Merge branch 'master' into extra-network-info

extensions-builtin/Lora/lora.py

@@ -178,6 +178,7 @@ def load_loras(names, multipliers=None):
 
 
 def lora_forward(module, input, res):
+    input = devices.cond_cast_unet(input)
     if len(loaded_loras) == 0:
         return res
 

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

@@ -89,22 +89,15 @@ function checkBrackets(evt, textArea, counterElt) {
 function setupBracketChecking(id_prompt, id_counter){
     var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
     var counter = gradioApp().getElementById(id_counter)
+
     textarea.addEventListener("input", function(evt){
         checkBrackets(evt, textarea, counter)
     });
 }
 
-var shadowRootLoaded = setInterval(function() {
-    var shadowRoot = document.querySelector('gradio-app').shadowRoot;
-    if(! shadowRoot)  return false;
-
-    var shadowTextArea = shadowRoot.querySelectorAll('#txt2img_prompt > label > textarea');
-    if(shadowTextArea.length < 1)  return false;
-
-    clearInterval(shadowRootLoaded);
-
+onUiLoaded(function(){
     setupBracketChecking('txt2img_prompt', 'txt2img_token_counter')
     setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter')
-    setupBracketChecking('img2img_prompt', 'imgimg_token_counter')
+    setupBracketChecking('img2img_prompt', 'img2img_token_counter')
     setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter')
-}, 1000);
+})
\ No newline at end of file

html/licenses.html

@@ -635,4 +635,30 @@ SOFTWARE.
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
+</pre>
+
+<h2><a href="https://github.com/explosion/curated-transformers/blob/main/LICENSE">Curated transformers</a></h2>
+<small>The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers</small>
+<pre>
+The MIT License (MIT)
+
+Copyright (C) 2021 ExplosionAI GmbH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
 </pre>
\ No newline at end of file

javascript/contextMenus.js

@@ -43,7 +43,7 @@ contextMenuInit = function(){
 
     })
 
-    gradioApp().getRootNode().appendChild(contextMenu)
+    gradioApp().appendChild(contextMenu)
 
     let menuWidth = contextMenu.offsetWidth + 4;
     let menuHeight = contextMenu.offsetHeight + 4;

javascript/edit-attention.js

 function keyupEditAttention(event){
 	let target = event.originalTarget || event.composedPath()[0];
-	if (!target.matches("[id*='_toprow'] textarea.gr-text-input[placeholder]")) return;
+	if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
 	if (! (event.metaKey || event.ctrlKey)) return;
 
 	let isPlus = event.key == "ArrowUp"

javascript/extraNetworks.js

@@ -141,3 +141,39 @@ function extraNetworksShowMetadata(event, text){
     
     event.stopPropagation()
 }
+
+function requestGet(url, data, handler, errorHandler){
+    var xhr = new XMLHttpRequest();
+    var args = Object.keys(data).map(function(k){ return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]) }).join('&')
+    xhr.open("GET", url + "?" + args, true);
+
+    xhr.onreadystatechange = function () {
+        if (xhr.readyState === 4) {
+            if (xhr.status === 200) {
+                try {
+                    var js = JSON.parse(xhr.responseText);
+                    handler(js)
+                } catch (error) {
+                    console.error(error);
+                    errorHandler()
+                }
+            } else{
+                errorHandler()
+            }
+        }
+    };
+    var js = JSON.stringify(data);
+    xhr.send(js);
+}
+
+function extraNetworksRequestMetadata(extraPage, cardName){
+    showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
+
+    requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
+        if(data && data.metadata){
+            extraNetworksShowMetadata(data.metadata)
+        } else{
+            showError()
+        }
+    }, showError)
+}

javascript/hints.js

@@ -18,7 +18,7 @@ titles = {
     "\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",
-    "\u{1f5d1}": "Clear prompt",
+    "\u{1f5d1}\ufe0f": "Clear prompt",
     "\u{1f4cb}": "Apply selected styles to current prompt",
     "\u{1f4d2}": "Paste available values into the field",
     "\u{1f3b4}": "Show extra networks",
@@ -40,8 +40,7 @@ titles = {
     "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
 
     "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
-    "Denoising strength change factor": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
-
+    
     "Skip": "Stop processing current image and continue processing.",
     "Interrupt": "Stop processing images and return any results accumulated so far.",
     "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.",
@@ -71,8 +70,10 @@ titles = {
     "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
     "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
 
-    "Loopback": "Process an image, use it as an input, repeat.",
-    "Loops": "How many times to repeat processing an image and using it as input for the next iteration",
+    "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
+    "Loops": "How many times to process an image. Each output is used as the input of the next loop. If set to 1, behavior will be as if this script were not used.",
+    "Final denoising strength": "The denoising strength for the final loop of each image in the batch.",
+    "Denoising strength curve": "The denoising curve controls the rate of denoising strength change each loop. Aggressive: Most of the change will happen towards the start of the loops. Linear: Change will be constant through all loops. Lazy: Most of the change will happen towards the end of the loops.",
 
