SD3 Lora page filter - detection not implemented

CODEOWNERS

@@ -1 +1,12 @@
-*       @AUTOMATIC1111 @w-e-w @catboxanon
+*       @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.
+
+

README.md

@@ -148,7 +148,6 @@ python_cmd="python3.11"
 2. Navigate to the directory you would like the webui to be installed and execute the following command:
 ```bash
 wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh
-chmod +x webui.sh
 ```
 Or just clone the repo wherever you want:
 ```bash

configs/sd_xl_v.yaml

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

extensions-builtin/Lora/network.py

@@ -19,6 +19,7 @@ class SdVersion(enum.Enum):
     SD1 = 2
     SD2 = 3
     SDXL = 4
+    SD3 = 5
 
 
 class NetworkOnDisk:
@@ -59,6 +60,7 @@ class NetworkOnDisk:
         self.sd_version = self.detect_version()
 
     def detect_version(self):
+        # TODO: SdVersion.SD3 detection
         if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"):
             return SdVersion.SDXL
         elif str(self.metadata.get('ss_v2', "")) == "True":

extensions-builtin/Lora/scripts/lora_script.py

@@ -38,7 +38,8 @@ shared.options_templates.update(shared.options_section(('extra_networks', "Extra
     "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
     "lora_bundled_ti_to_infotext": shared.OptionInfo(True, "Add Lora name as TI hashes for bundled Textual Inversion").info('"Add Textual Inversion hashes to infotext" needs to be enabled'),
     "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
-    "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
+    "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL", "SD3"]}),
+    "TEMP_setting_sd3_lora_filter": shared.OptionInfo(["SD1", "Unknown"], "For SD3 model also show Lora of other sd version", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL", "Unknown"]}).info('Temporary setting until SD3 Lora detection is implemented'),
     "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
     "lora_not_found_warning_console": shared.OptionInfo(False, "Lora not found warning in console"),
     "lora_not_found_gradio_warning": shared.OptionInfo(False, "Lora not found warning popup in webui"),

extensions-builtin/Lora/ui_edit_user_metadata.py

@@ -160,7 +160,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
     def create_extra_default_items_in_left_column(self):
 
         # this would be a lot better as gr.Radio but I can't make it work
-        self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True)
+        self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'SD3', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True)
 
     def create_editor(self):
         self.create_default_editor_elems()

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -3,7 +3,7 @@ import os
 import network
 import networks
 
-from modules import shared, ui_extra_networks
+from modules import shared, ui_extra_networks, sd_models_types
 from modules.ui_extra_networks import quote_js
 from ui_edit_user_metadata import LoraUserMetadataEditor
 
@@ -62,8 +62,14 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
 
         if shared.opts.lora_show_all or not enable_filter or not shared.sd_model:
             pass
+        elif shared.sd_model.is_sd3:
+            # TODO: add proper SD3 filtering when detection is implemented
+            # TODO: move after Unknown block when implemented
+            if sd_version is network.SdVersion.SD3 or sd_version.name in shared.opts.TEMP_setting_sd3_lora_filter:
+                return item
+            return None
         elif sd_version == network.SdVersion.Unknown:
-            model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1
+            model_version = self.sd_to_lora_version(shared.sd_model)
             if model_version.name in shared.opts.lora_hide_unknown_for_versions:
                 return None
         elif shared.sd_model.is_sdxl and sd_version != network.SdVersion.SDXL:
@@ -88,3 +94,14 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
 
     def create_user_metadata_editor(self, ui, tabname):
         return LoraUserMetadataEditor(ui, tabname, self)
+
+    @staticmethod
+    def sd_to_lora_version(sd_model: sd_models_types.WebuiSdModel):
+        if sd_model.is_sd1:
+            return network.SdVersion.SD1
+        elif sd_model.is_sd2:
+            return network.SdVersion.SD2
+        elif sd_model.is_sdxl:
+            return network.SdVersion.SDXL
+        elif sd_model.is_sd3:
+            return network.SdVersion.SD3

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

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

extensions-builtin/hypertile/hypertile.py

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

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

@@ -34,14 +34,14 @@ class ScriptPostprocessingAutosizedCrop(scripts_postprocessing.ScriptPostprocess
         with ui_components.InputAccordion(False, label="Auto-sized crop") as enable:
             gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
             with gr.Row():
-                mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id=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"))
