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15958))
+* feat: lora partial update precede full update ([#15943](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15943))
+* Fix bug where file extension had an extra '.' under some circumstances ([#15893](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15893))
+* Fix corrupt model initial load loop ([#15600](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15600))
+* Allow old sampler names in API ([#15656](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15656))
+* more old sampler scheduler compatibility ([#15681](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15681))
+* Fix Hypertile xyz ([#15831](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15831))
+* XYZ CSV skipinitialspace ([#15832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15832))
+* fix soft inpainting on mps and xpu, torch_utils.float64 ([#15815](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15815))
+* fix extention update when not on main branch ([#15797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15797))
+* update pickle safe filenames
+* use relative path for webui-assets css ([#15757](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15757))
+* When creating a virtual environment, upgrade pip in webui.bat/webui.sh ([#15750](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15750))
+* Fix AttributeError ([#15738](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15738))
+* use script_path for webui root in launch_utils ([#15705](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15705))
+* fix extra batch mode P Transparency ([#15664](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15664))
+* use gradio theme colors in css ([#15680](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15680))
+* Fix dragging text within prompt input ([#15657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15657))
+* Add correct mimetype for .mjs files ([#15654](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15654))
+* QOL Items - handle metadata issues more cleanly for SD models, Loras and embeddings ([#15632](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15632))
+* replace wsl-open with wslpath and explorer.exe ([#15968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15968))
+* Fix SDXL Inpaint ([#15976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15976))
+* multi size grid ([#15988](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15988))
+* fix Replace preview ([#16118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16118))
+* Possible fix of wrong scale in weight decomposition ([#16151](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16151))
+* Ensure use of python from venv on Mac and Linux ([#16116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16116))
+* Prioritize python3.10 over python3 if both are available on Linux and Mac (with fallback) ([#16092](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16092))
+* stoping generation extras ([#16085](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16085))
+* 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))
+* ReloadUI backgroundColor --background-fill-primary ([#15864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15864))
+* Use different torch versions for Intel and ARM Macs ([#15851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15851))
+* XYZ override rework ([#15836](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15836))
+* scroll extensions table on overflow ([#15830](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15830))
+* img2img batch upload method ([#15817](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15817))
+* chore: sync v1.8.0 packages according to changelog ([#15783](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15783))
+* Add AVIF MIME type support to mimetype definitions ([#15739](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15739))
+* Update imageviewer.js ([#15730](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15730))
+* no-referrer ([#15641](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15641))
+* .gitignore trace.json ([#15980](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15980))
+* 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), [#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

 ### Bug Fixes:
@@ -1,12 +1 @@
-*       @AUTOMATIC1111
-
-# 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.
-
-
+*       @AUTOMATIC1111 @w-e-w @catboxanon
@@ -148,6 +148,7 @@ python_cmd="python3.11"
 2. Navigate to the directory you would like the webui to be installed and execute the following command:
 ```bash
 wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh
+chmod +x webui.sh
 ```
 Or just clone the repo wherever you want:
 ```bash
@@ -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
@@ -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) {
@@ -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
@@ -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")
-                maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="postprocess_multicrop_maxdim")
+                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=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")
-                maxarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area upper bound", value=640 * 640, elem_id="postprocess_multicrop_maxarea")
+                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=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")
-                threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="postprocess_multicrop_threshold")
+                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=self.elem_id_suffix("postprocess_multicrop_threshold"))

        return {
            "enable": enable,
@@ -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")
-            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")
-            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")
