Merge branch 'AUTOMATIC1111:dev' into dev

.github/workflows/on_pull_request.yaml

@@ -18,7 +18,7 @@ jobs:
           #     not to have GHA download an (at the time of writing) 4 GB cache
           #     of PyTorch and other dependencies.
       - name: Install Ruff
-        run: pip install ruff==0.0.265
+        run: pip install ruff==0.0.272
       - name: Run Ruff
         run: ruff .
   lint-js:

.github/workflows/run_tests.yaml

@@ -50,7 +50,7 @@ jobs:
           python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
       - name: Kill test server
         if: always()
-        run: curl -vv -XPOST http://127.0.0.1:7860/_stop && sleep 10
+        run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10
       - name: Show coverage
         run: |
           python -m coverage combine .coverage*

extensions-builtin/LDSR/scripts/ldsr_model.py

@@ -1,7 +1,6 @@
 import os
 
-from basicsr.utils.download_util import load_file_from_url
-
+from modules.modelloader import load_file_from_url
 from modules.upscaler import Upscaler, UpscalerData
 from ldsr_model_arch import LDSR
 from modules import shared, script_callbacks, errors
@@ -43,20 +42,17 @@ class UpscalerLDSR(Upscaler):
         if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
             model = local_safetensors_path
         else:
-            model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="model.ckpt", progress=True)
+            model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt")
 
-        yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml", progress=True)
+        yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml")
 
-        try:
-            return LDSR(model, yaml)
-        except Exception:
-            errors.report("Error importing LDSR", exc_info=True)
-        return None
+        return LDSR(model, yaml)
 
     def do_upscale(self, img, path):
-        ldsr = self.load_model(path)
-        if ldsr is None:
-            print("NO LDSR!")
+        try:
+            ldsr = self.load_model(path)
+        except Exception:
+            errors.report(f"Failed loading LDSR model {path}", exc_info=True)
             return img
         ddim_steps = shared.opts.ldsr_steps
         return ldsr.super_resolution(img, ddim_steps, self.scale)

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -1,4 +1,3 @@
-import os.path
 import sys
 
 import PIL.Image
@@ -6,12 +5,11 @@ import numpy as np
 import torch
 from tqdm import tqdm
 
-from basicsr.utils.download_util import load_file_from_url
-
 import modules.upscaler
 from modules import devices, modelloader, script_callbacks, errors
-from scunet_model_arch import SCUNet as net
+from scunet_model_arch import SCUNet
 
+from modules.modelloader import load_file_from_url
 from modules.shared import opts
 
 
@@ -28,7 +26,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         scalers = []
         add_model2 = True
         for file in model_paths:
-            if "http" in file:
+            if file.startswith("http"):
                 name = self.model_name
             else:
                 name = modelloader.friendly_name(file)
@@ -89,9 +87,10 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
 
         torch.cuda.empty_cache()
 
-        model = self.load_model(selected_file)
-        if model is None:
-            print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
+        try:
+            model = self.load_model(selected_file)
+        except Exception as e:
+            print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
             return img
 
         device = devices.get_device_for('scunet')
@@ -119,15 +118,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
 
     def load_model(self, path: str):
         device = devices.get_device_for('scunet')
-        if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="%s.pth" % self.name, progress=True)
+        if path.startswith("http"):
+            # TODO: this doesn't use `path` at all?
+            filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
         else:
             filename = path
-        if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
-            print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
-            return None
-
-        model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
+        model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
         model.load_state_dict(torch.load(filename), strict=True)
         model.eval()
         for _, v in model.named_parameters():

extensions-builtin/SwinIR/scripts/swinir_model.py

@@ -1,17 +1,17 @@
-import os
+import sys
 
 import numpy as np
 import torch
 from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
 from tqdm import tqdm
 
 from modules import modelloader, devices, script_callbacks, shared
 from modules.shared import opts, state
-from swinir_model_arch import SwinIR as net
-from swinir_model_arch_v2 import Swin2SR as net2
+from swinir_model_arch import SwinIR
+from swinir_model_arch_v2 import Swin2SR
 from modules.upscaler import Upscaler, UpscalerData
 
+SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
 
 device_swinir = devices.get_device_for('swinir')
 
@@ -19,16 +19,14 @@ device_swinir = devices.get_device_for('swinir')
 class UpscalerSwinIR(Upscaler):
     def __init__(self, dirname):
         self.name = "SwinIR"
-        self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
-                         "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
-                         "-L_x4_GAN.pth "
+        self.model_url = SWINIR_MODEL_URL
         self.model_name = "SwinIR 4x"
         self.user_path = dirname
         super().__init__()
         scalers = []
         model_files = self.find_models(ext_filter=[".pt", ".pth"])
         for model in model_files:
-            if "http" in model:
+            if model.startswith("http"):
                 name = self.model_name
             else:
                 name = modelloader.friendly_name(model)
@@ -37,8 +35,10 @@ class UpscalerSwinIR(Upscaler):
         self.scalers = scalers
 
     def do_upscale(self, img, model_file):
-        model = self.load_model(model_file)
-        if model is None:
+        try:
+            model = self.load_model(model_file)
+        except Exception as e:
+            print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
             return img
         model = model.to(device_swinir, dtype=devices.dtype)
         img = upscale(img, model)
@@ -49,30 +49,31 @@ class UpscalerSwinIR(Upscaler):
         return img
 