     "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both",
     "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both",

javascript/imageviewer.js

@@ -50,7 +50,7 @@ function updateOnBackgroundChange() {
 }
 
 function modalImageSwitch(offset) {
-    var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
+    var allgalleryButtons = gradioApp().querySelectorAll(".gradio-gallery .thumbnail-item")
     var galleryButtons = []
     allgalleryButtons.forEach(function(elem) {
         if (elem.parentElement.offsetParent) {
@@ -59,7 +59,7 @@ function modalImageSwitch(offset) {
     })
 
     if (galleryButtons.length > 1) {
-        var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
+        var allcurrentButtons = gradioApp().querySelectorAll(".gradio-gallery .thumbnail-item.selected")
         var currentButton = null
         allcurrentButtons.forEach(function(elem) {
             if (elem.parentElement.offsetParent) {
@@ -136,37 +136,29 @@ function modalKeyHandler(event) {
     }
 }
 
-function showGalleryImage() {
-    setTimeout(function() {
-        fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain')
-
-        if (fullImg_preview != null) {
-            fullImg_preview.forEach(function function_name(e) {
-                if (e.dataset.modded)
-                    return;
-                e.dataset.modded = true;
-                if(e && e.parentElement.tagName == 'DIV'){
-                    e.style.cursor='pointer'
-                    e.style.userSelect='none'
-
-                    var isFirefox = isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1
-
-                    // For Firefox, listening on click first switched to next image then shows the lightbox.
-                    // If you know how to fix this without switching to mousedown event, please.
-                    // For other browsers the event is click to make it possiblr to drag picture.
-                    var event = isFirefox ? 'mousedown' : 'click'
-
-                    e.addEventListener(event, function (evt) {
-                        if(!opts.js_modal_lightbox || evt.button != 0) return;
-                        modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
-                        evt.preventDefault()
-                        showModal(evt)
-                    }, true);
-                }
-            });
-        }
+function setupImageForLightbox(e) {
+	if (e.dataset.modded)
+		return;
+
+	e.dataset.modded = true;
+	e.style.cursor='pointer'
+	e.style.userSelect='none'
+
+	var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1
+
+	// For Firefox, listening on click first switched to next image then shows the lightbox.
+	// If you know how to fix this without switching to mousedown event, please.
+	// For other browsers the event is click to make it possiblr to drag picture.
+	var event = isFirefox ? 'mousedown' : 'click'
+
+	e.addEventListener(event, function (evt) {
+		if(!opts.js_modal_lightbox || evt.button != 0) return;
+
+		modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
+		evt.preventDefault()
+		showModal(evt)
+	}, true);
 
-    }, 100);
 }
 
 function modalZoomSet(modalImage, enable) {
@@ -199,21 +191,21 @@ function modalTileImageToggle(event) {
 }
 
 function galleryImageHandler(e) {
-    if (e && e.parentElement.tagName == 'BUTTON') {
+    //if (e && e.parentElement.tagName == 'BUTTON') {
         e.onclick = showGalleryImage;
-    }
+    //}
 }
 
 onUiUpdate(function() {
-    fullImg_preview = gradioApp().querySelectorAll('img.w-full')
+    fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
     if (fullImg_preview != null) {
-        fullImg_preview.forEach(galleryImageHandler);
+        fullImg_preview.forEach(setupImageForLightbox);
     }
     updateOnBackgroundChange();
 })
 
 document.addEventListener("DOMContentLoaded", function() {
-    const modalFragment = document.createDocumentFragment();
+    //const modalFragment = document.createDocumentFragment();
     const modal = document.createElement('div')
     modal.onclick = closeModal;
     modal.id = "lightboxModal";
@@ -277,9 +269,9 @@ document.addEventListener("DOMContentLoaded", function() {
 
     modal.appendChild(modalNext)
 
+    gradioApp().appendChild(modal)
 
-    gradioApp().getRootNode().appendChild(modal)
 
-    document.body.appendChild(modalFragment);
+    document.body.appendChild(modal);
 
 });

javascript/progressbar.js

 // code related to showing and updating progressbar shown as the image is being made
 
-
-galleries = {}
-storedGallerySelections = {}
-galleryObservers = {}
-
 function rememberGallerySelection(id_gallery){
-    storedGallerySelections[id_gallery] = getGallerySelectedIndex(id_gallery)
-}
 
-function getGallerySelectedIndex(id_gallery){
-    let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
-    let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
-
-     let currentlySelectedIndex = -1
-     galleryButtons.forEach(function(v, i){ if(v==galleryBtnSelected) { currentlySelectedIndex = i } })
-
-     return currentlySelectedIndex
 }
 
