Merge branch 'master' into hot-reload-javascript

.github/workflows/on_pull_request.yaml

@@ -22,6 +22,12 @@ jobs:
         uses: actions/setup-python@v3
         with:
           python-version: 3.10.6
+      - uses: actions/cache@v2
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-
       - name: Install PyLint
         run: | 
           python -m pip install --upgrade pip

artists.csv

@@ -523,7 +523,6 @@ Affandi,0.7170285,nudity
 Diane Arbus,0.655138,digipa-high-impact
 Joseph Ducreux,0.65247905,digipa-high-impact
 Berthe Morisot,0.7165984,fineart
-Hilma AF Klint,0.71643853,scribbles
 Hilma af Klint,0.71643853,scribbles
 Filippino Lippi,0.7163017,fineart
 Leonid Afremov,0.7163005,fineart
@@ -738,14 +737,12 @@ Abraham Mignon,0.60605425,fineart
 Albert Bloch,0.69573116,nudity
 Charles Dana Gibson,0.67155975,fineart
 Alexandre-Évariste Fragonard,0.6507174,fineart
-Alexandre-Évariste Fragonard,0.6507174,fineart
 Ernst Fuchs,0.6953538,nudity
 Alfredo Jaar,0.6952965,digipa-high-impact
 Judy Chicago,0.6952246,weird
 Frans van Mieris the Younger,0.6951849,fineart
 Aertgen van Leyden,0.6951305,fineart
 Emily Carr,0.69512105,fineart
-Frances Macdonald,0.6950408,scribbles
 Frances MacDonald,0.6950408,scribbles
 Hannah Höch,0.69495845,scribbles
 Gillis Rombouts,0.58770025,fineart
@@ -895,7 +892,6 @@ Richard McGuire,0.6820089,scribbles
 Anni Albers,0.65708244,digipa-high-impact
 Aleksey Savrasov,0.65207493,fineart
 Wayne Barlowe,0.6537874,fineart
-Giorgio De Chirico,0.6815907,fineart
 Giorgio de Chirico,0.6815907,fineart
 Ernest Procter,0.6815795,fineart
 Adriaen Brouwer,0.6815058,fineart
@@ -1241,7 +1237,6 @@ Betty Churcher,0.65387225,fineart
 Claes Corneliszoon Moeyaert,0.65386075,fineart
 David Bomberg,0.6537477,fineart
 Abraham Bosschaert,0.6535562,fineart
-Giuseppe De Nittis,0.65354455,fineart
 Giuseppe de Nittis,0.65354455,fineart
 John La Farge,0.65342575,fineart
 Frits Thaulow,0.65341854,fineart
@@ -1522,7 +1517,6 @@ Gertrude Harvey,0.5903887,fineart
 Grant Wood,0.6266253,fineart
 Fyodor Vasilyev,0.5234919,digipa-med-impact
 Cagnaccio di San Pietro,0.6261671,fineart
-Cagnaccio Di San Pietro,0.6261671,fineart
 Doris Boulton-Maude,0.62593174,fineart
 Adolf Hirémy-Hirschl,0.5946784,fineart
 Harold von Schmidt,0.6256755,fineart
@@ -2411,7 +2405,6 @@ Hermann Feierabend,0.5346168,digipa-high-impact
 Antonio Donghi,0.4610982,digipa-low-impact
 Adonna Khare,0.4858036,digipa-med-impact
 James Stokoe,0.5015107,digipa-med-impact
-Art & Language,0.5341332,digipa-high-impact
 Agustín Fernández,0.53403986,fineart
 Germán Londoño,0.5338712,fineart
 Emmanuelle Moureaux,0.5335641,digipa-high-impact

javascript/edit-attention.js

@@ -9,9 +9,38 @@ addEventListener('keydown', (event) => {
 	let minus = "ArrowDown"
 	if (event.key != plus && event.key != minus) return;
 
-	selectionStart = target.selectionStart;
-	selectionEnd = target.selectionEnd;
-	if(selectionStart == selectionEnd) return;
+	let selectionStart = target.selectionStart;
+	let selectionEnd = target.selectionEnd;
+	// If the user hasn't selected anything, let's select their current parenthesis block
+	if (selectionStart === selectionEnd) {
+		// Find opening parenthesis around current cursor
+		const before = target.value.substring(0, selectionStart);
+		let beforeParen = before.lastIndexOf("(");
+		if (beforeParen == -1) return;
+		let beforeParenClose = before.lastIndexOf(")");
+		while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
+			beforeParen = before.lastIndexOf("(", beforeParen - 1);
+			beforeParenClose = before.lastIndexOf(")", beforeParenClose - 1);
+		}
+
+		// Find closing parenthesis around current cursor
+		const after = target.value.substring(selectionStart);
+		let afterParen = after.indexOf(")");
+		if (afterParen == -1) return;
+		let afterParenOpen = after.indexOf("(");
+		while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
+			afterParen = after.indexOf(")", afterParen + 1);
+			afterParenOpen = after.indexOf("(", afterParenOpen + 1);
+		}
+		if (beforeParen === -1 || afterParen === -1) return;
+
+		// Set the selection to the text between the parenthesis
+		const parenContent = target.value.substring(beforeParen + 1, selectionStart + afterParen);
+		const lastColon = parenContent.lastIndexOf(":");
+		selectionStart = beforeParen + 1;
+		selectionEnd = selectionStart + lastColon;
+		target.setSelectionRange(selectionStart, selectionEnd);
+	}
 
 	event.preventDefault();
 

javascript/imageMaskFix.js

@@ -31,8 +31,8 @@ function imageMaskResize() {
 
     wrapper.style.width = `${wW}px`;
     wrapper.style.height = `${wH}px`;
-    wrapper.style.left = `${(w-wW)/2}px`;
-    wrapper.style.top = `${(h-wH)/2}px`;
+    wrapper.style.left = `0px`;
+    wrapper.style.top = `0px`;
 
     canvases.forEach( c => {
         c.style.width = c.style.height = '';
@@ -42,4 +42,4 @@ function imageMaskResize() {
     });
  }
   