     def load_model(self, path, scale=4):
-        if "http" in path:
-            dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
-            filename = load_file_from_url(url=path, model_dir=self.model_download_path, file_name=dl_name, progress=True)
+        if path.startswith("http"):
+            filename = modelloader.load_file_from_url(
+                url=path,
+                model_dir=self.model_download_path,
+                file_name=f"{self.model_name.replace(' ', '_')}.pth",
+            )
         else:
             filename = path
-        if filename is None or not os.path.exists(filename):
-            return None
         if filename.endswith(".v2.pth"):
-            model = net2(
-            upscale=scale,
-            in_chans=3,
-            img_size=64,
-            window_size=8,
-            img_range=1.0,
-            depths=[6, 6, 6, 6, 6, 6],
-            embed_dim=180,
-            num_heads=[6, 6, 6, 6, 6, 6],
-            mlp_ratio=2,
-            upsampler="nearest+conv",
-            resi_connection="1conv",
+            model = Swin2SR(
+                upscale=scale,
+                in_chans=3,
+                img_size=64,
+                window_size=8,
+                img_range=1.0,
+                depths=[6, 6, 6, 6, 6, 6],
+                embed_dim=180,
+                num_heads=[6, 6, 6, 6, 6, 6],
+                mlp_ratio=2,
+                upsampler="nearest+conv",
+                resi_connection="1conv",
             )
             params = None
         else:
-            model = net(
+            model = SwinIR(
                 upscale=scale,
                 in_chans=3,
                 img_size=64,

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

@@ -4,12 +4,12 @@ onUiLoaded(async() => {
         inpaint: "#img2maskimg",
         inpaintSketch: "#inpaint_sketch",
         rangeGroup: "#img2img_column_size",
-        sketch: "#img2img_sketch",
+        sketch: "#img2img_sketch"
     };
     const tabNameToElementId = {
         "Inpaint sketch": elementIDs.inpaintSketch,
         "Inpaint": elementIDs.inpaint,
-        "Sketch": elementIDs.sketch,
+        "Sketch": elementIDs.sketch
     };
 
     // Helper functions
@@ -42,43 +42,110 @@ onUiLoaded(async() => {
         }
     }
 
-    // Check is hotkey valid
-    function isSingleLetter(value) {
+    // Function for defining the "Ctrl", "Shift" and "Alt" keys
+    function isModifierKey(event, key) {
+        switch (key) {
+        case "Ctrl":
+            return event.ctrlKey;
+        case "Shift":
+            return event.shiftKey;
+        case "Alt":
+            return event.altKey;
+        default:
+            return false;
+        }
+    }
+
+    // Check if hotkey is valid
+    function isValidHotkey(value) {
+        const specialKeys = ["Ctrl", "Alt", "Shift", "Disable"];
         return (
-            typeof value === "string" && value.length === 1 && /[a-z]/i.test(value)
+            (typeof value === "string" &&
+                value.length === 1 &&
+                /[a-z]/i.test(value)) ||
+            specialKeys.includes(value)
         );
     }
 
-    // Create hotkeyConfig from opts
-    function createHotkeyConfig(defaultHotkeysConfig, hotkeysConfigOpts) {
-        const result = {};
-        const usedKeys = new Set();
+    // Normalize hotkey
+    function normalizeHotkey(hotkey) {
+        return hotkey.length === 1 ? "Key" + hotkey.toUpperCase() : hotkey;
+    }
 
+    // Format hotkey for display
+    function formatHotkeyForDisplay(hotkey) {
+        return hotkey.startsWith("Key") ? hotkey.slice(3) : hotkey;
+    }
+
+    // Create hotkey configuration with the provided options
+    function createHotkeyConfig(defaultHotkeysConfig, hotkeysConfigOpts) {
+        const result = {}; // Resulting hotkey configuration
+        const usedKeys = new Set(); // Set of used hotkeys
+
+        // Iterate through defaultHotkeysConfig keys
         for (const key in defaultHotkeysConfig) {
-            if (typeof hotkeysConfigOpts[key] === "boolean") {
-                result[key] = hotkeysConfigOpts[key];
-                continue;
-            }
+            const userValue = hotkeysConfigOpts[key]; // User-provided hotkey value
+            const defaultValue = defaultHotkeysConfig[key]; // Default hotkey value
+
+            // Apply appropriate value for undefined, boolean, or object userValue
             if (
-                hotkeysConfigOpts[key] &&
-                isSingleLetter(hotkeysConfigOpts[key]) &&
-                !usedKeys.has(hotkeysConfigOpts[key].toUpperCase())
+                userValue === undefined ||
+                typeof userValue === "boolean" ||
+                typeof userValue === "object" ||
+                userValue === "disable"
             ) {
-                // If the property passed the test and has not yet been used, add 'Key' before it and save it
-                result[key] = "Key" + hotkeysConfigOpts[key].toUpperCase();
-                usedKeys.add(hotkeysConfigOpts[key].toUpperCase());
+                result[key] =
+                    userValue === undefined ? defaultValue : userValue;
+            } else if (isValidHotkey(userValue)) {
+                const normalizedUserValue = normalizeHotkey(userValue);
+
+                // Check for conflicting hotkeys
+                if (!usedKeys.has(normalizedUserValue)) {
+                    usedKeys.add(normalizedUserValue);
+                    result[key] = normalizedUserValue;
+                } else {
+                    console.error(
+                        `Hotkey: ${formatHotkeyForDisplay(
+                            userValue
+                        )} for ${key} is repeated and conflicts with another hotkey. The default hotkey is used: ${formatHotkeyForDisplay(
+                            defaultValue
+                        )}`
+                    );
+                    result[key] = defaultValue;
+                }
             } else {
-                // If the property does not pass the test or has already been used, we keep the default value
                 console.error(
-                    `Hotkey: ${hotkeysConfigOpts[key]} for ${key} is repeated and conflicts with another hotkey or is not 1 letter. The default hotkey is used: ${defaultHotkeysConfig[key][3]}`
+                    `Hotkey: ${formatHotkeyForDisplay(
+                        userValue
+                    )} for ${key} is not valid. The default hotkey is used: ${formatHotkeyForDisplay(
+                        defaultValue
+                    )}`
                 );
-                result[key] = defaultHotkeysConfig[key];
+                result[key] = defaultValue;
             }
         }
 