+                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")
             with gr.Row():
-                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"))
+                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")
             with gr.Row():
-                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"))
+                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")
 
         return {
             "enable": enable,

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

@@ -11,10 +11,10 @@ class ScriptPostprocessingFocalCrop(scripts_postprocessing.ScriptPostprocessing)
 
     def ui(self):
         with ui_components.InputAccordion(False, label="Auto focal point crop") as enable:
-            face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id=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"))
+            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")
 
         return {
             "enable": enable,

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

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

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

@@ -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) {
-    const counts = {};
-    textArea.value.matchAll(/(?<!\\)(?:\\\\)*?([(){}[\]])/g).forEach(bracket => {
-        counts[bracket[1]] = (counts[bracket[1]] || 0) + 1;
+    var counts = {};
+    (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => {
+        counts[bracket] = (counts[bracket] || 0) + 1;
     });
-    const errors = [];
+    var errors = [];
 
     function checkPair(open, close, kind) {
         if (counts[open] !== counts[close]) {

html/footer.html

@@ -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>
          • 

javascript/contextMenus.js

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

javascript/extraNetworks.js

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

javascript/imageviewer.js

@@ -13,7 +13,6 @@ function showModal(event) {
     if (modalImage.style.display === 'none') {
         lb.style.setProperty('background-image', 'url(' + source.src + ')');
     }
-    updateModalImage();
     lb.style.display = "flex";
     lb.focus();
 
@@ -32,26 +31,21 @@ 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(${modalImage.src})`);
-        }
-    }
-}
-
 function updateOnBackgroundChange() {
     const modalImage = gradioApp().getElementById("modalImage");
     if (modalImage && modalImage.offsetParent) {
-        updateModalImage();
+        let currentButton = selected_gallery_button();
+        let preview = gradioApp().querySelectorAll('.livePreview > img');
+        if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) {
+            // show preview image if available
+            modalImage.src = preview[preview.length - 1].src;
+        } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
+            modalImage.src = currentButton.children[0].src;
+            if (modalImage.style.display === 'none') {
+                const modal = gradioApp().getElementById("lightboxModal");
+                modal.style.setProperty('background-image', `url(${modalImage.src})`);
+            }
+        }
     }
 }
 

javascript/progressbar.js

@@ -79,12 +79,11 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
     var wakeLock = null;
 
     var requestWakeLock = async function() {
-        if (!opts.prevent_screen_sleep_during_generation || wakeLock !== null) return;
+        if (!opts.prevent_screen_sleep_during_generation || wakeLock) return;
         try {
             wakeLock = await navigator.wakeLock.request('screen');
         } catch (err) {
             console.error('Wake Lock is not supported.');
-            wakeLock = false;
         }
     };
 

javascript/resizeHandle.js

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

modules/api/api.py

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

modules/dat_model.py

@@ -1,7 +1,7 @@
 import os
 
 from modules import modelloader, errors
-from modules.shared import cmd_opts, opts, hf_endpoint
+from modules.shared import cmd_opts, opts
 from modules.upscaler import Upscaler, UpscalerData
 from modules.upscaler_utils import upscale_with_model
 
@@ -49,18 +49,7 @@ 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
@@ -71,23 +60,20 @@ def get_dat_models(scaler):
     return [
         UpscalerData(
             name="DAT x2",
-            path=f"{hf_endpoint}/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x2.pth",
+            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x2.pth",
             scale=2,
             upscaler=scaler,
-            sha256='7760aa96e4ee77e29d4f89c3a4486200042e019461fdb8aa286f49aa00b89b51',
         ),
         UpscalerData(
             name="DAT x3",
-            path=f"{hf_endpoint}/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x3.pth",
+            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x3.pth",
             scale=3,
             upscaler=scaler,
-            sha256='581973e02c06f90d4eb90acf743ec9604f56f3c2c6f9e1e2c2b38ded1f80d197',
