Merge branch 'master' into hn-activation

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -44,7 +44,7 @@ body:
     id: commit
     attributes:
       label: Commit where the problem happens
-      description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
+      description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
     validations:
       required: true
   - type: dropdown

.gitignore

@@ -29,4 +29,5 @@ notification.mp3
 /textual_inversion
 .vscode
 /extensions
-
+/test/stdout.txt
+/test/stderr.txt

CODEOWNERS

@@ -1 +1,13 @@
 *       @AUTOMATIC1111
+/localizations/ar_AR.json   @xmodar @blackneoo
+/localizations/de_DE.json   @LunixWasTaken
+/localizations/es_ES.json   @innovaciones
+/localizations/fr_FR.json   @tumbly
+/localizations/it_IT.json   @EugenioBuffo
+/localizations/ja_JP.json   @yuuki76
+/localizations/ko_KR.json   @36DB
+/localizations/pt_BR.json   @M-art-ucci
+/localizations/ru_RU.json   @kabachuha
+/localizations/tr_TR.json   @camenduru
+/localizations/zh_CN.json   @dtlnor @bgluminous
+/localizations/zh_TW.json   @benlisquare

javascript/extensions.js

@@ -0,0 +1,35 @@
+
+function extensions_apply(_, _){
+    disable = []
+    update = []
+    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
+        if(x.name.startsWith("enable_") && ! x.checked)
+            disable.push(x.name.substr(7))
+
+        if(x.name.startsWith("update_") && x.checked)
+            update.push(x.name.substr(7))
+    })
+
+    restart_reload()
+
+    return [JSON.stringify(disable), JSON.stringify(update)]
+}
+
+function extensions_check(){
+    gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
+        x.innerHTML = "Loading..."
+    })
+
+    return []
+}
+
+function install_extension_from_index(button, url){
+    button.disabled = "disabled"
+    button.value = "Installing..."
+
+    textarea = gradioApp().querySelector('#extension_to_install textarea')
+    textarea.value = url
+	textarea.dispatchEvent(new Event("input", { bubbles: true }))
+
+    gradioApp().querySelector('#install_extension_button').click()
+}

javascript/hints.js

@@ -75,6 +75,7 @@ titles = {
     "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.",
 
     "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
+    "Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.",
 
     "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).",
 

javascript/imageviewer.js

@@ -13,6 +13,15 @@ function showModal(event) {
     }
     lb.style.display = "block";
     lb.focus()
+
+    const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
+    const tabImg2Img = gradioApp().getElementById("tab_img2img")
+    // show the save button in modal only on txt2img or img2img tabs
+    if (tabTxt2Img.style.display != "none" || tabImg2Img.style.display != "none") {
+        gradioApp().getElementById("modal_save").style.display = "inline"
+    } else {
+        gradioApp().getElementById("modal_save").style.display = "none"
+    }
     event.stopPropagation()
 }
 
@@ -81,6 +90,25 @@ function modalImageSwitch(offset) {
     }
 }
 
+function saveImage(){
+    const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
+    const tabImg2Img = gradioApp().getElementById("tab_img2img")
+    const saveTxt2Img = "save_txt2img"
+    const saveImg2Img = "save_img2img"
+    if (tabTxt2Img.style.display != "none") {
+        gradioApp().getElementById(saveTxt2Img).click()
+    } else if (tabImg2Img.style.display != "none") {
+        gradioApp().getElementById(saveImg2Img).click()
+    } else {
+        console.error("missing implementation for saving modal of this type")
+    }
+}
+
+function modalSaveImage(event) {
+    saveImage()
+    event.stopPropagation()
+}
+
 function modalNextImage(event) {
     modalImageSwitch(1)
     event.stopPropagation()
@@ -93,6 +121,9 @@ function modalPrevImage(event) {
 
 function modalKeyHandler(event) {
     switch (event.key) {
+        case "s":
+            saveImage()
+            break;
         case "ArrowLeft":
             modalPrevImage(event)
             break;
@@ -198,6 +229,14 @@ document.addEventListener("DOMContentLoaded", function() {
     modalTileImage.title = "Preview tiling";
     modalControls.appendChild(modalTileImage)
 
+    const modalSave = document.createElement("span")
+    modalSave.className = "modalSave cursor"
+    modalSave.id = "modal_save"
+    modalSave.innerHTML = "🖫"
+    modalSave.addEventListener("click", modalSaveImage, true)
+    modalSave.title = "Save Image(s)"
+    modalControls.appendChild(modalSave)
+
     const modalClose = document.createElement('span')
     modalClose.className = 'modalClose cursor';
     modalClose.innerHTML = '×'

javascript/progressbar.js

@@ -3,8 +3,21 @@ global_progressbars = {}
 galleries = {}
 galleryObservers = {}
 
+// this tracks laumnches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
+timeoutIds = {}
+
 function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
-    var progressbar = gradioApp().getElementById(id_progressbar)
+    // gradio 3.8's enlightened approach allows them to create two nested div elements inside each other with same id
+    // every time you use gr.HTML(elem_id='xxx'), so we handle this here
+    var progressbar = gradioApp().querySelector("#"+id_progressbar+" #"+id_progressbar)
+    var progressbarParent
+    if(progressbar){
+        progressbarParent = gradioApp().querySelector("#"+id_progressbar)
+    } else{
+        progressbar = gradioApp().getElementById(id_progressbar)
+        progressbarParent = null
+    }
+
     var skip = id_skip ? gradioApp().getElementById(id_skip) : null
     var interrupt = gradioApp().getElementById(id_interrupt)
     
@@ -26,18 +39,26 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
 	    global_progressbars[id_progressbar] = progressbar
 
         var mutationObserver = new MutationObserver(function(m){
+            if(timeoutIds[id_part]) return;
+
             preview = gradioApp().getElementById(id_preview)
             gallery = gradioApp().getElementById(id_gallery)
 
             if(preview != null && gallery != null){
                 preview.style.width = gallery.clientWidth + "px"
                 preview.style.height = gallery.clientHeight + "px"
+                if(progressbarParent) progressbar.style.width = progressbarParent.clientWidth + "px"
 
 				//only watch gallery if there is a generation process going on
                 check_gallery(id_gallery);
 
                 var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
-                if(!progressDiv){
+                if(progressDiv){
+                    timeoutIds[id_part] = window.setTimeout(function() {
+                        timeoutIds[id_part] = null
+                        requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt)
+                    }, 500)
+                } else{
                     if (skip) {
                         skip.style.display = "none"
                     }
@@ -47,13 +68,10 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
                     if (galleryObservers[id_gallery]) {
                         galleryObservers[id_gallery].disconnect();
                         galleries[id_gallery] = null;
-                    }    
+                    }
                 }
-
-
             }
 
-            window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
         });
         mutationObserver.observe( progressbar, { childList:true, subtree:true })
 	}

javascript/ui.js

@@ -45,14 +45,14 @@ function switch_to_txt2img(){
     return args_to_array(arguments);
 }
 
-function switch_to_img2img_img2img(){
+function switch_to_img2img(){
     gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
     gradioApp().getElementById('mode_img2img').querySelectorAll('button')[0].click();
 
     return args_to_array(arguments);
 }
 
-function switch_to_img2img_inpaint(){
+function switch_to_inpaint(){
     gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
     gradioApp().getElementById('mode_img2img').querySelectorAll('button')[1].click();
 
@@ -65,26 +65,6 @@ function switch_to_extras(){
     return args_to_array(arguments);
 }
 
-function extract_image_from_gallery_txt2img(gallery){
-    switch_to_txt2img()
-    return extract_image_from_gallery(gallery);
-}
-
-function extract_image_from_gallery_img2img(gallery){
-    switch_to_img2img_img2img()
-    return extract_image_from_gallery(gallery);
-}
-
-function extract_image_from_gallery_inpaint(gallery){
-    switch_to_img2img_inpaint()
-    return extract_image_from_gallery(gallery);
-}
-
-function extract_image_from_gallery_extras(gallery){
-    switch_to_extras()
-    return extract_image_from_gallery(gallery);
-}
-
 function get_tab_index(tabId){
     var res = 0
 

launch.py

@@ -7,6 +7,7 @@ import shlex
 import platform
 
 dir_repos = "repositories"
+dir_extensions = "extensions"
 python = sys.executable
 git = os.environ.get('GIT', "git")
 index_url = os.environ.get('INDEX_URL', "")
@@ -16,11 +17,11 @@ def extract_arg(args, name):
     return [x for x in args if x != name], name in args
 
 
-def run(command, desc=None, errdesc=None):
+def run(command, desc=None, errdesc=None, custom_env=None):
     if desc is not None:
         print(desc)
 
-    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
+    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
 
     if result.returncode != 0:
 
@@ -101,9 +102,27 @@ def version_check(commit):
         else:
             print("Not a git clone, can't perform version check.")
     except Exception as e:
-        print("versipm check failed",e)
+        print("version check failed", e)
+
+
+def run_extensions_installers():
+    if not os.path.isdir(dir_extensions):
+        return
+
+    for dirname_extension in os.listdir(dir_extensions):
+        path_installer = os.path.join(dir_extensions, dirname_extension, "install.py")
+        if not os.path.isfile(path_installer):
+            continue
+
+        try:
+            env = os.environ.copy()
+            env['PYTHONPATH'] = os.path.abspath(".")
+
+            print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {dirname_extension}", custom_env=env))
+        except Exception as e:
+            print(e, file=sys.stderr)
+
 
-        
 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")
@@ -128,10 +147,12 @@ def prepare_enviroment():
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
     sys.argv += shlex.split(commandline_args)
+    test_argv = [x for x in sys.argv if x != '--tests']
 
     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')
+    sys.argv, run_tests = extract_arg(sys.argv, '--tests')
     xformers = '--xformers' in sys.argv
     deepdanbooru = '--deepdanbooru' in sys.argv
     ngrok = '--ngrok' in sys.argv
@@ -187,6 +208,8 @@ def prepare_enviroment():
 
     run_pip(f"install -r {requirements_file}", "requirements for Web UI")
 
+    run_extensions_installers()
+
     if update_check:
         version_check(commit)
     
@@ -194,6 +217,26 @@ def prepare_enviroment():
         print("Exiting because of --exit argument")
         exit(0)
 
+    if run_tests:
+        tests(test_argv)
+        exit(0)
+
+
+def tests(argv):
+    if "--api" not in argv:
+        argv.append("--api")
+
+    print(f"Launching Web UI in another process for testing with arguments: {' '.join(argv[1:])}")
+
+    with open('test/stdout.txt', "w", encoding="utf8") as stdout, open('test/stderr.txt', "w", encoding="utf8") as stderr:
+        proc = subprocess.Popen([sys.executable, *argv], stdout=stdout, stderr=stderr)
+
+    import test.server_poll
+    test.server_poll.run_tests()
+
+    print(f"Stopping Web UI process with id {proc.pid}")
+    proc.kill()
+
 
 def start_webui():
     print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")