         return result;
     }
 
+    // Disables functions in the config object based on the provided list of function names
+    function disableFunctions(config, disabledFunctions) {
+        // Bind the hasOwnProperty method to the functionMap object to avoid errors
+        const hasOwnProperty =
+            Object.prototype.hasOwnProperty.bind(functionMap);
+
+        // Loop through the disabledFunctions array and disable the corresponding functions in the config object
+        disabledFunctions.forEach(funcName => {
+            if (hasOwnProperty(funcName)) {
+                const key = functionMap[funcName];
+                config[key] = "disable";
+            }
+        });
+
+        // Return the updated config object
+        return config;
+    }
+
     /**
      * The restoreImgRedMask function displays a red mask around an image to indicate the aspect ratio.
      * If the image display property is set to 'none', the mask breaks. To fix this, the function
@@ -100,7 +167,9 @@ onUiLoaded(async() => {
         imageARPreview.style.transform = "";
         if (parseFloat(mainTab.style.width) > 865) {
             const transformString = mainTab.style.transform;
-            const scaleMatch = transformString.match(/scale\(([-+]?[0-9]*\.?[0-9]+)\)/);
+            const scaleMatch = transformString.match(
+                /scale\(([-+]?[0-9]*\.?[0-9]+)\)/
+            );
             let zoom = 1; // default zoom
 
             if (scaleMatch && scaleMatch[1]) {
@@ -124,31 +193,52 @@ onUiLoaded(async() => {
 
     // Default config
     const defaultHotkeysConfig = {
+        canvas_hotkey_zoom: "Alt",
+        canvas_hotkey_adjust: "Ctrl",
         canvas_hotkey_reset: "KeyR",
         canvas_hotkey_fullscreen: "KeyS",
         canvas_hotkey_move: "KeyF",
         canvas_hotkey_overlap: "KeyO",
-        canvas_show_tooltip: true,
-        canvas_swap_controls: false
+        canvas_disabled_functions: [],
+        canvas_show_tooltip: true
     };
-    // swap the actions for ctr + wheel and shift + wheel
-    const hotkeysConfig = createHotkeyConfig(
+
+    const functionMap = {
+        "Zoom": "canvas_hotkey_zoom",
+        "Adjust brush size": "canvas_hotkey_adjust",
+        "Moving canvas": "canvas_hotkey_move",
+        "Fullscreen": "canvas_hotkey_fullscreen",
+        "Reset Zoom": "canvas_hotkey_reset",
+        "Overlap": "canvas_hotkey_overlap"
+    };
+
+    // Loading the configuration from opts
+    const preHotkeysConfig = createHotkeyConfig(
         defaultHotkeysConfig,
         hotkeysConfigOpts
     );
 
+    // Disable functions that are not needed by the user
+    const hotkeysConfig = disableFunctions(
+        preHotkeysConfig,
+        preHotkeysConfig.canvas_disabled_functions
+    );
+
     let isMoving = false;
     let mouseX, mouseY;
     let activeElement;
 
-    const elements = Object.fromEntries(Object.keys(elementIDs).map((id) => [
-        id,
-        gradioApp().querySelector(elementIDs[id]),
-    ]));
+    const elements = Object.fromEntries(
+        Object.keys(elementIDs).map(id => [
+            id,
+            gradioApp().querySelector(elementIDs[id])
+        ])
+    );
     const elemData = {};
 
     // Apply functionality to the range inputs. Restore redmask and correct for long images.
-    const rangeInputs = elements.rangeGroup ? Array.from(elements.rangeGroup.querySelectorAll("input")) :
+    const rangeInputs = elements.rangeGroup ?
+        Array.from(elements.rangeGroup.querySelectorAll("input")) :
         [
             gradioApp().querySelector("#img2img_width input[type='range']"),
             gradioApp().querySelector("#img2img_height input[type='range']")
@@ -180,38 +270,56 @@ onUiLoaded(async() => {
             const toolTipElemnt =
                 targetElement.querySelector(".image-container");
             const tooltip = document.createElement("div");
-            tooltip.className = "tooltip";
+            tooltip.className = "canvas-tooltip";
 
             // Creating an item of information
             const info = document.createElement("i");
-            info.className = "tooltip-info";
+            info.className = "canvas-tooltip-info";
             info.textContent = "";
 
             // Create a container for the contents of the tooltip
             const tooltipContent = document.createElement("div");
-            tooltipContent.className = "tooltip-content";
+            tooltipContent.className = "canvas-tooltip-content";
 