         ),
         UpscalerData(
             name="DAT x4",
-            path=f"{hf_endpoint}/w-e-w/DAT/resolve/main/experiments/pretrained_models/DAT/DAT_x4.pth",
+            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x4.pth",
             scale=4,
             upscaler=scaler,
-            sha256='391a6ce69899dff5ea3214557e9d585608254579217169faf3d4c353caff049e',
         ),
     ]

modules/extras.py

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

modules/modelloader.py

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

modules/models/sd3/sd3_cond.py

@@ -24,7 +24,7 @@ class SafetensorsMapping(typing.Mapping):
         return self.file.get_tensor(key)
 
 
-CLIPL_URL = f"{shared.hf_endpoint}/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_l.safetensors"
+CLIPL_URL = "https://huggingface.co/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 = f"{shared.hf_endpoint}/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_g.safetensors"
+CLIPG_URL = "https://huggingface.co/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 = f"{shared.hf_endpoint}/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
+T5_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
 T5_CONFIG = {
     "d_ff": 10240,
     "d_model": 4096,

modules/processing.py

@@ -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, util
+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.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
@@ -457,20 +457,6 @@ 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)
@@ -494,10 +480,6 @@ 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]
@@ -505,13 +487,6 @@ 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]
 
@@ -527,8 +502,6 @@ 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
 
@@ -828,10 +801,10 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
 
     for key, value in generation_params.items():
         try:
-            if callable(value):
-                generation_params[key] = value(**locals())
-            elif isinstance(value, list):
+            if isinstance(value, list):
                 generation_params[key] = value[index]
+            elif callable(value):
+                generation_params[key] = value(**locals())
         except Exception:
             errors.report(f'Error creating infotext for key "{key}"', exc_info=True)
             generation_params[key] = None
@@ -965,7 +938,6 @@ 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]
@@ -993,6 +965,8 @@ 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
@@ -1285,10 +1259,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 if self.hr_checkpoint_info is None:
                     raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}')
 
-                if shared.sd_model.sd_checkpoint_info == self.hr_checkpoint_info:
-                    self.hr_checkpoint_info = None
-                else:
-                    self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title
+                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
@@ -1539,8 +1510,6 @@ 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

modules/scripts_auto_postprocessing.py

@@ -13,7 +13,6 @@ 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()
 
@@ -34,7 +33,7 @@ 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 or script.script_class.extra_only:
+        if script is None:
             continue
 
         constructor = lambda s=script: ScriptPostprocessingForMainUI(s.script_class())

modules/scripts_postprocessing.py

@@ -1,4 +1,3 @@
-import re
 import dataclasses
 import os
 import gradio as gr
@@ -60,10 +59,6 @@ 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"""
 
@@ -102,31 +97,6 @@ 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:
@@ -149,6 +119,10 @@ 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):
@@ -178,7 +152,7 @@ class ScriptPostprocessingRunner:
 
             return len(self.scripts)
 
-        filtered_scripts = [script for script in self.scripts if script.name not in scripts_filter_out and not script.main_ui_only]
+        filtered_scripts = [script for script in self.scripts if script.name not in scripts_filter_out]
         script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(filtered_scripts)}
 
         return sorted(filtered_scripts, key=lambda x: script_scores[x.name])

modules/sd_disable_initialization.py

@@ -76,7 +76,7 @@ class DisableInitialization(ReplaceHelper):
         def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
 
             # this file is always 404, prevent making request
-            if url == 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':
+            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':
                 return None
 
             try:

modules/sd_hijack.py

@@ -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, util
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
 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
@@ -321,14 +321,6 @@ 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 "-", "-"

modules/sd_hijack_clip.py

@@ -3,7 +3,7 @@ from collections import namedtuple
 
 import torch
 