-            debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
+            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=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=self.elem_id_suffix("postprocess_focal_crop_edges_weight"))
+            debug = gr.Checkbox(label='Create debug image', elem_id=self.elem_id_suffix("train_process_focal_crop_debug"))

        return {
            "enable": enable,
@@ -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")
-                overlap_ratio = gr.Slider(label='Overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="postprocess_overlap_ratio")
+                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=self.elem_id_suffix("postprocess_overlap_ratio"))

        return {
            "enable": enable,
@@ -4,11 +4,11 @@
 // 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) {
-    var counts = {};
-    (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => {
-        counts[bracket] = (counts[bracket] || 0) + 1;
+    const counts = {};
+    textArea.value.matchAll(/(?<!\\)(?:\\\\)*?([(){}[\]])/g).forEach(bracket => {
+        counts[bracket[1]] = (counts[bracket[1]] || 0) + 1;
    });
-    var errors = [];
+    const errors = [];

    function checkPair(open, close, kind) {
        if (counts[open] !== counts[close]) {
@@ -1,7 +1,7 @@
 <div>
        <a href="{api_docs}">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>
         • 
@@ -104,7 +104,7 @@ var contextMenuInit = function() {
                        e.preventDefault();
                    }
                });
-            });
+            }, {passive: false});
        });
        eventListenerApplied = true;

@@ -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 = /\(([^:^>]+:[\d.]+)\)/;
@@ -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();

@@ -31,21 +32,26 @@ function negmod(n, m) {
    return ((n % m) + m) % m;
 }

+function updateModalImage() {
+    const modalImage = gradioApp().getElementById("modalImage");
+    let currentButton = selected_gallery_button();
+    let preview = gradioApp().querySelectorAll('.livePreview > img');
+    if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) {
+        // show preview image if available
+        modalImage.src = preview[preview.length - 1].src;
+    } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
+        modalImage.src = currentButton.children[0].src;
+        if (modalImage.style.display === 'none') {
+            const modal = gradioApp().getElementById("lightboxModal");
+            modal.style.setProperty('background-image', `url(${modalImage.src})`);
+        }
+    }
+}
+
 function updateOnBackgroundChange() {
    const modalImage = gradioApp().getElementById("modalImage");
    if (modalImage && modalImage.offsetParent) {
-        let currentButton = selected_gallery_button();
-        let preview = gradioApp().querySelectorAll('.livePreview > img');
-        if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) {
-            // show preview image if available
-            modalImage.src = preview[preview.length - 1].src;
-        } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
-            modalImage.src = currentButton.children[0].src;
-            if (modalImage.style.display === 'none') {
-                const modal = gradioApp().getElementById("lightboxModal");
-                modal.style.setProperty('background-image', `url(${modalImage.src})`);
-            }
-        }
+        updateModalImage();
    }
 }

@@ -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;
        }
    };

@@ -124,7 +124,7 @@
                } else {
                    R.screenX = evt.changedTouches[0].screenX;
                }
-            });
+            }, {passive: false});
        });

        resizeHandle.addEventListener('dblclick', onDoubleClick);
@@ -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")
@@ -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',
        ),
    ]
@@ -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>
@@ -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.
@@ -24,7 +24,7 @@ class SafetensorsMapping(typing.Mapping):
        return self.file.get_tensor(key)


-CLIPL_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_l.safetensors"
+CLIPL_URL = f"{shared.hf_endpoint}/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_l.safetensors"
 CLIPL_CONFIG = {
    "hidden_act": "quick_gelu",
    "hidden_size": 768,
@@ -33,7 +33,7 @@ CLIPL_CONFIG = {
    "num_hidden_layers": 12,
 }

-CLIPG_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_g.safetensors"
+CLIPG_URL = f"{shared.hf_endpoint}/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_g.safetensors"
 CLIPG_CONFIG = {
    "hidden_act": "gelu",
    "hidden_size": 1280,
@@ -43,7 +43,7 @@ CLIPG_CONFIG = {
    "textual_inversion_key": "clip_g",
 }

-T5_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
+T5_URL = f"{shared.hf_endpoint}/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
 T5_CONFIG = {
    "d_ff": 10240,
    "d_model": 4096,
@@ -16,7 +16,7 @@ from skimage import exposure
 from typing import Any

 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext_utils, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng, profiling
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext_utils, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng, profiling, util
 from modules.rng import slerp # noqa: F401
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
@@ -457,6 +457,20 @@ class StableDiffusionProcessing:
            opts.emphasis,
        )

+    def apply_generation_params_list(self, generation_params_states):
+        """add and apply generation_params_states to self.extra_generation_params"""
+        for key, value in generation_params_states.items():
+            if key in self.extra_generation_params and isinstance(current_value := self.extra_generation_params[key], util.GenerationParametersList):
+                self.extra_generation_params[key] = current_value + value