- onUiUpdate(() => imageMaskResize());
\ No newline at end of file
+ onUiUpdate(() => imageMaskResize());

javascript/imageviewer.js

@@ -31,7 +31,7 @@ function updateOnBackgroundChange() {
             }
         })
 
-        if (modalImage.src != currentButton.children[0].src) {
+        if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
             modalImage.src = currentButton.children[0].src;
             if (modalImage.style.display === 'none') {
                 modal.style.setProperty('background-image', `url(${modalImage.src})`)
@@ -116,6 +116,7 @@ function showGalleryImage() {
                 e.dataset.modded = true;
                 if(e && e.parentElement.tagName == 'DIV'){
                     e.style.cursor='pointer'
+                    e.style.userSelect='none'
                     e.addEventListener('click', function (evt) {
                         if(!opts.js_modal_lightbox) return;
                         modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)

javascript/localization.js

+
+// localization = {} -- the dict with translations is created by the backend
+
+ignore_ids_for_localization={
+    setting_sd_hypernetwork: 'OPTION',
+    setting_sd_model_checkpoint: 'OPTION',
+    setting_realesrgan_enabled_models: 'OPTION',
+    modelmerger_primary_model_name: 'OPTION',
+    modelmerger_secondary_model_name: 'OPTION',
+    modelmerger_tertiary_model_name: 'OPTION',
+    train_embedding: 'OPTION',
+    train_hypernetwork: 'OPTION',
+    txt2img_style_index: 'OPTION',
+    txt2img_style2_index: 'OPTION',
+    img2img_style_index: 'OPTION',
+    img2img_style2_index: 'OPTION',
+    setting_random_artist_categories: 'SPAN',
+    setting_face_restoration_model: 'SPAN',
+    setting_realesrgan_enabled_models: 'SPAN',
+    extras_upscaler_1: 'SPAN',
+    extras_upscaler_2: 'SPAN',
+}
+
+re_num = /^[\.\d]+$/
+re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u
+
+original_lines = {}
+translated_lines = {}
+
+function textNodesUnder(el){
+    var n, a=[], walk=document.createTreeWalker(el,NodeFilter.SHOW_TEXT,null,false);
+    while(n=walk.nextNode()) a.push(n);
+    return a;
+}
+
+function canBeTranslated(node, text){
+    if(! text) return false;
+    if(! node.parentElement) return false;
+
+    parentType = node.parentElement.nodeName
+    if(parentType=='SCRIPT' || parentType=='STYLE' || parentType=='TEXTAREA') return false;
+
+    if (parentType=='OPTION' || parentType=='SPAN'){
+        pnode = node
+        for(var level=0; level<4; level++){
+            pnode = pnode.parentElement
+            if(! pnode) break;
+
+            if(ignore_ids_for_localization[pnode.id] == parentType) return false;
+        }
+    }
+
+    if(re_num.test(text)) return false;
+    if(re_emoji.test(text)) return false;
+    return true
+}
+
+function getTranslation(text){
+    if(! text) return undefined
+
+    if(translated_lines[text] === undefined){
+        original_lines[text] = 1
+    }
+
+    tl = localization[text]
+    if(tl !== undefined){
+        translated_lines[tl] = 1
+    }
+
+    return tl
+}
+
+function processTextNode(node){
+    text = node.textContent.trim()
+
+    if(! canBeTranslated(node, text)) return
+
+    tl = getTranslation(text)
+    if(tl !== undefined){
+        node.textContent = tl
+    }
+}
+
+function processNode(node){
+    if(node.nodeType == 3){
+        processTextNode(node)
+        return
+    }
+
+    if(node.title){
+        tl = getTranslation(node.title)
+        if(tl !== undefined){
+            node.title = tl
+        }
+    }
+
+    if(node.placeholder){
+        tl = getTranslation(node.placeholder)
+        if(tl !== undefined){
+            node.placeholder = tl
+        }
+    }
+
+    textNodesUnder(node).forEach(function(node){
+        processTextNode(node)
+    })
+}
+
+function dumpTranslations(){
+    dumped = {}
+
+    Object.keys(original_lines).forEach(function(text){
+        if(dumped[text] !== undefined)  return
+
+        dumped[text] = localization[text] || text
+    })
+
+    return dumped
+}
+
+onUiUpdate(function(m){
+    m.forEach(function(mutation){
+        mutation.addedNodes.forEach(function(node){
+            processNode(node)
+        })
+    });
+})
+
+
+document.addEventListener("DOMContentLoaded", function() {
+    processNode(gradioApp())
+})
+
+function download_localization() {
+    text = JSON.stringify(dumpTranslations(), null, 4)
+
+    var element = document.createElement('a');
+    element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
+    element.setAttribute('download', "localization.json");
+    element.style.display = 'none';
+    document.body.appendChild(element);
+
+    element.click();
+
+    document.body.removeChild(element);
+}

javascript/progressbar.js

@@ -34,7 +34,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
                 preview.style.height = gallery.clientHeight + "px"
 
 				//only watch gallery if there is a generation process going on
-                check_gallery(id_gallery);    
+                check_gallery(id_gallery);
 
                 var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
                 if(!progressDiv){
@@ -72,9 +72,17 @@ function check_gallery(id_gallery){
             let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
             let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
             if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
-                //automatically re-open previously selected index (if exists)
+                // automatically re-open previously selected index (if exists)
+                activeElement = gradioApp().activeElement;
+
                 galleryButtons[prevSelectedIndex].click();
-		showGalleryImage();
+                showGalleryImage();
+
+                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 somenoe has a better solution please by all means
+                    setTimeout(function() { activeElement.focus() }, 1);
+                }
             }
         })
         galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })

javascript/ui.js

 // various functions for interation with ui.py not large enough to warrant putting them in separate files
 