-from modules import prompt_parser, devices, sd_hijack, sd_emphasis, util
+from modules import prompt_parser, devices, sd_hijack, sd_emphasis
 from modules.shared import opts
 
 
@@ -27,30 +27,6 @@ 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__()
@@ -262,10 +238,12 @@ class TextConditionalModel(torch.nn.Module):
                 hashes.append(f"{name}: {shorthash}")
 
             if hashes:
-                self.hijack.extra_generation_params["TI hashes"] = util.GenerationParametersList(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)
 
-        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 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 self.return_pooled:
             return torch.hstack(zs), zs[0].pooled

modules/sd_models.py

@@ -159,7 +159,7 @@ def list_models():
         model_url = None
         expected_sha256 = None
     else:
-        model_url = f"{shared.hf_endpoint}/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
+        model_url = f"{shared.hf_endpoint}/runwayml/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)
@@ -423,10 +423,6 @@ 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)
@@ -665,7 +661,7 @@ 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" or (hasattr(sd_model, 'ztsnr') and sd_model.ztsnr):
+    if opts.sd_noise_schedule == "Zero Terminal SNR":
         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)
@@ -787,7 +783,7 @@ 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, checkpoint_config=None):
+def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     from modules import sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
 
@@ -805,8 +801,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, checkpoint_
     else:
         state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
 
-    if not checkpoint_config:
-        checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
+    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")
@@ -979,7 +974,7 @@ 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, checkpoint_config=checkpoint_config)
+        load_model(checkpoint_info, already_loaded_state_dict=state_dict)
         return model_data.sd_model
 
     try:

modules/sd_models_config.py

@@ -14,7 +14,6 @@ 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")
@@ -82,9 +81,6 @@ def guess_model_config_from_state_dict(sd, filename):
         if diffusion_model_input.shape[1] == 9:
             return config_sdxl_inpainting
         else:
-            if ('v_pred' in sd):
-                del sd['v_pred']
-                return config_sdxlv
             return config_sdxl
 
     if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:

modules/shared_items.py

@@ -16,12 +16,10 @@ def dat_models_names():
     return [x.name for x in modules.dat_model.get_dat_models(None)]
 
 
-def postprocessing_scripts(filter_out_extra_only=False, filter_out_main_ui_only=False):
+def postprocessing_scripts():
     import modules.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,
-    ))
+
+    return modules.scripts.scripts_postproc.scripts
 
 
 def sd_vae_items():

modules/shared_options.py

@@ -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/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": 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_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"),
@@ -291,7 +291,6 @@ 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"), {
@@ -405,15 +404,15 @@ 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; XYZ plot: Skip Early CFG"),
+    '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"),
     '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(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)]}),
+    '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()]}),
     'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     'postprocessing_existing_caption_action': OptionInfo("Ignore", "Action for existing captions", gr.Radio, {"choices": ["Ignore", "Keep", "Prepend", "Append"]}).info("when generating captions using postprocessing; Ignore = use generated; Keep = use original; Prepend/Append = combine both"),
 }))

modules/textual_inversion/textual_inversion.py

@@ -12,7 +12,7 @@ import safetensors.torch
 import numpy as np
 from PIL import Image, PngImagePlugin
 
-from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes, cache
+from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -116,7 +116,6 @@ 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)
@@ -155,31 +154,6 @@ 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()
@@ -189,10 +163,17 @@ class EmbeddingDatabase:
             if second_ext.upper() == '.PREVIEW':
                 return
 
-            data, name = self.read_embedding_from_image(path, name)
-            if data is None:
-                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
         elif ext in ['.BIN', '.PT']:
             data = torch.load(path, map_location="cpu")
         elif ext in ['.SAFETENSORS']:
@@ -210,6 +191,7 @@ class EmbeddingDatabase:
         else:
             print(f"Unable to load Textual inversion embedding due to data issue: '{name}'.")