localizations/ar_AR.json

@@ -1,45 +1,33 @@
 {
   "rtl": true,
-  "⤡": "⤡",
-  "⊞": "⊞",
-  "×": "×",
-  "❮": "❮",
-  "❯": "❯",
-  "Loading...": "جار التحميل...",
-  "view": "معاينة",
-  "api": "api",
-  "•": "•",
-  "built with gradio": "مبني باستخدام Gradio",
-  "Stable Diffusion checkpoint": "نماذج الانتشار المستقر",
-  "txt2img": "نص لصورة",
-  "img2img": "صورة لصورة",
-  "Extras": "الإضافات",
-  "PNG Info": "معلومات PNG",
-  "Checkpoint Merger": "دمج النماذج",
-  "Train": "التدريب",
-  "Create aesthetic embedding": "Create aesthetic embedding",
-  "Image Browser": "مستعرض الصور",
-  "Settings": "الإعدادات",
-  "Prompt": "الموجه",
-  "Negative prompt": "الموجه السلبي",
-  "Run": "تشغيل",
-  "Skip": "تخطي",
-  "Interrupt": "إيقاف",
-  "Generate": "إنشاء",
-  "Style 1": "نمط 1",
-  "Style 2": "نمط 2",
-  "Label": "الوسم",
-  "File": "ملف",
-  "Drop File Here": "اسحب الملف هنا",
-  "-": "-",
-  "or": "أو",
-  "Click to Upload": "انقر للتحميل",
-  "Image": "صورة",
-  "Check progress": "تحقق من التقدم",
-  "Check progress (first)": "تحقق من التقدم (الأول)",
-  "Sampling Steps": "خطوات أخذ العينة",
-  "Sampling method": "نظام أخذ العينات",
+  "Loading...": "لحظة...",
+  "view": "اعرض ",
+  "api": "واجهة البرمجة",
+  "built with gradio": "مبني باستخدام gradio",
+  "Stable Diffusion checkpoint": "أوزان نموذج الإنتشار المسقر",
+  "txt2img": "نص إلى صورة",
+  "Prompt": "الطلب",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "الطلب (لبدء الإنتاج Ctrl+Enter أو Alt+Enter اضغط)",
+  "Negative prompt": "عكس الطلب",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "عكس الطلب (لبدء الإنتاج Ctrl+Enter أو Alt+Enter اضغط)",
+  "Add a random artist to the prompt.": "أضف فنان عشوائي للطلب",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "اقرأ عوامل الإنتاج من الطلب أو من الإنتاج السابق إذا كان الطلب فارغا",
+  "Save style": "احتفظ بالطلب وعكسه كإضافة",
+  "Apply selected styles to current prompt": "ألحق الإضافات المحددة إلى الطلب وعكسه",
+  "Generate": "أنتج",
+  "Skip": "تخطى",
+  "Stop processing current image and continue processing.": "لا تكمل خطوات هذة الحزمة وانتقل إلى الحزمة التالية",
+  "Interrupt": "توقف",
+  "Stop processing images and return any results accumulated so far.": "توقف عن الإنتاج واعرض ما تم إلى الآن",
+  "Style 1": "الإضافة 1",
+  "Style to apply; styles have components for both positive and negative prompts and apply to both": "الإضافات (styles) عبارة عن كلمات تتكرر كثيرا يتم إلحاقها بالطلب وعكسه عند الرغبة",
+  "Style 2": "الإضافة 2",
+  "Do not do anything special": "لا يغير شيئا",
+  "Sampling Steps": "عدد الخطوات",
+  "Sampling method": "أسلوب الخطو",
+  "Which algorithm to use to produce the image": "Sampler: اسم نظام تحديد طريقة تغيير المسافات بين الخطوات",
   "Euler a": "Euler a",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral: طريقة مبدعة يمكن أن تنتج صور مختلفة على حسب عدد الخطوات، لا تتغير بعد 30-40 خطوة",
   "Euler": "Euler",
   "LMS": "LMS",
   "Heun": "Heun",
@@ -51,406 +39,480 @@
   "DPM2 Karras": "DPM2 Karras",
   "DPM2 a Karras": "DPM2 a Karras",
   "DDIM": "DDIM",
+  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models: الأفضل في الإنتاج الجزئي",
   "PLMS": "PLMS",
   "Width": "العرض",
-  "Height": "الارتفاع",
-  "Restore faces": "ترميم الوجوه",
-  "Tiling": "تبليط",
-  "Highres. fix": "إصلاح الصور عالية الدقة",
-  "Firstpass width": "عرض المرور الأول",
-  "Firstpass height": "ارتفاع المرور الأول",
-  "Denoising strength": "قوة تقليل الضوضاء",
-  "Batch count": "عدد الدُفعات",
-  "Batch size": "حجم الدفعة",
-  "CFG Scale": "مقياس التقارب من الموجه (CFG)",
+  "Height": "الإرتفاع",
+  "Restore faces": "تحسين الوجوه",
+  "Tiling": "ترصيف",
+  "Produce an image that can be tiled.": "أنتج صور يمكن ترصيفها بجانب بعضها كالبلاط",
+  "Highres. fix": "إصلاح الدقة العالية",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "أنتج صورة بدقة منخفضة ثم قم برفع الدقة فيما بعد لمنع التشوهات التي تحصل عندما تكون الدقة المطلوبة كبيرة",
+  "Firstpass width": "العرض الأولي",
+  "Firstpass height": "الإرتفاع الأولي",
+  "Denoising strength": "المدى",
+  "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: حدد مدى الإبتعاد عن الصورة (عدد الخطوات الفعلي = عدد الخطوات * المدى)",
+  "Batch count": "عدد الحزم",
+  "How many batches of images to create": "يتم إنتاج الصور على دفعات، كل دفعة فيها حزمة من الصور",
+  "Batch size": "حجم الحزمة",
+  "How many image to create in a single batch": "Batch size: إنتاج حزمة صور أسرع من إنتاجهم فرادى، حدد عدد الصور في كل حزمة",
+  "CFG Scale": "التركيز",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "CFG scale: يحدد مقدار التركيز على تلبية الطلب وتجنب عكسه، كلما زاد قل الإبداع",
   "Seed": "البذرة",
-  "Extra": "إضافي",
-  "Variation seed": "تباين البذرة",
-  "Variation strength": "قوة التباين",
-  "Resize seed from width": "تغيير حجم البذرة من العرض",
-  "Resize seed from height": "تغيير حجم البذرة من الارتفاع",
-  "Open for Clip Aesthetic!": "Open for Clip Aesthetic!",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Seed: رقم طبيعي عشوائي يسمح بإعادة إنتاج نفس الصورة إذا توافقت قيم العوامل الأخرى",
+  "Set seed to -1, which will cause a new random number to be used every time": "استخدم بذرة جديدة في كل مرة (نرمز لهذا الخيار بجعل قيمة البذرة 1-)",
+  "Reuse seed from last generation, mostly useful if it was randomed": "أعد استخدام البذرة من الإنتاج السابق",
+  "Extra": "مزج",
+  "Variation seed": "بذرة الممزوج",
+  "Seed of a different picture to be mixed into the generation.": "Variation seed: بذرة صورة أخرى ليتم مزجها مع الصورة الحالية",
+  "Variation strength": "أثر الممزوج",
+  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Variation seed strength: مقدار أثر الصورة المدمجة على النتيجة النهائية (0: لا أثر، 1: أثر كامل ما عدا عند استخدام أسلوب خطو سلفي Ancestral)",
+  "Resize seed from width": "عرض الممزوج",
+  "Resize seed from height": "إرتفاع الممزوج",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Seed resize from: حدد دقة صورة الممزوج (0: نفس دقة الإنتاج)",
+  "Open for Clip Aesthetic!": "تضمين تجميلي",
   "▼": "▼",
-  "Aesthetic weight": "Aesthetic weight",
-  "Aesthetic steps": "Aesthetic steps",
-  "Aesthetic learning rate": "Aesthetic learning rate",
-  "Slerp interpolation": "Slerp interpolation",
-  "Aesthetic imgs embedding": "Aesthetic imgs embedding",
-  "None": "لايوجد",
-  "Aesthetic text for imgs": "Aesthetic text for imgs",
-  "Slerp angle": "Slerp angle",
-  "Is negative text": "Is negative text",
-  "Script": "سكريبت",
-  "Prompt matrix": "مصفوفة الموجهات",
-  "Prompts from file or textbox": "موجهات من ملف أو مربع النص",
-  "X/Y plot": "الرسم البياني X/Y",
-  "Put variable parts at start of prompt": "ضع الأجزاء المتغيرة في بداية الموجه",
-  "Show Textbox": "إظهار مربع النص",
-  "File with inputs": "ملف يحتوي المدخلات",
-  "Prompts": "الموجهات",
-  "X type": "نوع X",
-  "Nothing": "لا شئ",
-  "Var. seed": "تغير البذرة",
-  "Var. strength": "قوة التغيير",
-  "Steps": "الخطوات",
-  "Prompt S/R": "موجه S / R",
-  "Prompt order": "ترتيب الموجهات",
-  "Sampler": "نظام أخذ العينات",
-  "Checkpoint name": "اسم النموذج",
+  "Aesthetic weight": "أثر التضمين",
+  "Aesthetic steps": "عدد الخطوات",
+  "Aesthetic learning rate": "معدل التعلم",
+  "Slerp interpolation": "امزج بطريقة كروية",
+  "Aesthetic imgs embedding": "التضمين",
+  "None": "بدون",
+  "Aesthetic text for imgs": "الطلب (اختياري)",
+  "This text is used to rotate the feature space of the imgs embs": "لإعادة توجيه التضمين التجميلي",
+  "Slerp angle": "أثر الطلب",
+  "Is negative text": "الطلب عكسي",
+  "Script": "أدوات خاصة",
+  "Prompt matrix": "مصفوفة طلبات",
+  "Put variable parts at start of prompt": "الجزء المتغير في بداية الطلب",
+  "Prompts from file or textbox": " قائمة طلبات",
+  "Iterate seed every line": "غير البذرة مع كل طلب",
+  "List of prompt inputs": "قائمة الطلبات",
+  "Upload prompt inputs": "اجلب الطلبات من ملف",
+  "Drop File Here": "اسقط ملف هنا",
+  "-": "-",
+  "or": "أو",
+  "Click to Upload": "انقر للرفع",
+  "X/Y plot": "مصفوفة عوامل",
+  "X type": "العامل الأول",
+  "Nothing": "لا شيء",
+  "Var. seed": "بذرة الممزوج",
+  "Var. strength": "أثر الممزوج",
+  "Steps": "عدد الخطوات",
+  "Prompt S/R": "كلمات بديلة",
+  "Prompt order": "ترتيب الكلمات",
+  "Sampler": "أسلوب الخطو",
+  "Checkpoint name": "ملف الأوزان",
   "Hypernetwork": "الشبكة الفائقة",
   "Hypernet str.": "قوة الشبكة الفائقة",
-  "Sigma Churn": "دفع سيجما",
-  "Sigma min": "أصغر سيجما",
-  "Sigma max": "أكبر سيجما",
-  "Sigma noise": "ضجة سيجما",
-  "Eta": "الوقت المتوقع",
-  "Clip skip": "تخطي Clip",
-  "Denoising": "تقليل الضوضاء",
-  "X values": "قيم X",
-  "Y type": "نوع Y",
-  "Y values": "قيم Y",
-  "Draw legend": "ارسم مفتاح التوضيح",
-  "Include Separate Images": "قم بتضمين الصور منفصلة",
-  "Keep -1 for seeds": "احتفظ بـقيمة -1 للبذور",
-  "Drop Image Here": "إسقاط الصورة هنا",
-  "Save": "حفظ",
-  "Send to img2img": "أرسل إلى صورة لصورة",
-  "Send to inpaint": "أرسل إلى إعادة الرسم الجزئي",
-  "Send to extras": "أرسل إلى الإضافات",
-  "Make Zip when Save?": "إنشاء ملف مضغوط عند الحفظ؟",
-  "Textbox": "مربع النص",
-  "Interrogate\nCLIP": "استجواب\n CLIP",
-  "Inpaint": "إعادة الرسم الجزئي",
-  "Batch img2img": "دفعات صورة لصورة",
-  "Image for img2img": "صورة (صورة لصورة)",
-  "Image for inpainting with mask": "صورة (إعادة الرسم الجزئي)",
-  "Mask": "القناع",
-  "Mask blur": "ضبابية القناع",
-  "Mask mode": "أسلوب القناع",
-  "Draw mask": "رسم القناع",
-  "Upload mask": "تحميل القناع",
-  "Masking mode": "أسلوب التقنيع",
-  "Inpaint masked": "إعادة الرسم الجزئي (المنطقة المقنعة)",
-  "Inpaint not masked": "إعادة الرسم الجزئي (المنطقة الغير مقنعة)",
-  "Masked content": "المحتوى المقنع",
-  "fill": "الملأ",
-  "original": "الأصلي",
-  "latent noise": "الضوضاء الكامنة",
-  "latent nothing": "لا شيء كامن",
-  "Inpaint at full resolution": "إعادة الرسم الجزئي بدقة كاملة",
-  "Inpaint at full resolution padding, pixels": "إعادة الرسم الجزئي بدقة كاملة, الحشو, بيكسل",
-  "Process images in a directory on the same machine where the server is running.": "معالجة الصور في المجلد على نفس الجهاز حيث يتم تشغيل الخادم.",
-  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "استخدم مجلد إخراج فارغ لحفظ الصور بشكل طبيعي بدلاً من الكتابة إلى مجلد المخرجات.",
-  "Input directory": "مجلد المدخلات",
-  "Output directory": "مجلد المخرجات",
-  "Resize mode": "وضعية تغيير الحجم",
-  "Just resize": "تغييير الحجم فقط",
-  "Crop and resize": "اقتصاص وتغيير الحجم",
-  "Resize and fill": "تغيير الحجم والتعبئة",
-  "img2img alternative test": "صورة لصورة البديلة",
-  "Loopback": "الحلقة الراجعة",
-  "Outpainting mk2": "الرسم الخارجي نسخة 2",
-  "Poor man's outpainting": "الرسم الخارجي للفقراء",
-  "SD upscale": "ترقية الانتشار المستقر",
-  "should be 2 or lower.": "should be 2 or lower.",
-  "Override `Sampling method` to Euler?(this method is built for it)": "Override `Sampling method` to Euler?(this method is built for it)",
-  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)",
-  "Original prompt": "Original prompt",
-  "Original negative prompt": "Original negative prompt",
-  "Override `Sampling Steps` to the same value as `Decode steps`?": "Override `Sampling Steps` to the same value as `Decode steps`?",
-  "Decode steps": "Decode steps",
-  "Override `Denoising strength` to 1?": "Override `Denoising strength` to 1?",
-  "Decode CFG scale": "Decode CFG scale",
-  "Randomness": "Randomness",
-  "Sigma adjustment for finding noise for image": "Sigma adjustment for finding noise for image",
-  "Loops": "Loops",
-  "Denoising strength change factor": "معامل قوة تقليل الضوضاء",
-  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "الإعدادات الموصى بها: خطوات أخذ العينات: 80-100 ، طريقة أخذ العينات: Euler a ، تقليل الضوضاء: 0.8",
-  "Pixels to expand": "عدد البكسل للتوسيع",
-  "Outpainting direction": "إتجاه الرسم الخارجي",
+  "Inpainting conditioning mask strength": "قوة قناع الإنتاج الجزئي",
+  "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "حدد مدى صرامة قناع الإنتاج، يصبح القناع شفاف إذا قوته 0 (لا يعمل إلا مع ملفات أوزان الإنتاج الجزئي: inpainting)",
+  "Sigma Churn": "العشوائية (Schurn)",
+  "Sigma min": "أدنى تشويش (Stmin)",
+  "Sigma max": "أقصى تشويش (Stmax)",
+  "Sigma noise": "التشويش (Snoise)",
+  "Eta": "العامل Eta η",
+  "Clip skip": "تخطي آخر طبقات CLIP",
+  "Denoising": "المدى",
+  "Cond. Image Mask Weight": "قوة قناع الإنتاج الجزئي",
+  "X values": "قيم العامل الأول",
+  "Separate values for X axis using commas.": "افصل القيم بفواصل (,) من اليسار إلى اليمين",
+  "Y type": "العامل الثاني",
+  "Y values": "قيم العامل الثاني",
+  "Separate values for Y axis using commas.": "افصل القيم بفواصل (,) من الأعلى إلى الأسفل",
+  "Draw legend": "أضف مفتاح التوضيح",
+  "Include Separate Images": "أضف الصور منفصلة",
+  "Keep -1 for seeds": "استخدم بذور عشوائية",
+  "Save": "احفظ",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "احفظ الصور مع ملف العوامل بصيغة CSV",
+  "Send to img2img": "أرسل لصورة إلى صورة",
+  "Send to inpaint": "أرسل للإنتاج الجزئي",
+  "Send to extras": "أرسل للمعالجة",
+  "Open images output directory": "افتح مجلد الصور المخرجة",
+  "Make Zip when Save?": "ضع النتائج في ملف مضغوط عند الحفظ",
+  "img2img": "صورة إلى صورة",
+  "Interrogate\nCLIP": "استجواب\nCLIP",
+  "Drop Image Here": "اسقط صورة هنا",
+  "Just resize": "تغيير الحجم فقط",
+  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "غير حجم الصورة بدون مراعات اتزان الأبعاد",
+  "Crop and resize": "تغيير الحجم وقص الأطراف",
+  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "غير حجم الصورة واقتص الأطراف الزائدة",
+  "Resize and fill": "تغيير الحجم وتبطين الأطراف",
+  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "غير حجم الصورة واملأ الأطراف الزائدة بألوان من الصورة",
+  "img2img alternative test": "استجواب الصورة (تجريبي)",
+  "should be 2 or lower.": "يفترض أن يكون 2 أو أقل",
+  "Override `Sampling method` to Euler?(this method is built for it)": "استخدم أسلوب خطو Euler (مستحسن)",
+  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "استبدل الطلب وعكسه في الأعلى بالطلب الأصلي وعكسه التاليين",
+  "Original prompt": "الطلب الأصلي",
+  "Original negative prompt": "عكس الطلب الأصلي",
+  "Override `Sampling Steps` to the same value as `Decode steps`?": "استبدل عدد الخطوات بعدد الخطوات الأصلية",
+  "Decode steps": "عدد الخطوات الأصلية",
+  "Override `Denoising strength` to 1?": "اجعل المدى 1",
+  "Decode CFG scale": "التركيز",
+  "Randomness": "العشوائية",
+  "Sigma adjustment for finding noise for image": "لا تسمح بتثبيت قيمة التباين",
+  "Loopback": "اجترار وتكرار",
+  "Loops": "عدد المرات",
+  "How many times to repeat processing an image and using it as input for the next iteration": "كم مرة يتم أخذ مخرجات الإنتاج كمدخلات وإعادة الإنتاج مرة أخرى",
+  "Denoising strength change factor": "معدل تغيير المدى",
+  "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.": "يتم ضرب المدى بهذا الرقم في كل مرة، إذا استخدمت رقم أصغر من 1 يمكن الرسو على نتيجة، وإذا استخدمت رقم أكبر من 1 تصبح النتيجة عشوائية",
+  "Outpainting mk2": "توسيع الصورة (mk2)",
+  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "يفضل استخدام: 80-100 خطوة، أسلوب Euler a، المدى 0.8",
+  "Pixels to expand": "عدد البيكسلات",
+  "Mask blur": "تنعيم القناع",
+  "How much to blur the mask before processing, in pixels.": "مقدرا تنعيم القناع قبل استخدامه (يقاس بالبيكسل)",
+  "Outpainting direction": "اتجاه توسيع الصورة",
   "left": "يسار",
   "right": "يمين",
   "up": "فوق",
   "down": "تحت",
-  "Fall-off exponent (lower=higher detail)": "أس التناقص (الأدنى = تفاصيل أعلى)",
-  "Color variation": "اختلاف اللون",
-  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "سيقوم بترقية الصورة إلى ضعف الأبعاد ؛ استخدم شريط تمرير العرض والارتفاع لضبط حجم التبليط",
-  "Tile overlap": "تداخل التبليط",
-  "Upscaler": "المرقي",
+  "Fall-off exponent (lower=higher detail)": "قوة السقوط (كلما قلت زادت التفاصيل)",
+  "Color variation": "تنوع الألوان",
+  "Poor man's outpainting": "توسيع الصورة (بدائي)",
+  "Masked content": "محتويات القناع",
+  "What to put inside the masked area before processing it with Stable Diffusion.": "ما يوضع مكان الفراغ في الصورة الذي نريد إنتاج محتوياته",
+  "fill": "ألوان",
+  "fill it with colors of the image": "املأ باستخدام ألوان مأخوذة من باقي الصورة",
+  "original": "بدون تغيير",
+  "keep whatever was there originally": "أبق محتويات ما تحت القناع كما هي",
+  "latent noise": "تشويش كامن",
+  "fill it with latent space noise": "املأه باستخدام تشويش من الفضاء الكامن",
+  "latent nothing": "تصفير كامن",
+  "fill it with latent space zeroes": "استبدل مكان القناع في الفضاء الكامن بأصفار",
+  "SD upscale": "مضاعفة الدقة بنموذج الإنتشار المستقر",
+  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "سيتم تكبير حجم الصورة إلى الضعف، استخدم الطول والإرتفاع في الأعلى لتحديد حجم نافذة المكبر",
+  "Tile overlap": "تداخل النافذة",
+  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "المكبر ينظر إلى أجزاء الصورة من خلال نافذة لتكبير المحتوى ثم ينتقل إلى الجزء المجاور، يفضل أن يكون هناك تداخل بين كل رقعة لكي لا يكون هناك اختلاف واضح بينهم",
+  "Upscaler": "طريقة التكبير",
   "Lanczos": "Lanczos",
+  "LDSR": "LDSR",
+  "ScuNET GAN": "ScuNET GAN",
+  "ScuNET PSNR": "ScuNET PSNR",
+  "ESRGAN_4x": "ESRGAN_4x",
+  "SwinIR 4x": "SwinIR 4x",
+  "Inpaint": "إنتاج جزئي",
+  "Draw mask": "ارسم القناع",
+  "Upload mask": "ارفع القناع",
+  "Inpaint masked": "أنتج ما بداخل القناع",
+  "Inpaint not masked": "أنتج ما حول القناع",
+  "Inpaint at full resolution": "إنتاج بالدقة الكاملة",
+  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "كبر ما يراد إعادة إنتاجه ثم صغر النتيجة وألصقها في مكانها",
+  "Inpaint at full resolution padding, pixels": "عدد بيكسلات التبطين",
+  "Batch img2img": "صور إلى صور",
+  "Process images in a directory on the same machine where the server is running.": "حدد مسار مجلد صور موجود في جهاز الخادم",
+  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "يمكنك أيضا تحديد مجلد حفظ النتائج (غير الإفتراضي)",
+  "Input directory": "مجلد المدخلات",
+  "Output directory": "مجلد المخرجات",
+  "Extras": "معالجة",
   "Single Image": "صورة واحدة",
-  "Batch Process": "معالجة الدفعات",
-  "Batch from Directory": "دفعة من المجلد",
-  "Source": "مصدر",
-  "Show result images": "إظهار نتائج الصور ",
-  "Scale by": "رفع الحجم بمقدار",
-  "Scale to": "رفع الحجم إلى",
+  "Source": "المصدر",
+  "Scale by": "مضاعفة الدقة",
   "Resize": "تغيير الحجم",
-  "Crop to fit": "اقتصاص للتوافق",
-  "Upscaler 2 visibility": "إظهار المرقي 2",
-  "GFPGAN visibility": "إظهار GFPGAN",
-  "CodeFormer visibility": "إظهار CodeFormer",
-  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "وزن CodeFormer (0 = أقصى تأثير ، 1 = تأثير أدنى)",
-  "Open output directory": "افتح مجلد المخرجات",
-  "Send to txt2img": "أرسل إلى كتابة لصورة",
-  "A merger of the two checkpoints will be generated in your": "سيتم إنشاء نموذجمدمج من النموذجين في",
-  "checkpoint": "النموذج",
-  "directory.": "المجلد.",
-  "Primary model (A)": "النموذج الأساسي (أ)",
-  "Secondary model (B)": "النموذج الثانوي (ب)",
-  "Tertiary model (C)": "النموذج الثالث (ج)",
-  "Custom Name (Optional)": "اسم مخصص (اختياري)",
-  "Multiplier (M) - set to 0 to get model A": "المضاعف (M) - اضبط على 0 للحصول على النموذج أ",
-  "Interpolation Method": "طريقة الاستنباط",
-  "Weighted sum": "المجموع الموزون",
-  "Add difference": "أضافة الاختلاف",
-  "Save as float16": "حفظ  float16",
-  "See": "شاهد",
-  "wiki": "ويكي",
-  "for detailed explanation.": "للحصول على شرح مفصل.",
-  "Create embedding": "إنشاء التضمين",
-  "Create hypernetwork": "إنشاء شبكة فائقة",
-  "Preprocess images": "تجهيز الصور",
+  "Upscaler 1": "المكبر الأول",
+  "Upscaler 2": "المكبر الثاني",
+  "Upscaler 2 visibility": "أثر المكبر الثاني",
+  "GFPGAN visibility": "أثر GFPGAN (محسن وجوه)",
+  "CodeFormer visibility": "أثر CodeFormer (محسن وجوه)",
+  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "وزن CodeFormer (يزيد التفاصيل على حساب الجودة)",
+  "Upscale Before Restoring Faces": "كبر قبل تحسين الوجوه",
+  "Scale to": "دقة محددة",
+  "Crop to fit": "قص الأطراف الزائدة إذا لم تتناسب الأبعاد",
+  "Batch Process": "حزمة صور",
+  "Batch from Directory": "حزمة من مجلد",
+  "A directory on the same machine where the server is running.": "مسار مجلد صور موجود في جهاز الخادم",
+  "Leave blank to save images to the default path.": "اتركه فارغا لاستخدام المسار الإفتراضي",
+  "Show result images": "اعرض الصور الناتجة",
+  "PNG Info": "عوامل الصورة",
+  "Send to txt2img": "أرسل لنص إلى صورة",
+  "Checkpoint Merger": "مزج الأوزان",
+  "A merger of the two checkpoints will be generated in your": "سيتم مزج الأوزان التالية وحفظ الأوزان المدجمة مع ",
+  "checkpoint": "الأوزان",
+  "directory.": " مجلد.",
+  "Primary model (A)": "الأوزان الأولى (A)",
+  "Secondary model (B)": "الأوزان الثانية (B)",
+  "Tertiary model (C)": "الأوزان الثالثة (C)",
+  "Custom Name (Optional)": "الاسم الجديد (اختياري)",
+  "Multiplier (M) - set to 0 to get model A": "العامل M: مسافة الإبتعاد عن الأوزان الأولى A",
+  "Interpolation Method": "طريقة المزج",
+  "Weighted sum": "خطية",
+  "Result = A * (1 - M) + B * M": "النتيجة = A * (1 - M) + B * M",
+  "Add difference": "جمع الفرق",
+  "Result = A + (B - C) * M": "النتيجة = A + (B - C) * M",
+  "Save as float16": "احفظ بدقة float16",
+  "Run": "تشغيل",
+  "Train": "تدريب",
+  "See": "اقرأ",
+  "wiki": " الـwiki ",
+  "for detailed explanation.": "لمعرفة المزيد",
+  "Create embedding": "إنشاء تضمين",
   "Name": "الاسم",
-  "Initialization text": "نص التهيئة",
-  "Number of vectors per token": "عدد المتجهات لكل رمز",
-  "Overwrite Old Embedding": "الكتابة فوق التضمين القديم",
-  "Modules": "الوحدات",
-  "Enter hypernetwork layer structure": "أدخل بنية طبقة الشبكة الفائقة",
-  "Select activation function of hypernetwork": "حدد وظيفة تنشيط الشبكة الفائقة",
-  "linear": "خطي (Linear)",
-  "relu": "الوحدة الخطية المعدلة (Relu)",
-  "leakyrelu": "الوحدة الخطية المعدلة المسربة (Leakyrelu)",
-  "elu": "الوحدة الأسية الخطية (Elu)",
-  "swish": "Swish",
-  "Add layer normalization": "أضف طبقة التسوية",
-  "Use dropout": "استخدم الهبوط",
-  "Overwrite Old Hypernetwork": "الكتابة فوق الشبكة الفائقة القديمة",
-  "Source directory": "مجلد المصدر",
-  "Destination directory": "مجلد النتائج",
-  "Existing Caption txt Action": "الإجراء النصي للتعليق المتوفر",
+  "Initialization text": "النص المبدأي",
+  "Number of vectors per token": "عدد المتجهات لكل وحدة لغوية",
+  "Overwrite Old Embedding": "استبدل التضمين القديم",
+  "Create hypernetwork": "إنشاء شبكة فائقة",
+  "Modules": "الأجزاء",
+  "Enter hypernetwork layer structure": "ترتيب مضاعفات عرض الطبقات",
+  "1st and last digit must be 1. ex:'1, 2, 1'": "المضاعفين الأول والأخير يجب أن يكونا 1، مثال: 1, 2, 1",
+  "Select activation function of hypernetwork": "دالة التنشيط",
+  "linear": "linear",
+  "relu": "relu",
+  "leakyrelu": "leakyrelu",
+  "elu": "elu",
+  "swish": "swish",
+  "tanh": "tanh",
+  "sigmoid": "sigmoid",
+  "celu": "celu",
+  "gelu": "gelu",
+  "glu": "glu",
+  "hardshrink": "hardshrink",
+  "hardsigmoid": "hardsigmoid",
+  "hardtanh": "hardtanh",
+  "logsigmoid": "logsigmoid",
+  "logsoftmax": "logsoftmax",
+  "mish": "mish",
+  "prelu": "prelu",
+  "rrelu": "rrelu",
+  "relu6": "relu6",
+  "selu": "selu",
+  "silu": "silu",
+  "softmax": "softmax",
+  "softmax2d": "softmax2d",
+  "softmin": "softmin",
+  "softplus": "softplus",
+  "softshrink": "softshrink",
+  "softsign": "softsign",
+  "tanhshrink": "tanhshrink",
+  "threshold": "threshold",
+  "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "تهيئة الأوزان (استخدم Kaiming مع relu وأمثالها وXavier مع sigmoid وأمثالها)",
+  "Normal": "Normal",
+  "KaimingUniform": "KaimingUniform",
+  "KaimingNormal": "KaimingNormal",
+  "XavierUniform": "XavierUniform",
+  "XavierNormal": "XavierNormal",
+  "Add layer normalization": "أضف تسوية الطبقات (LayerNorm)",
+  "Use dropout": "استخدم الإسقاط (Dropout)",
+  "Overwrite Old Hypernetwork": "استبدل الشبكة الفائقة القديمة",
+  "Preprocess images": "معالجة مسبقة للصور",
+  "Source directory": "مجلد المدخلات",
+  "Destination directory": "مجلد المخرجات",
+  "Existing Caption txt Action": "اذا كانت الصورة لديها توصيف (طلب)",
   "ignore": "تجاهل",
-  "copy": "نسخ",
-  "prepend": "أضف قبل",
+  "copy": "انسخ",
+  "prepend": "أسبق",
   "append": "ألحق",
-  "Create flipped copies": "قم بإنشاء نسخ مقلوبة",
-  "Split oversized images": "تقسيم الصور كبيرة الحجم",
-  "Use BLIP for caption": "استخدم BLIP للتعليق",
-  "Use deepbooru for caption": "استخدم deepbooru للتعليق",
-  "Split image threshold": "حد تقسيم الصورة",
-  "Split image overlap ratio": "نسبة تداخل الصورة المنقسمة",
-  "Preprocess": "تجهيز الصور",
-  "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "تدريب التضمين أو الشبكة الفائقة ؛ يجب تحديد مجلد بمجموعة من الصور بنسبة أبعاد 1: 1",
-  "[wiki]": "[ويكي]",
+  "Create flipped copies": "انشئ نسخ معكوسة للصور",
+  "Split oversized images": "قسّم الصور الكبيرة",
+  "Split image threshold": "حد تقسيم الصور الكبيرة",
+  "Split image overlap ratio": "نسبة تداخل اقسام الصور الكبيرة",
+  "Auto focal point crop": "اقتصاص تلقائي",
+  "Focal point face weight": "تمركز الوجوه",
+  "Focal point entropy weight": "تمركز التنوع",
+  "Focal point edges weight": "تمركز الحواف",
+  "Create debug image": "احفظ نتائج التحليل أيضا",
+  "Use BLIP for caption": "استخدم BLIP لتوصيف الصور",
+  "Use deepbooru for caption": "استخدم deepbooru لتوصيف الصور",
+  "Preprocess": "معالجة مسبقة",
+  "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "درب التضمين أو الشبكة الفائقة: يجب تحديد مجلد يحتوي صور مربعة فقط ",
+  "[wiki]": "[wiki]",
   "Embedding": "التضمين",
   "Embedding Learning rate": "معدل تعلم التضمين",
   "Hypernetwork Learning rate": "معدل تعلم الشبكة الفائقة",
   "Dataset directory": "مجلد مجموعة البيانات",
+  "Path to directory with input images": "مسار مجلد الصور المدخلة",
   "Log directory": "مجلد السجل",
-  "Prompt template file": "ملف قالب الموجهات",
-  "Max steps": "الخطوات القصوى",
-  "Save an image to log directory every N steps, 0 to disable": "حفظ صورة في مجلد السجل  كل N خطوات ، 0 للتعطيل",
-  "Save a copy of embedding to log directory every N steps, 0 to disable": "حفظ نسخة من التضمين في مجلد السجل كل N خطوات ، 0 للتعطيل",
-  "Save images with embedding in PNG chunks": "حفظ التضمين مع الصور في أجزاء PNG",
-  "Read parameters (prompt, etc...) from txt2img tab when making previews": "قراءة المتغيرات (الموجه ، إلخ ...) من علامة تبويب نص لصورة عند إجراء المعاينات",
-  "Train Hypernetwork": "تدريب الشبكة الفائقة",
-  "Train Embedding": "تدريب التضمين",
-  "Create an aesthetic embedding out of any number of images": "Create an aesthetic embedding out of any number of images",
-  "Create images embedding": "Create images embedding",
-  "extras": "إضافات",
-  "favorites": "المفضلة",
-  "custom fold": "custom fold",
-  "Load": "تحميل",
+  "Path to directory where to write outputs": "مسار مجلد الصور المخرجة",
+  "Prompt template file": "ملف صيغ الطلبات",
+  "Max steps": "أقصى عدد لخطوات التدريب",
+  "Save an image to log directory every N steps, 0 to disable": "احفظ صورة في السجل بعد كل كم خطوة تدريب (إذا 0 لا تحفظ)",
+  "Save a copy of embedding to log directory every N steps, 0 to disable": "احفظ التضمين في السجل بعد كل كم خطوة تدريب (إذا 0 لا تحفظ)",
+  "Save images with embedding in PNG chunks": "احفظ التضمين بداخل ملف الصورة كعامل يمكن استخراجه من عوامل الصورة (صيغة PNG)",
+  "Read parameters (prompt, etc...) from txt2img tab when making previews": "استخدم قيم العوامل الموجودة في تبويب نص إلى صورة لعرض نتائجهم أثناء التدريب",
+  "Train Hypernetwork": "درّب الشبكة الفائقة",
+  "Train Embedding": "درّب التضمين",
+  "Create aesthetic embedding": "تضمين تجميلي",
+  "Create an aesthetic embedding out of any number of images": "انشئ تضمين تجميلي يعبر عن مجموعة من الصور",
+  "Create images embedding": "انشئ التضمين التجميلي",
+  "Image Browser": "معرض الصور",
+  "Load": "حمّل",
   "Images directory": "مجلد الصور",
-  "Prev batch": "الدفعة السابقة",
-  "Next batch": "الدفعة التالية",
   "First Page": "الصفحة الأولى",
   "Prev Page": "الصفحة السابقة",
-  "Page Index": "فهرس الصفحات",
+  "Page Index": "رقم الصفحة",
   "Next Page": "الصفحة التالية",
-  "End Page": "صفحة النهاية",
-  "number of images to delete consecutively next": "عدد الصور المطلوب حذفها على التوالي بعد ذلك",
-  "Delete": "حذف",
-  "Generate Info": "معلومات الإنشاء",
+  "End Page": "الصفحة الأخيرة",
+  "number of images to delete consecutively next": "عدد الصور المتتالية للحذف",
+  "Delete": "احذف",
+  "Generate Info": "معلومات عامة",
   "File Name": "اسم الملف",
-  "Collect": "جمع",
-  "Refresh page": "إعادة تحميل الصفحة",
-  "Date to": "التاريخ إلى",
-  "Number": "الرقم",
-  "set_index": "وضع الفهرس",
-  "Checkbox": "صندوق اختيار",
-  "Apply settings": "تطبيق الإعدادات",
-  "Saving images/grids": "حفظ الصور / الإطار الشبكي",
-  "Always save all generated images": "احفظ دائمًا جميع الصور التي تم إنشائها",
-  "File format for images": "تنسيق ملفات الصور",
-  "Images filename pattern": "نمط اسم ملفات الصور",
-  "Add number to filename when saving": "Add number to filename when saving",
-  "Always save all generated image grids": "احفظ دائمًا جميع الإطارات الشبكية للصور التي تم إنشاؤها",
-  "File format for grids": "تنسيق ملفات الإطارات الشبكية",
-  "Add extended info (seed, prompt) to filename when saving grid": "أضف معلومات إضافية (البذرة ، الموجه) إلى اسم الملف عند حفظ الإطار الشبكي",
-  "Do not save grids consisting of one picture": "لا تحفظ الإطارات الشبكية التي تتكون من صورة واحدة",
-  "Prevent empty spots in grid (when set to autodetect)": "منع المناطق الفارغة في الإطار الشبكي (عند الضبط على الاكتشاف التلقائي)",
-  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "عدد صفوف الإطار الشبكي استخدم -1 للاكتشاف التلقائي و 0 ليكون نفس حجم الدُفعة",
-  "Save text information about generation parameters as chunks to png files": "احفظ معلومات نصية حول متغيرات الإنشاء كمقاطع في ملفات png",
-  "Create a text file next to every image with generation parameters.": "قم بإنشاء ملف نصي بجوار كل صورة باستخدام متغيرات الإنشاء.",