+function set_theme(theme){
+    gradioURL = window.location.href
+    if (!gradioURL.includes('?__theme=')) {
+      window.location.replace(gradioURL + '?__theme=' + theme);
+    }
+}
+
 function selected_gallery_index(){
     var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
     var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')

launch.py

@@ -86,7 +86,24 @@ def git_clone(url, dir, name, commithash=None):
     if commithash is not None:
         run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
 
-
+        
+def version_check(commit):
+    try:
+        import requests
+        commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
+        if commit != "<none>" and commits['commit']['sha'] != commit:
+            print("--------------------------------------------------------")
+            print("| You are not up to date with the most recent release. |")
+            print("| Consider running `git pull` to update.               |")
+            print("--------------------------------------------------------")
+        elif commits['commit']['sha'] == commit:
+            print("You are up to date with the most recent release.")
+        else:
+            print("Not a git clone, can't perform version check.")
+    except Exception as e:
+        print("versipm check failed",e)
+
+        
 def prepare_enviroment():
     torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
@@ -94,6 +111,15 @@ def prepare_enviroment():
 
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
+    deepdanbooru_package = os.environ.get('DEEPDANBOORU_PACKAGE', "git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26")
+
+    xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
+
+    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/CompVis/stable-diffusion.git")
+    taming_transformers_repo = os.environ.get('TAMING_REANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
+    k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
+    codeformer_repo = os.environ.get('CODEFORMET_REPO', 'https://github.com/sczhou/CodeFormer.git')
+    blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
     taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
@@ -101,13 +127,14 @@ def prepare_enviroment():
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
-    args = shlex.split(commandline_args)
+    sys.argv += shlex.split(commandline_args)
 
-    args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
-    args, reinstall_xformers = extract_arg(args, '--reinstall-xformers')
-    xformers = '--xformers' in args
-    deepdanbooru = '--deepdanbooru' in args
-    ngrok = '--ngrok' in args
+    sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
+    sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
+    sys.argv, update_check = extract_arg(sys.argv, '--update-check')
+    xformers = '--xformers' in sys.argv
+    deepdanbooru = '--deepdanbooru' in sys.argv
+    ngrok = '--ngrok' in sys.argv
 
     try:
         commit = run(f"{git} rev-parse HEAD").strip()
@@ -116,7 +143,7 @@ def prepare_enviroment():
 
     print(f"Python {sys.version}")
     print(f"Commit hash: {commit}")
-
+    
     if not is_installed("torch") or not is_installed("torchvision"):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
 
@@ -131,32 +158,33 @@ def prepare_enviroment():
 
     if (not is_installed("xformers") or reinstall_xformers) and xformers and platform.python_version().startswith("3.10"):
         if platform.system() == "Windows":
-            run_pip("install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+            run_pip(f"install -U -I --no-deps {xformers_windows_package}", "xformers")
         elif platform.system() == "Linux":
             run_pip("install xformers", "xformers")
 
     if not is_installed("deepdanbooru") and deepdanbooru:
-        run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
+        run_pip(f"install {deepdanbooru_package}#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
 
     if not is_installed("pyngrok") and ngrok:
         run_pip("install pyngrok", "ngrok")
 
     os.makedirs(dir_repos, exist_ok=True)
 
-    git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
-    git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
-    git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
-    git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
-    git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
+    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
+    git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
+    git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
+    git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
+    git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
 
     if not is_installed("lpips"):
         run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
 
     run_pip(f"install -r {requirements_file}", "requirements for Web UI")
 
-    sys.argv += args
-
-    if "--exit" in args:
+    if update_check:
+        version_check(commit)
+    
+    if "--exit" in sys.argv:
         print("Exiting because of --exit argument")
         exit(0)
 

modules/api/api.py

+from modules.api.processing import StableDiffusionProcessingAPI
+from modules.processing import StableDiffusionProcessingTxt2Img, process_images
+from modules.sd_samplers import all_samplers
+from modules.extras import run_pnginfo
+import modules.shared as shared
+import uvicorn
+from fastapi import Body, APIRouter, HTTPException
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field, Json
+import json
+import io
+import base64
+
+sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
+
+class TextToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: Json
+    info: Json
+
+
+class Api:
+    def __init__(self, app, queue_lock):
+        self.router = APIRouter()
+        self.app = app
+        self.queue_lock = queue_lock
+        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
+
+    def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
+        sampler_index = sampler_to_index(txt2imgreq.sampler_index)
+        
+        if sampler_index is None:
+            raise HTTPException(status_code=404, detail="Sampler not found") 
+        
+        populate = txt2imgreq.copy(update={ # Override __init__ params
+            "sd_model": shared.sd_model, 
+            "sampler_index": sampler_index[0],
+            "do_not_save_samples": True,
+            "do_not_save_grid": True
+            }
+        )
+        p = StableDiffusionProcessingTxt2Img(**vars(populate))
+        # Override object param
+        with self.queue_lock:
+            processed = process_images(p)
+        
+        b64images = []
+        for i in processed.images:
+            buffer = io.BytesIO()
+            i.save(buffer, format="png")
+            b64images.append(base64.b64encode(buffer.getvalue()))
+
+        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
+        
+        
+
+    def img2imgapi(self):
+        raise NotImplementedError
+
+    def extrasapi(self):
+        raise NotImplementedError
+
+    def pnginfoapi(self):
+        raise NotImplementedError
+
+    def launch(self, server_name, port):
+        self.app.include_router(self.router)
+        uvicorn.run(self.app, host=server_name, port=port)