 
+
     def load_from_dir(self, embdir):
         if not os.path.isdir(embdir.path):
             return

modules/ui.py

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

modules/ui_components.py

@@ -91,7 +91,6 @@ 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):
@@ -100,18 +99,6 @@ 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",
@@ -156,7 +143,3 @@ class InputAccordion(gr.Checkbox):
     def get_block_name(self):
         return "checkbox"
 
-    @classmethod
-    def reset(cls):
-        cls.global_index = 0
-        cls.accordion_id_set.clear()

modules/ui_extra_networks.py

@@ -177,8 +177,10 @@ 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: str):
-    return json.dumps(s, ensure_ascii=False)
+def quote_js(s):
+    s = s.replace('\\', '\\\\')
+    s = s.replace('"', '\\"')
+    return f'"{s}"'
 
 
 class ExtraNetworksPage:

modules/ui_loadsave.py

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

modules/upscaler.py

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

modules/util.py

@@ -211,126 +211,3 @@ 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__()
-

requirements_versions.txt

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

scripts/postprocessing_codeformer.py

@@ -12,8 +12,8 @@ class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing
     def ui(self):
         with ui_components.InputAccordion(False, label="CodeFormer") as enable:
             with gr.Row():
-                codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id=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"))
+                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")
 
         return {
             "enable": enable,

scripts/postprocessing_gfpgan.py

@@ -11,7 +11,7 @@ class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
 
     def ui(self):
         with ui_components.InputAccordion(False, label="GFPGAN") as enable:
-            gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id=self.elem_id_suffix("extras_gfpgan_visibility"))
+            gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Visibility", value=1.0, elem_id="extras_gfpgan_visibility")
 
         return {
             "enable": enable,

scripts/postprocessing_upscale.py

@@ -30,31 +30,31 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
     def ui(self):
         selected_tab = gr.Number(value=0, visible=False)
 
-        with InputAccordion(True, label="Upscale", elem_id=self.elem_id_suffix("extras_upscale")) as upscale_enabled:
+        with InputAccordion(True, label="Upscale", elem_id="extras_upscale") as upscale_enabled:
             with FormRow():
-                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)
+                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)
 
             with FormRow():
-                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"))
+                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")
 
             with FormRow():
-                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.Tabs(elem_id="extras_resize_mode"):
+                    with gr.TabItem('Scale by', elem_id="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=self.elem_id_suffix("extras_upscaling_resize"))
+                                upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
                             with gr.Column(scale=1, min_width=160):
-                                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)
+                                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)
 
-                    with gr.TabItem('Scale to', elem_id=self.elem_id_suffix("extras_scale_to_tab")) as tab_scale_to:
+                    with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
                         with FormRow():
-                            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"))
+                            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")
 
         def on_selected_upscale_method(upscale_method):
             if not shared.opts.set_scale_by_when_changing_upscaler:
@@ -169,7 +169,6 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
 class ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale):
     name = "Simple Upscale"
     order = 900
-    main_ui_only = True
 
     def ui(self):
         with FormRow():

scripts/xyz_grid.py

@@ -20,7 +20,7 @@ import modules.sd_models
 import modules.sd_vae
 import re
 
-from modules.ui_components import ToolButton, InputAccordion
+from modules.ui_components import ToolButton
 
 fill_values_symbol = "\U0001f4d2"  # 📒
 
@@ -259,7 +259,6 @@ 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")),
@@ -285,7 +284,7 @@ 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, draw_grid):
+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):
     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]
@@ -370,30 +369,29 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
         print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
         return Processed(p, [])
 
-    if draw_grid:
-        z_count = len(zs)
+    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))
-            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])))
+    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:
-            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])
+            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])
 
     return processed_result
 
@@ -443,6 +441,7 @@ 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.")
@@ -450,12 +449,9 @@ 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"))
-                csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode"))
-
-        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"))
+                csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode"))
+            with gr.Column():
                 margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
 
         with gr.Row(variant="compact", elem_id="swap_axes"):
@@ -537,9 +533,9 @@ 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, draw_grid]
+        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]
 
-    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):
+    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):
         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:
@@ -784,8 +780,7 @@ 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,
-                draw_grid=draw_grid,
+                margin_size=margin_size
             )
 
         if not processed.images:
@@ -794,15 +789,14 @@ class Script(scripts.Script):
 
         z_count = len(zs)
 
-        if draw_grid:
-            # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
-            processed.infotexts[:1 + z_count] = grid_infotext[:1 + z_count]
+        # 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] if draw_grid else []
+            processed.images = processed.images[:z_count + 1]
 
-        if draw_grid and opts.grid_save:
+        if 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):
@@ -812,7 +806,7 @@ class Script(scripts.Script):
                 if not include_sub_grids:  # if not include_sub_grids then skip saving after the first grid
                     break
 
-        if draw_grid and not include_sub_grids:
+        if not include_sub_grids:
             # Done with sub-grids, drop all related information:
             for _ in range(z_count):
                 del processed.images[1]

webui.bat

@@ -4,16 +4,7 @@ if exist webui.settings.bat (
     call webui.settings.bat
 )
 
-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 not defined PYTHON (set PYTHON=python)
 if defined GIT (set "GIT_PYTHON_GIT_EXECUTABLE=%GIT%")
 if not defined VENV_DIR (set "VENV_DIR=%~dp0%venv")
 

webui.py

@@ -45,44 +45,6 @@ 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
 
@@ -91,8 +53,6 @@ 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()