+            else:
+                self.extra_generation_params[key] = value
+
+    def clear_marked_generation_params(self):
+        """clears any generation parameters that are with the attribute to_be_clear_before_batch = True"""
+        for key, value in list(self.extra_generation_params.items()):
+            if getattr(value, 'to_be_clear_before_batch', False):
+                self.extra_generation_params.pop(key)
+
    def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
        """
        Returns the result of calling function(shared.sd_model, required_prompts, steps)
@@ -480,6 +494,10 @@ class StableDiffusionProcessing:

        for cache in caches:
            if cache[0] is not None and cached_params == cache[0]:
+                if len(cache) == 3:
+                    generation_params_states, cached_cached_params = cache[2]
+                    if cached_params == cached_cached_params:
+                        self.apply_generation_params_list(generation_params_states)
                return cache[1]

        cache = caches[0]
@@ -487,6 +505,13 @@ class StableDiffusionProcessing:
        with devices.autocast():
            cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)

+        generation_params_states = model_hijack.extract_generation_params_states()
+        self.apply_generation_params_list(generation_params_states)
+        if len(cache) == 2:
+            cache.append((generation_params_states, cached_params))
+        else:
+            cache[2] = (generation_params_states, cached_params)
+
        cache[0] = cached_params
        return cache[1]

@@ -502,6 +527,8 @@ class StableDiffusionProcessing:
        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)
        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)

+        self.extra_generation_params.update(model_hijack.extra_generation_params)
+
    def get_conds(self):
        return self.c, self.uc

@@ -801,10 +828,10 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter

    for key, value in generation_params.items():
        try:
-            if isinstance(value, list):
-                generation_params[key] = value[index]
-            elif callable(value):
+            if callable(value):
                generation_params[key] = value(**locals())
+            elif isinstance(value, list):
+                generation_params[key] = value[index]
        except Exception:
            errors.report(f'Error creating infotext for key "{key}"', exc_info=True)
            generation_params[key] = None
@@ -938,6 +965,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
            if state.interrupted or state.stopping_generation:
                break

+            p.clear_marked_generation_params()  # clean up some generation params are tagged to be cleared before batch
            sd_models.reload_model_weights()  # model can be changed for example by refiner

            p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
@@ -965,8 +993,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:

            p.setup_conds()

-            p.extra_generation_params.update(model_hijack.extra_generation_params)
-
            # params.txt should be saved after scripts.process_batch, since the
            # infotext could be modified by that callback
            # Example: a wildcard processed by process_batch sets an extra model
@@ -1259,7 +1285,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                if self.hr_checkpoint_info is None:
                    raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}')

-                self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title
+                if shared.sd_model.sd_checkpoint_info == self.hr_checkpoint_info:
+                    self.hr_checkpoint_info = None
+                else:
+                    self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title

            if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
                self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
@@ -1510,6 +1539,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data, total_steps)
        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data, total_steps)

+        self.extra_generation_params.update(model_hijack.extra_generation_params)
+
    def setup_conds(self):
        if self.is_hr_pass:
            # if we are in hr pass right now, the call is being made from the refiner, and we don't need to setup firstpass cons or switch model
@@ -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())
@@ -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])
@@ -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:
@@ -2,7 +2,7 @@ import torch
 from torch.nn.functional import silu
 from types import MethodType

-from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches, util
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
 from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, xlmr_m18
@@ -321,6 +321,14 @@ class StableDiffusionModelHijack:
        self.comments = []
        self.extra_generation_params = {}

+    def extract_generation_params_states(self):
+        """Extracts GenerationParametersList so that they can be cached and restored later"""
+        states = {}
+        for key in list(self.extra_generation_params):
+            if isinstance(self.extra_generation_params[key], util.GenerationParametersList):
+                states[key] = self.extra_generation_params.pop(key)
+        return states
+
    def get_prompt_lengths(self, text):
        if self.clip is None:
            return "-", "-"
@@ -3,7 +3,7 @@ from collections import namedtuple

 import torch

-from modules import prompt_parser, devices, sd_hijack, sd_emphasis
+from modules import prompt_parser, devices, sd_hijack, sd_emphasis, util
 from modules.shared import opts


@@ -27,6 +27,30 @@ chunk. Those objects are found in PromptChunk.fixes and, are placed into FrozenC
 are applied by sd_hijack.EmbeddingsWithFixes's forward function."""