modules/api/processing.py

+from inflection import underscore
+from typing import Any, Dict, Optional
+from pydantic import BaseModel, Field, create_model
+from modules.processing import StableDiffusionProcessingTxt2Img
+import inspect
+
+
+API_NOT_ALLOWED = [
+    "self",
+    "kwargs",
+    "sd_model",
+    "outpath_samples",
+    "outpath_grids",
+    "sampler_index",
+    "do_not_save_samples",
+    "do_not_save_grid",
+    "extra_generation_params",
+    "overlay_images",
+    "do_not_reload_embeddings",
+    "seed_enable_extras",
+    "prompt_for_display",
+    "sampler_noise_scheduler_override",
+    "ddim_discretize"
+]
+
+class ModelDef(BaseModel):
+    """Assistance Class for Pydantic Dynamic Model Generation"""
+
+    field: str
+    field_alias: str
+    field_type: Any
+    field_value: Any
+
+
+class PydanticModelGenerator:
+    """
+    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
+    source_data is a snapshot of the default values produced by the class
+    params are the names of the actual keys required by __init__
+    """
+
+    def __init__(
+        self,
+        model_name: str = None,
+        class_instance = None,
+        additional_fields = None,
+    ):
+        def field_type_generator(k, v):
+            # field_type = str if not overrides.get(k) else overrides[k]["type"]
+            # print(k, v.annotation, v.default)
+            field_type = v.annotation
+            
+            return Optional[field_type]
+        
+        def merge_class_params(class_):
+            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
+            parameters = {}
+            for classes in all_classes:
+                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
+            return parameters
+            
+                
+        self._model_name = model_name
+        self._class_data = merge_class_params(class_instance)
+        self._model_def = [
+            ModelDef(
+                field=underscore(k),
+                field_alias=k,
+                field_type=field_type_generator(k, v),
+                field_value=v.default
+            )
+            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
+        ]
+        
+        for fields in additional_fields:
+            self._model_def.append(ModelDef(
+                field=underscore(fields["key"]), 
+                field_alias=fields["key"], 
+                field_type=fields["type"],
+                field_value=fields["default"]))
+
+    def generate_model(self):
+        """
+        Creates a pydantic BaseModel
+        from the json and overrides provided at initialization
+        """
+        fields = {
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
+        }
+        DynamicModel = create_model(self._model_name, **fields)
+        DynamicModel.__config__.allow_population_by_field_name = True
+        DynamicModel.__config__.allow_mutation = True
+        return DynamicModel
+    
+StableDiffusionProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingTxt2Img", 
+    StableDiffusionProcessingTxt2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()
\ No newline at end of file

modules/deepbooru.py

@@ -157,8 +157,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
     # sort by reverse by likelihood and normal for alpha, and format tag text as requested
     unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
     for weight, tag in unsorted_tags_in_theshold:
-        # note: tag_outformat will still have a colon if include_ranks is True
-        tag_outformat = tag.replace(':', ' ')
+        tag_outformat = tag
         if use_spaces:
             tag_outformat = tag_outformat.replace('_', ' ')
         if use_escape:

modules/extras.py

@@ -20,26 +20,40 @@ import gradio as gr
 cached_images = {}
 
 
-def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
     devices.torch_gc()
 
     imageArr = []
     # Also keep track of original file names
     imageNameArr = []
-
+    outputs = []
+    
     if extras_mode == 1:
         #convert file to pillow image
         for img in image_folder:
             image = Image.open(img)
             imageArr.append(image)
             imageNameArr.append(os.path.splitext(img.orig_name)[0])
+    elif extras_mode == 2:
+        assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
+
+        if input_dir == '':
+            return outputs, "Please select an input directory.", ''
+        image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+        for img in image_list:
+            image = Image.open(img)
+            imageArr.append(image)
+            imageNameArr.append(img)
     else:
         imageArr.append(image)
         imageNameArr.append(None)
 
-    outpath = opts.outdir_samples or opts.outdir_extras_samples
+    if extras_mode == 2 and output_dir != '':
+        outpath = output_dir
+    else:
+        outpath = opts.outdir_samples or opts.outdir_extras_samples
 
-    outputs = []
+    
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
@@ -77,7 +91,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility,
             def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
                 small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
                 pixels = tuple(np.array(small).flatten().tolist())
-                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
+                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight, 
+                       resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
 
                 c = cached_images.get(key)
                 if c is None:
@@ -112,7 +127,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility,
             image.info = existing_pnginfo
             image.info["extras"] = info
 
-        outputs.append(image)
+        if extras_mode != 2 or show_extras_results :
+            outputs.append(image)
 
     devices.torch_gc()
 
@@ -160,11 +176,14 @@ def run_pnginfo(image):
 
 
 def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
-    def weighted_sum(theta0, theta1, theta2, alpha):
+    def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
-    def add_difference(theta0, theta1, theta2, alpha):
-        return theta0 + (theta1 - theta2) * alpha
+    def get_difference(theta1, theta2):
+        return theta1 - theta2
+
+    def add_difference(theta0, theta1_2_diff, alpha):
+        return theta0 + (alpha * theta1_2_diff)
 
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
@@ -183,23 +202,31 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
         teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
         theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
     else:
+        teritary_model = None
         theta_2 = None
 
     theta_funcs = {
-        "Weighted sum": weighted_sum,
-        "Add difference": add_difference,
+        "Weighted sum": (None, weighted_sum),
+        "Add difference": (get_difference, add_difference),
     }
-    theta_func = theta_funcs[interp_method]
+    theta_func1, theta_func2 = theta_funcs[interp_method]
 
     print(f"Merging...")
 