-// this is a workaround for https://github.com/gradio-app/gradio/issues/2984
-function check_gallery(id_gallery){
-    let gallery = gradioApp().getElementById(id_gallery)
-    // if gallery has no change, no need to setting up observer again.
-    if (gallery && galleries[id_gallery] !== gallery){
-        galleries[id_gallery] = gallery;
-        if(galleryObservers[id_gallery]){
-            galleryObservers[id_gallery].disconnect();
-        }
+function getGallerySelectedIndex(id_gallery){
 
-        storedGallerySelections[id_gallery] = -1
-
-        galleryObservers[id_gallery] = new MutationObserver(function (){
-            let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
-            let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
-            let currentlySelectedIndex = getGallerySelectedIndex(id_gallery)
-            prevSelectedIndex = storedGallerySelections[id_gallery]
-            storedGallerySelections[id_gallery] = -1
-
-            if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
-                // automatically re-open previously selected index (if exists)
-                activeElement = gradioApp().activeElement;
-                let scrollX = window.scrollX;
-                let scrollY = window.scrollY;
-
-                galleryButtons[prevSelectedIndex].click();
-                showGalleryImage();
-
-                // When the gallery button is clicked, it gains focus and scrolls itself into view
-                // We need to scroll back to the previous position
-                setTimeout(function (){
-                    window.scrollTo(scrollX, scrollY);
-                }, 50);
-
-                if(activeElement){
-                    // i fought this for about an hour; i don't know why the focus is lost or why this helps recover it
-                    // if someone has a better solution please by all means
-                    setTimeout(function (){
-                        activeElement.focus({
-                            preventScroll: true // Refocus the element that was focused before the gallery was opened without scrolling to it
-                        })
-                    }, 1);
-                }
-            }
-        })
-        galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
-    }
 }
 
-onUiUpdate(function(){
-    check_gallery('txt2img_gallery')
-    check_gallery('img2img_gallery')
-})
-
 function request(url, data, handler, errorHandler){
     var xhr = new XMLHttpRequest();
     var url = url;

javascript/ui.js

@@ -86,7 +86,7 @@ function get_tab_index(tabId){
     var res = 0
 
     gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button').forEach(function(button, i){
-        if(button.className.indexOf('bg-white') != -1)
+        if(button.className.indexOf('selected') != -1)
             res = i
     })
 
@@ -255,7 +255,6 @@ onUiUpdate(function(){
         }
 
         prompt.parentElement.insertBefore(counter, prompt)
-        counter.classList.add("token-counter")
         prompt.parentElement.style.position = "relative"
 
 		promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); }

launch.py

@@ -14,7 +14,7 @@ parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.real
 args, _ = parser.parse_known_args(sys.argv)
 
 script_path = os.path.dirname(__file__)
-data_path = os.getcwd()
+data_path = args.data_dir
 
 dir_repos = "repositories"
 dir_extensions = "extensions"
@@ -231,7 +231,7 @@ def run_extensions_installers(settings_file):
         return
 
     for dirname_extension in list_extensions(settings_file):
-        run_extension_installer(os.path.join(dir_extensions, dirname_extension))
+        run_extension_installer(os.path.join(data_path, dir_extensions, dirname_extension))
 
 
 def prepare_environment():

modules/api/api.py

@@ -18,7 +18,7 @@ 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
+from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights
 from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
@@ -150,6 +150,8 @@ class Api:
         self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
         self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
         self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse)
+        self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
+        self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
         self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList)
 
     def add_api_route(self, path: str, endpoint, **kwargs):
@@ -412,6 +414,16 @@ class Api:
 
         return {}
 
+    def unloadapi(self):
+        unload_model_weights()
+
+        return {}
+
+    def reloadapi(self):
+        reload_model_weights()
+
+        return {}
+
     def skip(self):
         shared.state.skip()
 

modules/generation_parameters_copypaste.py

@@ -401,9 +401,14 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
 
     button.click(
         fn=paste_func,
-        _js=f"recalculate_prompts_{tabname}",
         inputs=[input_comp],
         outputs=[x[0] for x in paste_fields],
     )
+    button.click(
+        fn=None,
+        _js=f"recalculate_prompts_{tabname}",
+        inputs=[],
+        outputs=[],
+    )
 
 

modules/images.py

@@ -645,6 +645,8 @@ Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}
 
 
 def image_data(data):
+    import gradio as gr
+
     try:
         image = Image.open(io.BytesIO(data))
         textinfo, _ = read_info_from_image(image)
@@ -660,7 +662,7 @@ def image_data(data):
     except Exception:
         pass
 
-    return '', None
+    return gr.update(), None
 
 
 def flatten(img, bgcolor):

modules/mac_specific.py

 import torch
+import platform
 from modules import paths
 from modules.sd_hijack_utils import CondFunc
 from packaging import version
@@ -32,6 +33,10 @@ if has_mps:
     # MPS fix for randn in torchsde
     CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps')
 