-            // Add info about hotkeys
-            const zoomKey = hotkeysConfig.canvas_swap_controls ? "Ctrl" : "Shift";
-            const adjustKey = hotkeysConfig.canvas_swap_controls ? "Shift" : "Ctrl";
-
-            const hotkeys = [
-                {key: `${zoomKey} + wheel`, action: "Zoom canvas"},
-                {key: `${adjustKey} + wheel`, action: "Adjust brush size"},
+            // Define an array with hotkey information and their actions
+            const hotkeysInfo = [
                 {
-                    key: hotkeysConfig.canvas_hotkey_reset.charAt(hotkeysConfig.canvas_hotkey_reset.length - 1),
-                    action: "Reset zoom"
+                    configKey: "canvas_hotkey_zoom",
+                    action: "Zoom canvas",
+                    keySuffix: " + wheel"
                 },
                 {
-                    key: hotkeysConfig.canvas_hotkey_fullscreen.charAt(hotkeysConfig.canvas_hotkey_fullscreen.length - 1),
+                    configKey: "canvas_hotkey_adjust",
+                    action: "Adjust brush size",
+                    keySuffix: " + wheel"
+                },
+                {configKey: "canvas_hotkey_reset", action: "Reset zoom"},
+                {
+                    configKey: "canvas_hotkey_fullscreen",
                     action: "Fullscreen mode"
                 },
-                {
-                    key: hotkeysConfig.canvas_hotkey_move.charAt(hotkeysConfig.canvas_hotkey_move.length - 1),
-                    action: "Move canvas"
-                }
+                {configKey: "canvas_hotkey_move", action: "Move canvas"},
+                {configKey: "canvas_hotkey_overlap", action: "Overlap"}
             ];
+
+            // Create hotkeys array with disabled property based on the config values
+            const hotkeys = hotkeysInfo.map(info => {
+                const configValue = hotkeysConfig[info.configKey];
+                const key = info.keySuffix ?
+                    `${configValue}${info.keySuffix}` :
+                    configValue.charAt(configValue.length - 1);
+                return {
+                    key,
+                    action: info.action,
+                    disabled: configValue === "disable"
+                };
+            });
+
             for (const hotkey of hotkeys) {
+                if (hotkey.disabled) {
+                    continue;
+                }
+
                 const p = document.createElement("p");
                 p.innerHTML = `<b>${hotkey.key}</b> - ${hotkey.action}`;
                 tooltipContent.appendChild(p);
@@ -346,10 +454,7 @@ onUiLoaded(async() => {
 
         // Change the zoom level based on user interaction
         function changeZoomLevel(operation, e) {
-            if (
-                (!hotkeysConfig.canvas_swap_controls && e.shiftKey) ||
-                (hotkeysConfig.canvas_swap_controls && e.ctrlKey)
-            ) {
+            if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) {
                 e.preventDefault();
 
                 let zoomPosX, zoomPosY;
@@ -514,6 +619,13 @@ onUiLoaded(async() => {
                 event.preventDefault();
                 action(event);
             }
+
+            if (
+                isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) ||
+                isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust)
+            ) {
+                event.preventDefault();
+            }
         }
 
         // Get Mouse position
@@ -564,11 +676,7 @@ onUiLoaded(async() => {
             changeZoomLevel(operation, e);
 
             // Handle brush size adjustment with ctrl key pressed
-            if (
-                (hotkeysConfig.canvas_swap_controls && e.shiftKey) ||
-                (!hotkeysConfig.canvas_swap_controls &&
-                    (e.ctrlKey || e.metaKey))
-            ) {
+            if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) {
                 e.preventDefault();
 
                 // Increase or decrease brush size based on scroll direction

extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py

@@ -1,10 +1,13 @@
+import gradio as gr
 from modules import shared
 
 shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), {
-    "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas"),
+    "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
+    "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
+    "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"),
     "canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "),
     "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
-    "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap ( Technical button, neededs for testing )"),
+    "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"),
     "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
-    "canvas_swap_controls": shared.OptionInfo(False, "Swap hotkey combinations for Zoom and Adjust brush resize"),
+    "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
 }))

extensions-builtin/canvas-zoom-and-pan/style.css

@@ -1,4 +1,4 @@
-.tooltip-info {
+.canvas-tooltip-info {
   position: absolute;
   top: 10px;
   left: 10px;
@@ -15,7 +15,7 @@
   z-index: 100;
 }
 
-.tooltip-info::after {
+.canvas-tooltip-info::after {
   content: '';
   display: block;
   width: 2px;
@@ -24,7 +24,7 @@
   margin-top: 2px; 
 }
 
-.tooltip-info::before {
+.canvas-tooltip-info::before {
   content: '';
   display: block;
   width: 2px;
@@ -32,7 +32,7 @@
   background-color: white;
 }
 
-.tooltip-content {
+.canvas-tooltip-content {
   display: none;
   background-color: #f9f9f9; 
   color: #333; 
@@ -50,7 +50,7 @@
   z-index: 100;
 }
 
-.tooltip:hover .tooltip-content {
+.canvas-tooltip:hover .canvas-tooltip-content {
   display: block;
   animation: fadeIn 0.5s; 
   opacity: 1;

javascript/extensions.js

@@ -72,3 +72,21 @@ function config_state_confirm_restore(_, config_state_name, config_restore_type)
     }
     return [confirmed, config_state_name, config_restore_type];
 }
+
+function toggle_all_extensions(event) {
+    gradioApp().querySelectorAll('#extensions .extension_toggle').forEach(function(checkbox_el) {
+        checkbox_el.checked = event.target.checked;
+    });
+}
+
+function toggle_extension() {
+    let all_extensions_toggled = true;
+    for (const checkbox_el of gradioApp().querySelectorAll('#extensions .extension_toggle')) {
+        if (!checkbox_el.checked) {
+            all_extensions_toggled = false;
+            break;
+        }
+    }
+
+    gradioApp().querySelector('#extensions .all_extensions_toggle').checked = all_extensions_toggled;
+}

javascript/hints.js

@@ -15,7 +15,7 @@ var titles = {
     "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
     "Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
     "\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
-    "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
+    "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomized",
     "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "\u{1f4c2}": "Open images output directory",
     "\u{1f4be}": "Save style",
@@ -112,7 +112,7 @@ var titles = {
     "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
     "Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.",
     "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
-    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.",
+    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order listed.",
     "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
 };
 

modules/api/api.py

@@ -14,7 +14,7 @@ from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -22,7 +22,7 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
-from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights
+from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights, checkpoint_alisases
 from modules.sd_vae import vae_dict
 from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
@@ -32,13 +32,6 @@ import piexif
 import piexif.helper
 
 
-def upscaler_to_index(name: str):
-    try:
-        return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}") from e
-
-
 def script_name_to_index(name, scripts):
     try:
         return [script.title().lower() for script in scripts].index(name.lower())
@@ -209,6 +202,11 @@ class Api:
         self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
         self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
 