+class EmphasisMode(util.GenerationParametersList):
+    def __init__(self, emphasis_mode:str = None):
+        super().__init__()
+        self.emphasis_mode = emphasis_mode
+
+    def __call__(self, *args, **kwargs):
+        return self.emphasis_mode
+
+    def __add__(self, other):
+        if isinstance(other, EmphasisMode):
+            return self if self.emphasis_mode else other
+        elif isinstance(other, str):
+            return self.__str__() + other
+        return NotImplemented
+
+    def __radd__(self, other):
+        if isinstance(other, str):
+            return other + self.__str__()
+        return NotImplemented
+
+    def __str__(self):
+        return self.emphasis_mode if self.emphasis_mode else ''
+
+
 class TextConditionalModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
@@ -238,12 +262,10 @@ class TextConditionalModel(torch.nn.Module):
                hashes.append(f"{name}: {shorthash}")

            if hashes:
-                if self.hijack.extra_generation_params.get("TI hashes"):
-                    hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
-                self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
+                self.hijack.extra_generation_params["TI hashes"] = util.GenerationParametersList(hashes)

-        if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
-            self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
+        if opts.emphasis != 'Original' and any(x for x in texts if '(' in x or '[' in x):
+            self.hijack.extra_generation_params["Emphasis"] = EmphasisMode(opts.emphasis)

        if self.return_pooled:
            return torch.hstack(zs), zs[0].pooled
@@ -159,7 +159,7 @@ 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)
@@ -423,6 +423,10 @@ 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 +665,7 @@ 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 +787,7 @@ def get_obj_from_str(string, reload=False):
    return getattr(importlib.import_module(module, package=None), cls)


-def load_model(checkpoint_info=None, already_loaded_state_dict=None):
+def load_model(checkpoint_info=None, already_loaded_state_dict=None, checkpoint_config=None):
    from modules import sd_hijack
    checkpoint_info = checkpoint_info or select_checkpoint()

@@ -801,7 +805,8 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
    else:
        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)

-    checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
+    if not checkpoint_config:
+        checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
    clip_is_included_into_sd = any(x for x in [sd1_clip_weight, sd2_clip_weight, sdxl_clip_weight, sdxl_refiner_clip_weight] if x in state_dict)

    timer.record("find config")
@@ -974,7 +979,7 @@ 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:
@@ -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:
@@ -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 modules.scripts.scripts_postproc.scripts
+    return list(filter(
+        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():
@@ -231,7 +231,7 @@ options_templates.update(options_section(('img2img', "img2img", "sd"), {

 options_templates.update(options_section(('optimizations', "Optimizations", "sd"), {
    "cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
-    "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}, infotext='NGMS').link("PR", "https://github.com/AUTOMATIC1111/stablediffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+    "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}, infotext='NGMS').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
    "s_min_uncond_all": OptionInfo(False, "Negative Guidance minimum sigma all steps", infotext='NGMS all steps').info("By default, NGMS above skips every other step; this makes it skip all steps"),
    "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
    "token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
@@ -291,6 +291,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks", "s
    "textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
    "textual_inversion_add_hashes_to_infotext": OptionInfo(True, "Add Textual Inversion hashes to infotext"),
    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *shared.hypernetworks]}, refresh=shared_items.reload_hypernetworks),
+    "textual_inversion_image_embedding_data_cache": OptionInfo(False, 'Cache the data of image embeddings').info('potentially increase TI load time at the cost some disk space'),
 }))

 options_templates.update(options_section(('ui_prompt_editing', "Prompt editing", "ui"), {
@@ -404,15 +405,15 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
    'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}, infotext='UniPC order').info("must be < sampling steps"),
    'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
    'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models"),
-    'skip_early_cond': OptionInfo(0.0, "Ignore negative prompt during early sampling", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext="Skip Early CFG").info("disables CFG on a proportion of steps at the beginning of generation; 0=skip none; 1=skip all; can both improve sample diversity/quality and speed up sampling"),
+    'skip_early_cond': OptionInfo(0.0, "Ignore negative prompt during early sampling", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext="Skip Early CFG").info("disables CFG on a proportion of steps at the beginning of generation; 0=skip none; 1=skip all; can both improve sample diversity/quality and speed up sampling; XYZ plot: Skip Early CFG"),
    'beta_dist_alpha': OptionInfo(0.6, "Beta scheduler - alpha", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler alpha').info('Default = 0.6; the alpha parameter of the beta distribution used in Beta sampling'),
    'beta_dist_beta': OptionInfo(0.6, "Beta scheduler - beta", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler beta').info('Default = 0.6; the beta parameter of the beta distribution used in Beta sampling'),
 }))

 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_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_operation_order': OptionInfo([], "Postprocessing operation order", 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(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(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"),
 }))
@@ -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)
-            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)
-            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
+            data, name = self.read_embedding_from_image(path, name)
+            if data is None:
+                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
@@ -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
@@ -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()
@@ -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):
-    s = s.replace('\\', '\\\\')
-    s = s.replace('"', '\\"')
-    return f'"{s}"'
+def quote_js(s: str):
+    return json.dumps(s, ensure_ascii=False)


 class ExtraNetworksPage:
@@ -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
@@ -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}>"
@@ -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():
@@ -211,3 +211,126 @@ 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())
+
+
+class GenerationParametersList(list):
+    """A special object used in sd_hijack.StableDiffusionModelHijack for setting extra_generation_params
+    due to StableDiffusionProcessing.get_conds_with_caching
+    extra_generation_params set in StableDiffusionModelHijack will be lost when cached is used
+
+    When an extra_generation_params is set in StableDiffusionModelHijack using this object,
+    the params will be extracted by StableDiffusionModelHijack.extract_generation_params_states
+    the extracted params will be cached in StableDiffusionProcessing.get_conds_with_caching
+    and applyed to StableDiffusionProcessing.extra_generation_params by StableDiffusionProcessing.apply_generation_params_states
+
+    Example see modules.sd_hijack_clip.TextConditionalModel.hijack.extra_generation_params 'TI hashes' 'Emphasis'
+
+    Depending on the use case the methods can be overwritten.