-  "Save a copy of image before doing face restoration.": "احفظ نسخة من الصورة قبل القيام بترميم الوجوه.",
-  "Quality for saved jpeg images": "جودة الصور المحفوظة بتنسيق jpeg",
-  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "إذا كانت صورة PNG أكبر من 4 ميجابايت أو كان أي بُعد أكبر من 4000 ، قم بتقليل حجم الصورة وحفظها بتنسيق JPG",
-  "Use original name for output filename during batch process in extras tab": "استخدم الاسم الأصلي لاسم ملف الإخراج أثناء عملية الدُفعات في علامة تبويب الإضافات",
-  "When using 'Save' button, only save a single selected image": "عند استخدام زر 'حفظ' ، احفظ فقط صورة واحدة محددة",
-  "Do not add watermark to images": "لا تقم بإضافة العلامة المائية للصور",
-  "Paths for saving": "مسارات الحفظ",
-  "Output directory for images; if empty, defaults to three directories below": "مجلد المخرجات للصور ؛ إذا كان فارغا ، يتم تعيينه افتراضيًا إلى المجلدات الثلاثة أدناه",
-  "Output directory for txt2img images": "مجلد المخرجات لصور نص لصورة",
-  "Output directory for img2img images": "مجلد المخرجات لصور صورة لصورة",
-  "Output directory for images from extras tab": "مجلد المخرجات لصور علامة تبويب الإضافات",
-  "Output directory for grids; if empty, defaults to two directories below": "مجلد المخرجات للإطارات الشبكية ؛ إذا كان فارغا ، يتم تعيينه افتراضيًا إلى المجلدين أدناه",
-  "Output directory for txt2img grids": "مجلد المخرجات للإطارات الشبكية نص لصورة",
-  "Output directory for img2img grids": "مجلد المخرجات للإطارات الشبكية صورة لصورة",
-  "Directory for saving images using the Save button": "مجلد لحفظ الصور باستخدام زر حفظ",
-  "Saving to a directory": "يتم الحفظ إلى المجلد..",
-  "Save images to a subdirectory": "حفظ الصور في مجلد فرعي",
-  "Save grids to a subdirectory": "حفظ الإطارات الشبكية في مجلد فرعي",
+  "Collect": "اجمع",
+  "extras": "معالجة",
+  "favorites": "المفضلة",
+  "custom fold": "مجلد آخر",
+  "Input images directory": "مجلد الصور المدخلة",
+  "Settings": "إعدادات",
+  "Apply settings": "طبق الإعدادت",
+  "Saving images/grids": "حفظ الصور وجداولها",
+  "Always save all generated images": "احفظ كل الصور المنتجة",
+  "File format for images": "صيغة ملفات الصور",
+  "Images filename pattern": "نمط تسمية الصور",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم الأوسمة التالية لتحديد كيفية تسمية الصور: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] أو اتركه فارغا إذا أردت",
+  "Add number to filename when saving": "دائما أضف رقم على اسم الملف",
+  "Always save all generated image grids": "احفظ جداول الصور دائما",
+  "File format for grids": "صيغة ملفات جداول الصور",
+  "Add extended info (seed, prompt) to filename when saving grid": "أضف عوامل الإنتاج (مثل الطلب والبذرة) لأسماء جداول الصور",
+  "Do not save grids consisting of one picture": "لا تحفظ جدول الصور عند إنتاج صورة واحدة فقط",
+  "Prevent empty spots in grid (when set to autodetect)": "في الوضع التلقائي امنع الفراغات في جداول الصور",
+  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "عدد صفوف جداول الصور (1-: تلقائي، 0: نفس حجم الحزمة)",
+  "Save text information about generation parameters as chunks to png files": "احفظ عوامل الإنتاج داخل ملفات الصور (صيغة PNG)",
+  "Create a text file next to every image with generation parameters.": "انشئ ملف نصي يحتوي على عوامل الإنتاج بجانب كل صورة",
+  "Save a copy of image before doing face restoration.": "احفظ نسخة من الصورة قبل تحسين الوجوه",
+  "Quality for saved jpeg images": "جودة حفظ صور JPEG",
+  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "إذا كان حجم ملف صورة PNG أكبر من 4MB أو أحد أبعاد الصورة أكبر من 4000 بيكسل، صغر الصورة واحفظها بصيغة JPEG",
+  "Use original name for output filename during batch process in extras tab": "استخدم الإسم الأصلي للصور عند معالجتهم في حزم تحت تبويب معالجة",
+  "When using 'Save' button, only save a single selected image": "احفظ صورة واحدة فقط عند الضغط على حفظ",
+  "Do not add watermark to images": "لا تضف علامة مائية على الصور",
+  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "الصور المنتجة لن تحتوي على علامة مائية عند تفعيل هذا الخيار، تحذير: ربما قد يدل هذا على أنك تتصرف بشكل غير أخلاقي",
+  "Paths for saving": "اماكن الحفظ",
+  "Output directory for images; if empty, defaults to three directories below": "مسار حفظ الصور المخرجة؛ يمكن أن يترك فارغا",
+  "Output directory for txt2img images": "مسار الصور المخرجة من تبويب نص إلى صورة",
+  "Output directory for img2img images": "مسار الصور المخرجة من تبويب صورة إلى صورة",
+  "Output directory for images from extras tab": "مسار الصور المخرجة من تبويب معالجة",
+  "Output directory for grids; if empty, defaults to two directories below": "مسار حفظ جداول الصور المخرجة؛ يمكن أن يترك فارغا",
+  "Output directory for txt2img grids": "مسار جداول الصور المخرجة من تبويب نص إلى صورة",
+  "Output directory for img2img grids": "مسار جداول الصور المخرجة من تبويب صورة إلى صورة",
+  "Directory for saving images using the Save button": "مسار حفظ الصور عند الضغط على زر الحفظ",
+  "Saving to a directory": "مجلدات الحفظ",
+  "Save images to a subdirectory": "احفظ الصور في مجلد فرعي",
+  "Save grids to a subdirectory": "احفظ جداول الصور في مجلد فرعي",
   "When using \"Save\" button, save images to a subdirectory": "احفظ الصور في مجلد فرعي عند الضغط على زر الحفظ",
   "Directory name pattern": "نمط اسم المجلد",
-  "Max prompt words for [prompt_words] pattern": "أقصى عدد لكلمات التوجيه لنمط [كلمات_التوجيه]",
-  "Upscaling": "الارتقاء",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "حجم التبليط لترقيات ESRGAN, القيمة 0= لا يوجد تبليط.",
-  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "تداخل المربعات ، بالبكسل لمرقي ESRGAN. القيم المنخفضة = تداخل مرئي.",
-  "Tile size for all SwinIR.": "حجم التليط لجميع عمليات SwinIR.",
-  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "تداخل المربعات بالبكسل لـ SwinIR. القيم المنخفضة = تداخل مرئي.",
-  "LDSR processing steps. Lower = faster": "خطوات معالجة LDSR. أقل = أسرع",
-  "Upscaler for img2img": "المرقي لصورة لصورة",
-  "Upscale latent space image when doing hires. fix": "ترقية صورة الفضاء الكامن عند التعيينات. تصليح",
-  "Face restoration": "ترميم الوجوه",
-  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "متغيرات وزن CodeFormer ؛ 0 = أقصى تأثير ؛ 1 = الحد الأدنى من التأثير",
-  "Move face restoration model from VRAM into RAM after processing": "انقل نموذج استعادة الوجوه من VRAM إلى RAM بعد المعالجة",
-  "System": "نظام",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "استطلاعات استخدام VRAM في الثانية أثناء الإنشاء. اضبط على 0 للتعطيل.",
-  "Always print all generation info to standard output": "قم دائمًا بطباعة جميع معلومات الإنشاء إلى المخرج القياسي",
-  "Add a second progress bar to the console that shows progress for an entire job.": "أضف شريط تقدم آخر إلى وحدة التحكم يُظهر التدقم الكلي للمهمة.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم الأوسمة التالية لتحديد كيفية تسمية المجلدات الفرعية للصور والصور المركبة: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] أو اتركه فارغا إذا أردت",
+  "Max prompt words for [prompt_words] pattern": "أقصى عدد كلمات الطلب عند استخدام وسم [prompt_words]",
+  "Upscaling": "تكبير الصور",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "حجم نافذة المكبر ESRGAN (يمكن أن يكون 0)",
+  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "تداخل نافذة المكبر ESRGAN (تتضح الفواصل إذا كان قليل)",
+  "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "حدد ما تريد عرضه كخيار لمكبر من نوع Real-ESRGAN يمكن استخدامه (يتطلب إعادة تشغيل)",
+  "Tile size for all SwinIR.": "حجم نافذة المكبر SwinIR",
+  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "تداخل نافذة المكبر SwinIR (تتضح الفواصل إذا كان قليل)",
+  "LDSR processing steps. Lower = faster": "عدد خطوات مكبر LDSR (أسرع كلما قلت)",
+  "Upscaler for img2img": "طريقة التكبير تحت تبويب صورة إلى صورة",
+  "Upscale latent space image when doing hires. fix": "عند اختيار \"إصلاح الدقة العالية\" قم بالتكبير في الفضاء الكامن",
+  "Face restoration": "تحسين الوجوه",
+  "Face restoration model": "نموذج تحسين الوجوه",
+  "Restore low quality faces using GFPGAN neural network": "استخدم نموذج GFPGAN لتحسين الوجوه",
+  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "وزن CodeFormer (يزيد التفاصيل على حساب الجودة)",
+  "Move face restoration model from VRAM into RAM after processing": "احذف محسن الوجوه من ذاكرة كرت الشاشة (VRAM) بعد استخدامه",
+  "System": "النظام",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "عدد مرات التحقق من ذاكرة كرت الشاشة المستخدمة (VRAM) في الثانية",
+  "Always print all generation info to standard output": "اطبع دائما كل عوامل الإنتاج",
+  "Add a second progress bar to the console that shows progress for an entire job.": "أضف شريط تقدم ثاني لعملية الإنتاج الكاملة",
   "Training": "التدريب",
-  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "تفريغ VAE و CLIP من VRAM عند التدريب",
-  "Filename word regex": "اسم ملف كلمات regex",
-  "Filename join string": "سلسلة أحرف دمج أسماء الملفات",
-  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "عدد التكرارات لصورة إدخال واحدة لكل حقبة ؛ تستخدم فقط لعرض رقم الحقبة",
-  "Save an csv containing the loss to log directory every N steps, 0 to disable": "حفظ ملف csv يحتوي على الخسارة إلى مجلد السجل كل N خطوات ، 0 للتعطيل",
-  "Stable Diffusion": "انتشار مستقر",
-  "Checkpoints to cache in RAM": "التخزين المؤقت للنماذج في RAM",
+  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "قلل استخدام ذاكرة كرت الشاشة (VRAM) عند تدريب الشبكة الفائقة بالتخلص من CLIP وVAE",
+  "Filename word regex": "التعبير النمطي (RegEx) لكلمات اسم الملف",
+  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "استخدم هذا التعبير لاستخراج كلمات من أسماء الملفات ليتم دمجها بالخيار التالي لتستخدم في التدريب، اتركه فارغا لتستخدم اسم الملف كما هو",
+  "Filename join string": "النص الفاصل للكلمات المدموجة",
+  "This string will be used to join split words into a single line if the option above is enabled.": "سيستخدم بين كل كلمة يتم استخراجها من الخيار السابق",
+  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "عدد مرات تكرار الصور في كل دورة (Epoch)، يستخدم للعرض فقط",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "احفظ خسارة التدريب (Loss) في السجل بعد كل كم خطوة (إذا 0 لا تحفظ)",
+  "Stable Diffusion": "نموذج الإنتشار المستقر (Stable Diffusion)",
+  "Checkpoints to cache in RAM": "عدد النماذج التي تترك في الذاكرة العشوائية (RAM)",
   "Hypernetwork strength": "قوة الشبكة الفائقة",
-  "Apply color correction to img2img results to match original colors.": "قم بتطبيق تصحيح الألوان على نتائج صورة لصورة لمطابقة الألوان الأصلية.",
-  "Save a copy of image before applying color correction to img2img results": "احفظ نسخة من الصورة قبل تطبيق تصحيح الألوان على نتائج  صورة لصورة",
-  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "باستخدام صورة لصورة ، قم بالضبط بعدد الخطوات التي يحددها شريط التمرير (عادةً ما سيكون أقل مع تقليل التشويش).",
-  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "تمكين تكميم عينات K للحصول على نتائج أوضح وأنظف. هذا قد يغير البذرة المستخدمة. يتطلب إعادة التشغيل للتطبيق.",
-  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "التأكيد: استخدم (النص) لجعل النموذج يولي مزيدًا من الاهتمام للنص و استخدم [النص] لجعل النموذج يولي اهتمامًا أقل  للنص",
-  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "استخدم طريقة التأكيد القديمة. يمكن أن يكون مفيدًا لإعادة إنتاج البذور القديمة.",
-  "Make K-diffusion samplers produce same images in a batch as when making a single image": "اجعل انظمة أخذ عينات الانتشار K تنتج نفس الصور في الدفعات كما هو الحال عند إنشاء صورة واحدة",
-  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "زيادة التناسق عن طريق المساحة المتروكة من الفاصلة الأخيرة ضمن عدد n من الرموز المميزة عند استخدام أكثر من 75 رمزًا مميزًا",
-  "Filter NSFW content": "تصفية المحتوى الإباحي",
-  "Stop At last layers of CLIP model": "توقف عند آخر طبقات نموذج CLIP",
+  "Apply color correction to img2img results to match original colors.": "صحح ألوان نتائج صورة إلى صورة لتشابه الصورة الأصلية",
+  "Save a copy of image before applying color correction to img2img results": "احفظ نسخة من الصور المنتجة من صورة إلى صورة قبل عملية تصحيح الألوان",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "استخدم بالضبط عدد الخطوات المحددة بالرغم عن المدى (عدد الخطوات الإفتراضي = عدد الخطوات / المدى)",
+  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "فعل التجزيء الكمي (Quantization) لأغلب أساليب الخطو (k-diffusion) للحصول على صور أنظف (يتطلب إعادة تشغيل)",
+  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "أحط أي كلمة أو عبارة في الطلب أو عكسه بأقواس () للتشديد أو [] للتيسير",
+  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "استخدم الطريقة القديمة للتشديد (إذا كانت لديك نتائج قديمة تريد تجربتها)",
+  "Make K-diffusion samplers produce same images in a batch as when making a single image": "عدم تأثير تحزيم الصور على البذور عند استخدام أساليب خطو (k-diffusion)",
+  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "عند استخدام طلب طويل أكبر من 75 وحدة لغوية، افصل الطلب عند أخر فاصلة قبل كم كلمة",
+  "Filter NSFW content": "انتق المحتوى النظيف فقط",
+  "Stop At last layers of CLIP model": "قف عند آخر كم طبقة لنموذج CLIP",
+  "Allowed categories for random artists selection when using the Roll button": "اختر اهتمامات الفنانين المسموح بإضافتهم للطلب",
   "Interrogate Options": "خيارات الاستجواب",
-  "Interrogate: keep models in VRAM": "الاستجواب: احتفظ بالنماذج في VRAM",
-  "Interrogate: use artists from artists.csv": "الاستجواب: استخدم  قائمة الفنانين من ملف artists.csv",
-  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "الاستجواب: قم بتضمين رتب علامات النموذج في النتائج (ليس له تأثير على الاستجواب المستند إلى التعليقات).",
-  "Interrogate: num_beams for BLIP": "الاستجواب: عدد الحزم لـ BLIP",
-  "Interrogate: minimum description length (excluding artists, etc..)": "الاستجواب: الحد الأدنى لطول الوصف (باستثناء الفنانين ،إلخ..)",
-  "Interrogate: maximum description length": "الاستجواب: الحد الأقصى لطول الوصف",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: أقصى عدد من الأسطر في الملف النصي (0 = بلا حدود)",
-  "Interrogate: deepbooru score threshold": "الاستجواب: عتبة درجات deepbooru",
-  "Interrogate: deepbooru sort alphabetically": "الاستجواب: الترتيب الأبجدي لـ deepbooru",
-  "use spaces for tags in deepbooru": "استخدام مسافات للعلامات في deepbooru",
+  "Interrogate: keep models in VRAM": "ابق نموذج الاستجواب في ذاكرة كرت الشاشة (VRAM)",
+  "Interrogate: use artists from artists.csv": "استخدم قائمة الفنانين في الاستجواب من ملف artists.csv",
+  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "أدرج رتب الشعارات عند الاستجواب (لا تعمل مع جميع النماذج)",
+  "Interrogate: num_beams for BLIP": "عدد أشعة الاستجواب لنموذج BLIP",
+  "Interrogate: minimum description length (excluding artists, etc..)": "أقل عدد كلمات للتوصيف (لا يتضمن الفنانين وغيرهم)",
+  "Interrogate: maximum description length": "أكثر عدد كلمات للتوصيف",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "أكثر عدد أسطر لملف نصي عند الاستجواب باستخدام CLIP (0 = بدون حد)",
+  "Interrogate: deepbooru score threshold": "حد درجة الاستجواب باستخدام deepbooru",
+  "Interrogate: deepbooru sort alphabetically": "رتب نتائج توصيف deepbooru أبجديا",
+  "use spaces for tags in deepbooru": "افصل شعارات deepbooru بمسافات",
   "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "تجاهل الأقواس عند استخدام deepbooru (كي لا تعامل كأقواس التشديد)",
   "User interface": "واجهة المستخدم",
-  "Show progressbar": "إظهار شريط التقدم",
-  "Show image creation progress every N sampling steps. Set 0 to disable.": "إظهار تقدم إنشاء الصورة لكل N خطوة من خطوات أخذ العينات. قم بتعيين 0 للتعطيل.",
-  "Show previews of all images generated in a batch as a grid": "إظهار معاينة لجميع الصور التي يتم إنشائها في الدفعة كإطار شبكي",
-  "Show grid in results for web": "إظهار الإطار الشبكي في نتائج الويب",
-  "Do not show any images in results for web": "لا تظهر أي صور في نتائج الويب",
-  "Add model hash to generation information": "أضف معلومات الهاش للنموذج إلى معلومات الإنشاء",
-  "Add model name to generation information": "أضف اسم النموذج إلى معلومات الإنشاء",
-  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "عند قراءة متغيرات الإنشاء من النص إلى واجهة المستخدم (من معلومات PNG أو النص الملصق) ، لا تقم بتغيير النموذج",
-  "Font for image grids that have text": "الخط المستخدم للإطارات الشبكية للصور التي تحتوي على نص",
-  "Enable full page image viewer": "تفعيل عارض الصور ذو الصفحة الكاملة",
-  "Show images zoomed in by default in full page image viewer": "إظهار الصور التي تم تكبيرها بشكل افتراضي في عارض الصور ذو الصفحة الكاملة",
-  "Show generation progress in window title.": "إظهار تقدم الإنشاء في عنوان الشاشة.",
+  "Show progressbar": "اظهر شريط التقدم",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "اعرض صورة بعد كل كم خطوة (إذا 0 لا تعرض)",
+  "Show previews of all images generated in a batch as a grid": "اعرض كل الصور التي تم إنتاجها في حزمة كجدول",
+  "Show grid in results for web": "أظهر نتائج جداول الصور",
+  "Do not show any images in results for web": "لا تظهر نتائج الصور",
+  "Add model hash to generation information": "أضف رمز تهشير (Hash) ملف الأوزان لعوامل الإنتاج",
+  "Add model name to generation information": "أضف اسم ملف الأوزان لعوامل الإنتاج",
+  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "لا تغير الأوزان المختارة عند قراءة عوامل الإنتاج من صورة أو من ملف",
+  "Send seed when sending prompt or image to other interface": "عند إرسال صورة أو طلب ألحق البذرة أيضا",
+  "Font for image grids that have text": "نوع الخط في جداول الصور التي تحتوي على نصوص",
+  "Enable full page image viewer": "اسمح بعرض الصور في وضع ملئ الشاشة",
+  "Show images zoomed in by default in full page image viewer": "اعرض الصور مقربة عند استخدام وضع ملئ الشاشة",
+  "Show generation progress in window title.": "أظهر التقدم في عنوان النافذة",
   "Quicksettings list": "قائمة الإعدادات السريعة",
-  "Localization (requires restart)": "الترجمة (يتطلب إعادة التشغيل)",
-  "Sampler parameters": "متغيرات أنظمة اخذ العينات",
-  "Hide samplers in user interface (requires restart)": "إخفاء أنظمة أخذ العينات في واجهة المستخدم (يتطلب إعادة التشغيل)",
-  "eta (noise multiplier) for DDIM": "الوقت المتبقي (مضاعف الضوضاء) لـ DDIM",
-  "eta (noise multiplier) for ancestral samplers": "الوقت المتبقي (مضاعف الضوضاء) لأنظمة أخذ العينات التي تعتمد على الأجداد",
-  "img2img DDIM discretize": "تفصل DDIM لصورة لصورة",
-  "uniform": "التوحيد",
-  "quad": "رباعية",
-  "sigma churn": "دفع سيجما",
-  "sigma tmin": "سيجما tmin",
-  "sigma noise": "ضجيج سيجما",
-  "Eta noise seed delta": "الوقت المتوقع لديلتا بذرة الضجيج ",
-  "Images Browser": "مستعرض الصور",
-  "Preload images at startup": "تحميل الصور مسبقًا عند بدء التشغيل",
-  "Number of columns on the page": "Number of columns on the page",
-  "Number of rows on the page": "Number of rows on the page",
-  "Minimum number of pages per load": "الحد الأدنى لعدد الصفحات لكل تحميل",
-  "Request browser notifications": "طلب إشعارات المتصفح",
-  "Download localization template": "تنزيل نموذج الترجمة",
-  "Reload custom script bodies (No ui updates, No restart)": "إعادة تحميل السكريبتات المخصصة (لا توجد تحديثات لواجهة المستخدم ، لا إعادة تشغيل)",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعادة تشغيل  Gradio و تحديث مكونات واجهة الاستخدام (السكريبتات و ui.py و js و css فقط)",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "الموجه (اضغط على Ctrl + Enter أو Alt + Enter للإنشاء)",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "الموجه السلبي (اضغط على Ctrl + Enter أو Alt + Enter للإنشاء)",
-  "Add a random artist to the prompt.": "أضف فنانًا عشوائيًا إلى الموجه.",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "قراءة متغيرات الإنشاء من الموجه أو الإنشاء الأخير إذا كان الموجه فارغا في واجهة المستخدم.",
-  "Save style": "حفظ النمط",
-  "Apply selected styles to current prompt": "تطبيق الأنماط المحددة للموجه الحالي",
-  "Stop processing current image and continue processing.": "توقف عن معالجة الصورة الحالية واستمر في المعالجة.",
-  "Stop processing images and return any results accumulated so far.": "توقف عن معالجة الصور وقم بإرجاع أي نتائج متراكمة حتى الآن.",
-  "Style to apply; styles have components for both positive and negative prompts and apply to both": "النمط المطلوب تطبيقه ؛ الأنماط لها مكونات لكل من الموجهات الإيجابية والسلبية وتنطبق على كليهما",
-  "Do not do anything special": "لا تفعل أي شيء خاص",
-  "Which algorithm to use to produce the image": "الخوارزمية التي يجب استخدامها لإنتاج الصورة",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - مبدع جدًا ، يمكن لكل منها الحصول على صورة مختلفة تمامًا اعتمادًا على عدد الخطوات ، تعيين الخطوات على أعلى من 30-40 لا يعطي نتيجة مفيدة",
-  "Denoising Diffusion Implicit Models - best at inpainting": "نماذج تقليل التشويش الضمني  - أفضل اسخدام في الرسم الجزئي",
-  "Produce an image that can be tiled.": "أنتج صورة يمكن تبليطها.",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "استخدم عملية من خطوتين لإنشاء صورة جزئيًا بدقة أقل ثم ترقيتها و تحسين تفاصيلها دون تغيير التكوين",
-  "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.": "يحدد مدى ضآلة احترام الخوارزمية لمحتوى الصورة. عند 0 ، لن يتغير شيء ، وعند 1 ستحصل على صورة لا صلة لها بالصورة الأصلية. مع وجود قيم أقل من 1.0 ، المعالجة ستأخذ خطوات أقل مما يحدده شريط تمرير خطوات المعالجة.",
-  "How many batches of images to create": "كم عدد دفعات الصور التي يتم إنشائها",
-  "How many image to create in a single batch": "كم صورة يتم إنشائها دفعة واحدة",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "مقياس التوجيه الحر للمصنف - إلى أي مدى يجب أن تتوافق الصورة مع الموجه - القيم المنخفضة تنتج نتائج أكثر إبداعًا",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "القيمة التي تحدد ناتج منشئ الأرقام العشوائية - إذا قمت بإنشاء صورة بنفس المتغيرات و بذرة الصورة ، فستحصل على نفس النتيجة",
-  "Set seed to -1, which will cause a new random number to be used every time": "عيّن البذرة إلى -1 ، مما سيؤدي إلى استخدام رقم عشوائي جديد في كل مرة",
-  "Reuse seed from last generation, mostly useful if it was randomed": "إعادة استخدام البذرة من الإنشاء الأخير ، وتكون مفيدة في الغالب إذا كانت عشوائية",
-  "Seed of a different picture to be mixed into the generation.": "استخدام بذرة من صورة مختلفة ليتم مزجها في الإنشاء.",
-  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "ما مدى قوة التباين عند الإنشاء. 0 ، لن يكون هناك أي تأثير. 1 ، ستحصل على الصورة الكاملة ببذور التباين (باستثناء أنظمة عينات الأسلاف ، حيث ستحصل على شيء ما فقط).",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "قم بمحاولة لإنتاج صورة مشابهة لما تم إنتاجه بنفس البذرة بناءا على دقة محددة",
-  "This text is used to rotate the feature space of the imgs embs": "This text is used to rotate the feature space of the imgs embs",
-  "Separate values for X axis using commas.": "افصل بين قيم المحور X باستخدام الفواصل.",
-  "Separate values for Y axis using commas.": "افصل بين قيم المحور Y باستخدام الفواصل.",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "اكتب الصورة إلى مجلد (الافتراضي -  log/images) وتوليد المتغيرات في ملف csv.",
-  "Open images output directory": "افتح مجلد مخرجات الصور",
-  "How much to blur the mask before processing, in pixels.": "كم يتم تضبيب القناع قبل المعالجة ، بالبكسل.",
-  "What to put inside the masked area before processing it with Stable Diffusion.": "ما يجب وضعه داخل المنطقة المقنعة قبل معالجتها باستخدام الانتشار المستقر.",
-  "fill it with colors of the image": "املأها بألوان الصورة",
-  "keep whatever was there originally": "احتفظ بكل ما كان هناك في الأصل",
-  "fill it with latent space noise": "املأه بضوضاء الفضاء الكامنة",
-  "fill it with latent space zeroes": "املأه بأصفار الفضاء الكامنة",
-  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "قم بترقية المنطقة المقنعة إلى الدقة المستهدفة و الرسم الجزئي ثم تقليص الحجم  واللصق في الصورة الأصلية",
-  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "تغيير حجم الصورة إلى الدقة المستهدفة. ما لم يتطابق الطول والعرض ، ستحصل على نسبة عرض إلى ارتفاع غير صحيحة.",
-  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "قم بتغيير حجم الصورة بحيث يتم ملء الدقة المستهدفة بالكامل بالصورة. يتم قطع المناطق التي تبرز.",
-  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "قم بتغيير حجم الصورة بحيث تكون الصورة بأكملها داخل الدقة المستهدفة. املأ المساحة الفارغة بألوان الصورة.",
-  "How many times to repeat processing an image and using it as input for the next iteration": "كم مرة يتم تكرار معالجة صورة واستخدامها  كمدخل للتكرار التالي",
-  "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.": "في وضع حلقة الاسترجاع ، يتم ضرب قوة تقليل الضوضاء بهذه القيمة في كل حلقة. يعني <1 تقليل التنوع بحيث يتقارب التسلسل الخاص بك على صورة ثابتة. > 1 يعني زيادة التنوع بحيث يصبح تسلسلك أكثر وأكثر فوضوية.",
-  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "الترقية باستخدام الانتشار المستقر ، كم يجب أن يكون مقدار التداخل بالبكسل بين المربعات. تتداخل المربعات بحيث لا يكون هناك خط واضح للعيان عند دمجها مرة أخرى في صورة واحدة.",
-  "A directory on the same machine where the server is running.": "مجلد على نفس الجهاز حيث يتم تشغيل الخادم.",
-  "Leave blank to save images to the default path.": "اتركه فارغًا لحفظ الصور في المسار الافتراضي.",
-  "Result = A * (1 - M) + B * M": "النتيجة = A * (1 - M) + B * M",
-  "Result = A + (B - C) * M": "النتيجة = A + (B - C) * M",
-  "1st and last digit must be 1. ex:'1, 2, 1'": "يجب أن يكون الرقم الأول والأخير 1. على سبيل المثال: '1 ، 2 ، 1'",
-  "Path to directory with input images": "المسار إلى مجلد الصور المدخلة",
-  "Path to directory where to write outputs": "المسار إلى مجلد كتابة النتائج",
-  "Input images directory": "مجلد الصور المدخلة",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم العلامات التالية لتعريف كيفية اختيار أسماء الملفات للصور: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] ؛ اتركه فارغا للوضع الافتراضي.",
-  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "إذا تم تمكين هذا الخيار ، فلن تتم إضافة العلامة المائية إلى الصور التي تم إنشاؤها. تحذير: إذا لم تقم بإضافة علامة مائية ، فقد تكون تتصرف بطريقة غير أخلاقية.",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم العلامات التالية لتحديد كيفية اختيار المجادت الفرعية للصور و الإطارات الشبكية : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] ؛ اتركه فارغا للوضع الافتراضي.",
-  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "سيتم استخدام هذا التعبير العادي لاستخراج الكلمات من اسم الملف ، وسيتم ضمها باستخدام الخيار أدناه في نص التسمية المستخدم للتدريب. اتركه فارغًا للاحتفاظ بنص اسم الملف كما هو.",
-  "This string will be used to join split words into a single line if the option above is enabled.": "سيتم استخدام هذا النص لضم الكلمات المقسمة إلى سطر واحد إذا تم تمكين الخيار أعلاه.",
-  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "قائمة أسماء الإعدادات ، مفصولة بفواصل ، للإعدادات التي يجب أن تنتقل إلى شريط الوصول السريع في الأعلى ، بدلاً من علامة تبويب الإعداد المعتادة. انظر modules / shared.py للتعرف على أسماء الإعدادات. يتطلب إعادة التشغيل للتطبيق.",
-  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "إذا كانت هذه القيم غير صفرية ، فستتم إضافتها إلى البذرة واستخدامها لتهيئة فوضى الضوضاء عند استخدام أنظمة أخذ العينات باستخدام الوقت المتبقي. يمكنك استخدام ذلك لإنتاج المزيد من التنوعات في الصور ، أو يمكنك استخدامه لمطابقة صور البرامج الأخرى إذا كنت تعرف ما تفعله."
-}
+  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "قائمة مقسمة بفواصل لأسماء الإعدادات التي يجب أن تظهر في الأعلى لتسهيل الوصول إليها، انظر إلى modules/shared.py لمعرفة الأسماء، يتطلب إعادة تشغيل",
+  "Localization (requires restart)": "اللغة (تتطلب إعادة تشغيل)",
+  "pt_BR": "البرتغالية",
+  "zh_CN": "الصينية",
+  "ko_KR": "الكورية",
+  "fr_FR": "الفرنسية",
+  "ru_RU": "الروسية",
+  "ar_AR": "العربية",
+  "tr_TR": "التركية",
+  "it_IT": "الإيطالية",
+  "ja_JP": "اليابانية",
+  "de_DE": "الألمانية",
+  "zh_TW": "الصينية (تايوان)",
+  "es_ES": "الإسبانية",
+  "Sampler parameters": "عوامل أساليب الخطو",
+  "Hide samplers in user interface (requires restart)": "اخف أساليب الخطو التالية (يتطلب إعادة تشغيل)",
+  "eta (noise multiplier) for DDIM": "العامل Eta η لأسلوب الخطو DDIM",
+  "eta (noise multiplier) for ancestral samplers": "العامل Eta η لأساليب الخطو السلفية (Ancestral)",
+  "img2img DDIM discretize": "طريقة التقطيع (Discretization) لأسلوب الخطو DDIM في وضع صورة إلى صورة",
+  "uniform": "خطية",
+  "quad": "تربيعية",
+  "sigma churn": "العشوائية (Schurn)",
+  "sigma tmin": "أدنى تشويش (Stmin)",
+  "sigma noise": "التشويش (Snoise)",
+  "Eta noise seed delta": "إزاحة بذرة أساليب الخطو التي تستعمل العامل Eta η",
+  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "هذا الرقم سيتم إضافته إلى البذرة عند استخدام أحد أساليب الخطو التي تستعمل العامل Eta η، يفيد هذا الخيار في مشابهة نتائج بعض البرامج الأخرى التي تستعمله",
+  "Images Browser": "معرض الصور",
+  "Preload images at startup": "حمل الصور عند بدء التشغيل",
+  "Number of columns on the page": "عدد الأعمدة في كل صفحة",
+  "Number of rows on the page": "عدد الصفوف في كل صفحة",
+  "Minimum number of pages per load": "أقل عدد صور يتم تحميلها كل مرة",
+  "Request browser notifications": "اطلب تنبيهات المتصفح",
+  "Download localization template": "حمل ملف اللغة",
+  "Reload custom script bodies (No ui updates, No restart)": "أعد تحميل الأدوات الخاصة (بدون واجهة المستخدم ولا يحتاج إعادة تشغيل)",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعد تشغيل gradio وتحميل الأدوات الخاصة وواجهة المستخدم",
+  "⤡": "⤡",
+  "⊞": "⊞",
+  "×": "×",
+  "❮": "❮",
+  "❯": "❯",
+  "•": "•",
+  "Label": "Label",
+  "File": "File",
+  "Image": "Image",
+  "Check progress": "Check progress",
+  "Check progress (first)": "Check progress (first)",
+  "Textbox": "Textbox",
+  "Image for img2img": "Image for img2img",
+  "Image for inpainting with mask": "Image for inpainting with mask",
+  "Mask": "Mask",
+  "Mask mode": "Mask mode",
+  "Masking mode": "Masking mode",
+  "Resize mode": "Resize mode",
+  "Prev batch": "Prev batch",
+  "Next batch": "Next batch",
+  "Refresh page": "Refresh page",
+  "Date to": "Date to",
+  "Number": "Number",
+  "set_index": "set_index",
+  "Checkbox": "Checkbox"
+}