+        if shared.cmd_opts.add_stop_route:
+            self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
+            self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"])
+            self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"])
+
         self.default_script_arg_txt2img = []
         self.default_script_arg_img2img = []
 
@@ -517,6 +515,10 @@ class Api:
         return options
 
     def set_config(self, req: Dict[str, Any]):
+        checkpoint_name = req.get("sd_model_checkpoint", None)
+        if checkpoint_name is not None and checkpoint_name not in checkpoint_alisases:
+            raise RuntimeError(f"model {checkpoint_name!r} not found")
+
         for k, v in req.items():
             shared.opts.set(k, v)
 
@@ -715,3 +717,15 @@ class Api:
     def launch(self, server_name, port):
         self.app.include_router(self.router)
         uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=0)
+
+    def kill_webui(self):
+        restart.stop_program()
+
+    def restart_webui(self):
+        if restart.is_restartable():
+            restart.restart_program()
+        return Response(status_code=501)
+
+    def stop_webui(request):
+        shared.state.server_command = "stop"
+        return Response("Stopping.")

modules/api/models.py

@@ -274,10 +274,6 @@ class PromptStyleItem(BaseModel):
     prompt: Optional[str] = Field(title="Prompt")
     negative_prompt: Optional[str] = Field(title="Negative Prompt")
 
-class ArtistItem(BaseModel):
-    name: str = Field(title="Name")
-    score: float = Field(title="Score")
-    category: str = Field(title="Category")
 
 class EmbeddingItem(BaseModel):
     step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")

modules/call_queue.py

@@ -1,3 +1,4 @@
+from functools import wraps
 import html
 import threading
 import time
@@ -18,6 +19,7 @@ def wrap_queued_call(func):
 
 
 def wrap_gradio_gpu_call(func, extra_outputs=None):
+    @wraps(func)
     def f(*args, **kwargs):
 
         # if the first argument is a string that says "task(...)", it is treated as a job id
@@ -45,6 +47,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
 
 def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+    @wraps(func)
     def f(*args, extra_outputs_array=extra_outputs, **kwargs):
         run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
         if run_memmon:

modules/cmd_args.py

@@ -106,4 +106,4 @@ parser.add_argument("--skip-version-check", action='store_true', help="Do not ch
 parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
 parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
 parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
-parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
+parser.add_argument('--add-stop-route', action='store_true', help='enable server stop/restart/kill via api')

modules/codeformer_model.py

@@ -15,14 +15,11 @@ model_dir = "Codeformer"
 model_path = os.path.join(models_path, model_dir)
 model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
 
-have_codeformer = False
 codeformer = None
 
 
 def setup_model(dirname):
-    global model_path
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
+    os.makedirs(model_path, exist_ok=True)
 
     path = modules.paths.paths.get("CodeFormer", None)
     if path is None:
@@ -125,9 +122,6 @@ def setup_model(dirname):
 
                 return restored_img
 
-        global have_codeformer
-        have_codeformer = True
-
         global codeformer
         codeformer = FaceRestorerCodeFormer(dirname)
         shared.face_restorers.append(codeformer)

modules/devices.py

@@ -15,13 +15,6 @@ def has_mps() -> bool:
     else:
         return mac_specific.has_mps
 
-def extract_device_id(args, name):
-    for x in range(len(args)):
-        if name in args[x]:
-            return args[x + 1]
-
-    return None
-
 
 def get_cuda_device_string():
     from modules import shared

modules/esrgan_model.py

@@ -1,15 +1,13 @@
-import os
+import sys
 
 import numpy as np
 import torch
 from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgan_model_arch as arch
 from modules import modelloader, images, devices
-from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
-
+from modules.upscaler import Upscaler, UpscalerData
 
 
 def mod2normal(state_dict):
@@ -134,7 +132,7 @@ class UpscalerESRGAN(Upscaler):
             scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
             scalers.append(scaler_data)
         for file in model_paths:
-            if "http" in file:
+            if file.startswith("http"):
                 name = self.model_name
             else:
                 name = modelloader.friendly_name(file)
@@ -143,26 +141,25 @@ class UpscalerESRGAN(Upscaler):
             self.scalers.append(scaler_data)
 
     def do_upscale(self, img, selected_model):
-        model = self.load_model(selected_model)
-        if model is None:
+        try:
+            model = self.load_model(selected_model)
+        except Exception as e:
+            print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
             return img
         model.to(devices.device_esrgan)
         img = esrgan_upscale(model, img)
         return img
 
     def load_model(self, path: str):
-        if "http" in path:
-            filename = load_file_from_url(
+        if path.startswith("http"):
+            # TODO: this doesn't use `path` at all?
+            filename = modelloader.load_file_from_url(
                 url=self.model_url,
                 model_dir=self.model_download_path,
                 file_name=f"{self.model_name}.pth",
-                progress=True,
             )
         else:
             filename = path
-        if not os.path.exists(filename) or filename is None:
-            print(f"Unable to load {self.model_path} from {filename}")
-            return None
 
         state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
 

modules/extensions.py

@@ -7,8 +7,7 @@ from modules.paths_internal import extensions_dir, extensions_builtin_dir, scrip
 
 extensions = []
 
-if not os.path.exists(extensions_dir):
-    os.makedirs(extensions_dir)
+os.makedirs(extensions_dir, exist_ok=True)
 
 
 def active():

modules/generation_parameters_copypaste.py

@@ -174,31 +174,6 @@ def send_image_and_dimensions(x):
     return img, w, h
 
 
-
-def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
-    """Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
-
-    Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config
-    parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to.
-
-    If the infotext has no hash, then a hypernet with the same name will be selected instead.
-    """
-    hypernet_name = hypernet_name.lower()
-    if hypernet_hash is not None:
-        # Try to match the hash in the name
-        for hypernet_key in shared.hypernetworks.keys():
-            result = re_hypernet_hash.search(hypernet_key)
-            if result is not None and result[1] == hypernet_hash:
-                return hypernet_key
-    else:
-        # Fall back to a hypernet with the same name
-        for hypernet_key in shared.hypernetworks.keys():
-            if hypernet_key.lower().startswith(hypernet_name):
-                return hypernet_key
-
-    return None
-
-
 def restore_old_hires_fix_params(res):
     """for infotexts that specify old First pass size parameter, convert it into
     width, height, and hr scale"""
@@ -332,10 +307,6 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     return res
 