+    if theta_func1:
+        for key in tqdm.tqdm(theta_1.keys()):
+            if 'model' in key:
+                if key in theta_2:
+                    t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
+                    theta_1[key] = theta_func1(theta_1[key], t2)
+                else:
+                    theta_1[key] = torch.zeros_like(theta_1[key])
+    del theta_2, teritary_model
+
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
-            t2 = (theta_2 or {}).get(key)
-            if t2 is None:
-                t2 = torch.zeros_like(theta_0[key])
 
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier)
+            theta_0[key] = theta_func2(theta_0[key], theta_1[key], multiplier)
 
             if save_as_half:
                 theta_0[key] = theta_0[key].half()

modules/hypernetworks/hypernetwork.py

@@ -196,7 +196,7 @@ def stack_conds(conds):
 
     return torch.stack(conds)
 
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -225,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)

modules/images_history.py

 import os
 import shutil
-
+import sys
 
 def traverse_all_files(output_dir, image_list, curr_dir=None):
     curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
@@ -24,10 +24,14 @@ def traverse_all_files(output_dir, image_list, curr_dir=None):
 
 def get_recent_images(dir_name, page_index, step, image_index, tabname):
     page_index = int(page_index)
-    f_list = os.listdir(dir_name)
     image_list = []
-    image_list = traverse_all_files(dir_name, image_list)
-    image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    if not os.path.exists(dir_name):
+        pass
+    elif os.path.isdir(dir_name):
+        image_list = traverse_all_files(dir_name, image_list)
+        image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    else:
+        print(f'ERROR: "{dir_name}" is not a directory. Check the path in the settings.', file=sys.stderr)
     num = 48 if tabname != "extras" else 12
     max_page_index = len(image_list) // num + 1
     page_index = max_page_index if page_index == -1 else page_index + step
@@ -105,10 +109,8 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         dir_name = opts.outdir_img2img_samples
     elif tabname == "extras":
         dir_name = opts.outdir_extras_samples
-    d = dir_name.split("/")
-    dir_name = "/" if dir_name.startswith("/") else d[0]
-    for p in d[1:]:
-        dir_name = os.path.join(dir_name, p)
+    else:
+        return
     with gr.Row():
         renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
         first_page = gr.Button('First Page')

modules/interrogate.py

@@ -123,7 +123,7 @@ class InterrogateModels:
 
         return caption[0]
 
-    def interrogate(self, pil_image, include_ranks=False):
+    def interrogate(self, pil_image):
         res = None
 
         try:
@@ -156,10 +156,10 @@ class InterrogateModels:
                 for name, topn, items in self.categories:
                     matches = self.rank(image_features, items, top_count=topn)
                     for match, score in matches:
-                        if include_ranks:
-                            res += ", " + match
+                        if shared.opts.interrogate_return_ranks:
+                            res += f", ({match}:{score/100:.3f})"
                         else:
-                            res += f", ({match}:{score})"
+                            res += ", " + match
 
         except Exception:
             print(f"Error interrogating", file=sys.stderr)

modules/localization.py

+import json
+import os
+import sys
+import traceback
+
+localizations = {}
+
+
+def list_localizations(dirname):
+    localizations.clear()
+
+    for file in os.listdir(dirname):
+        fn, ext = os.path.splitext(file)
+        if ext.lower() != ".json":
+            continue
+
+        localizations[fn] = os.path.join(dirname, file)
+
+
+def localization_js(current_localization_name):
+    fn = localizations.get(current_localization_name, None)
+    data = {}
+    if fn is not None:
+        try:
+            with open(fn, "r", encoding="utf8") as file:
+                data = json.load(file)
+        except Exception:
+            print(f"Error loading localization from {fn}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    return f"var localization = {json.dumps(data)}\n"

modules/ngrok.py

 from pyngrok import ngrok, conf, exception
 
 
-def connect(token, port):
+def connect(token, port, region):
     if token == None:
         token = 'None'
-    conf.get_default().auth_token = token
+    config = conf.PyngrokConfig(
+        auth_token=token, region=region
+    )
     try:
-        public_url = ngrok.connect(port).public_url
+        public_url = ngrok.connect(port, pyngrok_config=config).public_url
     except exception.PyngrokNgrokError:
         print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')

modules/processing.py

@@ -9,6 +9,7 @@ from PIL import Image, ImageFilter, ImageOps
 import random
 import cv2
 from skimage import exposure
+from typing import Any, Dict, List, Optional
 
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram
@@ -51,9 +52,15 @@ def get_correct_sampler(p):
         return sd_samplers.samplers
     elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
         return sd_samplers.samplers_for_img2img
+    elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
+        return sd_samplers.samplers
 
-class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
+class StableDiffusionProcessing():
+    """
+    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
+    
+    """
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str="", styles: List[str]=None, seed: int=-1, subseed: int=-1, subseed_strength: float=0, seed_resize_from_h: int=-1, seed_resize_from_w: int=-1, seed_enable_extras: bool=True, sampler_index: int=0, batch_size: int=1, n_iter: int=1, steps:int =50, cfg_scale:float=7.0, width:int=512, height:int=512, restore_faces:bool=False, tiling:bool=False, do_not_save_samples:bool=False, do_not_save_grid:bool=False, extra_generation_params: Dict[Any,Any]=None, overlay_images: Any=None, negative_prompt: str=None, eta: float =None, do_not_reload_embeddings: bool=False, denoising_strength: float = 0, ddim_discretize: str = "uniform", s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -80,15 +87,16 @@ class StableDiffusionProcessing:
         self.extra_generation_params: dict = extra_generation_params or {}
         self.overlay_images = overlay_images
         self.eta = eta
+        self.do_not_reload_embeddings = do_not_reload_embeddings
         self.paste_to = None
         self.color_corrections = None
         self.denoising_strength: float = 0
         self.sampler_noise_scheduler_override = None
         self.ddim_discretize = opts.ddim_discretize
-        self.s_churn = opts.s_churn
-        self.s_tmin = opts.s_tmin
-        self.s_tmax = float('inf')  # not representable as a standard ui option
-        self.s_noise = opts.s_noise
+        self.s_churn = s_churn or opts.s_churn
+        self.s_tmin = s_tmin or opts.s_tmin
+        self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
+        self.s_noise = s_noise or opts.s_noise
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -96,6 +104,7 @@ class StableDiffusionProcessing:
             self.seed_resize_from_h = 0
             self.seed_resize_from_w = 0
 