localizations/de_DE.json

@@ -0,0 +1,458 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "view": "API ",
+    "api": "anzeigen",
+    "•": " • ",
+    "built with gradio": "Mit Gradio erstellt",
+    "Loading...": "Lädt...",
+    "Stable Diffusion checkpoint": "Stable Diffusion Checkpoint",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Extras",
+    "PNG Info": "PNG Info",
+    "Checkpoint Merger": "Checkpoint Fusion",
+    "Train": "Trainieren",
+    "Settings": "Einstellungen",
+    "Prompt": "Prompt",
+    "Negative prompt": "Negative Prompt",
+    "Run": "Ausführen",
+    "Skip": "Überspringen",
+    "Interrupt": "Abbrechen",
+    "Generate": "Generieren",
+    "Style 1": "Stil 1",
+    "Style 2": "Stil 2",
+    "Label": "Bezeichnung",
+    "File": "Datei",
+    "Drop File Here": "Datei hier ablegen",
+    "-": "-",
+    "o": "oder",
+    "Click to Upload": "Hochladen",
+    "Image": "Bild",
+    "Check progress": "Fortschitt prüfen",
+    "Check progress (first)": "Fortschritt prüfen (Initial)",
+    "Sampling Steps": "Samplingschritte",
+    "Sampling method": "Samplingmethode",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Breite",
+    "Height": "Höhe",
+    "Restore faces": "Gesichter wiederherstellen",
+    "Tiling": "Kacheln",
+    "Highres. fix": "Highres. Fix",
+    "Firstpass width": "Breite Erstdurchlauf",
+    "Firstpass height": "Höhe Erstdurchlauf",
+    "Denoising strength": "Denoisingstärke",
+    "Batch count": "Batchanzahl",
+    "Batch size": "Batchgröße",
+    "CFG Scale": "CFG-Skala",
+    "Seed": "Seed",
+    "Extra": "Extra",
+    "Variation seed": "Variationsseed",
+    "Variation strength": "Variationsstärke",
+    "Resize seed from width": "Seed von Breite ändern",
+    "Resize seed from height": "Seed von Höhe ändern",
+    "Script": "Skript",
+    "None": "Nichts",
+    "Prompt matrix": "Promptmatrix",
+    "Prompts from file or textbox": "Prompts aus Datei oder Textfeld",
+    "X/Y plot": "X/Y Graph",
+    "Put variable parts at start of prompt": "Variable teile am start des Prompt setzen",
+    "Iterate seed every line": "Iterate seed every line",
+    "List of prompt inputs": "List of prompt inputs",
+    "Upload prompt inputs": "Upload prompt inputs",
+    "X type": "X-Typ",
+    "Nothing": "Nichts",
+    "Var. seed": "Var. seed",
+    "Var. strength": "Var. strength",
+    "Steps": "Schritte",
+    "Prompt S/R": "Prompt Suchen/Ersetzen",
+    "Prompt order": "Promptreihenfolge",
+    "Sampler": "Sampler",
+    "Checkpoint name": "Checkpointname",
+    "Hypernetwork": "Hypernetwork",
+    "Hypernet str.": "Hypernet str.",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "Sigma min",
+    "Sigma max": "Sigma max",
+    "Sigma noise": "Sigma noise",
+    "Eta": "Eta",
+    "Clip skip": "Clip skip",
+    "Denoising": "Denoising",
+    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
+    "X values": "X-Werte",
+    "Y type": "Y-Typ",
+    "Y values": "Y-Werte",
+    "Draw legend": "Legende zeichnen",
+    "Include Separate Images": "Seperate Bilder hinzufügen",
+    "Keep -1 for seeds": "-1 als Seed behalten",
+    "Save": "Speichern",
+    "Send to img2img": "An img2img senden",
+    "Send to inpaint": "An Inpaint senden",
+    "Send to extras": "An Extras senden",
+    "Make Zip when Save?": "Zip beim Speichern erstellen?",
+    "Textbox": "Textfeld",
+    "Interrogate\nCLIP": "Interrogate\nCLIP",
+    "Interrogate\nDeepBooru": "Interrogate\nDeepBooru",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Batch img2img",
+    "Image for img2img": "Bild für img2img",
+    "Drop Image Here": "Bild hier ablegen",
+    "Image for inpainting with mask": "Bild für inpainting mit Maske",
+    "Mask": "Maske",
+    "Mask blur": "Maskenunschärfe",
+    "Mask mode": "Maskenmodus",
+    "Draw mask": "Maske zeichnen",
+    "Upload mask": "Maske hochladen",
+    "Masking mode": "Maskierungsmodus",
+    "Inpaint masked": "Maskiertes inpainten",
+    "Inpaint not masked": "Nicht maskiertes inpainten",
+    "Masked content": "Maskierter Inhalt",
+    "fill": "ausfüllen",
+    "original": "original",
+    "latent noise": "latent noise",
+    "latent nothing": "latent nothing",
+    "Inpaint at full resolution": "Inpaint mit voller Auflösung",
+    "Inpaint at full resolution padding, pixels": "Inpaint bei voller Auflösung Abstand, Pixel",
+    "Process images in a directory on the same machine where the server is running.": "Bilder in einem Verzeichnis auf demselben Rechner verarbeiten, auf dem der Server läuft.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Ein leeres Ausgabeverzeichnis verwenden, um Bilder normal zu speichern, anstatt in das Ausgabeverzeichnis zu schreiben.",
+    "Input directory": "Eingabeverzeichnis",
+    "Output directory": "Ausgabeverzeichnis",
+    "Resize mode": "Größenänderungsmodus",
+    "Just resize": "Nur Größe anpassen",
+    "Crop and resize": "Zuschneiden und Größe anpassen",
+    "Resize and fill": "Größe anpassen und ausfüllen",
+    "img2img alternative test": "img2img alternativer Test",
+    "Loopback": "Loopback",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man's outpainting": "Poor man's outpainting",
+    "SD upscale": "SD-Upscale",
+    "should be 2 or lower.": "Sollte 2 oder niedriger sein.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "`Samplingmethode` auf Euler setzen? (Diese Methode is dafür ausgelegt)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "`Prompt` auf denselben Wert wie `Originale Prompt` (und `Negative Prompt`) setzen?",
+    "Original prompt": "Originale Prompt",
+    "Original negative prompt": "Originale negative Prompt",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "`Samplingschritte` auf denselben Wert wie `Dekodierschritte` setzen?",
+    "Decode steps": "Dekodierschritte",
+    "Override `Denoising strength` to 1?": "`Denoisingstärke auf 1 setzen?",
+    "Decode CFG scale": "CFG-Skala dekodieren",
+    "Randomness": "Zufälligkeit",
+    "Sigma adjustment for finding noise for image": "Sigma-Anpassung für die Suche nach Noise des Bildes",
+    "Loops": "Schleifen",
+    "Denoising strength change factor": "Denoising strength change factor",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Empfohlene Einstellungen: Samplingschritte: 80-100, Samplermethode: Euler a, Denoisingstärke: 0.8",
+    "Pixels to expand": "Pixel zum Erweitern",
+    "Outpainting direction": "Outpainting Richtung",
+    "left": "Links",
+    "right": "Rechts",
+    "up": "Hoch",
+    "down": "Runter",
+    "Fall-off exponent (lower=higher detail)": "Abfallexponent (niedriger=mehr Details)",
+    "Color variation": "Farbabweichung",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Skaliert das Bild auf die doppelte Größe; Benutze die Schieberegler für Breite und Höhe, um die Kachelgröße einzustellen",
+    "Tile overlap": "Kachelüberlappung",
+    "Upscaler": "Upscaler",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "SwinIR 4x": "SwinIR 4x",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "Single Image": "Einzelnes Bild",
+    "Batch Process": "Batchverarbeitung",
+    "Batch from Directory": "Batchverarbeitung aus Verzeichnis",
+    "Source": "Quelle",
+    "Show result images": "Bildergebnisse zeigen",
+    "Scale by": "Skalieren um",
+    "Scale to": "Skalieren zu",
+    "Resize": "Größe anpassen",
+    "Crop to fit": "Zuschneiden damit es passt",
+    "Upscaler 2 visibility": "Upscaler 2 Sichtbarkeit",
+    "GFPGAN visibility": "GFPGAN Sichtbarkeit",
+    "CodeFormer visibility": "CodeFormer Sichtbarkeit",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer Gewichtung (0 = maximale Wirkung, 1 = minimale Wirkung)",
+    "Upscale Before Restoring Faces": "Upscale Before Restoring Faces",
+    "Send to txt2img": "An txt2img senden",
+    "A merger of the two checkpoints will be generated in your": "Die zusammgeführten Checkpoints werden gespeichert unter",
+    "checkpoint": "Checkpoint",
+    "directory.": "Verzeichnis.",
+    "Primary model (A)": "Primäres Modell (A)",
+    "Secondary model (B)": "Sekundäres Modell (B)",
+    "Tertiary model (C)": "Tertiäres Modell (C)",
+    "Custom Name (Optional)": "Eigener Name (Optional)",
+    "Multiplier (M) - set to 0 to get model A": "Multiplikator (M) - auf 0 setzen, um Modell A zu erhalten",
+    "Interpolation Method": "Interpolationsmethode",
+    "Weighted sum": "Weighted sum",
+    "Add difference": "Add difference",
+    "Save as float16": "Speichern als float16",
+    "See": "Siehe ",
+    "wiki": "Wiki ",
+    "for detailed explanation.": "für eine ausführliche Erklärung.",
+    "Create embedding": "Embedding erstellen",
+    "Create hypernetwork": "Hypernetwork erstellen",
+    "Preprocess images": "Bilder vorbereiten",
+    "Name": "Name",
+    "Initialization text": "Initialisierungstext",
+    "Number of vectors per token": "Anzahl der Vektoren pro Token",
+    "Overwrite Old Embedding": "Alte Embeddings überschreiben",
+    "Modules": "Module",
+    "Enter hypernetwork layer structure": "Hypernetwork-Ebenenstruktur angeben",
+    "Select activation function of hypernetwork": "Aktivierungsfunktion des Hypernetwork auswählen",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "threshold",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Auswahl der Initialisierung der Ebenengewichte. Empfohlen wird relu-like - Kaiming, sigmoid-like - Xavier",
+    "Normal": "Normal",
+    "KaimingUniform": "KaimingUniform",
+    "KaimingNormal": "KaimingNormal",
+    "XavierUniform": "XavierUniform",
+    "XavierNormal": "XavierNormal",
+    "Add layer normalization": "Ebenennormalisierung hinzufügen",
+    "Use dropout": "Dropout benutzen",
+    "Overwrite Old Hypernetwork": "Altes Hypernetwork überschreiben",
+    "Source directory": "Quellenverzeichnis",
+    "Destination directory": "Zielverzeichnis",
+    "Existing Caption txt Action": "Vorhandene Beschriftung der txt",
+    "ignore": "ignorieren",
+    "copy": "kopieren",
+    "prepend": "voranstellen",
+    "append": "anhängen",
+    "Create flipped copies": "Gespiegelte Bilder erstellen",
+    "Split oversized images": "Übergroße Bilder aufteilen",
+    "Auto focal point crop": "Automatisch auf Fokuspunkt zuschneiden",
+    "Use BLIP for caption": "BLIP für Beschriftung nutzen",
+    "Use deepbooru for caption": "Deepbooru für Beschriftung nutzen",
+    "Split image threshold": "Schwellenwert für die Aufteilung von Bildern",
+    "Split image overlap ratio": "Überschneidungsverhältnis der Teilbilder",
+    "Focal point face weight": "Fokuspunkt Gesicht Gewicht",
+    "Focal point entropy weight": "Fokuspunkt Entropie Gewicht",
+    "Focal point edges weight": "Fokuspunkt Kanten Gewicht",
+    "Create debug image": "Testbild erstellen",
+    "Preprocess": "Vorbereiten",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Trainieren eines Embeddings oder eines Hypernetworks; Sie müssen ein Verzeichnis mit einem Satz von Bildern im Verhältnis 1:1 angeben",
+    "[wiki]": "[Wiki]",
+    "Embedding": "Embedding",
+    "Embedding Learning rate": "Embedding Lernrate",
+    "Hypernetwork Learning rate": "Hypernetwork Lernrate",
+    "Dataset directory": "Datensatzverzeichnis",
+    "Log directory": "Protokollverzeichnis",
+    "Prompt template file": "Prompt-Vorlagendatei",
+    "Max steps": "Max Schritte",
+    "Save an image to log directory every N steps, 0 to disable": "Speichere alle N Schritte ein Bild im Protokollverzeichnis, 0 zum Deaktivieren",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Speichere alle N Schritte eine Embeddingkopie im Protokollverzeichnis, 0 zum Deaktivieren",
+    "Save images with embedding in PNG chunks": "Speichere Bilder mit Embeddings in PNG Chunks",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Lese Parameter (Prompt, etc...) aus dem txt2img-Tab beim Erstellen von Vorschaubildern.",
+    "Train Hypernetwork": "Hypernetwork Trainieren",
+    "Train Embedding": "Embedding Trainieren",
+    "Apply settings": "Eintellungen anwenden",
+    "Saving images/grids": "Bilder/Raster speichern",
+    "Always save all generated images": "Immer alle generierten Bilder speichern",
+    "File format for images": "Dateiformat für Bilder",
+    "Images filename pattern": "Dateinamensmuster für Bilder",
+    "Add number to filename when saving": "Beim speichern, dem Dateinamen Nummer anhängen",
+    "Always save all generated image grids": "Immer alle generierten Bildraster speichern",
+    "File format for grids": "Dateiformat für Raster",
+    "Add extended info (seed, prompt) to filename when saving grid": "Beim Speichern von Rastern zusätzliche Information (Seed, Prompt) hinzufügen",
+    "Do not save grids consisting of one picture": "Keine Raster speichern, die nur aus einem Bild bestehen",
+    "Prevent empty spots in grid (when set to autodetect)": "Lücken im Raster verhindern (falls auf Auto-Erkennung gesetzt)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Rasterreihenanzahl; -1 für Auto-Erkennung und 0 für die gleiche wie die Batchanzahl",
+    "Save text information about generation parameters as chunks to png files": "Generationsparameter als Chunks in PNG-Dateien speichern",
+    "Create a text file next to every image with generation parameters.": "Erstelle zu jedem Bild eine Textdatei, die die Generationsparameter enthält",
+    "Save a copy of image before doing face restoration.": "Vor der Gesichtswiederhestellung eine Kopie des Bildes speichern",
+    "Quality for saved jpeg images": "Qualität der JPEG-Bilder",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Wenn ein PNG-Bild größer als 4MB oder die Dimensionen größer als 4000 ist, herunterskalieren und als JPG speichern.",
+    "Use original name for output filename during batch process in extras tab": "Orginale Dateinamen als Ausgabenamen bei der Batchverarbeitung im Extras-Tab verwenden",
+    "When using 'Save' button, only save a single selected image": "Bei der Benutzung des 'Speichern'-Knopfes, nur das ausgewählte Bild speichern",
+    "Do not add watermark to images": "Den Bildern kein Wasserzeichen hinzufügen",
+    "Paths for saving": "Pfade zum Speichern",
+    "Output directory for images; if empty, defaults to three directories below": "Ausgabeverzeichnis für Bilder; Falls leer, werden die Pfade unterhalb verwendet",
+    "Output directory for txt2img images": "Ausgabeverzeichnis für txt2img Bilder",
+    "Output directory for img2img images": "Ausgabeverzeichnis für img2img Bilder",
+    "Output directory for images from extras tab": "Ausgabeverzeichnis für Extras-Tab Bilder",
+    "Output directory for grids; if empty, defaults to two directories below": "Ausgabeverzeichnis für Raster; Falls leer, werden die Pfade unterhalb verwendet",
+    "Output directory for txt2img grids": "Ausgabeverzeichnis für txt2img Raster",
+    "Output directory for img2img grids": "Ausgabeverzeichnis für img2img Raster",
+    "Directory for saving images using the Save button": "Ausgabeverzeichnis für Bilder, die mit dem 'Speichern'-Knopf gespeichert wurden",
+    "Saving to a directory": "Speichern in ein Verzeichnis",
+    "Save images to a subdirectory": "Bilder in ein Unterverzeichnis speichern",
+    "Save grids to a subdirectory": "Raster in ein Unterverzeichnis speichern",
+    "When using \"Save\" button, save images to a subdirectory": "Bilder bei der Benutzung des 'Speichern'-Knopfes in ein Unterverzeichnis speichern",
+    "Directory name pattern": "Muster für Verzeichnisnamen",
+    "Max prompt words for [prompt_words] pattern": "Maximale Wortanzahl für [prompt_words] Muster",
+    "Upscaling": "Upscaling",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Kachelgröße für ESRGAN-Upscaler. 0 = keine Kacheln.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Kachelüberlappung in Pixeln für ESRGAN-Upscaler. Niedrige Werte = sichtbare Naht.",
+    "Tile size for all SwinIR.": "Kachelgröße für alle SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Kachelüberlappung in Pixeln für SwinIR. Niedrige Werte = sichtbare Naht.",
+    "LDSR processing steps. Lower = faster": "LDSR-Verarbeitungsschritte. Niedriger = schneller",
+    "Upscaler for img2img": "Upscaler für img2img",
+    "Upscale latent space image when doing hires. fix": "Bild des Latent Space upscalen, wenn Highres. Fix benutzt wird",
+    "Face restoration": "Gesichtswiederhestellung",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer Gewichtung (0 = maximale Wirkung, 1 = minimale Wirkung)",
+    "Move face restoration model from VRAM into RAM after processing": "Verschiebe Gesichtswiederhestellung-Modell nach der Verarbeitung vom VRAM in den RAM",
+    "System": "System",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM-Nutzungsabfragen pro Sekunde während der Generierung. Zum Deaktivieren auf 0 setzen.",
+    "Always print all generation info to standard output": "Immer alle Generationsinformationen in der Standardausgabe ausgeben",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Der Konsole einen zweiten Fortschrittsbalken hinzufügen, der den Fortschritt eines gesamten Auftrags anzeigt.",
+    "Training": "Training",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "VAE und CLIP während des Hypernetwork-Trainings in den RAM verschieben. Spart VRAM.",
+    "Filename word regex": "Filename word regex",
+    "Filename join string": "Filename join string",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Anzahl der Wiederholungen für ein einzelnes Eingabebild pro Epoche; wird nur für die Anzeige der Epochennummer verwendet",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Speichere eine csv-Datei, die den Verlust enthält, im Protokollverzeichnis alle N Schritte, 0 zum Deaktivieren",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Checkpoints zum Zwischenspeichern im RAM",
+    "Hypernetwork strength": "Hypernetworkstärke",
+    "Inpainting conditioning mask strength": "Inpainting Stärke der Konditionierungsmaske",
+    "Apply color correction to img2img results to match original colors.": "Farbkorrektur auf die img2img-Ergebnisse anwenden, damit sie den Originalfarben entsprechen.",
+    "Save a copy of image before applying color correction to img2img results": "Vor dem Anwenden der Farbkorrektur eine Kopie des Bildes speichern",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Mit img2img, die exakte Anzahl der Schritte ausführen, die vom Schieberegler angegeben sind (normalerweise weniger bei weniger Denoising).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Aktivieren der Quantisierung in K-Samplern für schärfere und sauberere Ergebnisse. Dies kann bestehende Seeds verändern. Erfordert Neustart.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Hervorhebung: Verwenden Sie (Text), damit das Modell dem Text mehr Aufmerksamkeit schenkt, und [Text], damit es ihm weniger Aufmerksamkeit schenkt",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Verwenden der alten Implementierung von Hervorhebungen. Kann nützlich sein, um alte Seeds zu reproduzieren.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-Diffusions-Sampler erzeugen in einem Batch die gleichen Bilder, wie bei der Erstellung eines einzelnen Bildes",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Erhöhung der Kohärenz durch Auffüllen ab dem letzten Komma innerhalb von n Token, wenn mehr als 75 Token verwendet werden",
+    "Filter NSFW content": "NSFW-Inhalte filtern",
+    "Stop At last layers of CLIP model": "Stoppe bei den letzten Schichten des CLIP-Modells",
+    "Interrogate Options": "Interrogate Optionen",
+    "Interrogate: keep models in VRAM": "Interrogate: Modelle im VRAM behalten",
+    "Interrogate: use artists from artists.csv": "Interrogate: Künstler aus 'artists.csv' nutzen",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: Die Rangfolge von Modell-Tags in den Ergebnissen einschließen (hat keine Auswirkung auf beschriftungsbasierte Interrogator).",
+    "Interrogate: num_beams for BLIP": "Interrogate: num_beams für BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimale Beschreibungslänge (Künstler, etc.. ausgenommen)",
+    "Interrogate: maximum description length": "Interrogate: maximale Beschreibungslänge",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximale Anzahl an Zeilen in Textdatei (0 = Kein Limit)",
+    "Interrogate: deepbooru score threshold": "Interrogate: Deepbooru minimale Punkteanzahl",
+    "Interrogate: deepbooru sort alphabetically": "Interrogate: Sortiere Deepbooru alphabetisch",
+    "use spaces for tags in deepbooru": "Benutze Leerzeichen für Deepbooru-Tags",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Escape-Klammern (\\) in Deepbooru (damit sie als normale Klammern und nicht zur Hervorhebung verwendet werden)",
+    "User interface": "Benutzeroberfläche",
+    "Show progressbar": "Fortschrittsleiste anzeigen",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Zeige eine Bildvorschau alle N Samplingschritte. Zum Deaktivieren auf 0 setzen.",
+    "Show previews of all images generated in a batch as a grid": "Zeige eine Vorschau aller erzeugten Bilder in einem Batch als Raster",
+    "Show grid in results for web": "Zeige Raster in der Web-UI Vorschau",
+    "Do not show any images in results for web": "Keine Bilder in der Web-UI Vorschau zeigen",
+    "Add model hash to generation information": "Hash des Modells zu den Generationsinformationen hinzufügen",
+    "Add model name to generation information": "Name des Modells zu den Generationsinformationen hinzufügen",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Beim Einlesen von Generierungsparametern aus Text in die Benutzeroberfläche (aus PNG-Info oder eingefügtem Text) wird das ausgewählte Modell/Checkpoint nicht geändert.",
+    "Send seed when sending prompt or image to other interface": "Den Seed, beim Senden des Bildes/Prompt zu einem anderen Tab, mitsenden",
+    "Font for image grids that have text": "Schriftart für Bildraster mit Text",
+    "Enable full page image viewer": "Ganzseitenbildbetrachter aktivieren",
+    "Show images zoomed in by default in full page image viewer": "Standardmäßig Bilder im Ganzseitenbildbetrachter vergrößert anzeigen",
+    "Show generation progress in window title.": "Generationsfortschritt im Fenstertitel anzeigen.",
+    "Quicksettings list": "Schnellzugriffsleiste",
+    "Localization (requires restart)": "Lokalisierung (Erfordert Neustart)",
+    "Sampler parameters": "Samplerparameter",
+    "Hide samplers in user interface (requires restart)": "Sampler in der Benutzeroberfläche verstecken (Erfordert Neustart)",
+    "eta (noise multiplier) for DDIM": "Eta (noise Multiplikator) für DDIM",
+    "eta (noise multiplier) for ancestral samplers": "Eta (noise Multiplikator) für Ancestral Sampler",
+    "img2img DDIM discretize": "img2img DDIM diskretisieren",
+    "uniform": "uniform",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "Eta noise seed delta",
+    "Request browser notifications": "Browserbenachrichtigungen anfordern",
+    "Download localization template": "Vorlage für Lokalisierung herunterladen",
+    "Reload custom script bodies (No ui updates, No restart)": "Benutzerdefinierte Skripte neu laden (keine Aktualisierung der Benutzeroberfläche, kein Neustart)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradio neu starten und Komponenten aktualisieren (nur Custom Scripts, ui.py, js und css)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (zum Erzeugen Strg+Eingabe oder Alt+Eingabe drücken)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Negative Prompt (zum Erzeugen Strg+Eingabe oder Alt+Eingabe drücken)",
+    "Add a random artist to the prompt.": "Zufälligen Künstler der Prompt hinzufügen.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lesen der Generationsparameter aus der Prompt oder der letzten Generation (wenn Prompt leer ist) in die Benutzeroberfläche.",
+    "Save style": "Stil speichern",
+    "Apply selected styles to current prompt": "Momentan ausgewählte Stile auf die Prompt anwenden",
+    "Stop processing current image and continue processing.": "Verarbeitung des momentanen Bildes abbrechen und Verarbeitung fortsetzen.",
+    "Stop processing images and return any results accumulated so far.": "Verarbeitung abbrechen und alle bisherigen Ergebnisse ausgeben.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stil, der angwendet werden soll. Stile haben sowohl positive als auch negative Promptanteile und werden auf beide angewandt.",
+    "Do not do anything special": "Nichts besonderes machen",
+    "Which algorithm to use to produce the image": "Der zu benutzende Algorithmus für die Bildgeneration",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - sehr kreativ, kann sehr unterschiedliche Bilder in Abhängigkeit von der Schrittanzahl bekommen. Werte höher als 30-40 helfen nicht.",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Modelle - am besten für inpainting",
+    "Produce an image that can be tiled.": "Bild erzeugen, dass gekachelt werden kann.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Verwendung eines zweistufigen Prozesses, um ein Bild mit geringerer Auflösung zu erstellen, hochzuskalieren und dann die Details zu verbessern, ohne die Komposition zu verändern.",
+    "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.": "Bestimmt, wie wenig Bezug der Algorithmus zu dem Inhalt des Bildes haben soll. Bei 0 ändert sich nichts, und bei 1 besitzt das Bild keinen Bezug. Bei Werten unter 1,0 erfolgt die Verarbeitung in weniger Schritten, als der Schieberegler angibt.",
+    "How many batches of images to create": "Wie viele Sätze von Bildern erstellt werden sollen",
+    "How many image to create in a single batch": "Wie viele Bilder in einem Batch erstellt werden sollen",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - wie stark das Bild der Prompt entsprechen soll - niedrigere Werte führen zu kreativeren Ergebnissen",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Ein Wert, der die Ausgabe des Zufallszahlengenerators bestimmt: Wenn ein Bild mit denselben Parametern und demselben Seed wie ein anderes Bild erstellt wird, erhält man dasselbe Ergebnis.",
+    "Set seed to -1, which will cause a new random number to be used every time": "Seed auf -1 setzen, so dass jedes Mal eine neue Zufallszahl verwendet wird",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Wiederverwendung des Seeds der letzten Generation, meist nützlich, wenn er zufällig gewählt wurde",
+    "Seed of a different picture to be mixed into the generation.": "Seed eines anderen Bildes, der bei der Erzeugung reingemischt wird.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Wie stark die Veränderung sein soll. Bei 0 gibt es keinen Effekt. Bei 1 erhält man das vollständige Bild mit dem Variationsseed (außer bei Ancestral Samplern, wie Euler A, wo man nur etwas erhält).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Versuche ein Bild zu erzeugen, das dem ähnelt, das mit dem Seed bei der angegebenen Auflösung erzeugt worden wäre.",
+    "Separate values for X axis using commas.": "Trenne die Werte für die X-Achse durch Kommas.",
+    "Separate values for Y axis using commas.": "Trenne die Werte für die Y-Achse durch Kommas.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Bild in ein Verzeichnis (Standard - log/images) und Generationsparameter in eine csv-Datei schreiben.",
+    "Open images output directory": "Ausgabeverzeichnis öffnen",
+    "How much to blur the mask before processing, in pixels.": "Wie stark die Maske vor der Verarbeitung weichgezeichnet werden soll, in Pixeln.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Was in den maskierten Bereich vor der Verarbeitung mit Stable Diffusion soll.",
+    "fill it with colors of the image": "Füllen mit den Farben des Bildes",
+    "keep whatever was there originally": "Originalen Inhalt behalten",
+    "fill it with latent space noise": "Füllen mit latent space noise",
+    "fill it with latent space zeroes": "Füllen mit latent space Nullen",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Hochskalieren des maskierten Bereichs auf die Zielauflösung, Inpainting, Zurückskalieren und Einfügen in das Originalbild.",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Die Größe des Bildes auf die gewünschte Auflösung ändern. Wenn Höhe und Breite nicht übereinstimmen, erhält man ein falsches Seitenverhältnis.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Die Größe des Bildes so ändern, dass die gesamte Zielauflösung mit dem Bild ausgefüllt wird. Herausragende Teile werden abgeschnitten.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Die Größe des Bildes so ändern, dass das gesamte Bild enthalten ist. Lücken werden mit Farben des Bildes ausgefüllt.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Wie oft die Verarbeitung eines Bildes wiederholt und als Eingabe für die nächste Iteration verwendet werden soll",
+    "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.": "Im Loopback-Modus wird die Denoisingstärke in jeder Schleife mit diesem Wert multipliziert. <1 bedeutet abnehmende Vielfalt, so dass die Sequenz zu einem festen Bild konvergiert. >1 bedeutet zunehmende Vielfalt, so dass die Sequenz immer chaotischer wird.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Wie viel Pixel sich beim SD-Upscale zwischen den Kacheln überlappen. Die Kacheln überlappen sich so, dass beim Zusammenfügen zu einem Bild keine deutlich sichtbare Naht entsteht.",
+    "A directory on the same machine where the server is running.": "Ein Verzeichnis auf demselben Rechner, auf dem der Server läuft.",
+    "Leave blank to save images to the default path.": "Leer lassen, um Bilder im Standardpfad zu speichern.",
+    "Result = A * (1 - M) + B * M": "Ergebnis = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Ergebnis = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "Erste und letzte Ziffer müssen 1 sein. Bspl:'1, 2, 1'",
+    "Path to directory with input images": "Pfad zum Verzeichnis mit den Eingabebildern",
+    "Path to directory where to write outputs": "Pfad zum Verzeichnis, wo die Ausgaben gespeichert werden",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Verwende die folgenden Tags, um festzulegen, wie die Dateinamen für Bilder ausgewählt werden: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leer lassen, um Standardwerte zu verwenden.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Wenn diese Option aktiviert ist, wird den erstellten Bildern kein Wasserzeichen hinzugefügt. Achtung: Wenn Sie kein Wasserzeichen hinzufügen, verhalten Sie sich möglicherweise unethisch.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Verwenden Sie die folgenden Tags, um festzulegen, wie Unterverzeichnisse für Bilder und Raster ausgewählt werden: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leer lassen, um Standardwerte zu verwenden.",
+    "Restore low quality faces using GFPGAN neural network": "Wiederherstellung von Gesichtern schlechter Qualität mit dem neuralen Netzwerk GFPGAN",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Dieser reguläre Ausdruck wird verwendet, um Wörter aus dem Dateinamen zu extrahieren, die dann mit der unten stehenden Option zu einem Beschriftungstext für das Training zusammengefügt werden. Leer lassen, um den Text des Dateinamens so zu belassen, wie er ist.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Diese Zeichenfolge wird verwendet, um getrennte Wörter in einer einzigen Zeile zu verbinden, wenn die obige Option aktiviert ist.",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Gilt nur für Inpainting-Modelle. Legt fest, wie stark das Originalbild für Inpainting und img2img maskiert werden soll. 1.0 bedeutet vollständig maskiert, was das Standardverhalten ist. 0.0 bedeutet eine vollständig unmaskierte Konditionierung. Niedrigere Werte tragen dazu bei, die Gesamtkomposition des Bildes zu erhalten, sind aber bei großen Änderungen problematisch.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Liste von Einstellungsnamen, getrennt durch Kommas, für Einstellungen, die in der Schnellzugriffsleiste oben erscheinen sollen, anstatt in dem üblichen Einstellungs-Tab. Siehe modules/shared.py für Einstellungsnamen. Erfordert einen Neustart zur Anwendung.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Wenn dieser Wert ungleich Null ist, wird er zum Seed addiert und zur Initialisierung des RNG für Noise bei der Verwendung von Samplern mit Eta verwendet. Dies kann verwendet werden, um noch mehr Variationen von Bildern zu erzeugen, oder um Bilder von anderer Software zu erzeugen, wenn Sie wissen, was Sie tun."
+}