+    In general __call__ method should return str or None, as normally it's called in modules.processing.create_infotext.
+    When called by create_infotext it will access to the locals() of the caller,
+    if return str, the value will be written to infotext, if return None will be ignored.
+    """
+
+    def __init__(self, *args, to_be_clear_before_batch=True, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._to_be_clear_before_batch = to_be_clear_before_batch
+
+    def __call__(self, *args, **kwargs):
+        return ', '.join(sorted(set(self), key=natural_sort_key))
+
+    @property
+    def to_be_clear_before_batch(self):
+        return self._to_be_clear_before_batch
+
+    def __add__(self, other):
+        if isinstance(other, GenerationParametersList):
+            return self.__class__([*self, *other])
+        elif isinstance(other, str):
+            return self.__str__() + other
+        return NotImplemented
+
+    def __radd__(self, other):
+        if isinstance(other, str):
+            return other + self.__str__()
+        return NotImplemented
+
+    def __str__(self):
+        return self.__call__()
+
@@ -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
@@ -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_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_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=self.elem_id_suffix("extras_codeformer_weight"))

        return {
            "enable": enable,
@@ -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,
@@ -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_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 = 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=self.elem_id_suffix("extras_upscaler_2_visibility"))

            with FormRow():
-                with gr.Tabs(elem_id="extras_resize_mode"):
-                    with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
+                with gr.Tabs(elem_id=self.elem_id_suffix("extras_resize_mode")):
+                    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):
-                                upscaling_resize_w = gr.Slider(minimum=64, maximum=8192, step=8, label="Width", value=512, elem_id="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")
-                            with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn", tooltip="Switch width/height")
-                                upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+                            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=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=self.elem_id_suffix("extras_upscaling_resize_h"))
+                            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=self.elem_id_suffix("upscaling_res_switch_btn"), tooltip="Switch width/height")
+                                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():
@@ -20,7 +20,7 @@ import modules.sd_models
 import modules.sd_vae
 import re

-from modules.ui_components import ToolButton
+from modules.ui_components import ToolButton, InputAccordion

 fill_values_symbol = "\U0001f4d2"  # 📒

@@ -259,6 +259,7 @@ axis_options = [
    AxisOption("Schedule min sigma", float, apply_override("sigma_min")),
    AxisOption("Schedule max sigma", float, apply_override("sigma_max")),
    AxisOption("Schedule rho", float, apply_override("rho")),
+    AxisOption("Skip Early CFG", float, apply_override('skip_early_cond')),
    AxisOption("Beta schedule alpha", float, apply_override("beta_dist_alpha")),
    AxisOption("Beta schedule beta", float, apply_override("beta_dist_beta")),
    AxisOption("Eta", float, apply_field("eta")),
@@ -284,7 +285,7 @@ axis_options = [
 ]


-def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size):
+def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size, draw_grid):
    hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
    ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
    title_texts = [[images.GridAnnotation(z)] for z in z_labels]
@@ -369,29 +370,30 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
        print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
        return Processed(p, [])

-    z_count = len(zs)
+    if draw_grid:
+        z_count = len(zs)

-    for i in range(z_count):