+    if platform.mac_ver()[0].startswith("13.2."):
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
+        CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760)
+
     if version.parse(torch.__version__) < version.parse("1.13"):
         # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
 
@@ -49,4 +54,6 @@ if has_mps:
         CondFunc('torch.cumsum', cumsum_fix_func, None)
         CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
         CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
-
+    if version.parse(torch.__version__) == version.parse("2.0"):
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/96113
+        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda *args, **kwargs: len(args) == 6)

modules/modelloader.py

@@ -4,7 +4,6 @@ import shutil
 import importlib
 from urllib.parse import urlparse
 
-from basicsr.utils.download_util import load_file_from_url
 from modules import shared
 from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
 from modules.paths import script_path, models_path
@@ -59,6 +58,7 @@ 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, model_path, True, download_name)
                 output.append(dl)
             else:

modules/processing.py

@@ -689,6 +689,22 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image.info["parameters"] = text
                 output_images.append(image)
 
+                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
+                    image_mask = p.mask_for_overlay.convert('RGB')
+                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA')
+
+                    if opts.save_mask:
+                        images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+
+                    if opts.save_mask_composite:
+                        images.save_image(image_mask_composite, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+
+                    if opts.return_mask:
+                        output_images.append(image_mask)
+                    
+                    if opts.return_mask_composite:
+                        output_images.append(image_mask_composite)
+
             del x_samples_ddim
 
             devices.torch_gc()

modules/scripts.py

@@ -239,7 +239,15 @@ def load_scripts():
             elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
                 postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
 
-    for scriptfile in sorted(scripts_list):
+    def orderby(basedir):
+        # 1st webui, 2nd extensions-builtin, 3rd extensions
+        priority = {os.path.join(paths.script_path, "extensions-builtin"):1, paths.script_path:0}
+        for key in priority:
+            if basedir.startswith(key):
+                return priority[key]
+        return 9999
+
+    for scriptfile in sorted(scripts_list, key=lambda x: [orderby(x.basedir), x]):
         try:
             if scriptfile.basedir != paths.script_path:
                 sys.path = [scriptfile.basedir] + sys.path
@@ -513,6 +521,18 @@ def reload_scripts():
     scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
 
 
+def add_classes_to_gradio_component(comp):
+    """
+    this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
+    """
+
+    comp.elem_classes = ["gradio-" + comp.get_block_name(), *(comp.elem_classes or [])]
+
+    if getattr(comp, 'multiselect', False):
+        comp.elem_classes.append('multiselect')
+
+
+
 def IOComponent_init(self, *args, **kwargs):
     if scripts_current is not None:
         scripts_current.before_component(self, **kwargs)
@@ -521,6 +541,8 @@ def IOComponent_init(self, *args, **kwargs):
 
     res = original_IOComponent_init(self, *args, **kwargs)
 
+    add_classes_to_gradio_component(self)
+
     script_callbacks.after_component_callback(self, **kwargs)
 
     if scripts_current is not None:

modules/scripts_postprocessing.py

@@ -109,7 +109,7 @@ class ScriptPostprocessingRunner:
         inputs = []
 
         for script in self.scripts_in_preferred_order():
-            with gr.Box() as group:
+            with gr.Row() as group:
                 self.create_script_ui(script, inputs)
 
             script.group = group

modules/sd_hijack_optimizations.py

@@ -337,7 +337,7 @@ def xformers_attention_forward(self, x, context=None, mask=None):
 
     dtype = q.dtype
     if shared.opts.upcast_attn:
-        q, k = q.float(), k.float()
+        q, k, v = q.float(), k.float(), v.float()
 
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
 
@@ -372,7 +372,7 @@ def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
 
     dtype = q.dtype
     if shared.opts.upcast_attn:
-        q, k = q.float(), k.float()
+        q, k, v = q.float(), k.float(), v.float()
 
     # the output of sdp = (batch, num_heads, seq_len, head_dim)
     hidden_states = torch.nn.functional.scaled_dot_product_attention(

modules/sd_hijack_unet.py

@@ -67,7 +67,7 @@ def hijack_ddpm_edit():
 unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
 CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
-if version.parse(torch.__version__) <= version.parse("1.13.1"):
+if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
     CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
     CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
     CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)

modules/sd_models.py

@@ -178,7 +178,7 @@ def select_checkpoint():
     return checkpoint_info
 
 
-chckpoint_dict_replacements = {
+checkpoint_dict_replacements = {
     'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
     'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
     'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
@@ -186,7 +186,7 @@ chckpoint_dict_replacements = {
 
 
 def transform_checkpoint_dict_key(k):
-    for text, replacement in chckpoint_dict_replacements.items():
+    for text, replacement in checkpoint_dict_replacements.items():
         if k.startswith(text):
             k = replacement + k[len(text):]
 