 
-settings_map = {}
-
-
-
 infotext_to_setting_name_mapping = [
     ('Clip skip', 'CLIP_stop_at_last_layers', ),
     ('Conditional mask weight', 'inpainting_mask_weight'),
@@ -357,6 +328,7 @@ infotext_to_setting_name_mapping = [
     ('Token merging ratio hr', 'token_merging_ratio_hr'),
     ('RNG', 'randn_source'),
     ('NGMS', 's_min_uncond'),
+    ('Pad conds', 'pad_cond_uncond'),
 ]
 
 

modules/gfpgan_model.py

@@ -25,7 +25,7 @@ def gfpgann():
         return None
 
     models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
-    if len(models) == 1 and "http" in models[0]:
+    if len(models) == 1 and models[0].startswith("http"):
         model_file = models[0]
     elif len(models) != 0:
         latest_file = max(models, key=os.path.getctime)
@@ -70,11 +70,8 @@ gfpgan_constructor = None
 
 
 def setup_model(dirname):
-    global model_path
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
-
     try:
+        os.makedirs(model_path, exist_ok=True)
         from gfpgan import GFPGANer
         from facexlib import detection, parsing  # noqa: F401
         global user_path

modules/hypernetworks/hypernetwork.py

@@ -353,17 +353,6 @@ def load_hypernetworks(names, multipliers=None):
         shared.loaded_hypernetworks.append(hypernetwork)
 
 
-def find_closest_hypernetwork_name(search: str):
-    if not search:
-        return None
-    search = search.lower()
-    applicable = [name for name in shared.hypernetworks if search in name.lower()]
-    if not applicable:
-        return None
-    applicable = sorted(applicable, key=lambda name: len(name))
-    return applicable[0]
-
-
 def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None)
 
@@ -446,18 +435,6 @@ def statistics(data):
     return total_information, recent_information
 
 
-def report_statistics(loss_info:dict):
-    keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
-    for key in keys:
-        try:
-            print("Loss statistics for file " + key)
-            info, recent = statistics(list(loss_info[key]))
-            print(info)
-            print(recent)
-        except Exception as e:
-            print(e)
-
-
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
     # Remove illegal characters from name.
     name = "".join( x for x in name if (x.isalnum() or x in "._- "))
@@ -770,7 +747,6 @@ Last saved image: {html.escape(last_saved_image)}<br/>
         pbar.leave = False
         pbar.close()
         hypernetwork.eval()
-        #report_statistics(loss_dict)
         sd_hijack_checkpoint.remove()
 
 

modules/images.py

@@ -372,8 +372,8 @@ class FilenameGenerator:
         'hasprompt': lambda self, *args: self.hasprompt(*args),  # accepts formats:[hasprompt<prompt1|default><prompt2>..]
         'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
         'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
+        'user': lambda self: self.p.user,
         'vae_filename': lambda self: self.get_vae_filename(),
-
     }
     default_time_format = '%Y%m%d%H%M%S'
 

modules/img2img.py

@@ -3,6 +3,7 @@ from pathlib import Path
 
 import numpy as np
 from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
+import gradio as gr
 
 from modules import sd_samplers
 from modules.generation_parameters_copypaste import create_override_settings_dict
@@ -97,7 +98,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, request: gr.Request, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     is_batch = mode == 5
@@ -180,6 +181,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
     p.scripts = modules.scripts.scripts_img2img
     p.script_args = args
 
+    p.user = request.username
+
     if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
 

modules/launch_utils.py

@@ -147,10 +147,10 @@ def git_clone(url, dir, name, commithash=None):
             return
 
         run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
-        run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
+        run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
         return
 
-    run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
+    run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
 
     if commithash is not None:
         run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")

modules/modelloader.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import os
 import shutil
 import importlib
@@ -8,6 +10,29 @@ from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, Upscale
 from modules.paths import script_path, models_path
 
 
+def load_file_from_url(
+    url: str,
+    *,
+    model_dir: str,
+    progress: bool = True,
+    file_name: 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)
+    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) -> list:
     """
     A one-and done loader to try finding the desired models in specified directories.
@@ -46,9 +71,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
 
         if model_url is not None and len(output) == 0:
             if download_name is not None:
-                from basicsr.utils.download_util import load_file_from_url
-                dl = load_file_from_url(model_url, places[0], True, download_name)
-                output.append(dl)
+                output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name))
             else:
                 output.append(model_url)
 
@@ -59,7 +82,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
 
 
 def friendly_name(file: str):
-    if "http" in file:
+    if file.startswith("http"):
         file = urlparse(file).path
 
     file = os.path.basename(file)
@@ -95,8 +118,7 @@ def cleanup_models():
 
 def move_files(src_path: str, dest_path: str, ext_filter: str = None):
     try:
-        if not os.path.exists(dest_path):
-            os.makedirs(dest_path)
+        os.makedirs(dest_path, exist_ok=True)
         if os.path.exists(src_path):
             for file in os.listdir(src_path):
                 fullpath = os.path.join(src_path, file)

modules/paths.py

@@ -38,17 +38,3 @@ for d, must_exist, what, options in path_dirs:
         else:
             sys.path.append(d)
         paths[what] = d
-
-
-class Prioritize:
-    def __init__(self, name):
-        self.name = name
-        self.path = None
-
-    def __enter__(self):
-        self.path = sys.path.copy()
-        sys.path = [paths[self.name]] + sys.path
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        sys.path = self.path
-        self.path = None

modules/processing.py

@@ -184,6 +184,8 @@ class StableDiffusionProcessing:
         self.uc = None
         self.c = None
 