localizations/es_ES.json

@@ -5,18 +5,21 @@
     "❮": "❮",
     "❯": "❯",
     "Loading...": "Cargando...",
-    "view": "mostrar",
-    "api": "api",
-    "•": "•",
-    "built with gradio": "Construido con Gradio",
+    "view": "Mostrar ",
+    "api": "API",
+    "•": " • ",
+    "Construido con Gradio": "Construido con Gradio",
     "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
     "txt2img": "txt2img",
     "img2img": "img2img",
     "Extras": "Extras",
-    "PNG Info": "PNG Info",
-    "Checkpoint Merger": "Checkpoint Merger",
-    "Train": "Train",
+    "PNG Info": "Info PNG",
+    "Checkpoint Merger": "Fusionar Checkpoints",
+    "Train": "Entrenar",
+    "Deforum": "Deforum",
+    "Image Browser": "Navegador de Imágenes",
     "Settings": "Ajustes",
+    "Extensions": "Extensiones",
     "Prompt": "Prompt",
     "Negative prompt": "Prompt negativo",
     "Run": "Ejecutar",
@@ -27,15 +30,15 @@
     "Style 2": "Estilo 2",
     "Label": "Etiqueta",
     "File": "Archivo",
-    "Drop File Here": "Suelta el Archivo Aquí",
+    "Coloque el archivo aquí": "Suelta el archivo aquí",
     "-": "-",
     "o": "o",
-    "Click to Upload": "Click para Cargar",
+    "Haga click para cargar": "Haz click para cargar",
     "Image": "Imagen",
     "Check progress": "Comprobar progreso",
     "Check progress (first)": "Comprobar progreso (inicial)",
     "Sampling Steps": "Sampling Steps",
-    "Sampling method": "Sampling method",
+    "Sampling method": "Método de Sampling",
     "Euler a": "Euler a",
     "Euler": "Euler",
     "LMS": "LMS",
@@ -57,33 +60,34 @@
     "Firstpass width": "Ancho original",
     "Firstpass height": "Alto original",
     "Denoising strength": "Denoising strength",
-    "Batch count": "Batch count",
-    "Batch size": "Batch size",
-    "CFG Scale": "CFG Scale",
+    "Batch count": "Cantidad del Batch",
+    "Batch size": "Tamaño del Batch",
+    "CFG Scale": "Escala CFG",
     "Seed": "Seed",
     "Extra": "Extra",
-    "Variation seed": "Variation seed",
-    "Variation strength": "Variation strength",
+    "Variation seed": "Seed de variación",
+    "Variation strength": "Fuerza de variación",
     "Resize seed from width": "Redimensionar seed del ancho",
     "Resize seed from height": "Redimensionar seed del alto",
     "Script": "Script",
     "None": "Ninguno",
-    "Prompt matrix": "Prompt matrix",
+    "Prompt matrix": "Prompt en matriz",
     "Prompts from file or textbox": "Prompts desde archivo o campo de texto",
-    "X/Y plot": "X/Y plot",
+    "X/Y plot": "Tabla X/Y",
     "Put variable parts at start of prompt": "Poner partes variables al inicio del prompt",
-    "Show Textbox": "Mostrar Campo de texto",
-    "File with inputs": "Archivo con inputs",
-    "Prompts": "Prompts",
-    "X type": "X type",
+    "Iterate seed every line": "Repetir seed en cada línea",
+    "Use same random seed for all lines": "Utiliza el mismo seed aleatorio para todas las líneas",
+    "List of prompt inputs": "Listado de prompts",
+    "Upload prompt inputs": "Cargar archivo de prompts",
+    "X type": "X",
     "Nothing": "Nada",
     "Var. seed": "Var. seed",
-    "Var. strength": "Var. strength",
+    "Var. strength": "Var. fuerza",
     "Steps": "Steps",
     "Prompt S/R": "Prompt S/R",
     "Prompt order": "Prompt order",
     "Sampler": "Sampler",
-    "Checkpoint name": "Checkpoint name",
+    "Checkpoint name": "Nombre Checkpoint",
     "Hypernetwork": "Hypernetwork",
     "Hypernet str.": "Hypernet str.",
     "Sigma Churn": "Sigma Churn",
@@ -93,13 +97,13 @@
     "Eta": "Eta",
     "Clip skip": "Clip skip",
     "Denoising": "Denoising",
-    "X values": "X values",
-    "Y type": "Y type",
-    "Y values": "Y values",
+    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
+    "X values": "Valores X",
+    "Y type": "Y",
+    "Y values": "Valores Y",
     "Draw legend": "Agregar leyenda",
     "Include Separate Images": "Incluir Imágenes Separadas",
     "Keep -1 for seeds": "Mantener -1 para seeds",
-    "Drop Image Here": "Suelta la Imagen Aquí",
     "Save": "Guardar",
     "Send to img2img": "Enviar a img2img",
     "Send to inpaint": "Enviar a inpaint",
@@ -110,22 +114,23 @@
     "Inpaint": "Inpaint",
     "Batch img2img": "Batch img2img",
     "Image for img2img": "Imagen para img2img",
-    "Image for inpainting with mask": "Imagen para inpainting con mask",
-    "Mask": "Mask",
-    "Mask blur": "Mask blur",
-    "Mask mode": "Mask mode",
-    "Draw mask": "Dibujar mask",
-    "Upload mask": "Cargar mask",
-    "Masking mode": "Masking mode",
-    "Inpaint masked": "Inpaint masked",
-    "Inpaint not masked": "Inpaint not masked",
-    "Masked content": "Masked content",
-    "fill": "fill",
+    "Coloque la imagen aquí": "Suelta la imagen aquí",
+    "Image for inpainting with mask": "Imagen para inpainting con máscara",
+    "Mask": "Máscara",
+    "Mask blur": "Difuminar máscara",
+    "Mask mode": "Modo máscara",
+    "Draw mask": "Dibujar máscara",
+    "Upload mask": "Cargar máscara",
+    "Masking mode": "Modo de enmascarado",
+    "Inpaint masked": "Inpaint con enmascarado",
+    "Inpaint not masked": "Inpaint sin enmascarado",
+    "Masked content": "Contenido enmascarado",
+    "fill": "rellenar",
     "original": "original",
     "latent noise": "latent noise",
     "latent nothing": "latent nothing",
     "Inpaint at full resolution": "Inpaint a resolución completa",
-    "Inpaint at full resolution padding, pixels": "Inpaint a resolución completa padding, pixeles",
+    "Inpaint at full resolution padding, pixels": "Inpaint a resolución completa con relleno, en pixeles",
     "Process images in a directory on the same machine where the server is running.": "Procesa imágenes en un directorio en la misma máquina donde se ejecuta el servidor.",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa un directorio de salida vacío para guardar imágenes normalmente en lugar de escribir en el directorio de salida.",
     "Input directory": "Directorio de entrada",
@@ -139,15 +144,16 @@
     "Outpainting mk2": "Outpainting mk2",
     "Poor man's outpainting": "Poor man's outpainting",
     "SD upscale": "SD upscale",
+    "Deforum-webui (use tab extension instead!)": "Deforum-webui (utiliza la extensión en su lugar!)",
     "should be 2 or lower.": "debe ser 2 o menos.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Anular `Sampling method` a Euler? (este método está diseñado para ello)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Anular `prompt` al mismo valor `prompt original`? (y `prompt negativo`)",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Invalidar `Sampling method` a Euler? (este método está diseñado para ello)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Invalidar `prompt` al mismo valor `prompt original`? (y `prompt negativo`)",
     "Original prompt": "Prompt original",
     "Original negative prompt": "Promp negativo original",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Anular `Sampling Steps` al mismo valor de `Decode steps`?",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Invalidar `Sampling Steps` al mismo valor de `Decode steps`?",
     "Decode steps": "Decode steps",
-    "Override `Denoising strength` to 1?": "Anular `Denoising strength` a 1?",
-    "Decode CFG scale": "Decode CFG scale",
+    "Override `Denoising strength` to 1?": "Invalidar `Denoising strength` a 1?",
+    "Decode CFG scale": "Decodificar escala CFG",
     "Randomness": "Aleatoriedad",
     "Sigma adjustment for finding noise for image": "Ajuste Sigma para encontrar ruido para la imagen.",
     "Loops": "Loops",
@@ -165,29 +171,35 @@
     "Tile overlap": "Solapar mosaicos",
     "Upscaler": "Upscaler",
     "Lanczos": "Lanczos",
+    "Nearest": "Nearest",
     "LDSR": "LDSR",
-    "SwinIR 4x": "SwinIR 4x",
+    "ESRGAN_4x": "ESRGAN_4x",
     "ScuNET GAN": "ScuNET GAN",
     "ScuNET PSNR": "ScuNET PSNR",
-    "ESRGAN_4x": "ESRGAN_4x",
+    "SwinIR 4x": "SwinIR 4x",
+    "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
+    "This script is deprecated. Please use the full Deforum extension instead.": "Este script está obsoleto. Utiliza la extensión completa de Deforum en su lugar.",
+    "Update instructions:": "Instrucciones para actualizar:",
+    "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
+    "discord.gg/deforum": "discord.gg/deforum",
     "Single Image": "Imagen Única",
     "Batch Process": "Batch Process",
     "Batch from Directory": "Batch desde Directorio",
     "Source": "Origen",
-    "Show result images": "Mostrar resultados de imágenes",
+    "Show result images": "Mostrar imágenes generadas",
     "Scale by": "Escalar por",
     "Scale to": "Escalar a",
     "Resize": "Redimensionar",
     "Crop to fit": "Recortar para ajustar",
-    "Upscaler 2 visibility": "Upscaler 2 visibilidad",
-    "GFPGAN visibility": "GFPGAN visibilidad",
-    "CodeFormer visibility": "CodeFormer visibilidad",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer peso (0 = efecto máximo, 1 = efecto mínimo)",
-    "Open output directory": "Abrir directorio de salida",
+    "Upscaler 2 visibility": "Visibilidad Upscaler 2",
+    "GFPGAN visibility": "Visibilidad GFPGAN",
+    "CodeFormer visibility": "Visibilidad CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Influencia CodeFormer (0 = efecto máximo, 1 = efecto mínimo)",
+    "Upscale Before Restoring Faces": "Escalar antes de Restaurar Rostros",
     "Send to txt2img": "Enviar a txt2img",
-    "A merger of the two checkpoints will be generated in your": "Se generará una fusión de los dos checkpoints en su",
-    "checkpoint": "checkpoint",
-    "directory.": "directorio.",
+    "A merger of the two checkpoints will be generated in your": "Se generará una fusión de los dos checkpoints en tu ",
+    "checkpoint": "directorio ",
+    "directory.": "de modelos.",
     "Primary model (A)": "Modelo primario (A)",
     "Secondary model (B)": "Modelo secundario (B)",
     "Tertiary model (C)": "Modelo terciario (C)",
@@ -215,6 +227,36 @@
     "leakyrelu": "leakyrelu",
     "elu": "elu",
     "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "threshold",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleccionar inicialización de modelos de capa. relu-like - Kaiming, sigmoid-like - Xavier es el recomendado",
+    "Normal": "Normal",
+    "KaimingUniform": "KaimingUniform",
+    "KaimingNormal": "KaimingNormal",
+    "XavierUniform": "XavierUniform",
+    "XavierNormal": "XavierNormal",
     "Add layer normalization": "Agregar normalización de capa",
     "Use dropout": "Usar dropout",
     "Overwrite Old Hypernetwork": "Sobrescribir Hypernetwork Anterior",
@@ -226,11 +268,16 @@
     "prepend": "anteponer",
     "append": "añadir",
     "Create flipped copies": "Crear copias volteadas",
-    "Split oversized images": "Dividir imágenes grandes",
-    "Use BLIP for caption": "Usar BLIP para subtítulo",
-    "Use deepbooru for caption": "Usar deepbooru para subtítulo",
-    "Split image threshold": "Threshold imagen dividida",
-    "Split image overlap ratio": "Overlap ratio imagen dividida",
+    "Split oversized images": "Dividir imágenes muy grandes",
+    "Auto focal point crop": "Recorte de punto focal automático",
+    "Use BLIP for caption": "Usar BLIP para leyenda",
+    "Use deepbooru for caption": "Usar deepbooru para leyenda",
+    "Split image threshold": "Umbral en imagen dividida",
+    "Split image overlap ratio": "Relación de superposición en imagen dividida",
+    "Focal point face weight": "Peso de la cara del punto focal",
+    "Focal point entropy weight": "Focal point entropy weight",
+    "Focal point edges weight": "Focal point edges weight",
+    "Create debug image": "Crear imagen de depuración",
     "Preprocess": "Preproceso",
     "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1 ",
     "[wiki]": "[wiki]",
@@ -245,13 +292,194 @@
     "Save a copy of embedding to log directory every N steps, 0 to disable": "Guarda una copia de embedding en el directorio log cada N pasos, 0 para deshabilitar",
     "Save images with embedding in PNG chunks": "Guarda imágenes con embedding en fragmentos PNG",
     "Read parameters (prompt, etc...) from txt2img tab when making previews": "Leer parámetros (prompt, etc...) desde la pestaña txt2img al hacer vistas previas",
-    "Train Hypernetwork": "Train Hypernetwork",
-    "Train Embedding": "Train Embedding",
+    "Train Hypernetwork": "Entrenar Hypernetwork",
+    "Train Embedding": "Entrenar Embedding",
+    "Info and links": "Información y enlaces",
+    "▼": "▼",
+    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Desarrolado por deforum.github.io, port para AUTOMATIC1111's webui mantenido por kabachuha",
+    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Deforum Github repo  github.com/deforum/stable-diffusion",
+    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "Fork para auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui",
+    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Únete al Discord oficial Deforum discord.gg/deforum para compartir tus creaciones y sugerencias",
+    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Guía de usuario v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
+    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Explicación de fotogramas matemáticos docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
+    "Keyframes": "Keyframes",
+    "Prompts": "Prompts",
+    "Init": "Init",
+    "Video output": "Salida de vídeo",
+    "Run settings": "Ejecutar ajustes",
+    "Import settings from file": "Importar ajustes desde archivo",
+    "Override settings": "Invalidar ajustes",
+    "Custom settings file": "Archivo de ajustes personalizados",
+    "Sampling settings": "Ajustes de sampling",
+    "override_these_with_webui": "override_these_with_webui",
+    "W": "Ancho",
+    "H": "Alto",
+    "seed": "seed",
+    "sampler": "sampler",
+    "Enable extras": "Activar extras",
+    "subseed": "subseed",
+    "subseed_strength": "subseed_strength",
+    "steps": "steps",
+    "ddim_eta": "ddim_eta",
+    "n_batch": "n_batch",
+    "make_grid": "make_grid",
+    "grid_rows": "grid_rows",
+    "save_settings": "save_settings",
+    "save_samples": "save_samples",
+    "display_samples": "display_samples",
+    "save_sample_per_step": "save_sample_per_step",
+    "show_sample_per_step": "show_sample_per_step",
+    "Batch settings": "Ajustes de batch",
+    "batch_name": "batch_name",
+    "filename_format": "filename_format",
+    "seed_behavior": "seed_behavior",
+    "iter": "iter",
+    "fixed": "fixed",
+    "random": "random",
+    "schedule": "schedule",
+    "Animation settings": "Ajustes de animación",
+    "animation_mode": "animation_mode",
+    "2D": "2D",
+    "3D": "3D",
+    "Video Input": "Entrada de Video",
+    "max_frames": "max_frames",
+    "border": "border",
+    "replicate": "replicate",
+    "wrap": "wrap",
+    "Motion parameters:": "Parámetros de movimiento:",
+    "2D and 3D settings": "Ajustes 2D y 3D",
+    "angle": "angle",
+    "zoom": "zoom",
+    "translation_x": "translation_x",
+    "translation_y": "translation_y",
+    "3D settings": "Ajustes 3D",
+    "translation_z": "translation_z",
+    "rotation_3d_x": "rotation_3d_x",
+    "rotation_3d_y": "rotation_3d_y",
+    "rotation_3d_z": "rotation_3d_z",
+    "Prespective flip — Low VRAM pseudo-3D mode:": "Prespective flip — Modo Low VRAM pseudo-3D:",
+    "flip_2d_perspective": "flip_2d_perspective",
+    "perspective_flip_theta": "perspective_flip_theta",
+    "perspective_flip_phi": "perspective_flip_phi",
+    "perspective_flip_gamma": "perspective_flip_gamma",
+    "perspective_flip_fv": "perspective_flip_fv",
+    "Generation settings:": "Ajustes de generación:",
+    "noise_schedule": "noise_schedule",
+    "strength_schedule": "strength_schedule",
+    "contrast_schedule": "contrast_schedule",
+    "cfg_scale_schedule": "cfg_scale_schedule",
+    "3D Fov settings:": "Ajustes 3D Fov:",
+    "fov_schedule": "fov_schedule",
+    "near_schedule": "near_schedule",
+    "far_schedule": "far_schedule",
+    "To enable seed schedule select seed behavior — 'schedule'": "Para habilitar el seed schedule, selecciona el comportamiento del seed — 'schedule'",
+    "seed_schedule": "seed_schedule",
+    "Coherence:": "Coherencia:",
+    "color_coherence": "color_coherence",
+    "Match Frame 0 HSV": "Match Frame 0 HSV",
+    "Match Frame 0 LAB": "Match Frame 0 LAB",
+    "Match Frame 0 RGB": "Match Frame 0 RGB",
+    "diffusion_cadence": "diffusion_cadence",
+    "3D Depth Warping:": "3D Depth Warping:",
+    "use_depth_warping": "use_depth_warping",
+    "midas_weight": "midas_weight",
+    "near_plane": "near_plane",
+    "far_plane": "far_plane",
+    "fov": "fov",
+    "padding_mode": "padding_mode",
+    "reflection": "reflection",
+    "zeros": "zeros",
+    "sampling_mode": "sampling_mode",
+    "bicubic": "bicubic",
+    "bilinear": "bilinear",
+    "nearest": "nearest",
+    "save_depth_maps": "save_depth_maps",
+    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`animation_mode: None` batches en lista de *prompts*. (Modo batch deshabilitado por el momento, solamente animation_prompts esta funcionando)",
+    "*Important change from vanilla Deforum!*": "*Cambios importantes en Deforum!*",
+    "This script uses the built-in webui weighting settings.": "Este script utiliza la configuración de pesos integrados.",
+    "So if you want to use math functions as prompt weights,": "Entonces, si deseas usar funciones matemáticas con pesos en los prompts,",
+    "keep the values above zero in both parts": "manten los valores por encima de cero en ambas partes",
+    "Negative prompt part can be specified with --neg": "La parte de prompt negativo se puede especificar utilizando --neg",
+    "batch_prompts (disabled atm)": "batch_prompts (deshabilitado por el momento)",
+    "animation_prompts": "animation_prompts",
+    "Init settings": "Ajustes Init",
+    "use_init": "use_init",
+    "from_img2img_instead_of_link": "from_img2img_instead_of_link",
+    "strength_0_no_init": "strength_0_no_init",
+    "strength": "strength",
+    "init_image": "init_image",
+    "use_mask": "use_mask",
+    "use_alpha_as_mask": "use_alpha_as_mask",
+    "invert_mask": "invert_mask",
+    "overlay_mask": "overlay_mask",
+    "mask_file": "mask_file",
+    "mask_brightness_adjust": "mask_brightness_adjust",
+    "mask_overlay_blur": "mask_overlay_blur",
+    "Video Input:": "Entrada de Video:",
+    "video_init_path": "video_init_path",
+    "extract_nth_frame": "extract_nth_frame",
+    "overwrite_extracted_frames": "overwrite_extracted_frames",
+    "use_mask_video": "use_mask_video",
+    "video_mask_path": "video_mask_path",
+    "Interpolation (turned off atm)": "Interpolación (apagado por el momento)",
+    "interpolate_key_frames": "interpolate_key_frames",
+    "interpolate_x_frames": "interpolate_x_frames",
+    "Resume animation:": "Reanudar animación:",
+    "resume_from_timestring": "resume_from_timestring",
+    "resume_timestring": "resume_timestring",
+    "Video output settings": "Ajustes video de salida",
+    "skip_video_for_run_all": "skip_video_for_run_all",
+    "fps": "fps",
+    "output_format": "output_format",
+    "PIL gif": "PIL gif",
+    "FFMPEG mp4": "FFMPEG mp4",
+    "ffmpeg_location": "ffmpeg_location",
+    "add_soundtrack": "add_soundtrack",
+    "soundtrack_path": "soundtrack_path",
+    "use_manual_settings": "use_manual_settings",
+    "render_steps": "render_steps",
+    "max_video_frames": "max_video_frames",
+    "path_name_modifier": "path_name_modifier",
+    "x0_pred": "x0_pred",
+    "x": "x",
+    "image_path": "image_path",
+    "mp4_path": "mp4_path",
+    "Click here after the generation to show the video": "Haz click aquí después de la generación para mostrar el video",
+    "NOTE: If the 'Generate' button doesn't work, go in Settings and click 'Restart Gradio and Refresh...'.": "NOTA: Si el botón 'Generar' no funciona, ve a los Ajustes y presiona 'Reinciar Gradio y Refrescar...'.",
+    "Save Settings": "Guardar Ajustes",
+    "Load Settings": "Cargar Ajustes",
+    "Path relative to the webui folder": "Ruta relativa al folder principal",
+    "Save Video Settings": "Guardar Ajustes de Video",
+    "Load Video Settings": "Cargar Ajustes de Video",
+    "Favorites": "Favoritos",
+    "Others": "Otros",
+    "Images directory": "Directorio de Imágenes",
+    "Dropdown": "Menú desplegable",
+    "First Page": "Primera Página",
+    "Prev Page": "Página Anterior",
+    "Page Index": "Índice de Página",
+    "Next Page": "Página Siguiente",
+    "End Page": "Última Página",
+    "delete next": "eliminar siguiente",
+    "Delete": "Eliminar",
+    "sort by": "ordenar por",
+    "path name": "nombre de ruta",
+    "date": "fecha",
+    "keyword": "palabra clave",
+    "Generate Info": "Generar Info",
+    "File Name": "Nombre de Archivo",
+    "Move to favorites": "Mover a favoritos",
+    "Renew Page": "Recargar Página",
+    "Number": "Número",
+    "set_index": "set_index",
+    "load_switch": "load_switch",
+    "turn_page_switch": "turn_page_switch",
+    "Checkbox": "Checkbox",
     "Apply settings": "Aplicar ajustes",
     "Saving images/grids": "Guardar imágenes/grids",
     "Always save all generated images": "Siempre guardar imágenes generadas",
     "File format for images": "Formato de archivo para imágenes",
-    "Images filename pattern": "Patrón nombre archivo imágenes",
+    "Images filename pattern": "Patrón en nombre archivo imágenes",
     "Add number to filename when saving": "Agregar número al nombre de archivo al guardar",
     "Always save all generated image grids": "Siempre guardar grids de imágenes generadas",
     "File format for grids": "Formato de archivo para grids",
@@ -262,6 +490,8 @@
     "Save text information about generation parameters as chunks to png files": "Guardar información de texto sobre parámetros de generación como fragmentos en archivos png",
     "Create a text file next to every image with generation parameters.": "Crear un archivo de texto junto a cada imagen con parámetros de generación.",
     "Save a copy of image before doing face restoration.": "Guardar una copia de la imagen antes de restaurar rostro.",
+    "Save a copy of image before applying highres fix.": "Guardar una copia de la imagen antes de aplicar highres fix.",
+    "Save a copy of image before applying color correction to img2img results": "Guarda una copia de la imagen antes de aplicar la corrección de color a los resultados de img2img",
     "Quality for saved jpeg images": "Calidad para imágenes jpeg guardadas",
     "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Si la imagen PNG es más grande de 4 MB o cualquier dimensión es más grande que 4000, reduce la escala y guarda la copia como JPG",
     "Use original name for output filename during batch process in extras tab": "Use el nombre original para el nombre del archivo de salida durante el batch process en la pestaña extras",
@@ -291,23 +521,26 @@
     "Upscaler for img2img": "Upscaler para img2img",
     "Upscale latent space image when doing hires. fix": "Upscale latent space al aplicar hires. fix",
     "Face restoration": "Restauración de rostro",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer parámetro peso; 0 = máximo efecto; 1 = mínimo efecto",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Parámetro influencia CodeFormer; 0 = máximo efecto; 1 = mínimo efecto",
     "Move face restoration model from VRAM into RAM after processing": "Mover modelo de restauración de rostro del VRAM al RAM después de procesar",
     "System": "Sistema",
     "VRAM usage polls per second during generation. Set to 0 to disable.": "Sondeos de uso de VRAM por segundo durante la generación. Establecer en 0 para deshabilitar.",
     "Always print all generation info to standard output": "Imprime siempre toda la información de generación en la salida estándar",
     "Add a second progress bar to the console that shows progress for an entire job.": "Agrega una segunda barra de progreso a la consola que muestra el progreso de un trabajo completo.",
     "Training": "Entrenamiento",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Mover VAE y CLIP al RAM al entrenar un hypernetwork. Ahorra VRAM.",
+    "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "Mover VAE y CLIP al RAM al entrenar cuando sea posible. Ahorra VRAM.",
     "Filename word regex": "Filename word regex",
     "Filename join string": "Filename join string",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repeticiones para una sola imagen de entrada por epoch; utilizado solo para mostrar el número epoch",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "Guarda un csv que contenga la pérdida en el directorio log cada N pasos, 0 para deshabilitar",
+    "Use cross attention optimizations while training": "Utiliza optimizaciones de atención cruzada durante el entrenamiento",
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "Checkpoints al cache en RAM",
+    "SD VAE": "SD VAE",
+    "auto": "auto",
     "Hypernetwork strength": "Hypernetwork strength",
+    "Inpainting conditioning mask strength": "Fuerza de la máscara en acondicionamiento Inpainting",
     "Apply color correction to img2img results to match original colors.": "Aplica la corrección de color a los resultados de img2img para que coincidan con los colores originales.",
-    "Save a copy of image before applying color correction to img2img results": "Guarda una copia de la imagen antes de aplicar la corrección de color a los resultados de img2img",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, hace exactamente la cantidad de pasos que especifica el slider (normalmente haría menos con menos eliminación de ruido).",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Habilita la cuantificación en K samplers para obtener resultados más nítidos y limpios. Esto puede cambiar los seeds existentes. Requiere reiniciar para aplicar.",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Énfasis: utiliza (texto) para que el modelo preste más atención al texto y [texto] para que preste menos atención",
@@ -324,7 +557,7 @@
     "Interrogate: minimum description length (excluding artists, etc..)": "Interrogar: longitud mínima de la descripción (excluyendo artistas, etc.)",
     "Interrogate: maximum description length": "Interrogar: longitud máxima de la descripción",
     "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: número máximo de líneas en el archivo de texto (0 = Sin límite)",
-    "Interrogate: deepbooru score threshold": "Interrogar: deepbooru score threshold",
+    "Interrogate: deepbooru score threshold": "Interrogar: deepbooru umbral de puntuación",
     "Interrogate: deepbooru sort alphabetically": "Interrogar: deepbooru ordenar alfabéticamente",
     "use spaces for tags in deepbooru": "usar espacios para etiquetas en deepbooru",
     "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "corchetes de escape (\\) en deepbooru (por lo que se usan como corchetes literales y no para enfatizar)",
@@ -337,13 +570,26 @@
     "Add model hash to generation information": "Agregar hash de modelo a la información de generación",
     "Add model name to generation information": "Agregar nombre de modelo a la información de generación",
     "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Al leer los parámetros de generación del texto en la interfaz de usuario (desde PNG Info o texto pegado), no cambia el modelo/checkpoint seleccionado.",
+    "Send seed when sending prompt or image to other interface": "Enviar seed cuando se envíe el prompt o imagen a otra interfase",
     "Font for image grids that have text": "Tipografía para grids de imágenes que tienen texto",
     "Enable full page image viewer": "Habilitar visor de imágenes de página completa",
     "Show images zoomed in by default in full page image viewer": "Mostrar imágenes ampliadas de forma predeterminada en el visor de imágenes de página completa",
     "Show generation progress in window title.": "Muestra el progreso de la generación en el título de la ventana del navegador.",
     "Quicksettings list": "Lista de ajustes rápidos",
     "Localization (requires restart)": "Traducción (requiere reiniciar)",
-    "Sampler parameters": "Parámetros sampler",
+    "fr_FR": "fr_FR",
+    "tr_TR": "tr_TR",
+    "it_IT": "it_IT",
+    "de_DE": "de_DE",
+    "ru_RU": "ru_RU",
+    "ja_JP": "ja_JP",
+    "es_ES": "es_ES",
+    "ko_KR": "ko_KR",
+    "zh_TW": "zh_TW",
+    "zh_CN": "zh_CN",
+    "ar_AR": "ar_AR",
+    "pt_BR": "pt_BR",
+    "Sampler parameters": "Parámetros del sampler",
     "Hide samplers in user interface (requires restart)": "Ocultar samplers en interfaz de usuario (requiere reiniciar)",
     "eta (noise multiplier) for DDIM": "eta (noise multiplier) para DDIM",
     "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) para ancestral samplers",
@@ -354,10 +600,35 @@
     "sigma tmin": "sigma tmin",
     "sigma noise": "sigma noise",
     "Eta noise seed delta": "Eta noise seed delta",
+    "Images Browser": "Navegador de Imágenes",
+    "Preload images at startup": "Precargar imágenes al iniciar",
+    "Number of columns on the page": "Número de columnas en la página",
+    "Number of rows on the page": "Número de filas en la página",
+    "Minimum number of pages per load": "Número mínimo de páginas por carga",
     "Request browser notifications": "Solicitar notificaciones del navegador",
     "Download localization template": "Descargar plantilla de traducción",
     "Reload custom script bodies (No ui updates, No restart)": "Recargar custom script bodies (Sin actualizar UI, Sin reiniciar)",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Reiniciar Gradio y Refrescar componentes (Custom Scripts, ui.py, js y css)",
+    "Installed": "Instaladas",
+    "Available": "Disponibles",
+    "Install from URL": "Instalar desde URL",
+    "Apply and restart UI": "Aplicar y reiniciar UI",
+    "Check for updates": "Buscar actualizaciones",
+    "Extension": "Extensión",
+    "URL": "URL",
+    "Update": "Actualizar",
+    "deforum-for-automatic1111-webui": "deforum-for-automatic1111-webui",
+    "https://github.com/deforum-art/deforum-for-automatic1111-webui": "https://github.com/deforum-art/deforum-for-automatic1111-webui",
+    "unknown": "desconocido",
+    "stable-diffusion-webui-images-browser": "stable-diffusion-webui-images-browser",
+    "https://github.com/yfszzx/stable-diffusion-webui-images-browser": "https://github.com/yfszzx/stable-diffusion-webui-images-browser",
+    "Load from:": "Cargar desde:",
+    "Extension index URL": "URL índice de extensiones",
+    "URL for extension's git repository": "URL repositorio git de extensión",
+    "Local directory name": "Nombre directorio local",
+    "Install": "Instalar",
+    "Ver": "Ver",
+    "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1": "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (presiona Ctrl+Enter o Alt+Enter para generar)",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (presiona Ctrl+Enter o Alt+Enter para generar)",
     "Add a random artist to the prompt.": "Agregar un artista aleatorio al prompt.",
@@ -387,16 +658,16 @@
     "Separate values for Y axis using commas.": "Separar valores para Y usando comas.",
     "Write image to a directory (default - log/images) and generation parameters into csv file.": "Escribe la imagen en un directorio (predeterminado: log/images) y los parámetros de generación en el archivo csv.",
     "Open images output directory": "Abrir directorio de imágenes",
-    "How much to blur the mask before processing, in pixels.": "Cuánto blur al mask antes de procesar, en píxeles.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Qué poner dentro del área con mask antes de procesarlo con Stable Diffusion.",
+    "How much to blur the mask before processing, in pixels.": "Cuánto difuminado a la máscara antes de procesarla, en píxeles.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Qué poner dentro del área enmascarada antes de procesarla con Stable Diffusion.",
     "fill it with colors of the image": "rellenarlo con los colores de la imagen",
     "keep whatever was there originally": "mantener lo que estaba allí originalmente",
     "fill it with latent space noise": "rellenarlo con latent space noise",
     "fill it with latent space zeroes": "rellenarlo con latent space zeroes",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Upscale la región con mask a la resolución objetivo, vuelve a pintar, reduce la escala hacia atrás y pégala en la imagen original",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Cambia el tamaño de la imagen a la resolución destino. A menos que la altura y el ancho coincidan, obtendrás una relación de aspecto incorrecta.",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Escalar la región con máscara a la resolución objetivo, vuelve a pintar, reduce la escala hacia atrás y pégala en la imagen original",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Cambia el tamaño de la imagen a la resolución objetivo. A menos que la altura y el ancho coincidan, obtendrás una relación de aspecto incorrecta.",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Cambia el tamaño de la imagen para que la totalidad de la resolución destino se llene con la imagen. Recorta las partes que sobresalen.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Cambia el tamaño de la imagen para que la totalidad de la imagen esté dentro de la resolución de destino. Rellena el espacio vacío con los colores de la imagen.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Cambia el tamaño de la imagen para que la totalidad de la imagen esté dentro de la resolución objetivo. Rellena el espacio vacío con los colores de la imagen.",
     "How many times to repeat processing an image and using it as input for the next iteration": "Cuántas veces repetir el procesamiento de una imagen y usarla como entrada para la próxima iteración",
     "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.": "En modo loopback, en cada bucle, la fuerza de eliminación de ruido se multiplica por este valor. <1 significa variedad decreciente, por lo que su secuencia convergerá en una imagen fija. >1 significa aumentar la variedad, por lo que su secuencia se volverá cada vez más caótica.",
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Para SD upscale, cuánta superposición en píxeles debe haber entre mosaicos. Los mosaicos se superponen de modo que cuando se fusionan nuevamente en una imagen, no hay una unión claramente visible.",
@@ -407,12 +678,15 @@
     "1st and last digit must be 1. ex:'1, 2, 1'": "Primer y último dígito debe ser 1. ej:'1, 2, 1'",
     "Path to directory with input images": "Ruta al directorio con imágenes de entrada",
     "Path to directory where to write outputs": "Ruta al directorio donde escribir salidas",
+    "Input images directory": "Directorio de imágenes de entrada",
     "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa las siguientes etiquetas para definir cómo se eligen los nombres de archivo para las imágenes: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; dejar vacío para utilizar predeterminados.",
     "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Si esta opción está habilitada, el watermark no se agregará a las imágenes creadas. Advertencia: si no agregas un watermark, es posible que te estés comportando de manera poco ética.",
     "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa las siguiente etiquetas para definir cómo los subdirectorios para imágenes y grids son seleccionados: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; dejar vacío para utilizar predeterminados.",
     "Restore low quality faces using GFPGAN neural network": "Restaurar rostros de baja calidad utilizando GFPGAN neural network",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Esta expresión regular se usará para extraer palabras del nombre de archivo y se unirán usando la opción a continuación en el texto de la etiqueta que se usa para el entrenamiento. Dejar vacío para mantener el texto del nombre de archivo tal como está.",
     "This string will be used to join split words into a single line if the option above is enabled.": "Esta cadena se usará para unir palabras divididas en una sola línea si la opción anterior está habilitada.",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Solo se aplica a modelos inpainting. Determina con qué fuerza enmascarar la imagen original para inpainting en img2img. 1.0 significa totalmente enmascarado, que es el comportamiento predeterminado. 0.0 significa un condicionamiento totalmente desenmascarado. Los valores más bajos ayudarán a preservar la composición general de la imagen, pero tendrán problemas con los grandes cambios.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nombres de configuración, separados por comas, para configuraciones que deben ir a la barra de acceso rápido en la parte superior, en lugar de la pestaña de configuración habitual. Ver modules/shared.py para configurar los nombres. Requiere reiniciar para aplicar.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si este valor no es cero, se agregará al seed y se usará para inicializar RNG para ruidos cuando se usan samplers con Eta. Puedes usar esto para producir aún más variaciones de imágenes, o puedes usar esto para hacer coincidir imágenes de otro software si sabes lo que estás haciendo."
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si este valor no es cero, se agregará al seed y se usará para inicializar RNG para ruidos cuando se usan samplers con Eta. Puedes usar esto para producir aún más variaciones de imágenes, o puedes usar esto para hacer coincidir imágenes de otro software si sabes lo que estás haciendo.",
+    "Leave empty for auto": "Dejar vacío para automático"
 }