@@ -494,7 +494,7 @@ def reload_model_weights(sd_model=None, info=None):
     if sd_model is None or checkpoint_config != sd_model.used_config:
         del sd_model
         checkpoints_loaded.clear()
-        load_model(checkpoint_info, already_loaded_state_dict=state_dict, time_taken_to_load_state_dict=timer.records["load weights from disk"])
+        load_model(checkpoint_info, already_loaded_state_dict=state_dict)
         return shared.sd_model
 
     try:
@@ -517,3 +517,23 @@ def reload_model_weights(sd_model=None, info=None):
     print(f"Weights loaded in {timer.summary()}.")
 
     return sd_model
+
+def unload_model_weights(sd_model=None, info=None):
+    from modules import lowvram, devices, sd_hijack
+    timer = Timer()
+
+    if shared.sd_model:
+
+        # shared.sd_model.cond_stage_model.to(devices.cpu)
+        # shared.sd_model.first_stage_model.to(devices.cpu)
+        shared.sd_model.to(devices.cpu)
+        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+        shared.sd_model = None
+        sd_model = None
+        gc.collect()
+        devices.torch_gc()
+        torch.cuda.empty_cache()
+
+    print(f"Unloaded weights {timer.summary()}.")
+
+    return sd_model
\ No newline at end of file

modules/shared.py

@@ -107,7 +107,8 @@ parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS o
 parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
 parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
-parser.add_argument("--gradio-queue", action='store_true', help="Uses gradio queue; experimental option; breaks restart UI button")
+parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
+parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
 parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
 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)
@@ -332,6 +333,8 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
     "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
+    "save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
+    "save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
     "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
     "export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
@@ -454,6 +457,8 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
 
 options_templates.update(options_section(('ui', "User interface"), {
     "return_grid": OptionInfo(True, "Show grid in results for web"),
+    "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
+    "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "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"),

modules/textual_inversion/textual_inversion.py

@@ -152,7 +152,11 @@ class EmbeddingDatabase:
                 name = data.get('name', name)
             else:
                 data = extract_image_data_embed(embed_image)
-                name = data.get('name', name)
+                if data:
+                    name = data.get('name', name)
+                else:
+                    # if data is None, means this is not an embeding, just a preview image
+                    return
         elif ext in ['.BIN', '.PT']:
             data = torch.load(path, map_location="cpu")
         elif ext in ['.SAFETENSORS']:

modules/ui_common.py

@@ -129,8 +129,8 @@ Requested path was: {f}
 
         generation_info = None
         with gr.Column():
-            with gr.Row(elem_id=f"image_buttons_{tabname}"):
-                open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
+            with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
+                open_folder_button = gr.Button(folder_symbol, visible=not shared.cmd_opts.hide_ui_dir_config)
 
                 if tabname != "extras":
                     save = gr.Button('Save', elem_id=f'save_{tabname}')
@@ -160,6 +160,7 @@ Requested path was: {f}
                             _js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
                             inputs=[generation_info, html_info, html_info],
                             outputs=[html_info, html_info],
+                            show_progress=False,
                         )
 
                     save.click(

modules/ui_components.py

 import gradio as gr
 
 
-class ToolButton(gr.Button, gr.components.FormComponent):
-    """Small button with single emoji as text, fits inside gradio forms"""
+class FormComponent:
+    def get_expected_parent(self):
+        return gr.components.Form
 
-    def __init__(self, **kwargs):
-        super().__init__(variant="tool", **kwargs)
 
-    def get_block_name(self):
-        return "button"
+gr.Dropdown.get_expected_parent = FormComponent.get_expected_parent
 
 
-class ToolButtonTop(gr.Button, gr.components.FormComponent):
-    """Small button with single emoji as text, with extra margin at top, fits inside gradio forms"""
+class ToolButton(FormComponent, gr.Button):
+    """Small button with single emoji as text, fits inside gradio forms"""
 
-    def __init__(self, **kwargs):
-        super().__init__(variant="tool-top", **kwargs)
+    def __init__(self, *args, **kwargs):
+        classes = kwargs.pop("elem_classes", [])
+        super().__init__(*args, elem_classes=["tool", *classes], **kwargs)
 
     def get_block_name(self):
         return "button"
 
 
-class FormRow(gr.Row, gr.components.FormComponent):
+class FormRow(FormComponent, gr.Row):
     """Same as gr.Row but fits inside gradio forms"""
 
     def get_block_name(self):
         return "row"
 
 
-class FormGroup(gr.Group, gr.components.FormComponent):
+class FormColumn(FormComponent, gr.Column):
+    """Same as gr.Column but fits inside gradio forms"""
+
+    def get_block_name(self):
+        return "column"
+
+
+class FormGroup(FormComponent, gr.Group):
     """Same as gr.Row but fits inside gradio forms"""
 
     def get_block_name(self):
         return "group"
 
 
-class FormHTML(gr.HTML, gr.components.FormComponent):
+class FormHTML(FormComponent, gr.HTML):
     """Same as gr.HTML but fits inside gradio forms"""
 
     def get_block_name(self):
         return "html"
 