+        self.user = None
+
     @property
     def sd_model(self):
         return shared.sd_model
@@ -549,7 +551,7 @@ def program_version():
     return res
 
 
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False):
     index = position_in_batch + iteration * p.batch_size
 
     clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
@@ -585,13 +587,15 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
         **p.extra_generation_params,
         "Version": program_version() if opts.add_version_to_infotext else None,
+        "User": p.user if opts.add_user_name_to_info else None,
     }
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
+    prompt_text = p.prompt if use_main_prompt else all_prompts[index]
     negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
 
-    return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
+    return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
 
 
 def process_images(p: StableDiffusionProcessing) -> Processed:
@@ -663,8 +667,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     else:
         p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
-    def infotext(iteration=0, position_in_batch=0):
-        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
+    def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
+        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
@@ -824,7 +828,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             grid = images.image_grid(output_images, p.batch_size)
 
             if opts.return_grid:
-                text = infotext()
+                text = infotext(use_main_prompt=True)
                 infotexts.insert(0, text)
                 if opts.enable_pnginfo:
                     grid.info["parameters"] = text
@@ -832,7 +836,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 index_of_first_image = 1
 
             if opts.grid_save:
-                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
+                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(use_main_prompt=True), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     if not p.disable_extra_networks and p.extra_network_data:
         extra_networks.deactivate(p, p.extra_network_data)

modules/realesrgan_model.py

@@ -2,7 +2,6 @@ import os
 
 import numpy as np
 from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
 from realesrgan import RealESRGANer
 
 from modules.upscaler import Upscaler, UpscalerData
@@ -43,9 +42,10 @@ class UpscalerRealESRGAN(Upscaler):
         if not self.enable:
             return img
 
-        info = self.load_model(path)
-        if not os.path.exists(info.local_data_path):
-            print(f"Unable to load RealESRGAN model: {info.name}")
+        try:
+            info = self.load_model(path)
+        except Exception:
+            errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
             return img
 
         upsampler = RealESRGANer(
@@ -63,20 +63,17 @@ class UpscalerRealESRGAN(Upscaler):
         return image
 
     def load_model(self, path):
-        try:
-            info = next(iter([scaler for scaler in self.scalers if scaler.data_path == path]), None)
-
-            if info is None:
-                print(f"Unable to find model info: {path}")
-                return None
-
-            if info.local_data_path.startswith("http"):
-                info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_download_path, progress=True)
-
-            return info
-        except Exception:
-            errors.report("Error making Real-ESRGAN models list", exc_info=True)
-        return None
+        for scaler in self.scalers:
+            if scaler.data_path == path:
+                if scaler.local_data_path.startswith("http"):
+                    scaler.local_data_path = modelloader.load_file_from_url(
+                        scaler.data_path,
+                        model_dir=self.model_download_path,
+                    )
+                if not os.path.exists(scaler.local_data_path):
+                    raise FileNotFoundError(f"RealESRGAN data missing: {scaler.local_data_path}")
+                return scaler
+        raise ValueError(f"Unable to find model info: {path}")
 
     def load_models(self, _):
         return get_realesrgan_models(self)

modules/scripts.py

@@ -1,6 +1,7 @@
 import os
 import re
 import sys
+import inspect
 from collections import namedtuple
 
 import gradio as gr
@@ -249,7 +250,7 @@ def load_scripts():
 
     def register_scripts_from_module(module):
         for script_class in module.__dict__.values():
-            if type(script_class) != type:
+            if not inspect.isclass(script_class):
                 continue
 
             if issubclass(script_class, Script):

modules/sd_models.py

@@ -95,8 +95,7 @@ except Exception:
 
 
 def setup_model():
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
+    os.makedirs(model_path, exist_ok=True)
 
     enable_midas_autodownload()
 
@@ -248,7 +247,12 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
     _, extension = os.path.splitext(checkpoint_file)
     if extension.lower() == ".safetensors":
         device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
-        pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
+
+        if not shared.opts.disable_mmap_load_safetensors:
+            pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
+        else:
+            pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
+            pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
     else:
         pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
 

modules/sd_samplers_kdiffusion.py

@@ -69,6 +69,7 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None
         self.step = 0
         self.image_cfg_scale = None
+        self.padded_cond_uncond = False
 
     def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
         denoised_uncond = x_out[-uncond.shape[0]:]
@@ -133,15 +134,17 @@ class CFGDenoiser(torch.nn.Module):
             x_in = x_in[:-batch_size]
             sigma_in = sigma_in[:-batch_size]
 
-        # TODO add infotext entry
+        self.padded_cond_uncond = False
         if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
             empty = shared.sd_model.cond_stage_model_empty_prompt
             num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
 
             if num_repeats < 0:
                 tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
+                self.padded_cond_uncond = True
             elif num_repeats > 0:
                 uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
+                self.padded_cond_uncond = True
 
         if tensor.shape[1] == uncond.shape[1] or skip_uncond:
             if is_edit_model:
@@ -405,6 +408,9 @@ class KDiffusionSampler:
 
         samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
         return samples
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
@@ -438,5 +444,8 @@ class KDiffusionSampler:
             's_min_uncond': self.s_min_uncond
         }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
         return samples
 

modules/shared.py

@@ -376,6 +376,7 @@ options_templates.update(options_section(('system', "System"), {
     "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
     "print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
     "list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
+    "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
 }))
 
 options_templates.update(options_section(('training', "Training"), {
@@ -409,7 +410,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
-    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP nrtwork; 1 ignores none, 2 ignores one layer"),
+    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
     "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
     "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
 }))
@@ -493,6 +494,7 @@ options_templates.update(options_section(('ui', "User interface"), {
 options_templates.update(options_section(('infotext', "Infotext"), {
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
+    "add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
     "add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
     "disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
     "infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>

modules/textual_inversion/autocrop.py

@@ -298,8 +298,7 @@ def download_and_cache_models(dirname):
     download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
     model_file_name = 'face_detection_yunet.onnx'
 