localizations/ko_KR.json

@@ -9,11 +9,13 @@
   " images in this directory. Loaded ": "개의 이미지가 이 경로에 존재합니다. ",
   " pages": "페이지로 나뉘어 표시합니다.",
   ", divided into ": "입니다. ",
+  ". Use Installed tab to restart.": "에 성공적으로 설치하였습니다. 설치된 확장기능 탭에서 UI를 재시작해주세요.",
   "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
   "[wiki]": " [위키] 참조",
   "A directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리를 선택해 주세요.",
   "A merger of the two checkpoints will be generated in your": "체크포인트들이 병합된 결과물이 당신의",
   "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
+  "Action": "작업",
   "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
   "Add a second progress bar to the console that shows progress for an entire job.": "콘솔에 전체 작업의 진행도를 보여주는 2번째 프로그레스 바 추가하기",
   "Add difference": "차이점 추가",
@@ -21,6 +23,9 @@
   "Add layer normalization": "레이어 정규화(normalization) 추가",
   "Add model hash to generation information": "생성 정보에 모델 해시 추가",
   "Add model name to generation information": "생성 정보에 모델 이름 추가",
+  "Add number to filename when saving": "이미지를 저장할 때 파일명에 숫자 추가하기",
+  "Aesthetic Gradients": "스타일 그라디언트",
+  "Aesthetic Image Scorer": "스타일 이미지 스코어러",
   "Aesthetic imgs embedding": "스타일 이미지 임베딩",
   "Aesthetic learning rate": "스타일 학습 수",
   "Aesthetic steps": "스타일 스텝 수",
@@ -32,21 +37,32 @@
   "Always save all generated images": "생성된 이미지 항상 저장하기",
   "api": "",
   "append": "뒤에 삽입",
+  "Append commas": "쉼표 삽입",
+  "Apply and restart UI": "적용 후 UI 재시작",
   "Apply color correction to img2img results to match original colors.": "이미지→이미지 결과물이 기존 색상과 일치하도록 색상 보정 적용하기",
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
   "Apply settings": "설정 적용하기",
+  "Artists to study": "연구할만한 작가들",
+  "auto": "자동",
+  "Auto focal point crop": "초점 기준 크롭(자동 감지)",
+  "Autocomplete options": "자동완성 설정",
+  "Available": "지원되는 확장기능 목록",
   "Batch count": "배치 수",
   "Batch from Directory": "저장 경로로부터 여러장 처리",
   "Batch img2img": "이미지→이미지 배치",
   "Batch Process": "이미지 여러장 처리",
   "Batch size": "배치 크기",
+  "behind": "최신 아님",
   "BSRGAN 4x": "BSRGAN 4x",
   "built with gradio": "gradio로 제작되었습니다",
+  "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Chad 스코어러를 기반으로 한 CLIP+MLP 스타일 점수 예측기를 이용해 생성된 이미지의 스타일 점수를 계산합니다.",
   "Cancel generate forever": "반복 생성 취소",
+  "cfg cnt": "CFG 변화 횟수",
   "cfg count": "CFG 변화 횟수",
   "CFG Scale": "CFG 스케일",
   "cfg1 min/max": "CFG1 최소/최대",
   "cfg2 min/max": "CFG2 최소/최대",
+  "Check for updates": "업데이트 확인",
   "Check progress": "진행도 체크",
   "Check progress (first)": "진행도 체크 (처음)",
   "checkpoint": " 체크포인트 ",
@@ -62,26 +78,36 @@
   "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 가중치 설정값 (0 = 최대 효과, 1 = 최소 효과)",
   "Collect": "즐겨찾기",
   "Color variation": "색깔 다양성",
+  "Combinations": "조합",
+  "Combinatorial batches": "조합 배치 수",
+  "Combinatorial generation": "조합 생성",
   "copy": "복사",
   "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
   "Create a text file next to every image with generation parameters.": "생성된 이미지마다 생성 설정값을 담은 텍스트 파일 생성하기",
   "Create aesthetic images embedding": "스타일 이미지 임베딩 생성하기",
+  "Create an embedding from one or few pictures and use it to apply their style to generated images.": "하나 혹은 그 이상의 이미지들로부터 임베딩을 생성해, 그 이미지들의 스타일을 다른 이미지 생성 시 적용할 수 있게 해줍니다.",
+  "Create debug image": "디버그 이미지 생성",
   "Create embedding": "임베딩 생성",
   "Create flipped copies": "좌우로 뒤집은 복사본 생성",
   "Create hypernetwork": "하이퍼네트워크 생성",
   "Create images embedding": "이미지 임베딩 생성하기",
   "Crop and resize": "잘라낸 후 리사이징",
   "Crop to fit": "잘라내서 맞추기",
+  "custom fold": "커스텀 경로",
   "Custom Name (Optional)": "병합 모델 이름 (선택사항)",
   "Dataset directory": "데이터셋 경로",
+  "Dataset Tag Editor": "데이터셋 태그 편집기",
+  "date": "생성 일자",
   "DDIM": "DDIM",
   "Decode CFG scale": "디코딩 CFG 스케일",
   "Decode steps": "디코딩 스텝 수",
   "Delete": "삭제",
+  "delete next": "선택한 이미지부터 시작해서 삭제할 이미지 갯수",
   "Denoising": "디노이징",
   "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
   "Denoising strength": "디노이즈 강도",
   "Denoising strength change factor": "디노이즈 강도 변경 배수",
+  "Description": "설명",
   "Destination directory": "결과물 저장 경로",
   "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.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
   "Directory for saving images using the Save button": "저장 버튼을 이용해 저장하는 이미지들의 저장 경로",
@@ -104,9 +130,12 @@
   "Draw mask": "마스크 직접 그리기",
   "Drop File Here": "파일을 끌어 놓으세요",
   "Drop Image Here": "이미지를 끌어 놓으세요",
+  "Dropdown": "드롭다운",
+  "Dynamic Prompts": "다이나믹 프롬프트",
   "Embedding": "임베딩",
   "Embedding Learning rate": "임베딩 학습률",
   "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "강조 : (텍스트)를 이용해 모델의 텍스트에 대한 가중치를 더 강하게 주고 [텍스트]를 이용해 더 약하게 줍니다.",
+  "Enable Autocomplete": "태그 자동완성 사용",
   "Enable full page image viewer": "전체 페이지 이미지 뷰어 활성화",
   "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "더 예리하고 깔끔한 결과물을 위해 K 샘플러들에 양자화를 적용합니다. 존재하는 시드가 변경될 수 있습니다. 재시작이 필요합니다.",
   "End Page": "마지막 페이지",
@@ -122,6 +151,9 @@
   "Euler a": "Euler a",
   "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
   "Existing Caption txt Action": "이미 존재하는 캡션 텍스트 처리",
+  "Extension": "확장기능",
+  "Extension index URL": "확장기능 목록 URL",
+  "Extensions": "확장기능",
   "Extra": "고급",
   "Extras": "부가기능",
   "extras": "부가기능",
@@ -129,7 +161,7 @@
   "Face restoration": "얼굴 보정",
   "Face restoration model": "얼굴 보정 모델",
   "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
-  "favorites": "즐겨찾기",
+  "Favorites": "즐겨찾기",
   "File": "파일",
   "File format for grids": "그리드 이미지 파일 형식",
   "File format for images": "이미지 파일 형식",
@@ -145,6 +177,10 @@
   "First Page": "처음 페이지",
   "Firstpass height": "초기 세로길이",
   "Firstpass width": "초기 가로길이",
+  "Fixed seed": "시드 고정",
+  "Focal point edges weight": "경계면 가중치",
+  "Focal point entropy weight": "엔트로피 가중치",
+  "Focal point face weight": "얼굴 가중치",
   "Font for image grids that have text": "텍스트가 존재하는 그리드 이미지의 폰트",
   "for detailed explanation.": "를 참조하십시오.",
   "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
@@ -176,8 +212,10 @@
   "ignore": "무시",
   "Image": "이미지",
   "Image Browser": "이미지 브라우저",
+  "Image browser": "이미지 브라우저",
   "Image for img2img": "Image for img2img",
   "Image for inpainting with mask": "마스크로 인페인팅할 이미지",
+  "Image not found (may have been already moved)": "이미지를 찾을 수 없습니다 (이미 옮겨졌을 수 있음)",
   "Images Browser": "이미지 브라우저",
   "Images directory": "이미지 경로",
   "Images filename pattern": "이미지 파일명 패턴",
@@ -185,6 +223,7 @@
   "img2img alternative test": "이미지→이미지 대체버전 테스트",
   "img2img DDIM discretize": "이미지→이미지 DDIM 이산화",
   "img2img history": "이미지→이미지 기록",
+  "Implements an expressive template language for random or combinatorial prompt generation along with features to support deep wildcard directory structures.": "무작위/조합 프롬프트 생성을 위한 문법과 복잡한 와일드카드 구조를 지원합니다.",
   "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.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
   "Include Separate Images": "분리된 이미지 포함하기",
   "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "75개보다 많은 토큰을 사용시 마지막 쉼표로부터 N개의 토큰 이내에 패딩을 추가해 통일성 증가시키기",
@@ -194,7 +233,14 @@
   "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트시 패딩값(픽셀 단위)",
   "Inpaint masked": "마스크만 처리",
   "Inpaint not masked": "마스크 이외만 처리",
+  "Inpainting conditioning mask strength": "인페인팅 조절 마스크 강도",
   "Input directory": "인풋 이미지 경로",
+  "Input images directory": "이미지 경로 입력",
+  "Inspiration": "\"영감\"",
+  "Install": "설치",
+  "Install from URL": "URL로부터 확장기능 설치",
+  "Installed": "설치된 확장기능",
+  "Installed into ": "확장기능을 ",
   "Interpolation Method": "보간 방법",
   "Interrogate\nCLIP": "CLIP\n분석",
   "Interrogate\nDeepBooru": "DeepBooru\n분석",
@@ -209,9 +255,11 @@
   "Interrogate: use artists from artists.csv": "분석 : artists.csv의 작가들 사용하기",
   "Interrupt": "중단",
   "Is negative text": "네거티브 텍스트일시 체크",
+  "Iterate seed every line": "줄마다 시드 반복하기",
   "Just resize": "리사이징",
   "Keep -1 for seeds": "시드값 -1로 유지",
   "keep whatever was there originally": "이미지 원본 유지",
+  "keyword": "프롬프트",
   "Label": "라벨",
   "Lanczos": "Lanczos",
   "Last prompt:": "마지막 프롬프트 : ",
@@ -219,22 +267,29 @@
   "Last saved image:": "마지막으로 저장된 이미지 : ",
   "latent noise": "잠재 노이즈",
   "latent nothing": "잠재 공백",
+  "latest": "최신 버전",
   "LDSR": "LDSR",
   "LDSR processing steps. Lower = faster": "LDSR 스텝 수. 낮은 값 = 빠른 속도",
   "leakyrelu": "leakyrelu",
   "Leave blank to save images to the default path.": "기존 저장 경로에 이미지들을 저장하려면 비워두세요.",
+  "Leave empty for auto": "자동 설정하려면 비워두십시오",
   "left": "왼쪽",
+  "Lets you edit captions in training datasets.": "훈련에 사용되는 데이터셋의 캡션을 수정할 수 있게 해줍니다.",
   "linear": "linear",
+  "List of prompt inputs": "프롬프트 입력 리스트",
   "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "설정 탭이 아니라 상단의 빠른 설정 바에 위치시킬 설정 이름을 쉼표로 분리해서 입력하십시오. 설정 이름은 modules/shared.py에서 찾을 수 있습니다. 재시작이 필요합니다.",
   "LMS": "LMS",
   "LMS Karras": "LMS Karras",
   "Load": "불러오기",
+  "Load from:": "URL로부터 불러오기",
   "Loading...": "로딩 중...",
+  "Local directory name": "로컬 경로 이름",
   "Localization (requires restart)": "현지화 (재시작 필요)",
   "Log directory": "로그 경로",
   "Loopback": "루프백",
   "Loops": "루프 수",
   "Loss:": "손실(Loss) : ",
+  "Magic prompt": "매직 프롬프트",
   "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
   "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-diffusion 샘플러들이 단일 이미지를 생성하는 것처럼 배치에서도 동일한 이미지를 생성하게 하기",
   "Make Zip when Save?": "저장 시 Zip 생성하기",
@@ -248,21 +303,26 @@
   "Minimum number of pages per load": "한번 불러올 때마다 불러올 최소 페이지 수",
   "Modules": "모듈",
   "Move face restoration model from VRAM into RAM after processing": "처리가 완료되면 얼굴 보정 모델을 VRAM에서 RAM으로 옮기기",
-  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "하이퍼네트워크 훈련 진행 시 VAE와 CLIP을 RAM으로 옮기기. VRAM이 절약됩니다.",
+  "Move to favorites": "즐겨찾기로 옮기기",
+  "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "훈련 진행 시 가능하면 VAE와 CLIP을 RAM으로 옮기기. VRAM이 절약됩니다.",
+  "Moved to favorites": "즐겨찾기로 옮겨짐",
   "Multiplier (M) - set to 0 to get model A": "배율 (M) - 0으로 적용하면 모델 A를 얻게 됩니다",
   "Name": "이름",
   "Negative prompt": "네거티브 프롬프트",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트(Prompt) 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
   "Next batch": "다음 묶음",
   "Next Page": "다음 페이지",
   "None": "없음",
   "Nothing": "없음",
   "Nothing found in the image.": "Nothing found in the image.",
+  "Number of columns on the page": "각 페이지마다 표시할 가로줄 수",
   "Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
   "number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
   "Number of pictures displayed on each page": "각 페이지에 표시될 이미지 수",
   "Number of repeats for a single input image per epoch; used only for displaying epoch number": "세대(Epoch)당 단일 인풋 이미지의 반복 횟수 - 세대(Epoch) 숫자를 표시하는 데에만 사용됩니다. ",
+  "Number of rows on the page": "각 페이지마다 표시할 세로줄 수",
   "Number of vectors per token": "토큰별 벡터 수",
+  "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "인페인팅 모델에만 적용됩니다. 인페인팅과 이미지→이미지에서 원본 이미지를 얼마나 마스킹 처리할지 결정하는 값입니다. 1.0은 완전히 마스킹함(기본 설정)을 의미하고, 0.0은 완전히 언마스킹된 이미지를 의미합니다. 낮은 값일수록 이미지의 전체적인 구성을 유지하는 데에 도움되겠지만, 변화량이 많을수록 불안정해집니다.",
   "Open for Clip Aesthetic!": "클립 스타일 기능을 활성화하려면 클릭!",
   "Open images output directory": "이미지 저장 경로 열기",
   "Open output directory": "저장 경로 열기",
@@ -270,6 +330,7 @@
   "original": "원본 유지",
   "Original negative prompt": "기존 네거티브 프롬프트",
   "Original prompt": "기존 프롬프트",
+  "Others": "기타",
   "Outpainting direction": "아웃페인팅 방향",
   "Outpainting mk2": "아웃페인팅 마크 2",
   "Output directory": "이미지 저장 경로",
@@ -288,6 +349,7 @@
   "Overwrite Old Hypernetwork": "기존 하이퍼네트워크 덮어쓰기",
   "Page Index": "페이지 인덱스",
   "parameters": "설정값",
+  "path name": "경로 이름",
   "Path to directory where to write outputs": "결과물을 출력할 경로",
   "Path to directory with input images": "인풋 이미지가 있는 경로",
   "Paths for saving": "저장 경로",
@@ -308,13 +370,14 @@
   "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
   "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
   "Prompt": "프롬프트",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트(Prompt) 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
   "Prompt matrix": "프롬프트 매트릭스",
   "Prompt order": "프롬프트 순서",
   "Prompt S/R": "프롬프트 스타일 변경",
   "Prompt template file": "프롬프트 템플릿 파일 경로",
   "Prompts": "프롬프트",
   "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
+  "Provides an interface to browse created images in the web browser.": "생성된 이미지를 브라우저 내에서 볼 수 있는 인터페이스를 추가합니다.",
   "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
   "quad": "quad",
   "Quality for saved jpeg images": "저장된 jpeg 이미지들의 품질",
@@ -322,11 +385,13 @@
   "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
   "Random": "랜덤",
   "Random grid": "랜덤 그리드",
+  "Randomly display the pictures of the artist's or artistic genres typical style, more pictures of this artist or genre is displayed after selecting. So you don't have to worry about how hard it is to choose the right style of art when you create.": "특정 작가 또는 스타일의 이미지들 중 하나를 무작위로 보여줍니다. 선택 후 선택한 작가 또는 스타일의 이미지들이 더 나타나게 됩니다. 고르기 어려워도 걱정하실 필요 없어요!",
   "Randomness": "랜덤성",
   "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
   "Read parameters (prompt, etc...) from txt2img tab when making previews": "프리뷰 이미지 생성 시 텍스트→이미지 탭에서 설정값(프롬프트 등) 읽어오기",
   "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
   "Reload custom script bodies (No ui updates, No restart)": "커스텀 스크립트 리로드하기(UI 업데이트 없음, 재시작 없음)",
+  "Reloading...": "재시작 중...",
   "relu": "relu",
   "Renew Page": "Renew Page",
   "Request browser notifications": "브라우저 알림 권한 요청",
@@ -346,6 +411,7 @@
   "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
   "right": "오른쪽",
   "Run": "가동",
+  "Sample extension. Allows you to use __name__ syntax in your prompt to get a random line from a file named name.txt in the wildcards directory. Also see Dynamic Prompts for similar functionality.": "샘플 확장기능입니다. __이름__형식의 문법을 사용해 와일드카드 경로 내의 이름.txt파일로부터 무작위 프롬프트를 적용할 수 있게 해줍니다. 유사한 확장기능으로 다이나믹 프롬프트가 있습니다.",
   "Sampler": "샘플러",
   "Sampler parameters": "샘플러 설정값",
   "Sampling method": "샘플링 방법",
@@ -353,6 +419,7 @@
   "Save": "저장",
   "Save a copy of embedding to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 임베딩을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
   "Save a copy of image before applying color correction to img2img results": "이미지→이미지 결과물에 색상 보정을 진행하기 전 이미지의 복사본을 저장하기",
+  "Save a copy of image before applying highres fix.": "고해상도 보정을 진행하기 전 이미지의 복사본을 저장하기",
   "Save a copy of image before doing face restoration.": "얼굴 보정을 진행하기 전 이미지의 복사본을 저장하기",
   "Save an csv containing the loss to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 손실(Loss)을 포함하는 csv 파일을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
   "Save an image to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 이미지를 저장합니다, 비활성화하려면 0으로 설정하십시오.",
@@ -375,7 +442,9 @@
   "Seed": "시드",
   "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
   "Select activation function of hypernetwork": "하이퍼네트워크 활성화 함수 선택",
+  "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "레이어 가중치 초기화 방식 선택 - relu류 : Kaiming 추천, sigmoid류 : Xavier 추천",
   "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "WebUI에 표시할 Real-ESRGAN 모델을 선택하십시오. (재시작 필요)",
+  "Send seed when sending prompt or image to other interface": "다른 화면으로 프롬프트나 이미지를 보낼 때 시드도 함께 보내기",
   "Send to extras": "부가기능으로 전송",
   "Send to img2img": "이미지→이미지로 전송",
   "Send to inpaint": "인페인트로 전송",
@@ -395,6 +464,7 @@
   "Show progressbar": "프로그레스 바 보이기",
   "Show result images": "이미지 결과 보이기",
   "Show Textbox": "텍스트박스 보이기",
+  "Shows a gallery of generated pictures by artists separated into categories.": "생성된 이미지들을 작가별로 분류해 보여줍니다. 원본 - https://artiststostudy.pages.dev",
   "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
   "Sigma Churn": "시그마 섞기",
   "sigma churn": "시그마 섞기",
@@ -407,6 +477,7 @@
   "Skip": "건너뛰기",
   "Slerp angle": "구면 선형 보간 각도",
   "Slerp interpolation": "구면 선형 보간",
+  "sort by": "정렬 기준",
   "Source": "원본",
   "Source directory": "원본 경로",
   "Split image overlap ratio": "이미지 분할 겹침 비율",
@@ -414,6 +485,7 @@
   "Split oversized images": "사이즈가 큰 이미지 분할하기",
   "Stable Diffusion": "Stable Diffusion",
   "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "step cnt": "스텝 변화 횟수",
   "step count": "스텝 변화 횟수",
   "step1 min/max": "스텝1 최소/최대",
   "step2 min/max": "스텝2 최소/최대",
@@ -430,6 +502,7 @@
   "System": "시스템",
   "Tertiary model (C)": "3차 모델 (C)",
   "Textbox": "텍스트박스",
+  "The official port of Deforum, an extensive script for 2D and 3D animations, supporting keyframable sequences, dynamic math parameters (even inside the prompts), dynamic masking, depth estimation and warping.": "Deforum의 공식 포팅 버전입니다. 2D와 3D 애니메이션, 키프레임 시퀀스, 수학적 매개변수, 다이나믹 마스킹 등을 지원합니다.",
   "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "이 정규표현식은 파일명으로부터 단어를 추출하는 데 사용됩니다. 추출된 단어들은 하단의 설정을 이용해 라벨 텍스트로 변환되어 훈련에 사용됩니다. 파일명 텍스트를 유지하려면 비워두십시오.",
   "This string will be used to join split words into a single line if the option above is enabled.": "이 문자열은 상단 설정이 활성화되어있을 때 분리된 단어들을 한 줄로 합치는 데 사용됩니다.",
   "This text is used to rotate the feature space of the imgs embs": "이 텍스트는 이미지 임베딩의 특징 공간을 회전하는 데 사용됩니다.",
@@ -450,8 +523,12 @@
   "txt2img": "텍스트→이미지",
   "txt2img history": "텍스트→이미지 기록",
   "uniform": "uniform",
+  "unknown": "알수 없음",
   "up": "위쪽",
+  "Update": "업데이트",
   "Upload mask": "마스크 업로드하기",
+  "Upload prompt inputs": "입력할 프롬프트를 업로드하십시오",
+  "Upscale Before Restoring Faces": "얼굴 보정을 진행하기 전에 업스케일링 먼저 진행하기",
   "Upscale latent space image when doing hires. fix": "고해상도 보정 사용시 잠재 공간 이미지 업스케일하기",
   "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
   "Upscaler": "업스케일러",
@@ -460,15 +537,21 @@
   "Upscaler 2 visibility": "업스케일러 2 가시성",
   "Upscaler for img2img": "이미지→이미지 업스케일러",
   "Upscaling": "업스케일링",
+  "URL for extension's git repository": "확장기능의 git 레포 URL",
   "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
   "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
   "Use BLIP for caption": "캡션에 BLIP 사용",
+  "Use checkbox to enable the extension; it will be enabled or disabled when you click apply button": "체크박스를 이용해 적용할 확장기능을 선택하세요. 변경사항은 적용 후 UI 재시작 버튼을 눌러야 적용됩니다.",
+  "Use checkbox to mark the extension for update; it will be updated when you click apply button": "체크박스를 이용해 업데이트할 확장기능을 선택하세요. 업데이트는 적용 후 UI 재시작 버튼을 눌러야 적용됩니다.",
+  "Use cross attention optimizations while training": "훈련 진행 시 크로스 어텐션 최적화 사용",
   "Use deepbooru for caption": "캡션에 deepbooru 사용",
   "Use dropout": "드롭아웃 사용",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지 파일명 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지와 그리드의 하위 디렉토리명의 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지 파일명 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지와 그리드의 하위 디렉토리명의 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
   "Use old emphasis implementation. Can be useful to reproduce old seeds.": "옛 방식의 강조 구현을 사용합니다. 옛 시드를 재현하는 데 효과적일 수 있습니다.",
   "Use original name for output filename during batch process in extras tab": "부가기능 탭에서 이미지를 여러장 처리 시 결과물 파일명에 기존 파일명 사용하기",
+  "Use same random seed for all lines": "모든 줄에 동일한 시드 사용",
+  "Use same seed for each image": "각 이미지에 동일한 시드 사용",
   "use spaces for tags in deepbooru": "deepbooru에서 태그에 공백 사용",
   "User interface": "사용자 인터페이스",
   "Var. seed": "바리에이션 시드",
@@ -485,6 +568,7 @@
   "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
   "Width": "가로",
   "wiki": " 위키",
+  "Wildcards": "와일드카드",
   "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
   "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "이미지→이미지 진행 시, 슬라이더로 설정한 스텝 수를 정확히 실행하기 (일반적으로 디노이즈 강도가 낮을수록 실제 설정된 스텝 수보다 적게 진행됨)",
   "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",