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -333,12 +342,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     seed = get_fixed_seed(p.seed)
     subseed = get_fixed_seed(p.subseed)
 
-    if p.outpath_samples is not None:
-        os.makedirs(p.outpath_samples, exist_ok=True)
-
-    if p.outpath_grids is not None:
-        os.makedirs(p.outpath_grids, exist_ok=True)
-
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
     modules.sd_hijack.model_hijack.clear_comments()
 
@@ -364,7 +367,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     def infotext(iteration=0, position_in_batch=0):
         return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
 
-    if os.path.exists(cmd_opts.embeddings_dir):
+    if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     infotexts = []
@@ -407,12 +410,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             with devices.autocast():
                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
 
-            if state.interrupted or state.skipped:
-
-                # if we are interrupted, sample returns just noise
-                # use the image collected previously in sampler loop
-                samples_ddim = shared.state.current_latent
-
             samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@@ -502,7 +499,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
+    def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.denoising_strength = denoising_strength
@@ -728,4 +725,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         del x
         devices.torch_gc()
 
-        return samples
+        return samples
\ No newline at end of file

modules/scripts.py

@@ -58,6 +58,9 @@ def load_scripts(basedir):
     for filename in sorted(os.listdir(basedir)):
         path = os.path.join(basedir, filename)
 
+        if os.path.splitext(path)[1].lower() != '.py':
+            continue
+
         if not os.path.isfile(path):
             continue
 
@@ -93,6 +96,7 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
 class ScriptRunner:
     def __init__(self):
         self.scripts = []
+        self.titles = []
 
     def setup_ui(self, is_img2img):
         for script_class, path in scripts_data:
@@ -104,9 +108,10 @@ class ScriptRunner:
 
             self.scripts.append(script)
 
-        titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
+        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
 
-        dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index")
+        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
+        dropdown.save_to_config = True
         inputs = [dropdown]
 
         for script in self.scripts:
@@ -136,6 +141,15 @@ class ScriptRunner:
 
             return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
 
+        def init_field(title):
+            if title == 'None':
+                return
+            script_index = self.titles.index(title)
+            script = self.scripts[script_index]
+            for i in range(script.args_from, script.args_to):
+                inputs[i].visible = True
+
+        dropdown.init_field = init_field
         dropdown.change(
             fn=select_script,
             inputs=[dropdown],

modules/sd_hijack_optimizations.py

@@ -181,7 +181,7 @@ def einsum_op_cuda(q, k, v):
     mem_free_torch = mem_reserved - mem_active
     mem_free_total = mem_free_cuda + mem_free_torch
     # Divide factor of safety as there's copying and fragmentation
-    return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
+    return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
 
 def einsum_op(q, k, v):
     if q.device.type == 'cuda':
@@ -296,10 +296,16 @@ def xformers_attnblock_forward(self, x):
     try:
         h_ = x
         h_ = self.norm(h_)
-        q1 = self.q(h_).contiguous()
-        k1 = self.k(h_).contiguous()
-        v = self.v(h_).contiguous()
-        out = xformers.ops.memory_efficient_attention(q1, k1, v)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+        b, c, h, w = q.shape
+        q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+        q = q.contiguous()
+        k = k.contiguous()
+        v = v.contiguous()
+        out = xformers.ops.memory_efficient_attention(q, k, v)
+        out = rearrange(out, 'b (h w) c -> b c h w', h=h)
         out = self.proj_out(out)
         return x + out
     except NotImplementedError:

modules/sd_models.py

@@ -122,11 +122,33 @@ def select_checkpoint():
     return checkpoint_info
 
 
+chckpoint_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.',
+}
+
+
+def transform_checkpoint_dict_key(k):
+    for text, replacement in chckpoint_dict_replacements.items():
+        if k.startswith(text):
+            k = replacement + k[len(text):]
+
+    return k
+
+
 def get_state_dict_from_checkpoint(pl_sd):
     if "state_dict" in pl_sd:
-        return pl_sd["state_dict"]
+        pl_sd = pl_sd["state_dict"]
+
+    sd = {}
+    for k, v in pl_sd.items():
+        new_key = transform_checkpoint_dict_key(k)
+
+        if new_key is not None:
+            sd[new_key] = v
 
-    return pl_sd
+    return sd
 
 
 def load_model_weights(model, checkpoint_info):
@@ -141,7 +163,7 @@ def load_model_weights(model, checkpoint_info):
             print(f"Global Step: {pl_sd['global_step']}")
 
         sd = get_state_dict_from_checkpoint(pl_sd)
-        model.load_state_dict(sd, strict=False)
+        missing, extra = model.load_state_dict(sd, strict=False)
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)

modules/sd_samplers.py

@@ -98,25 +98,8 @@ def store_latent(decoded):
             shared.state.current_image = sample_to_image(decoded)
 
 
-
-def extended_tdqm(sequence, *args, desc=None, **kwargs):
-    state.sampling_steps = len(sequence)
-    state.sampling_step = 0
-
-    seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
-    for x in seq:
-        if state.interrupted or state.skipped:
-            break
-
-        yield x
-
-        state.sampling_step += 1
-        shared.total_tqdm.update()
-
-
-ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
-ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
+class InterruptedException(BaseException):
+    pass
 
 
 class VanillaStableDiffusionSampler:
@@ -128,14 +111,32 @@ class VanillaStableDiffusionSampler:
         self.init_latent = None
         self.sampler_noises = None
         self.step = 0
+        self.stop_at = None
         self.eta = None
         self.default_eta = 0.0
         self.config = None
+        self.last_latent = None
 
     def number_of_needed_noises(self, p):
         return 0
 
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except InterruptedException:
+            return self.last_latent
+
     def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+        if state.interrupted or state.skipped:
+            raise InterruptedException
+
+        if self.stop_at is not None and self.step > self.stop_at:
+            raise InterruptedException
+
+
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
 