-        start_index = (i * len(xs) * len(ys)) + i
-        end_index = start_index + len(xs) * len(ys)
-        grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
+        for i in range(z_count):
+            start_index = (i * len(xs) * len(ys)) + i
+            end_index = start_index + len(xs) * len(ys)
+            grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
+            if draw_legend:
+                grid_max_w, grid_max_h = map(max, zip(*(img.size for img in processed_result.images[start_index:end_index])))
+                grid = images.draw_grid_annotations(grid, grid_max_w, grid_max_h, hor_texts, ver_texts, margin_size)
+            processed_result.images.insert(i, grid)
+            processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
+            processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
+            processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
+
+        z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
+        z_sub_grid_max_w, z_sub_grid_max_h = map(max, zip(*(img.size for img in processed_result.images[:z_count])))
        if draw_legend:
-            grid_max_w, grid_max_h = map(max, zip(*(img.size for img in processed_result.images[start_index:end_index])))
-            grid = images.draw_grid_annotations(grid, grid_max_w, grid_max_h, hor_texts, ver_texts, margin_size)
-        processed_result.images.insert(i, grid)
-        processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
-        processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
-        processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
-
-    z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
-    z_sub_grid_max_w, z_sub_grid_max_h = map(max, zip(*(img.size for img in processed_result.images[:z_count])))
-    if draw_legend:
-        z_grid = images.draw_grid_annotations(z_grid, z_sub_grid_max_w, z_sub_grid_max_h, title_texts, [[images.GridAnnotation()]])
-    processed_result.images.insert(0, z_grid)
-    # TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
-    # processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
-    # processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
-    processed_result.infotexts.insert(0, processed_result.infotexts[0])
+            z_grid = images.draw_grid_annotations(z_grid, z_sub_grid_max_w, z_sub_grid_max_h, title_texts, [[images.GridAnnotation()]])
+        processed_result.images.insert(0, z_grid)
+        # TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
+        # processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
+        # processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
+        processed_result.infotexts.insert(0, processed_result.infotexts[0])

    return processed_result

@@ -441,7 +443,6 @@ class Script(scripts.Script):

        with gr.Row(variant="compact", elem_id="axis_options"):
            with gr.Column():
-                draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
                no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
                with gr.Row():
                    vary_seeds_x = gr.Checkbox(label='Vary seeds for X', value=False, min_width=80, elem_id=self.elem_id("vary_seeds_x"), tooltip="Use different seeds for images along X axis.")
@@ -449,9 +450,12 @@ class Script(scripts.Script):
                    vary_seeds_z = gr.Checkbox(label='Vary seeds for Z', value=False, min_width=80, elem_id=self.elem_id("vary_seeds_z"), tooltip="Use different seeds for images along Z axis.")
            with gr.Column():
                include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id("include_lone_images"))
-                include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
                csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode"))
-            with gr.Column():
+
+        with InputAccordion(True, label='Draw grid', elem_id=self.elem_id('draw_grid')) as draw_grid:
+            with gr.Row():
+                include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
+                draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
                margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))

        with gr.Row(variant="compact", elem_id="swap_axes"):