localizations/pt_BR.json

@@ -0,0 +1,485 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Carregando...",
+    "view": "ver",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "criado com gradio",
+    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Extras",
+    "PNG Info": "Informações de PNG",
+    "Checkpoint Merger": "Fusão de Checkpoint",
+    "Train": "Treinar",
+    "Settings": "Configurações",
+    "Extensions": "Extensões",
+    "Prompt": "Prompt",
+    "Negative prompt": "Prompt negativo",
+    "Run": "Executar",
+    "Skip": "Pular",
+    "Interrupt": "Interromper",
+    "Generate": "Gerar",
+    "Style 1": "Estilo 1",
+    "Style 2": "Estilo 2",
+    "Label": "Rótulo",
+    "File": "Arquivo",
+    "Drop File Here": "Solte Aqui o Arquivo",
+    "-": "-",
+    "or": "ou",
+    "Click to Upload": "Clique para Carregar um Arquivo",
+    "Image": "Imagem",
+    "Check progress": "Checar progresso",
+    "Check progress (first)": "Checar progresso (primeiro)",
+    "Sampling Steps": "Passos de Amostragem",
+    "Sampling method": "Método de amostragem",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Largura",
+    "Height": "Altura",
+    "Restore faces": "Restaurar rostos",
+    "Tiling": "Ladrilhar",
+    "Highres. fix": "Ajuste de alta resolução",
+    "Firstpass width": "Primeira Passagem da largura",
+    "Firstpass height": "Primeira Passagem da altura",
+    "Denoising strength": "Força do denoise",
+    "Batch count": "Quantidade por lote",
+    "Batch size": "Quantidade de lotes",
+    "CFG Scale": "Escala CFG",
+    "Seed": "Seed",
+    "Extra": "Extra",
+    "Variation seed": "Variação de seed",
+    "Variation strength": "Força da variação",
+    "Resize seed from width": "Redimensionar a seed a partir da largura",
+    "Resize seed from height": "Redimensionar a seed a partir da altura",
+    "Script": "Script",
+    "None": "Nenhum",
+    "Prompt matrix": "Matriz de prompt",
+    "Prompts from file or textbox": "Prompts a partir de arquivo ou caixa de texto",
+    "X/Y plot": "X/Y plot",
+    "Put variable parts at start of prompt": "Coloca partes variáveis no começo do prompt",
+    "Iterate seed every line": "Iterar seed a cada linha",
+    "List of prompt inputs": "Lista de entrada de texto para prompt",
+    "Upload prompt inputs": "Carregar entrada de texto para prompt",
+    "X type": "Tipo do X",
+    "Nothing": "Nenhum",
+    "Var. seed": "Var. seed",
+    "Var. strength": "Var. da força",
+    "Steps": "Passos",
+    "Prompt S/R": "Prompt S/R",
+    "Prompt order": "Ordem de Prompt",
+    "Sampler": "Sampler",
+    "Checkpoint name": "Nome do Checkpoint",
+    "Hypernetwork": "Hypernetwork",
+    "Hypernet str.": "Força da Hypernet",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "Sigma min",
+    "Sigma max": "Sigma max",
+    "Sigma noise": "Sigma noise",
+    "Eta": "Tempo estimado",
+    "Clip skip": "Pular Clip",
+    "Denoising": "Denoising",
+    "Cond. Image Mask Weight": "Peso da Máscara Condicional de Imagem",
+    "X values": "Valores de X",
+    "Y type": "Tipo de Y",
+    "Y values": "Valores de Y",
+    "Draw legend": "Desenhar a legenda",
+    "Include Separate Images": "Incluir Imagens Separadas",
+    "Keep -1 for seeds": "Manter em -1 para seeds",
+    "Save": "Salvar",
+    "Send to img2img": "Mandar para img2img",
+    "Send to inpaint": "Mandar para inpaint",
+    "Send to extras": "Mandar para extras",
+    "Make Zip when Save?": "Criar um Zip quando salvar?",
+    "Textbox": "Caixa de texto",
+    "Interrogate\nCLIP": "Interrogatório\nCLIP",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Lote img2img",
+    "Image for img2img": "Imagem para img2img",
+    "Drop Image Here": "Solte a imagem aqui",
+    "Image for inpainting with mask": "Imagem para inpainting com máscara",
+    "Mask": "Máscara",
+    "Mask blur": "Desfoque da máscara",
+    "Mask mode": "Modo de máscara",
+    "Draw mask": "Desenhar máscara",
+    "Upload mask": "Carregar máscara",
+    "Masking mode": "Modo de máscara",
+    "Inpaint masked": "Inpaint o que está dentro da máscara",
+    "Inpaint not masked": "Inpaint o que está fora da máscara",
+    "Masked content": "Conteúdo mascarado",
+    "fill": "preencher",
+    "original": "original",
+    "latent noise": "latent noise",
+    "latent nothing": "latent nothing",
+    "Inpaint at full resolution": "Inpaint em resolução total",
+    "Inpaint at full resolution padding, pixels": "Inpaint de preenchimento em resolução total, pixels",
+    "Process images in a directory on the same machine where the server is running.": "Processar imagens no diretório da mesma maquina onde o servidor está rodando.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usar um diretório vazio para salvar imagens, ao invés de salvá-las no diretório output.",
+    "Input directory": "Diretório de entrada",
+    "Output directory": "Diretório de saída",
+    "Resize mode": "Modo de redimensionamento",
+    "Just resize": "Apenas redimensionar",
+    "Crop and resize": "Cortar e redimensionar",
+    "Resize and fill": "Redimensionar e preencher",
+    "img2img alternative test": "Teste alternativo de img2img",
+    "Loopback": "Loopback",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man's outpainting": "Poor man`s outpainting",
+    "SD upscale": "Ampliamento SD",
+    "should be 2 or lower.": "deve ser 2 ou menos.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Substituir `Método de amostragem` por Euler? (este método foi feito para isso)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Substituir `prompt` para o mesmo valor que o `prompt original`? (também para o `prompt negativo`)",
+    "Original prompt": "Prompt original",
+    "Original negative prompt": "Prompt negativo original",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Substituir `Passos de Amostragem` para o mesmo valor que `Decodificar Passos`?",
+    "Decode steps": "Decode steps",
+    "Override `Denoising strength` to 1?": "Substituir `Quantidade do Denoise` para 1?",
+    "Decode CFG scale": "Decodificar escala CFG",
+    "Randomness": "Aleatoriedade",
+    "Sigma adjustment for finding noise for image": "Ajuste Sigma para encontrar ruído para imagem",
+    "Loops": "Loops",
+    "Denoising strength change factor": "Fator de mudança na quantidade do Denoise",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Configurações recomendadas: Passos de amostragem: 80-100: Euler a, força do Denoise: 0.8",
+    "Pixels to expand": "Pixels para expandir",
+    "Outpainting direction": "Direção do outpainting",
+    "left": "esquerda",
+    "right": "direita",
+    "up": "cima",
+    "down": "baixo",
+    "Fall-off exponent (lower=higher detail)": "Expoente de queda (menor=mais detalhes)",
+    "Color variation": "Variação de cor",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Amplia a imagem em dobro; ajusta a largura e altura para definir o tamanho do ladrilho",
+    "Tile overlap": "Sobreposição de ladrilho",
+    "Upscaler": "Ampliador",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR 4x": "SwinIR 4x",
+    "Single Image": "Uma imagem",
+    "Batch Process": "Processo em lote",
+    "Batch from Directory": "Lote apartir de diretório",
+    "Source": "Origem",
+    "Show result images": "Mostrar imagens resultantes",
+    "Scale by": "Aumentar proporcionalmente em",
+    "Scale to": "Aumentar proporcionalmente para",
+    "Resize": "Redimensionar",
+    "Crop to fit": "Cortar para caber",
+    "Upscaler 2 visibility": "Visibilidade da ferramenta de ampliação 2",
+    "GFPGAN visibility": "Visibilidade GFPGAN",
+    "CodeFormer visibility": "Visibilidade CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso do CodeFormer (0 = efeito máximo, 1 = efeito mínimo)",
+    "Upscale Before Restoring Faces": "Ampliar Antes de Refinar Rostos",
+    "Send to txt2img": "Mandar para txt2img",
+    "A merger of the two checkpoints will be generated in your": "Uma fusão dos dois checkpoints será gerada em seu",
+    "checkpoint": "checkpoint",
+    "directory.": "diretório.",
+    "Primary model (A)": "Modelo primário (A)",
+    "Secondary model (B)": "Modelo secundário (B)",
+    "Tertiary model (C)": "Modelo terciário (C)",
+    "Custom Name (Optional)": "Nome personalizado (Opcional)",
+    "Multiplier (M) - set to 0 to get model A": "Multiplicador (M) - definir em 0 para obter o modelo A",
+    "Interpolation Method": "Método de Interpolação",
+    "Weighted sum": "Soma de pesos",
+    "Add difference": "Acrescentar diferença",
+    "Save as float16": "Salvar como float16",
+    "See": "Ver",
+    "wiki": "wiki",
+    "for detailed explanation.": "para explicação detalhada.",
+    "Create embedding": "Criar incorporação",
+    "Create hypernetwork": "Criar hypernetwork",
+    "Preprocess images": "Pré-processar imagens",
+    "Name": "Nome",
+    "Initialization text": "Texto de inicialização",
+    "Number of vectors per token": "Número de vetores por token",
+    "Overwrite Old Embedding": "Substituir Incorporação anterior",
+    "Modules": "Módulos",
+    "Enter hypernetwork layer structure": "Entrar na estrutura de camadas da hypernetwork",
+    "Select activation function of hypernetwork": "Selecionar a função de ativação de hypernetwork",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "threshold",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Selecionar a inicialização de pesos de camada. relu-like - Kaiming, sigmoid-like - Xavier é recomendado",
+    "Normal": "Normal",
+    "KaimingUniform": "KaimingUniform",
+    "KaimingNormal": "KaimingNormal",
+    "XavierUniform": "XavierUniform",
+    "XavierNormal": "XavierNormal",
+    "Add layer normalization": "Adicionar normalização de camada",
+    "Use dropout": "Usar dropout",
+    "Overwrite Old Hypernetwork": "Sobrescrever Hypernetwork Anterior",
+    "Source directory": "Diretório de origem",
+    "Destination directory": "Diretório de destino",
+    "Existing Caption txt Action": "Ação de Título txt Já Existente",
+    "ignore": "ignorar",
+    "copy": "copiar",
+    "prepend": "adicionar ao início",
+    "append": "adicionar ao final",
+    "Create flipped copies": "Criar cópias espelhadas",
+    "Split oversized images into two": "Dividir imagens maiores em duas",
+    "Auto focal point crop": "Ajuste de corte em ponto focal automático",
+    "Use BLIP for caption": "Usar BLIP para o título",
+    "Use deepbooru for caption": "Usar deepbooru para o título",
+    "Split image threshold": "Limite de divisão de imagem",
+    "Split image overlap ratio": "Proporção de sobreposição da divisão de imagem",
+    "Focal point face weight": "Peso de ponto focal para rosto",
+    "Focal point entropy weight": "Peso de ponto focal para entropia",
+    "Focal point edges weight": "Peso de ponto focal para bordas",
+    "Create debug image": "Criar imagem de depuração",
+    "Preprocess": "Pré-processar",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Treinar uma incorporação; precisa especificar um diretório com imagens de proporção 1:1",
+    "[wiki]": "[wiki]",
+    "Embedding": "Incorporação",
+    "Embedding Learning rate": "Taxa de aprendizagem da incorporação",
+    "Hypernetwork Learning rate": "Taxa de aprendizagem de Hypernetwork",
+    "Dataset directory": "Diretório de Dataset",
+    "Log directory": "Diretório de Log",
+    "Prompt template file": "Arquivo padrão de Prompt",
+    "Max steps": "Passos máximos",
+    "Save an image to log directory every N steps, 0 to disable": "Salvar uma imagem no diretório de log a cada N passos. 0 para desativar",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva uma cópia da incorporação no diretório de log a cada N passos. 0 para desativar",
+    "Save images with embedding in PNG chunks": "Salva imagens com incorporação em segmentos de PNG",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Ler parâmetros (prompt, etc...) para a aba txt2img durante os previews",
+    "Train Hypernetwork": "Treinar Hypernetwork",
+    "Train Embedding": "Treinar Incorporação",
+    "Apply settings": "Aplicar configurações",
+    "Saving images/grids": "Salvar imagens/grades",
+    "Always save all generated images": "Sempre salvar todas as imagens geradas",
+    "File format for images": "Tipo de formato das imagens salvas",
+    "Images filename pattern": "Padrão de nomeação para imagens salvas",
+    "Add number to filename when saving": "Adicionar número para o nome do arquivo quando salvar",
+    "Always save all generated image grids": "Sempre salvar todas as grades de imagens",
+    "File format for grids": "Tipo de formato das grades de imagens salvas",
+    "Add extended info (seed, prompt) to filename when saving grid": "Adicionar informações extras (seed, prompt) para os arquivos quando gerar uma grade",
+    "Do not save grids consisting of one picture": "Não salvar grades de apenas uma imagem",
+    "Prevent empty spots in grid (when set to autodetect)": "Previnir espaços vazios na grade (quando marcado para autodetectar)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Contagem de linhas da grade; -1 para autodetectar e 0 para ser igual ao valor do tamanho das levas",
+    "Save text information about generation parameters as chunks to png files": "Salvar informações de parâmetros de geração como segmentos png",
+    "Create a text file next to every image with generation parameters.": "Criar um arquivo de texto com informações de geração junto a cada imagem gerada.",
+    "Save a copy of image before doing face restoration.": "Salva uma cópia de cada imagem antes do refinamento facial.",
+    "Quality for saved jpeg images": "Qualidade das imagens jpeg",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se a imagem PNG for maior que 4MB ou qualquer dimensão maior que 4000, diminuir e salvar uma cópia em JPG",
+    "Use original name for output filename during batch process in extras tab": "Usar o nome original para os arquivos de output durante o processo de levas da aba Extras",
+    "When using 'Save' button, only save a single selected image": "Quando usar o botão `Salvar`, somente salvar as imagens selecionadas.",
+    "Do not add watermark to images": "Não adicionar marca dágua nas imagens",
+    "Paths for saving": "Caminhos para salvar",
+    "Output directory for images; if empty, defaults to three directories below": "Diretório de saída para imagens; se deixado em branco, as imagens vao para os seguintes diretórios",
+    "Output directory for txt2img images": "Diretório de Saída para imagens txt2img",
+    "Output directory for img2img images": "Diretório de Saída para imagens img2img",
+    "Output directory for images from extras tab": "Diretório de Saída para a aba Extras",
+    "Output directory for grids; if empty, defaults to two directories below": "Diretório de Saída para grades; se vazio, vão para os diretórios seguintes",
+    "Output directory for txt2img grids": "Diretório de Saída para grades de imagens txt2img",
+    "Output directory for img2img grids": "Diretório de Saída para grades de imagens img2img",
+    "Directory for saving images using the Save button": "Diretório para imagens salvas utilizando o botão de salvar",
+    "Saving to a directory": "Salvando para um diretório",
+    "Save images to a subdirectory": "Salvar imagens para um subdiretório",
+    "Save grids to a subdirectory": "Salvar grades de imagens para um subdiretório",
+    "When using \"Save\" button, save images to a subdirectory": "Quando usar o botão \"Salvar\", salvar imagens para um subdiretório",
+    "Directory name pattern": "Padrão de nome de diretório",
+    "Max prompt words for [prompt_words] pattern": "Número máximo de palavras do padrão de prompt [prompt_words]",
+    "Upscaling": "Ampliando",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tamanho do ladrilho para ampliação ESRGAN. 0 = sem ladrilho.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sobreposição de azulejo, em pixels, para amplicação  ESRGAN. Valores baixos = linhas de fusão mais aparente.",
+    "Tile size for all SwinIR.": "Tamanho do ladrilho para todo SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sobreposição de azulejo, em pixels,  para SwinIR. Valores baixos = junção mais aparente.",
+    "LDSR processing steps. Lower = faster": "Steps de processamento LDSR. Menos = rápido",
+    "Upscaler for img2img": "Ampliação para img2img",
+    "Upscale latent space image when doing hires. fix": "Ampliar a imagem do espaço latente quando usando o ajuste de alta definição - hires. fix",
+    "Face restoration": "Refinamento de rosto",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Parâmento de peso do CodeFormer; 0 = efeito máximo; 1 = efeito mínimo",
+    "Move face restoration model from VRAM into RAM after processing": "Mover o processo de refinamento de rosto da VRAM da placa de vídeo para a RAM do computador depois do processamento.",
+    "System": "Sistema",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Levantamento de uso de VRAM por segundo durante gerações. Deixar em 0 para desativar.",
+    "Always print all generation info to standard output": "Sempre mostrar as informações de todas as gerações no padrão de output",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Adicionar uma segunda barra de processamento no console que mostra a progressão de todo o trabalho.",
+    "Training": "Treinamento",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Mover VAE e CLIP para a RAM quando treinando hypernetwork. Preserva VRAM.",
+    "Filename word regex": "Palavra de nome de arquivo regex",
+    "Filename join string": "Nome de arquivo join string",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repetições para entrada única de imagens por época; serve apenas para mostrar o número de época",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salvar um csv com as perdas para o diretório de log a cada N steps, 0 para desativar",
+    "Use cross attention optimizations while training": "Usar otimizações de atenção cruzada enquanto treinando",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Checkpoints para manter no cache da RAM",
+    "Hypernetwork strength": "Força da Hypernetwork",
+     "Inpainting conditioning mask strength": "Força do inpaint para máscaras condicioniais",
+    "Apply color correction to img2img results to match original colors.": "Aplicar correção de cor nas imagens geradas em img2img, usando a imagem original como base.",
+    "Save a copy of image before applying color correction to img2img results": "Salvar uma cópia das imagens geradas em img2img antes de aplicar a correção de cor",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Durante gerações img2img, fazer examente o número de steps definidos na barra (normalmente você faz menos steps com denoising menor).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Ativar quantização em K samples para resultados mais nítidos e visíveis. Pode alterar seeds ja existentes. Precisa reiniciar para funcionar.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Ênfase: usar parênteses ao redor de palavras (texto de exemplo) para fazer o modelo dar mais atenção para aquela palavra ou frase, e chaves [texto de exemplo] para tirar atenção",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usar método anterior de implementação de ênfase. Útil para reproduzir seeds antigas.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Faz as amostragens K-diffusion produzirem imagens iguais em lotes quando criando uma única imagem",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta a coerência por preenchimento apartir da ultima vírgula dentro de n tokens quando usando mais de 75 tokens",
+    "Filter NSFW content": "Filtra conteúdos inadequados(geralmente +18)",
+    "Stop At last layers of CLIP model": "Para na última camada do modelo CLIP",
+    "Interrogate Options": "Opções de Interrogatório",
+    "Interrogate: keep models in VRAM": "Interrogar: manter modelos na VRAM",
+    "Interrogate: use artists from artists.csv": "Interrogar: usa artistas e estilos do documento artists.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogar: incluir classificação de tags de modelo combinando nos resultados (Não tem efeito na interrogação feita por legenda).",
+    "Interrogate: num_beams for BLIP": "Interrogar: num_beams para BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interrogar: tamanho mínimo da descrição (tirando artistas, etc..)",
+    "Interrogate: maximum description length": "Interrogar: tamanho máximo da descrição",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: número máximo de linhas no arquivo de texto(0 = Sem limites)",
+    "Interrogate: deepbooru score threshold": "Interrogatório: limite de score deepbooru",
+    "Interrogate: deepbooru sort alphabetically": "Interrogar: organizar deepbooru por ordem alfabética",
+    "use spaces for tags in deepbooru": "usar espaços para tags em deepbooru",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "espaço (\\) colchetes em deepbooru (são usados como colchetes ao invés de dar ênfase)",
+    "User interface": "Interface de usuário",
+    "Show progressbar": "Mostrar barra de progresso",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostrar a criação de imagens a cada N sampling steps. Em 1 já dá para ver o processo de geração. Marcar como 0 para desativar.",
+    "Show previews of all images generated in a batch as a grid": "Mostrar previsualização de todas as imagens geradas em leva numa grade",
+    "Show grid in results for web": "Mostrar grade em resultados para web",
+    "Do not show any images in results for web": "Não mostrar nenhuma imagem em resultados para web",
+    "Add model hash to generation information": "Adicionar hash do modelo para informação de geração",
+    "Add model name to generation information": "Adicionar nome do modelo para informação de geração",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Quando ler parâmetros de texto para a interface (de informações de PNG ou texto copiado), não alterar o modelo/intervalo selecionado.",
+    "Send seed when sending prompt or image to other interface": "Enviar seed quando enviar prompt ou imagem para outra interface",
+    "Font for image grids that have text": "Fonte para grade de imagens que têm texto",
+    "Enable full page image viewer": "Ativar visualizador de página inteira",
+    "Show images zoomed in by default in full page image viewer": "Mostrar imagens com zoom por definição no visualizador de página inteira",
+    "Show generation progress in window title.": "Mostrar barra de progresso no nome da janela.",
+    "Quicksettings list": "Lista de configurações rapidas",
+    "Localization (requires restart)": "Localização (precisa reiniciar)",
+    "ar_AR": "ar_AR",
+    "de_DE": "de_DE",
+    "es_ES": "es_ES",
+    "fr_FR": "fr_FR",
+    "it_IT": "it_IT",
+    "ja_JP": "ja_JP",
+    "ko_KR": "ko_KR",
+    "pt_BR": "pt_BR",
+    "ru_RU": "ru_RU",
+    "tr_TR": "tr_TR",
+    "zh_CN": "zh_CN",
+    "zh_TW": "zh_TW",
+    "Sampler parameters": "Parâmetros de Amostragem",
+    "Hide samplers in user interface (requires restart)": "Esconder amostragens na interface de usuário (precisa reiniciar)",
+    "eta (noise multiplier) for DDIM": "tempo estimado (multiplicador de ruído) para DDIM",
+    "eta (noise multiplier) for ancestral samplers": "tempo estimado (multiplicador de ruído) para amostragens ancestrais",
+    "img2img DDIM discretize": "Discretização de img2img DDIM",
+    "uniform": "uniforme",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "tempo estimado para ruído seed delta",
+    "Request browser notifications": "Solicitar notificações do navegador",
+    "Download localization template": "Baixar arquivo modelo de localização",
+    "Reload custom script bodies (No ui updates, No restart)": "Recarregar scripts personalizados (Sem atualizar a interface, Sem reiniciar)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Reiniciar Gradio e atualizar componentes (Scripts personalizados, ui.py, js e css)",
+    "Installed": "Instalado",
+    "Available": "Disponível",
+    "Install from URL": "Instalado de URL",
+    "Apply and restart UI": "Apicar e reiniciar a interface",
+    "Check for updates": "Procurar por atualizações",
+    "Extension": "Extensão",
+    "URL": "URL",
+    "Update": "Atualização",
+    "Load from:": "Carregar de:",
+    "Extension index URL": "Índice de extensão URL",
+    "URL for extension's git repository": "URL para repositório git da extensão",
+    "Local directory name": "Nome do diretório local",
+    "Install": "Instalar",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (apertar Ctrl+Enter ou Alt+Enter para gerar)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt Negativo (apertar Ctrl+Enter ou Alt+Enter para gerar)",
+    "Add a random artist to the prompt.": "Adicionar um artista aleatório para o prompt.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lê os parâmetros de geração do prompt ou da última geraçao, caso o prompt esteja vazio.",
+    "Save style": "Salva um estilo de prompt.",
+    "Apply selected styles to current prompt": "Aplica o estilo para o prompt atual.",
+    "Stop processing current image and continue processing.": "Pula a imagem sendo gerada e vai para a próxima.",
+    "Stop processing images and return any results accumulated so far.": "Interrompe o processo e mostra o que foi gerado até então.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Estilo para aplicar; também serve para o prompt negativo e vai preencher se usado.",
+    "Do not do anything special": "Não faça nada de especial",
+    "Which algorithm to use to produce the image": "O tipo de algoritmo para gerar imagens.",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - cria mais variações para as imagens em diferentes passos. Mais que 40 passos cancela o efeito.",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - Funciona melhor para inpainting.",
+    "Produce an image that can be tiled.": "Produz uma imagem que pode ser ladrilhada.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Cria um processo em duas etapas, com uma imagem em baixa qualidade primeiro, aumenta a imagem e refina os detalhes sem alterar a composição da imagem",
+    "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.": "Quanto o algoritmo deve manter da imagem original. Em 0, nada muda. Em 1 o algoritmo ignora a imagem original. Valores menores que 1.0 demoram mais.",
+    "How many batches of images to create": "Quantos lotes de imagens criar",
+    "How many image to create in a single batch": "Quantas imagens criar em um único lote",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - Quanto maior o valor, mais segue o prompt e quanto menor, menor segue.",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Codigo de geração de uma imagem - criando uma imagem com os mesmos parâmetros e seed trazem o mesmo resultado.",
+    "Set seed to -1, which will cause a new random number to be used every time": "Define seed como -1, deixando o valor que vai aparecer como aleatório.",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Reutilizar a seed da última geração, útil principalmente se ela foi aleatória",
+    "Seed of a different picture to be mixed into the generation.": "Seed de uma imagem diferente é misturada na geração.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Qual a variação a ser gerada. Em 0, não tem efeito. Em 1, gera uma imagem completa com a variação de seed, (exceto com amostragens a).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Tenta gerar uma imagem similar ao que teria sido feito com a mesma seed em dimensões especifica.",
+    "Separate values for X axis using commas.": "Separa os valores para o eixo X usando vírgulas.",
+    "Separate values for Y axis using commas.": "Separa os valores para o eixo Y usando vírgulas.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva a imagem no diretório padrão ou escolhido e cria um arquivo csv com os parâmetros da geração.",
+    "Open images output directory": "Abre o diretório de saída de imagens.",
+    "How much to blur the mask before processing, in pixels.": "Transição do contorno da máscara, em pixels.",
+    "What to put inside the masked area before processing it with Stable Diffusion.":  "O que vai dentro da máscara antes de processá-la com Stable Diffusion.",
+	"fill it with colors of the image": "Preenche usando as cores da imagem.",
+    "keep whatever was there originally": "manter usando o que estava lá originalmente",
+    "fill it with latent space noise": "Preenche com ruídos do espaço latente.",
+    "fill it with latent space zeroes": "Preenche com zeros do espaço latente.",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Faz ampliação na região com máscara para atingir a resolução desejada, faz inpainting, faz downscale para voltar à resolução original e cola na imagem original",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Redimensiona a imagem para a resolução desejada. A menos que a altura e a largura sejam iguais, você obterá uma proporção incorreta.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Redimensiona a imagem para que toda a resolução desejada seja preenchida com a imagem. Corta as partes que ficaram pra fora.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Redimensiona a imagem para que toda a imagem esteja dentro da resolução desejada. Preenche o espaço vazio com as cores da imagem.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Número de vezes que vai repetir o processamento da imagem e usar como entrada para a próxima iteração",
+    "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.": "No modo de loopback, em cada loop a força do denoise é multiplicado por este valor. <1 significa diminuir a variedade para que sua sequência converta em uma imagem fixa. >1 significa aumentar a variedade para que sua sequência se torne cada vez mais caótica.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Para ampliação SD, quantidade de sobreposição em pixels que deve haver entre os ladrilhos. Os ladrilhos se sobrepõem para que, quando forem mesclados de volta em uma imagem, não haja linhas de junção claramente visíveis.",
+    "A directory on the same machine where the server is running.": "Um diretório na mesma máquina onde o servidor está rodando.",
+    "Leave blank to save images to the default path.": "Deixar em branco para salvar imagens no caminho padrão.",
+    "Result = A * (1 - M) + B * M": "Resultado = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Resultado = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "Primeiro e último dígito precisam ser 1. ex:`1, 2, 1`",
+    "Path to directory with input images": "Caminho para o diretório com imagens de entrada",
+    "Path to directory where to write outputs": "Caminho para o diretório para gravar as saídas",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos arquivos sao escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrão.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se esta opção estiver marcada, as imagens não vão ter marca d`água. Aviso: se você não quer a marca d`água, você pode estar se envolvendo em comportamentos antiéticos",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos subdiretorios e grades são escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrão.",
+    "Restore low quality faces using GFPGAN neural network": "Restaurar rostos de baixa qualidade usando a rede neural GFPGAN",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Esta expressão regular vai retirar palavras do nome do arquivo e serão juntadas via regex usando a opção abaixo em etiquetas usadas em treinamento. Não mexer para manter os nomes como estão.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Esta string será usada para unir palavras divididas em uma única linha se a opção acima estiver habilitada.",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Aplicável somente para modelos de inpaint. Determina quanto deve mascarar da imagem original para inpaint e img2img. 1.0 significa totalmente mascarado, que é o comportamento padrão. 0.0 significa uma condição totalmente não mascarada. Valores baixos ajudam a preservar a composição geral da imagem, mas vai encontrar dificuldades com grandes mudanças.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nomes de configurações, separados por vírgulas, para configurações que devem ir para a barra de acesso rápido na parte superior, em vez da guia de configuração usual. Veja modules/shared.py para nomes de configuração. Necessita reinicialização para aplicar.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se este valor for diferente de zero, ele será adicionado à seed e usado para inicializar o RNG para ruídos ao usar amostragens com Tempo Estimado. Você pode usar isso para produzir ainda mais variações de imagens ou pode usar isso para combinar imagens de outro software se souber o que está fazendo.",
+    "Leave empty for auto": "Deixar desmarcado para automático"
+}