 
-class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
+class FormColorPicker(FormComponent, gr.ColorPicker):
     """Same as gr.ColorPicker but fits inside gradio forms"""
 
     def get_block_name(self):
         return "colorpicker"
 
 
-class DropdownMulti(gr.Dropdown):
+class DropdownMulti(FormComponent, gr.Dropdown):
     """Same as gr.Dropdown but always multiselect"""
     def __init__(self, **kwargs):
         super().__init__(multiselect=True, **kwargs)

modules/ui_extensions.py

 import json
 import os.path
-import shutil
 import sys
 import time
 import traceback
@@ -141,22 +140,20 @@ def install_extension_from_url(dirname, url):
 
     try:
         shutil.rmtree(tmpdir, True)
-
-        repo = git.Repo.clone_from(url, tmpdir)
-        repo.remote().fetch()
-
+        with git.Repo.clone_from(url, tmpdir) as repo:
+            repo.remote().fetch()
+            for submodule in repo.submodules:
+                submodule.update()
         try:
             os.rename(tmpdir, target_dir)
         except OSError as err:
-            # TODO what does this do on windows? I think it'll be a different error code but I don't have a system to check it
-            # Shouldn't cause any new issues at least but we probably want to handle it there too.
             if err.errno == errno.EXDEV:
                 # Cross device link, typical in docker or when tmp/ and extensions/ are on different file systems
                 # Since we can't use a rename, do the slower but more versitile shutil.move()
                 shutil.move(tmpdir, target_dir)
             else:
                 # Something else, not enough free space, permissions, etc.  rethrow it so that it gets handled.
-                raise(err)
+                raise err
 
         import launch
         launch.run_extension_installer(target_dir)
@@ -244,7 +241,7 @@ def refresh_available_extensions_from_data(hide_tags, sort_column):
             hidden += 1
             continue
 
-        install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
+        install_code = f"""<button onclick="install_extension_from_index(this, '{html.escape(url)}')" {"disabled=disabled" if existing else ""} class="lg secondary gradio-button custom-button">{"Install" if not existing else "Installed"}</button>"""
 
         tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
 

modules/ui_extra_networks.py

@@ -22,21 +22,37 @@ def register_page(page):
     allowed_dirs.update(set(sum([x.allowed_directories_for_previews() for x in extra_pages], [])))
 
 
-def add_pages_to_demo(app):
-    def fetch_file(filename: str = ""):
-        from starlette.responses import FileResponse
+def fetch_file(filename: str = ""):
+    from starlette.responses import FileResponse
+
+    if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
+        raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
+
+    ext = os.path.splitext(filename)[1].lower()
+    if ext not in (".png", ".jpg", ".webp"):
+        raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.")
+
+    # would profit from returning 304
+    return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
+
+
+def get_metadata(page: str = "", item: str = ""):
+    from starlette.responses import JSONResponse
 
-        if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
-            raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
+    page = next(iter([x for x in extra_pages if x.name == page]), None)
+    if page is None:
+        return JSONResponse({})
 
-        ext = os.path.splitext(filename)[1].lower()
-        if ext not in (".png", ".jpg", ".webp"):
-            raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.")
+    metadata = page.metadata.get(item)
+    if metadata is None:
+        return JSONResponse({})
 
-        # would profit from returning 304
-        return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
+    return JSONResponse({"metadata": metadata})
 
+
+def add_pages_to_demo(app):
     app.add_api_route("/sd_extra_networks/thumb", fetch_file, methods=["GET"])
+    app.add_api_route("/sd_extra_networks/metadata", get_metadata, methods=["GET"])
 
 
 class ExtraNetworksPage:
@@ -45,6 +61,7 @@ class ExtraNetworksPage:
         self.name = title.lower()
         self.card_page = shared.html("extra-networks-card.html")
         self.allow_negative_prompt = False
+        self.metadata = {}
 
     def refresh(self):
         pass
@@ -66,6 +83,8 @@ class ExtraNetworksPage:
         view = shared.opts.extra_networks_default_view
         items_html = ''
 
+        self.metadata = {}
+
         subdirs = {}
         for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
             for x in glob.glob(os.path.join(parentdir, '**/*'), recursive=True):
@@ -86,12 +105,16 @@ class ExtraNetworksPage:
             subdirs = {"": 1, **subdirs}
 
         subdirs_html = "".join([f"""
-<button class='gr-button gr-button-lg gr-button-secondary{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
+<button class='lg secondary gradio-button custom-button{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
 {html.escape(subdir if subdir!="" else "all")}
 </button>
 """ for subdir in subdirs])
 
         for item in self.list_items():
+            metadata = item.get("metadata")
+            if metadata:
+                self.metadata[item["name"]] = metadata
+
             items_html += self.create_html_for_item(item, tabname)
 