@@ -533,9 +537,9 @@ class Script(scripts.Script):
            (z_values_dropdown, lambda params: get_dropdown_update_from_params("Z", params)),
        )

-        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode]
+        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode, draw_grid]

-    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode):
+    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode, draw_grid):
        x_type, y_type, z_type = x_type or 0, y_type or 0, z_type or 0  # if axle type is None set to 0

        if not no_fixed_seeds:
@@ -780,7 +784,8 @@ class Script(scripts.Script):
                include_sub_grids=include_sub_grids,
                first_axes_processed=first_axes_processed,
                second_axes_processed=second_axes_processed,
-                margin_size=margin_size
+                margin_size=margin_size,
+                draw_grid=draw_grid,
            )

        if not processed.images:
@@ -789,14 +794,15 @@ class Script(scripts.Script):

        z_count = len(zs)

-        # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
-        processed.infotexts[:1 + z_count] = grid_infotext[:1 + z_count]
+        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):
@@ -806,7 +812,7 @@ class Script(scripts.Script):
                if not include_sub_grids:  # if not include_sub_grids then skip saving after the first grid
                    break

-        if not include_sub_grids:
+        if draw_grid and not include_sub_grids:
            # Done with sub-grids, drop all related information:
            for _ in range(z_count):
                del processed.images[1]
@@ -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")

@@ -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()
Author	SHA1	Message	Date
w-e-w	ac8c05398b	clear GenerationParametersList before batch clears any generation parameters that are with the attribute to_be_clear_before_batch = True prevent buildup of some parameters	2024-11-24 20:16:54 +09:00
w-e-w	025080218f	fix missing infotext cased by conda cache some generation params such as TI hashes or Emphasis is added in sd_hijack / sd_hijack_clip if conda are fetche from cache sd_hijack_clip will not be executed and it won't have a chance to to add generation params the generation params will also be missing if in non low-vram mode because the hijack.extra_generation_params was never read after calculate_hr_conds	2024-11-24 17:58:14 +09:00
w-e-w	023454b49e	fix passing of literal backslash (#16671 )	2024-11-20 21:33:59 -05:00
w-e-w	cd869bb7a3	fix prompt-bracket-checker miscounting of literal tokens (#16669 )	2024-11-20 12:29:41 -05:00
w-e-w	957888a100	Merge pull request #16667 from AUTOMATIC1111/fix/safetensors-bump Bump safetensors to v0.4.5	2024-11-19 14:41:37 +09:00
catboxanon	d2c9efb1f3	Bump safetensors to v0.4.5 Resolves #16650	2024-11-18 20:48:36 -05:00
w-e-w	7799859f9f	Merge pull request #16620 from AUTOMATIC1111/fix/api-webp-lossless Honor lossless WebP compression option in API	2024-11-02 01:28:12 +09:00
catboxanon	ca3bedbd02	Honor lossless webp compression option in API	2024-11-01 11:32:52 -04:00
w-e-w	1b16c62608	use shared.hf_endpoint (#16611 )	2024-10-30 13:01:32 -04:00
w-e-w	91de919451	Warn if WebUI is installed under a dot directory (#16584 )	2024-10-30 09:34:37 -04:00
w-e-w	aa52408aab	Merge pull request #16606 from AUTOMATIC1111/fix/vweighting Fix config for SDXL v-pred	2024-10-30 09:51:41 +09:00
catboxanon	e6f36d9cdc	sd_xl_v.yaml: use_checkpoint = False In accordance with https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15803	2024-10-29 13:27:32 -04:00
w-e-w	28323cf8ee	XYZ option to disable grid (#16416 )	2024-10-29 13:18:38 -04:00
w-e-w	533c7b7b7f	Fix Default system None filter logic (#16309 )	2024-10-29 13:13:16 -04:00
catboxanon	ac28cad998	Fix weighting config for SDXL v-pred Fixes a small oversight I made.	2024-10-29 11:49:09 -04:00
w-e-w	5206b938ef	InputAccordion duplicate elem_id handling (#16381 )	2024-10-29 11:03:21 -04:00
w-e-w	59481435f4	addEventListener {passive: false} (#16575 )	2024-10-29 10:59:04 -04:00
viking1304	f31faf6f14	pyenv-win compatibility - another approach (#16287 )	2024-10-29 10:54:58 -04:00
w-e-w	14c6d6cb3a	Disable Hires checkpoint if same as First pass checkpoint (#16269 )	2024-10-29 10:45:45 -04:00