-    if not os.path.exists(dirname):
-        os.makedirs(dirname)
+    os.makedirs(dirname, exist_ok=True)
 
     cache_file = os.path.join(dirname, model_file_name)
     if not os.path.exists(cache_file):

modules/textual_inversion/logging.py

@@ -2,11 +2,51 @@ import datetime
 import json
 import os
 
-saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "gradient_step", "latent_sampling_method"}
-saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
-saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
+saved_params_shared = {
+    "batch_size",
+    "clip_grad_mode",
+    "clip_grad_value",
+    "create_image_every",
+    "data_root",
+    "gradient_step",
+    "initial_step",
+    "latent_sampling_method",
+    "learn_rate",
+    "log_directory",
+    "model_hash",
+    "model_name",
+    "num_of_dataset_images",
+    "steps",
+    "template_file",
+    "training_height",
+    "training_width",
+}
+saved_params_ti = {
+    "embedding_name",
+    "num_vectors_per_token",
+    "save_embedding_every",
+    "save_image_with_stored_embedding",
+}
+saved_params_hypernet = {
+    "activation_func",
+    "add_layer_norm",
+    "hypernetwork_name",
+    "layer_structure",
+    "save_hypernetwork_every",
+    "use_dropout",
+    "weight_init",
+}
 saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
-saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"}
+saved_params_previews = {
+    "preview_cfg_scale",
+    "preview_height",
+    "preview_negative_prompt",
+    "preview_prompt",
+    "preview_sampler_index",
+    "preview_seed",
+    "preview_steps",
+    "preview_width",
+}
 
 
 def save_settings_to_file(log_directory, all_params):

modules/txt2img.py

@@ -4,10 +4,10 @@ from modules.generation_parameters_copypaste import create_override_settings_dic
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
 from modules.ui import plaintext_to_html
+import gradio as gr
 
 
-
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, *args):
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     p = processing.StableDiffusionProcessingTxt2Img(
@@ -48,6 +48,8 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
     p.scripts = modules.scripts.scripts_txt2img
     p.script_args = args
 
+    p.user = request.username
+
     if cmd_opts.enable_console_prompts:
         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
 

modules/ui.py

@@ -773,7 +773,7 @@ def create_ui():
                                 selected_scale_tab = gr.State(value=0)
 
                                 with gr.Tabs():
-                                    with gr.Tab(label="Resize to") as tab_scale_to:
+                                    with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
                                         with FormRow():
                                             with gr.Column(elem_id="img2img_column_size", scale=4):
                                                 width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
@@ -782,7 +782,7 @@ def create_ui():
                                                 res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
                                                 detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
 
-                                    with gr.Tab(label="Resize by") as tab_scale_by:
+                                    with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
                                         scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
 
                                         with FormRow():

modules/ui_extensions.py

@@ -138,7 +138,10 @@ def extension_table():
     <table id="extensions">
         <thead>
             <tr>
-                <th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
+                <th>
+                    <input class="gr-check-radio gr-checkbox all_extensions_toggle" type="checkbox" {'checked="checked"' if all(ext.enabled for ext in extensions.extensions) else ''} onchange="toggle_all_extensions(event)" />
+                    <abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr>
+                </th>
                 <th>URL</th>
                 <th>Branch</th>
                 <th>Version</th>
@@ -170,7 +173,7 @@ def extension_table():
 
         code += f"""
             <tr>
-                <td><label{style}><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
+                <td><label{style}><input class="gr-check-radio gr-checkbox extension_toggle" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''} onchange="toggle_extension(event)" />{html.escape(ext.name)}</label></td>
                 <td>{remote}</td>
                 <td>{ext.branch}</td>
                 <td>{version_link}</td>
@@ -325,6 +328,11 @@ def normalize_git_url(url):
 def install_extension_from_url(dirname, url, branch_name=None):
     check_access()
 
+    if isinstance(dirname, str):
+        dirname = dirname.strip()
+    if isinstance(url, str):
+        url = url.strip()
+
     assert url, 'No URL specified'
 
     if dirname is None or dirname == "":
@@ -563,9 +571,9 @@ def create_ui():
                 available_extensions_table = gr.HTML()
 
                 refresh_available_extensions_button.click(
-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update(), gr.update()]),
                     inputs=[available_extensions_index, hide_tags, sort_column],
-                    outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result, search_extensions_text],
+                    outputs=[available_extensions_index, available_extensions_table, hide_tags, search_extensions_text, install_result],
                 )
 
                 install_extension_button.click(

webui.py

@@ -11,7 +11,7 @@ import json
 from threading import Thread
 from typing import Iterable
 
-from fastapi import FastAPI, Response
+from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
 from packaging import version
@@ -362,11 +362,6 @@ def api_only():
     api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
 
 
-def stop_route(request):
-    shared.state.server_command = "stop"
-    return Response("Stopping.")
-
-
 def webui():
     launch_api = cmd_opts.api
     initialize()
@@ -404,8 +399,6 @@ def webui():
                 "redoc_url": "/redoc",
             },
         )
-        if cmd_opts.add_stop_route:
-            app.add_route("/_stop", stop_route, methods=["POST"])
 
         # after initial launch, disable --autolaunch for subsequent restarts
         cmd_opts.autolaunch = False