         if items_html == '':
@@ -127,8 +150,7 @@ class ExtraNetworksPage:
         metadata_button = ""
         metadata = item.get("metadata")
         if metadata:
-            metadata_onclick = '"' + html.escape(f"""return extraNetworksShowMetadata(event, {json.dumps(metadata)})""") + '"'
-            metadata_button = f"<div class='metadata-button' title='Show metadata' onclick={metadata_onclick}></div>"
+            metadata_button = f"<div class='metadata-button' title='Show metadata' onclick='extraNetworksRequestMetadata(event, {json.dumps(self.name)}, {json.dumps(item['name'])})'></div>"
 
         args = {
             "preview_html": "style='background-image: url(\"" + html.escape(preview) + "\")'" if preview else '',
@@ -215,6 +237,7 @@ def create_ui(container, button, tabname):
     with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
         for page in ui.stored_extra_pages:
             with gr.Tab(page.title):
+
                 page_elem = gr.HTML(page.create_html(ui.tabname))
                 ui.pages.append(page_elem)
 

requirements.txt

@@ -4,7 +4,7 @@ basicsr
 fonts
 font-roboto
 gfpgan
-gradio==3.16.2
+gradio==3.23
 invisible-watermark
 numpy
 omegaconf

requirements_versions.txt

@@ -3,7 +3,7 @@ transformers==4.25.1
 accelerate==0.12.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.16.2
+gradio==3.23
 numpy==1.23.3
 Pillow==9.4.0
 realesrgan==0.3.0

script.js

 function gradioApp() {
     const elems = document.getElementsByTagName('gradio-app')
-    const gradioShadowRoot = elems.length == 0 ? null : elems[0].shadowRoot
-    return !!gradioShadowRoot ? gradioShadowRoot : document;
+    const elem = elems.length == 0 ? document : elems[0]
+
+    elem.getElementById = function(id){ return document.getElementById(id) }
+    return elem.shadowRoot ? elem.shadowRoot : elem
 }
 
 function get_uiCurrentTab() {

scripts/loopback.py

-import numpy as np
-from tqdm import trange
+import math
 
-import modules.scripts as scripts
 import gradio as gr
-
-from modules import processing, shared, sd_samplers, images
+import modules.scripts as scripts
+from modules import deepbooru, images, processing, shared
 from modules.processing import Processed
-from modules.sd_samplers import samplers
-from modules.shared import opts, cmd_opts, state
-from modules import deepbooru
+from modules.shared import opts, state
 
 
 class Script(scripts.Script):
@@ -20,39 +16,68 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):        
         loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
-        denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, elem_id=self.elem_id("denoising_strength_change_factor"))
+        final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
+        denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
         append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
 
-        return [loops, denoising_strength_change_factor, append_interrogation]
+        return [loops, final_denoising_strength, denoising_curve, append_interrogation]
 
-    def run(self, p, loops, denoising_strength_change_factor, append_interrogation):
+    def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
         processing.fix_seed(p)
         batch_count = p.n_iter
         p.extra_generation_params = {
-            "Denoising strength change factor": denoising_strength_change_factor,
+            "Final denoising strength": final_denoising_strength,
+            "Denoising curve": denoising_curve
         }
 
         p.batch_size = 1
         p.n_iter = 1
 
-        output_images, info = None, None
+        info = None
         initial_seed = None
         initial_info = None
+        initial_denoising_strength = p.denoising_strength
 
         grids = []
         all_images = []
         original_init_image = p.init_images
         original_prompt = p.prompt
+        original_inpainting_fill = p.inpainting_fill
         state.job_count = loops * batch_count
 
         initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
 
-        for n in range(batch_count):
-            history = []
+        def calculate_denoising_strength(loop):
+            strength = initial_denoising_strength
+
+            if loops == 1:
+                return strength
+
+            progress = loop / (loops - 1)
+            match denoising_curve:
+                case "Aggressive":
+                    strength = math.sin((progress) * math.pi * 0.5)
+
+                case "Lazy":
+                    strength = 1 - math.cos((progress) * math.pi * 0.5)
+
+                case _:
+                    strength = progress
+
+            change = (final_denoising_strength - initial_denoising_strength) * strength
+            return initial_denoising_strength + change
+
+        history = []
 
+        for n in range(batch_count):
             # Reset to original init image at the start of each batch
             p.init_images = original_init_image
 
+            # Reset to original denoising strength
+            p.denoising_strength = initial_denoising_strength
+
+            last_image = None
+
             for i in range(loops):
                 p.n_iter = 1
                 p.batch_size = 1
@@ -72,26 +97,46 @@ class Script(scripts.Script):
 
                 processed = processing.process_images(p)
 
+                # Generation cancelled.
+                if state.interrupted:
+                    break
+
                 if initial_seed is None:
                     initial_seed = processed.seed
                     initial_info = processed.info
 