@@ -159,11 +160,16 @@ class VanillaStableDiffusionSampler:
         res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
 
         if self.mask is not None:
-            store_latent(self.init_latent * self.mask + self.nmask * res[1])
+            self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
         else:
-            store_latent(res[1])
+            self.last_latent = res[1]
+
+        store_latent(self.last_latent)
 
         self.step += 1
+        state.sampling_step = self.step
+        shared.total_tqdm.update()
+
         return res
 
     def initialize(self, p):
@@ -192,7 +198,7 @@ class VanillaStableDiffusionSampler:
         self.init_latent = x
         self.step = 0
 
-        samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
+        samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
 
         return samples
 
@@ -206,9 +212,9 @@ class VanillaStableDiffusionSampler:
 
         # existing code fails with certain step counts, like 9
         try:
-            samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
+            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
         except Exception:
-            samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
+            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
 
         return samples_ddim
 
@@ -223,6 +229,9 @@ class CFGDenoiser(torch.nn.Module):
         self.step = 0
 
     def forward(self, x, sigma, uncond, cond, cond_scale):
+        if state.interrupted or state.skipped:
+            raise InterruptedException
+
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
 
@@ -268,25 +277,6 @@ class CFGDenoiser(torch.nn.Module):
         return denoised
 
 
-def extended_trange(sampler, count, *args, **kwargs):
-    state.sampling_steps = count
-    state.sampling_step = 0
-
-    seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
-    for x in seq:
-        if state.interrupted or state.skipped:
-            break
-
-        if sampler.stop_at is not None and x > sampler.stop_at:
-            break
-
-        yield x
-
-        state.sampling_step += 1
-        shared.total_tqdm.update()
-
-
 class TorchHijack:
     def __init__(self, kdiff_sampler):
         self.kdiff_sampler = kdiff_sampler
@@ -314,9 +304,28 @@ class KDiffusionSampler:
         self.eta = None
         self.default_eta = 1.0
         self.config = None
+        self.last_latent = None
 
     def callback_state(self, d):
-        store_latent(d["denoised"])
+        step = d['i']
+        latent = d["denoised"]
+        store_latent(latent)
+        self.last_latent = latent
+
+        if self.stop_at is not None and step > self.stop_at:
+            raise InterruptedException
+
+        state.sampling_step = step
+        shared.total_tqdm.update()
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except InterruptedException:
+            return self.last_latent
 
     def number_of_needed_noises(self, p):
         return p.steps
@@ -339,9 +348,6 @@ class KDiffusionSampler:
         self.sampler_noise_index = 0
         self.eta = p.eta or opts.eta_ancestral
 
-        if hasattr(k_diffusion.sampling, 'trange'):
-            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
-
         if self.sampler_noises is not None:
             k_diffusion.sampling.torch = TorchHijack(self)
 
@@ -383,8 +389,9 @@ class KDiffusionSampler:
 
         self.model_wrap_cfg.init_latent = x
 
-        return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
+        return samples
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
         steps = steps or p.steps
@@ -406,6 +413,8 @@ class KDiffusionSampler:
                 extra_params_kwargs['n'] = steps
         else:
             extra_params_kwargs['sigmas'] = sigmas
-        samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
         return samples
 

modules/shared.py

@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models
+from modules import sd_samplers, sd_models, localization
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -31,6 +31,7 @@ parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
 parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
+parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
@@ -40,6 +41,7 @@ parser.add_argument("--unload-gfpgan", action='store_true', help="does not do an
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
+parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
@@ -68,14 +70,26 @@ parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image upload
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
+parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-
+parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
+parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 
 cmd_opts = parser.parse_args()
+restricted_opts = [
+    "samples_filename_pattern",
+    "outdir_samples",
+    "outdir_txt2img_samples",
+    "outdir_img2img_samples",
+    "outdir_extras_samples",
+    "outdir_grids",
+    "outdir_txt2img_grids",
+    "outdir_save",
+]
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
@@ -92,7 +106,6 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
-
 def reload_hypernetworks():
     global hypernetworks
 
@@ -140,6 +153,8 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
 
+localization.list_localizations(cmd_opts.localizations_dir)
+
 
 def realesrgan_models_names():
     import modules.realesrgan_model
@@ -280,11 +295,13 @@ options_templates.update(options_section(('ui', "User interface"), {
     "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(False, "Add model name to generation information"),
+    "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
+    'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {

modules/styles.py

@@ -45,7 +45,7 @@ class StyleDatabase:
         if not os.path.exists(path):
             return
 
-        with open(path, "r", encoding="utf8", newline='') as file:
+        with open(path, "r", encoding="utf-8-sig", newline='') as file:
             reader = csv.DictReader(file)
             for row in reader:
                 # Support loading old CSV format with "name, text"-columns
@@ -79,7 +79,7 @@ class StyleDatabase:
     def save_styles(self, path: str) -> None:
         # Write to temporary file first, so we don't nuke the file if something goes wrong
         fd, temp_path = tempfile.mkstemp(".csv")
-        with os.fdopen(fd, "w", encoding="utf8", newline='') as file:
+        with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
             # _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
             # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
             writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)

modules/textual_inversion/textual_inversion.py

@@ -137,6 +137,7 @@ class EmbeddingDatabase:
                 continue
 
         print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
+        print("Embeddings:", ', '.join(self.word_embeddings.keys()))
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
@@ -296,6 +297,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 sd_model=shared.sd_model,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
+                do_not_reload_embeddings=True,
             )
 
             if preview_from_txt2img:

requirements.txt

@@ -23,3 +23,4 @@ resize-right
 torchdiffeq
 kornia
 lark
+inflection

requirements_versions.txt

@@ -22,3 +22,4 @@ resize-right==0.0.2
 torchdiffeq==0.2.3
 kornia==0.6.7
 lark==1.1.2
+inflection==0.5.1

script.js

@@ -21,20 +21,20 @@ function onUiTabChange(callback){
     uiTabChangeCallbacks.push(callback)
 }
 