w-e-w	4ec10bcdc1	Fix postprocessing_enable_in_main_ui ScriptPostprocessing elem_id (#16373 )	2024-10-29 10:38:55 -04:00
w-e-w	0bf36cf2ad	extra_only / main_ui_only ScriptPostprocessing (#16374 )	2024-10-29 10:35:46 -04:00
w-e-w	820fe8d2b5	Allow newline in Extra Network activation text (#16428 )	2024-10-29 10:30:08 -04:00
w-e-w	deb3803a3a	image embedding data cache (#16556 )	2024-10-29 10:28:21 -04:00
w-e-w	d88a3c15f7	Merge pull request #16588 from bluelovers/patch-3 chore(js): avoid lots of `Wake Lock is not supported.`	2024-10-27 10:28:23 +09:00
w-e-w	38c8043229	Merge pull request #16523 from changeworld/fix/typo Fix typo: Github -> GitHub	2024-10-27 01:09:41 +09:00
bluelovers	ee0ad5cceb	chore(js): avoid lots of `Wake Lock is not supported.`	2024-10-25 09:59:45 +08:00
w-e-w	984b952eb3	Fix DAT models download (#16302 )	2024-10-24 09:05:51 -04:00
w-e-w	5865da28d1	Merge pull request #16569 from AUTOMATIC1111/feat/ztsnr-auto Automatically enable ztSNR based on existence of key in `state_dict`	2024-10-20 11:40:02 +09:00
w-e-w	bb1f39196e	clarify readme: weget ... chmod +x webui.sh (#16251 )	2024-10-19 20:58:53 -04:00
w-e-w	6a59766313	Add Skip Early CFG to XYZ (#16282 ) Co-authored-by: Yevhenii Hurin <evgeny.gurin@gmail.com>	2024-10-19 20:56:12 -04:00
w-e-w	65423d2b33	MIME type text/css (#16406 )	2024-10-19 20:52:47 -04:00
w-e-w	c2bc187ce7	fix modalImageViewer preview/result flicker (#16426 )	2024-10-19 20:51:59 -04:00
w-e-w	d0b27dc906	Merge pull request #16300 from hello2564/fix_NGMS_pr_typo fix NGMS pr typo	2024-10-20 09:42:21 +09:00
catboxanon	c2ce1d3b9c	Automatically enable ztSNR based on existence of key in state_dict	2024-10-19 19:58:13 -04:00
w-e-w	bb4cbaf0cf	Merge pull request #16341 from gutris1/devv add break-word for geninfo in pnginfo	2024-10-20 08:27:12 +09:00
catboxanon	c462e5a575	Merge pull request #16460 from AUTOMATIC1111/sd-1.5-url	2024-10-19 10:51:16 -04:00
AUTOMATIC1111	8b19b75270	Merge pull request #16567 from AUTOMATIC1111/feat/sdxl-vpred Support and automatically detect SDXL V-prediction models	2024-10-19 17:40:56 +03:00
AUTOMATIC1111	907bfb5ef0	add w-e-w and catboxanon to codeowners file	2024-10-19 17:33:58 +03:00
catboxanon	1ae073c052	Support SDXL v-pred models	2024-10-19 06:53:19 -04:00
missionfloyd	c9a06d1093	Use stable-diffusion-v1-5 repo instead	2024-10-08 16:50:39 -06:00
Takashi Takebayashi	d8ad364617	Fix typo Github -> GitHub	2024-10-03 14:33:37 +09:00
missionfloyd	f57ec2b53b	Update stable diffusion 1.5 URL	2024-09-03 19:58:29 -06:00
gutris1	9677b09b7c	add break-word for geninfo in pnginfo	2024-08-07 17:37:23 +07:00
hello2564	cbaaf0af0e	fix NGMS pr typo	2024-07-31 14:55:30 +08:00
AUTOMATIC1111	48239090f1	Merge branch 'master' into dev	2024-07-27 15:50:26 +03:00
AUTOMATIC1111	82a973c043	changelog	2024-07-27 15:49:39 +03:00
AUTOMATIC1111	1d7e9eca09	Merge pull request #16275 from AUTOMATIC1111/fix-image-upscale-on-cpu fix image upscale on cpu	2024-07-27 15:48:22 +03:00
AUTOMATIC1111	850e14923e	Merge pull request #16275 from AUTOMATIC1111/fix-image-upscale-on-cpu fix image upscale on cpu	2024-07-27 15:47:49 +03:00
w-e-w	8e0881d9ab	fix image upscale on cpu for some reason upscale using cpu will fail with RuntimeError: Inplace update to inference tensor outside InferenceMode switch from no_grad to inference_mode seems to have fixed it	2024-07-27 21:28:10 +09:00
AUTOMATIC1111	834297b13d	Merge branch 'master' into dev	2024-07-27 07:09:08 +03:00
AUTOMATIC1111	c19d044364	Merge branch 'release_candidate'	2024-07-27 06:53:05 +03:00
AUTOMATIC1111	8b3d98c5a5	update CHANGELOG	2024-07-20 11:54:14 +03:00
AUTOMATIC1111	5bbbda473f	Merge branch 'dev' into release_candidate	2024-07-20 11:51:12 +03:00
AUTOMATIC1111	48dd4d9eae	Merge pull request #16170 from AUTOMATIC1111/shlex.quote-launch-args-in-console-log shlex.join launch args in console log	2024-07-08 18:36:56 +03:00
AUTOMATIC1111	6ca7a453d4	Merge pull request #16169 from AUTOMATIC1111/py-3.9-compatibility Py 3.9 compatibility	2024-07-08 08:27:40 +03:00
AUTOMATIC1111	780c70f6ea	update changelog	2024-07-07 08:40:19 +03:00
AUTOMATIC1111	b5481c6195	Merge pull request #16153 from light-and-ray/fix_ui_flashing_on_reload_and_fast_scrollong fix ui flashing on reloading and fast scrollong	2024-07-07 08:38:26 +03:00
AUTOMATIC1111	340a9108ca	update changelog	2024-07-06 11:26:14 +03:00