-                init_img = processed.images[0]
-
-                p.init_images = [init_img]
                 p.seed = processed.seed + 1
-                p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1)
-                history.append(processed.images[0])
+                p.denoising_strength = calculate_denoising_strength(i + 1)
+                
+                if state.skipped:
+                    break
 
+                last_image = processed.images[0]
+                p.init_images = [last_image]
+                p.inpainting_fill = 1 # Set "masked content" to "original" for next loop.
+
+                if batch_count == 1:
+                    history.append(last_image)
+                    all_images.append(last_image)
+
+            if batch_count > 1 and not state.skipped and not state.interrupted:
+                history.append(last_image)
+                all_images.append(last_image)
+
+            p.inpainting_fill = original_inpainting_fill
+                
+            if state.interrupted:
+                    break
+
+        if len(history) > 1:
             grid = images.image_grid(history, rows=1)
             if opts.grid_save:
                 images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
 
-            grids.append(grid)
-            all_images += history
-
-        if opts.return_grid:
-            all_images = grids + all_images
+            if opts.return_grid:
+                grids.append(grid)
+                
+        all_images = grids + all_images
 
         processed = Processed(p, all_images, initial_seed, initial_info)
 

scripts/postprocessing_upscale.py

@@ -17,22 +17,24 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
     def ui(self):
         selected_tab = gr.State(value=0)
 
-        with gr.Tabs(elem_id="extras_resize_mode"):
-            with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
-                upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
-
-            with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
-                with FormRow():
-                    upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
-                    upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
-                    upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
-
-        with FormRow():
-            extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
-
-        with FormRow():
-            extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
-            extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
+        with gr.Column():
+            with FormRow():
+                with gr.Tabs(elem_id="extras_resize_mode"):
+                    with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
+
+                    with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
+                        with FormRow():
+                            upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
+                            upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+
+            with FormRow():
+                extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+
+            with FormRow():
+                extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+                extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
 
         tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
         tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])

scripts/xyz_grid.py

@@ -247,7 +247,7 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
 
         state.job = f"{index(ix, iy, iz) + 1} out of {list_size}"
 
-        processed: Processed = cell(x, y, z)
+        processed: Processed = cell(x, y, z, ix, iy, iz)
 
         if processed_result is None:
             # Use our first processed result object as a template container to hold our full results
@@ -558,8 +558,6 @@ class Script(scripts.Script):
         print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})")
         shared.total_tqdm.updateTotal(total_steps)
 
-        grid_infotext = [None]
-
         state.xyz_plot_x = AxisInfo(x_opt, xs)
         state.xyz_plot_y = AxisInfo(y_opt, ys)
         state.xyz_plot_z = AxisInfo(z_opt, zs)
@@ -588,7 +586,9 @@ class Script(scripts.Script):
             else:
                 second_axes_processed = 'y'
 
-        def cell(x, y, z):
+        grid_infotext = [None] * (1 + len(zs))
+
+        def cell(x, y, z, ix, iy, iz):
             if shared.state.interrupted:
                 return Processed(p, [], p.seed, "")
 
@@ -600,7 +600,9 @@ class Script(scripts.Script):
 
             res = process_images(pc)
 
-            if grid_infotext[0] is None:
+            # Sets subgrid infotexts
+            subgrid_index = 1 + iz
+            if grid_infotext[subgrid_index] is None and ix == 0 and iy == 0:
                 pc.extra_generation_params = copy(pc.extra_generation_params)
                 pc.extra_generation_params['Script'] = self.title()
 
@@ -616,6 +618,12 @@ class Script(scripts.Script):
                     if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
                         pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])
 
+                grid_infotext[subgrid_index] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
+
+            # Sets main grid infotext
+            if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0:
+                pc.extra_generation_params = copy(pc.extra_generation_params)
+
                 if z_opt.label != 'Nothing':
                     pc.extra_generation_params["Z Type"] = z_opt.label
                     pc.extra_generation_params["Z Values"] = z_values
@@ -650,6 +658,9 @@ class Script(scripts.Script):
 
         z_count = len(zs)
 
+        # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
+        processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]
+
         if not include_lone_images:
             # Don't need sub-images anymore, drop from list:
             processed.images = processed.images[:z_count+1]

webui.py

@@ -240,7 +240,7 @@ def webui():
         shared.demo = modules.ui.create_ui()
         startup_timer.record("create ui")
 
-        if cmd_opts.gradio_queue:
+        if not cmd_opts.no_gradio_queue:
             shared.demo.queue(64)
 
         gradio_auth_creds = []
@@ -262,6 +262,9 @@ def webui():
             inbrowser=cmd_opts.autolaunch,
             prevent_thread_lock=True
         )
+        for dep in shared.demo.dependencies:
+            dep['show_progress'] = False  # disable gradio css animation on component update
+
         # after initial launch, disable --autolaunch for subsequent restarts
         cmd_opts.autolaunch = False