-function runCallback(x){
+function runCallback(x, m){
     try {
-        x()
+        x(m)
     } catch (e) {
         (console.error || console.log).call(console, e.message, e);
     }
 }
-function executeCallbacks(queue) {
-    queue.forEach(runCallback)
+function executeCallbacks(queue, m) {
+    queue.forEach(function(x){runCallback(x, m)})
 }
 
 document.addEventListener("DOMContentLoaded", function() {
     var mutationObserver = new MutationObserver(function(m){
-        executeCallbacks(uiUpdateCallbacks);
+        executeCallbacks(uiUpdateCallbacks, m);
         const newTab = get_uiCurrentTab();
         if ( newTab && ( newTab !== uiCurrentTab ) ) {
             uiCurrentTab = newTab;

scripts/xy_grid.py

@@ -233,6 +233,21 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_
     return processed_result
 
 
+class SharedSettingsStackHelper(object):
+    def __enter__(self):
+        self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
+        self.hypernetwork = opts.sd_hypernetwork
+        self.model = shared.sd_model
+  
+    def __exit__(self, exc_type, exc_value, tb):
+        modules.sd_models.reload_model_weights(self.model)
+
+        hypernetwork.load_hypernetwork(self.hypernetwork)
+        hypernetwork.apply_strength()
+
+        opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
+
+
 re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
 re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
 
@@ -267,9 +282,6 @@ class Script(scripts.Script):
         if not opts.return_grid:
             p.batch_size = 1
 
-
-        CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
-
         def process_axis(opt, vals):
             if opt.label == 'Nothing':
                 return [0]
@@ -367,27 +379,19 @@ class Script(scripts.Script):
 
             return process_images(pc)
 
-        processed = draw_xy_grid(
-            p,
-            xs=xs,
-            ys=ys,
-            x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
-            y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
-            cell=cell,
-            draw_legend=draw_legend,
-            include_lone_images=include_lone_images
-        )
+        with SharedSettingsStackHelper():
+            processed = draw_xy_grid(
+                p,
+                xs=xs,
+                ys=ys,
+                x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
+                y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
+                cell=cell,
+                draw_legend=draw_legend,
+                include_lone_images=include_lone_images
+            )
 
         if opts.grid_save:
             images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p)
 
-        # restore checkpoint in case it was changed by axes
-        modules.sd_models.reload_model_weights(shared.sd_model)
-
-        hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
-        hypernetwork.apply_strength()
-
-
-        opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers
-
         return processed

style.css

@@ -478,7 +478,7 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork{
+#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
     max-width: 2.5em;
     min-width: 2.5em;
     height: 2.4em;

webui.bat

@@ -33,7 +33,7 @@ goto :launch
 :skip_venv
 
 :launch
-%PYTHON% launch.py
+%PYTHON% launch.py %*
 pause
 exit /b
 

webui.py

@@ -4,7 +4,7 @@ import time
 import importlib
 import signal
 import threading
-
+from fastapi import FastAPI
 from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
@@ -31,7 +31,6 @@ from modules.paths import script_path
 from modules.shared import cmd_opts
 import modules.hypernetworks.hypernetwork
 
-
 queue_lock = threading.Lock()
 
 
@@ -87,10 +86,6 @@ def initialize():
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
 
-
-def webui():
-    initialize()
-    
     # make the program just exit at ctrl+c without waiting for anything
     def sigint_handler(sig, frame):
         print(f'Interrupted with signal {sig} in {frame}')
@@ -98,10 +93,37 @@ def webui():
 
     signal.signal(signal.SIGINT, sigint_handler)
 
+
+def create_api(app):
+    from modules.api.api import Api
+    api = Api(app, queue_lock)
+    return api
+
+def wait_on_server(demo=None):
     while 1:
+        time.sleep(0.5)
+        if demo and getattr(demo, 'do_restart', False):
+            time.sleep(0.5)
+            demo.close()
+            time.sleep(0.5)
+            break
+
+def api_only():
+    initialize()
+
+    app = FastAPI()
+    app.add_middleware(GZipMiddleware, minimum_size=1000)
+    api = create_api(app)
+
+    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 webui(launch_api=False):
+    initialize()
+
+    while 1:
         demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
-        
+
         app, local_url, share_url = demo.launch(
             share=cmd_opts.share,
             server_name="0.0.0.0" if cmd_opts.listen else None,
@@ -111,17 +133,14 @@ def webui():
             inbrowser=cmd_opts.autolaunch,
             prevent_thread_lock=True
         )
-        
+
         app.add_middleware(GZipMiddleware, minimum_size=1000)
 
-        while 1:
-            time.sleep(0.5)
-            if getattr(demo, 'do_restart', False):
-                time.sleep(0.5)
-                demo.close()
-                time.sleep(0.5)
-                break
+        if (launch_api):
+            create_api(app)
 
+        wait_on_server(demo)
+        
         sd_samplers.set_samplers()
 
         print('Reloading Custom Scripts')
@@ -133,5 +152,10 @@ def webui():
         print('Restarting Gradio')
 
 
+
+task = []
 if __name__ == "__main__":
-    webui()
+    if cmd_opts.nowebui:
+        api_only()
+    else:
+        webui(cmd_opts.api)
\ No newline at end of file

webui.sh

@@ -138,4 +138,4 @@ fi
 printf "\n%s\n" "${delimiter}"
 printf "Launching launch.py..."
 printf "\n%s\n" "${delimiter}"
-"${python_cmd}" "${LAUNCH_SCRIPT}"
+"${python_cmd}" "${LAUNCH_SCRIPT}" "$@"