localizations/tr_TR.json

@@ -0,0 +1,423 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Yükleniyor...",
+    "view": "arayüz",
+    "api": "",
+    "•": "-",
+    "built with gradio": "gradio ile inşa edildi",
+    "Stable Diffusion checkpoint": "Kararlı Difüzyon kontrol noktası",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Ekstralar",
+    "PNG Info": "PNG Bilgisi",
+    "Checkpoint Merger": "Checkpoint Birleştir",
+    "Train": "Eğitim",
+    "Settings": "Ayarlar",
+    "Prompt": "İstem",
+    "Negative prompt": "Negatif istem",
+    "Run": "Koşmak",
+    "Skip": "Atla",
+    "Interrupt": "Durdur",
+    "Generate": "Oluştur",
+    "Style 1": "Stil 1",
+    "Style 2": "Stil 2",
+    "Label": "Etiket",
+    "File": "Dosya",
+    "Drop File Here": "Dosyayı Buraya Bırakın",
+    "-": "-",
+    "or": "veya",
+    "Click to Upload": "Yüklemek için Tıklayınız",
+    "Image": "Resim",
+    "Check progress": "İlerlemeyi kontrol edin",
+    "Check progress (first)": "Önce ilerlemeyi kontrol edin",
+    "Sampling Steps": "Örnekleme Adımları",
+    "Sampling method": "Örnekleme yöntemi",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM hızlı",
+    "DPM adaptive": "DPM uyarlanabilir",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Genişlik",
+    "Height": "Yükseklik",
+    "Restore faces": "Yüzleri düzeltme",
+    "Tiling": "Döşeme Oluştur",
+    "Highres. fix": "Highres. düzeltme",
+    "Firstpass width": "İlk geçiş genişliği",
+    "Firstpass height": "İlk geçiş yüksekliği",
+    "Denoising strength": "Gürültü arındırma gücü",
+    "Batch count": "Grup sayısı",
+    "Batch size": "Grup büyüklüğü",
+    "CFG Scale": "CFG Ölçeği",
+    "Seed": "Tohum",
+    "Extra": "Ekstra",
+    "Variation seed": "Varyasyon tohumu",
+    "Variation strength": "Varyasyon gücü",
+    "Resize seed from width": "Tohumu genişlik ile yeniden boyutlandırma",
+    "Resize seed from height": "Tohumu yükseklik ile yeniden boyutlandırma",
+    "Script": "Scriptler",
+    "None": "Hiçbiri",
+    "Prompt matrix": "İstem matrisi",
+    "Prompts from file or textbox": "Dosyadan veya metin kutusundan istemler",
+    "X/Y plot": "X/Y grafiği",
+    "Put variable parts at start of prompt": "Değişken parçaları komut isteminin başına koyun",
+    "Show Textbox": "Metin Kutusunu Göster",
+    "File with inputs": "Girdileri içeren dosya",
+    "Prompts": "İpuçları",
+    "X type": "X tipi",
+    "Nothing": "Hiçbir şey",
+    "Var. seed": "Var. tohum",
+    "Var. strength": "Var. güç",
+    "Steps": "Adımlar",
+    "Prompt S/R": "İstem S/R",
+    "Prompt order": "İstem sırası",
+    "Sampler": "Örnekleyici",
+    "Checkpoint name": "Kontrol noktası adı",
+    "Hypernetwork": "Hipernetwork",
+    "Hypernet str.": "Hypernet str.",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "Sigma dakika",
+    "Sigma max": "Sigma maksimum",
+    "Sigma noise": "Sigma gürültüsü",
+    "Eta": "Eta",
+    "Clip skip": "Klip atlama",
+    "Denoising": "Denoising",
+    "X values": "X değerleri",
+    "Y type": "Y tipi",
+    "Y values": "Y değerleri",
+    "Draw legend": "Gösterge çizin",
+    "Include Separate Images": "Ayrı Görseller Ekleyin",
+    "Keep -1 for seeds": "Tohumlar için -1'i saklayın",
+    "Drop Image Here": "Resmi Buraya Bırakın",
+    "Save": "Kaydet",
+    "Send to img2img": "img2img'ye gönder",
+    "Send to inpaint": "Inpaint'e gönder",
+    "Send to extras": "Ekstralara gönder",
+    "Make Zip when Save?": "Kaydederken Zip Yap?",
+    "Textbox": "Metin Kutusu",
+    "Interrogate\nCLIP": "Sorgula\nCLIP",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Toplu img2img",
+    "Image for img2img": "img2img için resim",
+    "Image for inpainting with mask": "Maske ile inpainting için görüntü",
+    "Mask": "Maske",
+    "Mask blur": "Maske bulanıklığı",
+    "Mask mode": "Maske modu",
+    "Draw mask": "Maske çizin",
+    "Upload mask": "Maske yükle",
+    "Masking mode": "Maskeleme modu",
+    "Inpaint masked": "Maskeli inpaint",
+    "Inpaint not masked": "Boya maskelenmemiş",
+    "Masked content": "Maskelenmiş içerik",
+    "fill": "doldurun",
+    "original": "orijinal",
+    "latent noise": "gizli gürültü",
+    "latent nothing": "gizli hiçbir şey",
+    "Inpaint at full resolution": "Tam çözünürlükte inpaint",
+    "Inpaint at full resolution padding, pixels": "Tam çözünürlükte inpaint dolgu, piksel",
+    "Process images in a directory on the same machine where the server is running.": "Görüntüleri sunucunun çalıştığı makinedeki bir dizinde işleyin.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Resimleri çıktı dizinine yazmak yerine normal şekilde kaydetmek için boş bir çıktı dizini kullanın.",
+    "Input directory": "Girdi dizini",
+    "Output directory": "Çıktı dizini",
+    "Resize mode": "Yeniden boyutlandırma modu",
+    "Just resize": "Sadece yeniden boyutlandır",
+    "Crop and resize": "Kırpma ve yeniden boyutlandırma",
+    "Resize and fill": "Yeniden boyutlandırın ve doldurun",
+    "img2img alternative test": "img2img alternatif test",
+    "Loopback": "Geri Döngü",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man's outpainting": "Zavallı adamın dış boyaması",
+    "SD upscale": "SD lüks",
+    "should be 2 or lower.": "2 veya daha düşük olmalıdır.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Euler için `Örnekleme yöntemini` geçersiz kılın (bu yöntem bunun için oluşturulmuştur)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Prompt` değerini `orijinal prompt` ile aynı değere geçersiz kılma (ve `negatif prompt`)",
+    "Original prompt": "Orijinal bilgi istemi",
+    "Original negative prompt": "Orijinal negatif istem",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Örnekleme Adımlarını `Kod çözme adımları` ile aynı değere mi geçersiz kılıyorsunuz?",
+    "Decode steps": "Kod çözme adımları",
+    "Override `Denoising strength` to 1?": "`Denoising strength` değerini 1 olarak geçersiz kıl?",
+    "Decode CFG scale": "CFG ölçeğinin kodunu çöz",
+    "Randomness": "Rastgelelik",
+    "Sigma adjustment for finding noise for image": "Görüntü için gürültü bulmaya yönelik Sigma ayarı",
+    "Loops": "Döngüler",
+    "Denoising strength change factor": "Denoising gücü değişim faktörü",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Önerilen ayarlar: Örnekleme Adımları: 80-100, Örnekleyici: Euler a, Denoising gücü: 0.8",
+    "Pixels to expand": "Genişletilecek pikseller",
+    "Outpainting direction": "Dış boyama yönü",
+    "left": "sol",
+    "right": "doğru",
+    "up": "yukarı",
+    "down": "aşağı",
+    "Fall-off exponent (lower=higher detail)": "Düşme üssü (düşük=daha yüksek detay)",
+    "Color variation": "Renk çeşitliliği",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Görüntüyü boyutlarının iki katına yükseltir; döşeme boyutunu ayarlamak için genişlik ve yükseklik kaydırıcılarını kullanın",
+    "Tile overlap": "Karo örtüşmesi",
+    "Upscaler": "Upscaler",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "SwinIR 4x": "SwinIR 4x",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "Single Image": "Tek Resim",
+    "Batch Process": "Toplu İşlem",
+    "Batch from Directory": "Dizinden Toplu İş",
+    "Source": "Kaynak",
+    "Show result images": "Sonuç resimlerini göster",
+    "Scale by": "Ölçek tarafından",
+    "Scale to": "Ölçeklendir",
+    "Resize": "Yeniden Boyutlandır",
+    "Crop to fit": "Sığdırmak için kırpın",
+    "Upscaler 2 visibility": "Upscaler 2 görünürlüğü",
+    "GFPGAN visibility": "GFPGAN görünürlüğü",
+    "CodeFormer visibility": "CodeFormer görünürlüğü",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer ağırlığı (0 = maksimum etki, 1 = minimum etki)",
+    "Open output directory": "Çıktı dizinini aç",
+    "Send to txt2img": "txt2img'ye gönder",
+    "A merger of the two checkpoints will be generated in your": "İki kontrol noktasının bir birleşimi sizin kontrol noktanızda oluşturulacaktır.",
+    "checkpoint": "kontrol noktası",
+    "directory.": "dizin.",
+    "Primary model (A)": "Birincil model (A)",
+    "Secondary model (B)": "İkincil model (B)",
+    "Tertiary model (C)": "Üçüncü model (C)",
+    "Custom Name (Optional)": "Özel Ad (İsteğe Bağlı)",
+    "Multiplier (M) - set to 0 to get model A": "Çarpan (M) - A modelini elde etmek için 0'a ayarlayın",
+    "Interpolation Method": "İnterpolasyon Yöntemi",
+    "Weighted sum": "Ağırlıklı toplam",
+    "Add difference": "Farklılık ekleyin",
+    "Save as float16": "float16 olarak kaydet",
+    "See": "Bkz. ",
+    "wiki": "wiki",
+    "for detailed explanation.": " ayrıntılı açıklama için.",
+    "Create embedding": "Yerleştirme oluşturma",
+    "Create hypernetwork": "Hipernet oluşturun",
+    "Preprocess images": "Görüntüleri ön işleme",
+    "Name": "İsim",
+    "Initialization text": "Başlatma metni",
+    "Number of vectors per token": "Belirteç başına vektör sayısı",
+    "Overwrite Old Embedding": "Eski Yerleştirmenin Üzerine Yaz",
+    "Modules": "Modüller",
+    "Enter hypernetwork layer structure": "Hipernetwork katman yapısına girin",
+    "Select activation function of hypernetwork": "Hipernetwork'ün aktivasyon fonksiyonunu seçin",
+    "linear": "doğrusal",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "Add layer normalization": "Katman normalizasyonu ekleyin",
+    "Use dropout": "Bırakmayı kullanın",
+    "Overwrite Old Hypernetwork": "Eski Hipernetwork'ün Üzerine Yazma",
+    "Source directory": "Kaynak dizini",
+    "Destination directory": "Hedef dizini",
+    "Existing Caption txt Action": "Mevcut Başlık txt Eylem",
+    "ignore": "görmezden gel",
+    "copy": "kopya",
+    "prepend": "prepend",
+    "append": "ekle",
+    "Create flipped copies": "Ters çevrilmiş kopyalar oluşturun",
+    "Split oversized images": "Büyük boyutlu görüntüleri bölme",
+    "Use BLIP for caption": "Başlık için BLIP kullanın",
+    "Use deepbooru for caption": "Başlık için deepbooru kullanın",
+    "Split image threshold": "Bölünmüş görüntü eşiği",
+    "Split image overlap ratio": "Bölünmüş görüntü örtüşme oranı",
+    "Preprocess": "Ön işlem",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Bir gömme veya Hipernetwork eğitin; 1:1 oranlı görüntülerin bulunduğu bir dizin belirtmelisiniz",
+    "[wiki]": "[wiki]",
+    "Embedding": "Yerleştirme",
+    "Embedding Learning rate": "Gömme Öğrenme oranı",
+    "Hypernetwork Learning rate": "Hypernetwork Öğrenme oranı",
+    "Dataset directory": "Veri seti dizini",
+    "Log directory": "Günlük dizini",
+    "Prompt template file": "Komut istemi şablon dosyası",
+    "Max steps": "Maksimum adım",
+    "Save an image to log directory every N steps, 0 to disable": "Her N adımda bir görüntüyü günlük dizinine kaydet, 0 devre dışı bırakmak için",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Katıştırmanın bir kopyasını her N adımda bir günlük dizinine kaydedin, devre dışı bırakmak için 0",
+    "Save images with embedding in PNG chunks": "Görüntüleri PNG parçalarına yerleştirerek kaydedin",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Önizleme yaparken txt2img sekmesinden parametreleri (istem, vb...) okuma",
+    "Train Hypernetwork": "Tren Hipernetwork",
+    "Train Embedding": "Tren Gömme",
+    "Apply settings": "Ayarları uygula",
+    "Saving images/grids": "Görüntüleri/gridleri kaydetme",
+    "Always save all generated images": "Oluşturulan tüm görüntüleri her zaman kaydedin",
+    "File format for images": "Görüntüler için dosya formatı",
+    "Images filename pattern": "Görüntü dosya adı deseni",
+    "Add number to filename when saving": "Kaydederken dosya adına numara ekle",
+    "Always save all generated image grids": "Oluşturulan tüm görüntü ızgaralarını her zaman kaydedin",
+    "File format for grids": "Izgaralar için dosya formatı",
+    "Add extended info (seed, prompt) to filename when saving grid": "Izgarayı kaydederken dosya adına genişletilmiş bilgi (tohum, istem) ekleyin",
+    "Do not save grids consisting of one picture": "Tek resimden oluşan ızgaraları kaydetmeyin",
+    "Prevent empty spots in grid (when set to autodetect)": "Izgaradaki boş noktaları önleme (otomatik algılamaya ayarlandığında)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Izgara satır sayısı; otomatik algılama için -1, yığın boyutuyla aynı olması için 0 kullanın",
+    "Save text information about generation parameters as chunks to png files": "Üretim parametreleri hakkındaki metin bilgilerini png dosyalarına parçalar halinde kaydedin",
+    "Create a text file next to every image with generation parameters.": "Oluşturma parametreleri ile her görüntünün yanında bir metin dosyası oluşturun.",
+    "Save a copy of image before doing face restoration.": "Yüz restorasyonu yapmadan önce görüntünün bir kopyasını kaydedin.",
+    "Quality for saved jpeg images": "Kaydedilen jpeg görüntüleri için kalite",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "PNG görüntüsü 4MB'den büyükse veya herhangi bir boyut 4000'den büyükse, ölçeği küçültün ve kopyayı JPG olarak kaydedin",
+    "Use original name for output filename during batch process in extras tab": "Ekstralar sekmesinde toplu işlem sırasında çıktı dosya adı için orijinal adı kullan",
+    "When using 'Save' button, only save a single selected image": "'Kaydet' düğmesini kullanırken, yalnızca seçilen tek bir resmi kaydedin",
+    "Do not add watermark to images": "Görüntülere filigran eklemeyin",
+    "Paths for saving": "Tasarruf için yollar",
+    "Output directory for images; if empty, defaults to three directories below": "Görüntüler için çıktı dizini; boşsa, varsayılan olarak aşağıdaki üç dizine gider",
+    "Output directory for txt2img images": "txt2img görüntüleri için çıktı dizini",
+    "Output directory for img2img images": "img2img görüntüleri için çıktı dizini",
+    "Output directory for images from extras tab": "Ekstralar sekmesindeki görüntüler için çıktı dizini",
+    "Output directory for grids; if empty, defaults to two directories below": "Izgaralar için çıktı dizini; boşsa, varsayılan olarak aşağıdaki iki dizine gider",
+    "Output directory for txt2img grids": "txt2img ızgaraları için çıktı dizini",
+    "Output directory for img2img grids": "img2img ızgaraları için çıktı dizini",
+    "Directory for saving images using the Save button": "Kaydet düğmesini kullanarak görüntüleri kaydetmek için dizin",
+    "Saving to a directory": "Bir dizine kaydetme",
+    "Save images to a subdirectory": "Görüntüleri bir alt dizine kaydetme",
+    "Save grids to a subdirectory": "Izgaraları bir alt dizine kaydetme",
+    "When using \"Save\" button, save images to a subdirectory": "\"Kaydet\" düğmesini kullanırken, görüntüleri bir alt dizine kaydedin",
+    "Directory name pattern": "Dizin adı kalıbı",
+    "Max prompt words for [prompt_words] pattern": "prompt_words] kalıbı için maksimum istem sözcükleri",
+    "Upscaling": "Yükseltme",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN yükselticileri için döşeme boyutu. 0 = döşeme yok.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN yükselticileri için piksel cinsinden döşeme örtüşmesi. Düşük değerler = görünür bağlantı hattı.",
+    "Tile size for all SwinIR.": "Tüm SwinIR için döşeme boyutu.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR için piksel cinsinden döşeme örtüşmesi. Düşük değerler = görünür dikiş.",
+    "LDSR processing steps. Lower = faster": "LDSR işleme adımları. Düşük = daha hızlı",
+    "Upscaler for img2img": "img2img için üst ölçekleyici",
+    "Upscale latent space image when doing hires. fix": "İşe alım yaparken gizli alan görüntüsünü yükselt. düzelt",
+    "Face restoration": "Yüz restorasyonu",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer ağırlık parametresi; 0 = maksimum etki; 1 = minimum etki",
+    "Move face restoration model from VRAM into RAM after processing": "İşlemden sonra yüz restorasyon modelini VRAM'den RAM'e taşıma",
+    "System": "Sistem",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Üretim sırasında saniye başına VRAM kullanım yoklamaları. Devre dışı bırakmak için 0 olarak ayarlayın.",
+    "Always print all generation info to standard output": "Tüm üretim bilgilerini her zaman standart çıktıya yazdır",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Konsola tüm iş için ilerlemeyi gösteren ikinci bir ilerleme çubuğu ekleyin.",
+    "Training": "Eğitim",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Hiperneti eğitirken VAE ve CLIP'i RAM'e taşıyın. VRAM'den tasarruf sağlar.",
+    "Filename word regex": "Dosya adı kelime regex",
+    "Filename join string": "Dosya adı birleştirme dizesi",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Epok başına tek bir girdi görüntüsü için tekrar sayısı; yalnızca epok numarasını görüntülemek için kullanılır",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Her N adımda bir günlük dizinine kaybı içeren bir csv kaydedin, devre dışı bırakmak için 0",
+    "Stable Diffusion": "Kararlı Difüzyon",
+    "Checkpoints to cache in RAM": "RAM'de önbelleğe alınacak kontrol noktaları",
+    "Hypernetwork strength": "Hipernetwork gücü",
+    "Apply color correction to img2img results to match original colors.": "Orijinal renklerle eşleştirmek için img2img sonuçlarına renk düzeltmesi uygulayın.",
+    "Save a copy of image before applying color correction to img2img results": "img2img sonuçlarına renk düzeltmesi uygulamadan önce görüntünün bir kopyasını kaydedin",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "img2img ile, kaydırıcının belirttiği adım miktarını tam olarak yapın (normalde daha az denoising ile daha az yaparsınız).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Daha keskin ve temiz sonuçlar için K örnekleyicilerinde nicelemeyi etkinleştirin. Bu, mevcut tohumları değiştirebilir. Uygulamak için yeniden başlatma gerektirir.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Vurgu: modelin metne daha fazla dikkat etmesini sağlamak için (metin) ve daha az dikkat etmesini sağlamak için [metin] kullanın",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Eski vurgu uygulamasını kullanın. Eski tohumları yeniden üretmek faydalı olabilir.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-difüzyon örnekleyicilerinin tek bir görüntü oluştururken olduğu gibi toplu halde aynı görüntüleri üretmesini sağlayın",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "75'ten fazla belirteç kullanıldığında n belirteç içindeki son virgülden itibaren dolgu yaparak tutarlılığı artırın",
+    "Filter NSFW content": "NSFW içeriği filtreleme",
+    "Stop At last layers of CLIP model": "Durdur CLIP modelinin son katmanlarında",
+    "Interrogate Options": "Sorgulama Seçenekleri",
+    "Interrogate: keep models in VRAM": "Sorgula: modelleri VRAM'de tut",
+    "Interrogate: use artists from artists.csv": "Sorgula: artists.csv dosyasındaki sanatçıları kullan",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: sonuçlara eşleşen model etiketlerinin sıralarını dahil et (Başlık tabanlı sorgulayıcılar üzerinde etkisi yoktur).",
+    "Interrogate: num_beams for BLIP": "Sorgula: BLIP için num_beams",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Sorgula: minimum açıklama uzunluğu (sanatçılar vb. hariç)",
+    "Interrogate: maximum description length": "Sorgula: maksimum açıklama uzunluğu",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: metin dosyasındaki maksimum satır sayısı (0 = Sınır yok)",
+    "Interrogate: deepbooru score threshold": "Sorgula: deepbooru puan eşiği",
+    "Interrogate: deepbooru sort alphabetically": "Sorgula: deepbooru alfabetik olarak sırala",
+    "use spaces for tags in deepbooru": "deepbooru'da etiketler için boşluk kullanın",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "deepbooru'da kaçış (\\) parantezleri (böylece vurgu için değil, gerçek parantez olarak kullanılırlar)",
+    "User interface": "Kullanıcı arayüzü",
+    "Show progressbar": "İlerleme çubuğunu göster",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Her N örnekleme adımında görüntü oluşturma ilerlemesini gösterir. Devre dışı bırakmak için 0 olarak ayarlayın.",
+    "Show previews of all images generated in a batch as a grid": "Bir toplu işte oluşturulan tüm görüntülerin önizlemelerini ızgara olarak göster",
+    "Show grid in results for web": "Web için sonuçlarda ızgarayı göster",
+    "Do not show any images in results for web": "Web için sonuçlarda herhangi bir resim gösterme",
+    "Add model hash to generation information": "Üretim bilgilerine model karması ekleyin",
+    "Add model name to generation information": "Üretim bilgilerine model adı ekleme",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Üretim parametrelerini metinden kullanıcı arayüzüne okurken (PNG bilgisinden veya yapıştırılan metinden), seçilen modeli/denetim noktasını değiştirmeyin.",
+    "Font for image grids that have text": "Metin içeren görüntü ızgaraları için yazı tipi",
+    "Enable full page image viewer": "Tam sayfa resim görüntüleyiciyi etkinleştir",
+    "Show images zoomed in by default in full page image viewer": "Tam sayfa resim görüntüleyicide resimleri varsayılan olarak yakınlaştırılmış olarak gösterme",
+    "Show generation progress in window title.": "Pencere başlığında üretim ilerlemesini göster.",
+    "Quicksettings list": "Hızlı ayarlar listesi",
+    "Localization (requires restart)": "Yerelleştirme (yeniden başlatma gerektirir)",
+    "ko_KR": "ko_KR",
+    "ru_RU": "ru_RU",
+    "es_ES": "es_ES",
+    "ja_JP": "ja_JP",
+    "ar_AR": "ar_AR",
+    "Sampler parameters": "Örnekleyici parametreleri",
+    "Hide samplers in user interface (requires restart)": "Kullanıcı arayüzünde örnekleyicileri gizle (yeniden başlatma gerektirir)",
+    "eta (noise multiplier) for DDIM": "DDIM için eta (gürültü çarpanı)",
+    "eta (noise multiplier) for ancestral samplers": "eta örnekleyiciler için eta (gürültü çarpanı)",
+    "img2img DDIM discretize": "img2img DDIM discretize",
+    "uniform": "üniforma",
+    "quad": "dörtlü",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma gürültüsü",
+    "Eta noise seed delta": "Eta gürültü tohum deltası",
+    "Request browser notifications": "Tarayıcı bildirimleri isteyin",
+    "Download localization template": "Dil şablonunu indirin",
+    "Reload custom script bodies (No ui updates, No restart)": "Kişisel komut dosyası gövdelerini yeniden yükle (Kullanıcı arayüzü güncellemesi yok, yeniden başlatma yok)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradio'yu yeniden başlatın ve bileşenleri yenileyin (yalnızca Özel Komut Dosyaları, ui.py, js ve css)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "İstem (oluşturmak için Ctrl+Enter veya Alt+Enter tuşlarına basın)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Negatif istem (oluşturmak için Ctrl+Enter veya Alt+Enter tuşlarına basın)",
+    "Add a random artist to the prompt.": "Komut istemine rastgele bir sanatçı ekleyin.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Kullanıcı arayüzüne istemden veya istem boşsa son üretimden üretim parametrelerini okuyun.",
+    "Save style": "Stil kaydet",
+    "Apply selected styles to current prompt": "Seçilen stilleri geçerli komut istemine uygulama",
+    "Stop processing current image and continue processing.": "Geçerli görüntüyü işlemeyi durdurun ve işlemeye devam edin.",
+    "Stop processing images and return any results accumulated so far.": "Görüntüleri işlemeyi durdurun ve o ana kadar biriken tüm sonuçları döndürün.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Uygulanacak stil; stillerin hem pozitif hem de negatif istemler için bileşenleri vardır ve her ikisine de uygulanır",
+    "Do not do anything special": "Özel bir şey yapmayın",
+    "Which algorithm to use to produce the image": "Görüntüyü üretmek için hangi algoritmanın kullanılacağı",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - çok yaratıcı, adım sayısına bağlı olarak her biri tamamen farklı bir resim elde edebilir, adımları 30-40'tan daha yükseğe ayarlamak yardımcı olmaz",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Difüzyon Örtük Modelleri - en iyi inpainting",
+    "Produce an image that can be tiled.": "Döşenebilen bir görüntü üretin.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Bir görüntüyü kısmen daha düşük çözünürlükte oluşturmak, büyütmek ve ardından kompozisyonu değiştirmeden ayrıntıları iyileştirmek için iki adımlı bir işlem kullanın",
+    "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.": "Algoritmanın resmin içeriğine ne kadar az saygı göstermesi gerektiğini belirler. 0'da hiçbir şey değişmez ve 1'de ilgisiz bir görüntü elde edersiniz. 1,0'ın altındaki değerlerde işleme, Örnekleme Adımları kaydırıcısının belirttiğinden daha az adım atacaktır.",
+    "How many batches of images to create": "Kaç görüntü grubu oluşturulacağı",
+    "How many image to create in a single batch": "Tek bir partide kaç görüntü oluşturulacağı",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Sınıflandırıcı Serbest Rehberlik Ölçeği - görüntünün istemle ne kadar uyumlu olması gerektiği - düşük değerler daha yaratıcı sonuçlar üretir",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Rastgele sayı üretecinin çıktısını belirleyen bir değer - başka bir resimle aynı parametrelere ve tohuma sahip bir resim oluşturursanız, aynı sonucu alırsınız",
+    "Set seed to -1, which will cause a new random number to be used every time": "Tohum değerini -1 olarak ayarlayın, bu her seferinde yeni bir rastgele sayı kullanılmasına neden olacaktır",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Son nesilden tohumu yeniden kullanın, çoğunlukla rastgele ise kullanışlıdır",
+    "Seed of a different picture to be mixed into the generation.": "Nesle karıştırılacak farklı bir resmin tohumu.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Ne kadar güçlü bir varyasyon üretileceği. 0'da hiçbir etki olmayacaktır. 1'de, varyasyon tohumu ile tam bir resim elde edersiniz (sadece bir şey alacağınız atasal örnekleyiciler hariç).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Belirtilen çözünürlükte aynı tohumla üretilecek olana benzer bir resim üretme girişiminde bulunun",
+    "Separate values for X axis using commas.": "X ekseni için değerleri virgül kullanarak ayırın.",
+    "Separate values for Y axis using commas.": "Y ekseni için değerleri virgül kullanarak ayırın.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Görüntüyü bir dizine (varsayılan - log/images) ve üretim parametrelerini csv dosyasına yazın.",
+    "Open images output directory": "Görüntü çıktı dizinini açın",
+    "How much to blur the mask before processing, in pixels.": "İşlemeden önce maskenin piksel cinsinden ne kadar bulanıklaştırılacağı.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Kararlı Difüzyon ile işlemeden önce maskelenmiş alanın içine ne konulacağı.",
+    "fill it with colors of the image": "Görüntünün renkleriyle doldurun",
+    "keep whatever was there originally": "başlangıçta orada ne varsa saklayın",
+    "fill it with latent space noise": "gizli alan gürültüsü ile doldurun",
+    "fill it with latent space zeroes": "gizli uzay sıfırları ile doldurun",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Maskelenmiş bölgeyi hedef çözünürlüğe yükseltme, inpainting yapma, ölçeği küçültme ve orijinal görüntüye yapıştırma",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Görüntüyü hedef çözünürlüğe göre yeniden boyutlandırın. Yükseklik ve genişlik eşleşmediği sürece, yanlış en boy oranı elde edersiniz.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Görüntüyü, hedef çözünürlüğün tamamı görüntüyle dolacak şekilde yeniden boyutlandırın. Dışarıda kalan kısımları kırpın.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Görüntünün tamamı hedef çözünürlüğün içinde olacak şekilde görüntüyü yeniden boyutlandırın. Boş alanı görüntünün renkleriyle doldurun.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Bir görüntüyü işlemeyi kaç kez tekrarlamak ve bir sonraki yineleme için girdi olarak kullanmak",
+    "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.": "Geri döngü modunda, her döngüde denoising gücü bu değerle çarpılır. <1 çeşitliliğin azalması anlamına gelir, böylece diziniz sabit bir resme yakınsayacaktır. >1'den büyük olması çeşitliliğin artması anlamına gelir, böylece sekansınız gittikçe daha kaotik hale gelecektir.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD yükseltme için karolar arasında piksel olarak ne kadar örtüşme olmalıdır. Döşemeler, tekrar tek bir resimde birleştirildiklerinde açıkça görülebilen bir dikiş olmayacak şekilde üst üste biner.",
+    "A directory on the same machine where the server is running.": "Sunucunun çalıştığı makinedeki bir dizin.",
+    "Leave blank to save images to the default path.": "Görüntüleri varsayılan yola kaydetmek için boş bırakın.",
+    "Result = A * (1 - M) + B * M": "Sonuç = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Sonuç = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "1. ve son rakam 1 olmalıdır. örn:'1, 2, 1'",
+    "Path to directory with input images": "Girdi resimlerinin bulunduğu dizinin yolu",
+    "Path to directory where to write outputs": "Çıktıların yazılacağı dizinin yolu",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Görüntülerin dosya adlarının nasıl seçileceğini tanımlamak için aşağıdaki etiketleri kullanın: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; varsayılan için boş bırakın.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Bu seçenek etkinleştirilirse, oluşturulan görüntülere filigran eklenmeyecektir. Uyarı: filigran eklemezseniz, etik olmayan bir şekilde davranıyor olabilirsiniz.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Görüntüler ve ızgaralar için alt dizinlerin nasıl seçileceğini tanımlamak için aşağıdaki etiketleri kullanın: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; varsayılan için boş bırakın.",
+    "Restore low quality faces using GFPGAN neural network": "GFPGAN sinir ağını kullanarak düşük kaliteli yüzleri geri yükleme",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Bu düzenli ifade, dosya adından sözcükleri ayıklamak için kullanılır ve bunlar aşağıdaki seçenek kullanılarak eğitim için kullanılan etiket metnine birleştirilir. Dosya adı metnini olduğu gibi tutmak için boş bırakın.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Bu dize, yukarıdaki seçenek etkinleştirilirse bölünmüş kelimeleri tek bir satırda birleştirmek için kullanılacaktır.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Normal ayar sekmesi yerine üstteki hızlı erişim çubuğuna gitmesi gereken ayarlar için virgülle ayrılmış ayar adlarının listesi. Ayar adları için modules/shared.py dosyasına bakın. Uygulanması için yeniden başlatma gerekir.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Bu değer sıfır değilse, tohuma eklenecek ve Eta ile örnekleyiciler kullanılırken gürültüler için RNG'yi başlatmak için kullanılacaktır. Bunu daha fazla görüntü çeşitliliği üretmek için kullanabilir veya ne yaptığınızı biliyorsanız diğer yazılımların görüntülerini eşleştirmek için kullanabilirsiniz.."
+}

localizations/zh_CN.json

@@ -0,0 +1,624 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "载入中...",
+    "view": "查看",
+    "api": "api",
+    "•": " • ",
+    "built with gradio": "基于 Gradio 构建",
+    "Stable Diffusion checkpoint": "Stable Diffusion 模型(ckpt)",
+    "txt2img": "文生图",
+    "img2img": "图生图",
+    "Extras": "更多",
+    "PNG Info": "图片信息",
+    "Checkpoint Merger": "模型(ckpt)合并",
+    "Train": "训练",
+    "Create aesthetic embedding": "生成美术风格",
+    "Image Browser": "图库浏览器",
+    "Settings": "设置",
+    "Extensions": "扩展",
+    "Prompt": "提示词",
+    "Negative prompt": "反向提示词",
+    "Run": "运行",
+    "Skip": "跳过",
+    "Interrupt": "中止",
+    "Generate": "生成",
+    "Style 1": "模版风格 1",
+    "Style 2": "模版风格 2",
+    "Label": "标签",
+    "File": "文件",
+    "Drop File Here": "拖拽文件到此",
+    "-": "-",
+    "or": "或",
+    "Click to Upload": "点击上传",
+    "Image": "图像",
+    "Check progress": "查看进度",
+    "Check progress (first)": "(首次)查看进度",
+    "Sampling Steps": "采样迭代步数 (Steps)",
+    "Sampling method": "采样方法 (Sampler)",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "宽度",
+    "Height": "高度",
+    "Restore faces": "面部修复",
+    "Tiling": "可平铺(Tiling)",
+    "Highres. fix": "高分辨率修复",
+    "Firstpass width": "第一遍的宽度",
+    "Firstpass height": "第一遍的高度",
+    "Denoising strength": "重绘幅度(Denoising strength)",
+    "Batch count": "生成批次",
+    "Batch size": "每批数量",
+    "CFG Scale": "提示词相关性(CFG Scale)",
+    "Seed": "随机种子(seed)",
+    "Extra": "▼",
+    "Variation seed": "差异随机种子",
+    "Variation strength": "差异强度",
+    "Resize seed from width": "自宽度缩放随机种子",
+    "Resize seed from height": "自高度缩放随机种子",
+    "Open for Clip Aesthetic!": "打开以调整 Clip 的美术风格！",
+    "Aesthetic weight": "美术风格权重",
+    "Aesthetic steps": "美术风格迭代步数",
+    "Aesthetic learning rate": "美术风格学习率",
+    "Slerp interpolation": "球面线性插值",
+    "Aesthetic imgs embedding": "美术风格图集 embedding",
+    "None": "无",
+    "Aesthetic text for imgs": "该图集的美术风格描述",
+    "Slerp angle": "球面线性插值角度",
+    "Is negative text": "是反向提示词",
+    "Script": "脚本",
+    "Embedding to Shareable PNG": "将 Embedding 转换为可分享的 PNG 图片文件",
+    "Prompt matrix": "提示词矩阵",
+    "Prompts from file or textbox": "从文本框或文件载入提示词",
+    "X/Y plot": "X/Y 图表",
+    "Source embedding to convert": "用于转换的源 Embedding",
+    "Embedding token": "Embedding 的 token (关键词)",
+    "Output directory": "输出目录",
+    "Put variable parts at start of prompt": "把变量部分放在提示词文本的开头",
+    "Iterate seed every line": "每行输入都换一个种子",
+    "Use same random seed for all lines": "每行输入都使用同一个随机种子",
+    "List of prompt inputs": "提示词输入列表",
+    "Upload prompt inputs": "上传提示词输入文件",
+    "X type": "X轴类型",
+    "Nothing": "无",
+    "Var. seed": "差异随机种子",
+    "Var. strength": "差异强度",
+    "Steps": "迭代步数",
+    "Prompt S/R": "提示词替换",
+    "Prompt order": "提示词顺序",
+    "Sampler": "采样器",
+    "Checkpoint name": "模型(ckpt)名",
+    "Hypernetwork": "超网络(Hypernetwork)",
+    "Hypernet str.": "超网络(Hypernetwork) 强度",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "最小 Sigma",
+    "Sigma max": "最大 Sigma",
+    "Sigma noise": "Sigma noise",
+    "Eta": "Eta",
+    "Clip skip": "Clip 跳过",
+    "Denoising": "去噪",
+    "Cond. Image Mask Weight": "图像调节屏蔽度",
+    "X values": "X轴数值",
+    "Y type": "Y轴类型",
+    "Y values": "Y轴数值",
+    "Draw legend": "在图表中包括轴标题",
+    "Include Separate Images": "包括独立的图像",
+    "Keep -1 for seeds": "保持随机种子为-1",
+    "Save": "保存",
+    "Send to img2img": ">> 图生图",
+    "Send to inpaint": ">> 局部重绘",
+    "Send to extras": ">> 更多",
+    "Make Zip when Save?": "保存时生成zip压缩文件?",
+    "Textbox": "文本框",
+    "Interrogate\nCLIP": "CLIP\n反推提示词",
+    "Interrogate\nDeepBooru": "DeepBooru\n反推提示词",
+    "Inpaint": "局部重绘",
+    "Batch img2img": "批量图生图",
+    "Image for img2img": "图生图的图像",
+    "Drop Image Here": "拖拽图像到此",
+    "Image for inpainting with mask": "用于局部重绘并手动画蒙版的图像",
+    "Mask": "蒙版",
+    "Mask blur": "蒙版模糊",
+    "Mask mode": "蒙版模式",
+    "Draw mask": "绘制蒙版",
+    "Upload mask": "上传蒙版",
+    "Masking mode": "蒙版模式",
+    "Inpaint masked": "重绘蒙版内容",
+    "Inpaint not masked": "重绘非蒙版内容",
+    "Masked content": "蒙版蒙住的内容",
+    "fill": "填充",
+    "original": "原图",
+    "latent noise": "潜空间噪声",
+    "latent nothing": "潜空间数值零",
+    "Inpaint at full resolution": "全分辨率局部重绘",
+    "Inpaint at full resolution padding, pixels": "预留像素",
+    "Process images in a directory on the same machine where the server is running.": "使用服务器主机上的一个目录，作为输入目录处理图像",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "使用一个空的文件夹作为输出目录，而不是使用默认的 output 文件夹作为输出目录",
+    "Input directory": "输入目录",
+    "Resize mode": "缩放模式",
+    "Just resize": "拉伸",
+    "Crop and resize": "裁剪",
+    "Resize and fill": "填充",
+    "img2img alternative test": "图生图的另一种测试",
+    "Loopback": "回送",
+    "Outpainting mk2": "向外绘制第二版",
+    "Poor man's outpainting": "效果稍差的向外绘制",
+    "SD upscale": "使用 SD 放大(SD upscale)",
+    "should be 2 or lower.": "必须小于等于2",
+    "Override `Sampling method` to Euler?(this method is built for it)": "覆写 `采样方法` 为 Euler?(这个方法就是为这样做设计的)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆写 `提示词` 为 `初始提示词`?(包括`反向提示词`)",
+    "Original prompt": "初始提示词",
+    "Original negative prompt": "初始反向提示词",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "覆写 `采样迭代步数` 为 `解码迭代步数`?",
+    "Decode steps": "解码迭代步数",
+    "Override `Denoising strength` to 1?": "覆写 `重绘幅度` 为 1?",
+    "Decode CFG scale": "解码提示词相关性(CFG scale)",
+    "Randomness": "随机度",
+    "Sigma adjustment for finding noise for image": "为寻找图中噪点的 Sigma 调整",
+    "Loops": "迭代次数",
+    "Denoising strength change factor": "重绘幅度的调整系数",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，重绘幅度：0.8",
+    "Pixels to expand": "拓展的像素数",
+    "Outpainting direction": "向外绘制的方向",
+    "left": "左",
+    "right": "右",
+    "up": "上",
+    "down": "下",
+    "Fall-off exponent (lower=higher detail)": "衰减指数(越低细节越好)",
+    "Color variation": "色彩变化",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块尺寸(tile size)",
+    "Tile overlap": "图块重叠的像素(Tile overlap)",
+    "Upscaler": "放大算法",
+    "Lanczos": "Lanczos",
+    "Nearest": "最邻近(整数缩放)",
+    "LDSR": "LDSR",
+    "BSRGAN 4x": "BSRGAN 4x",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR_4x": "SwinIR 4x",
+    "Single Image": "单个图像",
+    "Batch Process": "批量处理",
+    "Batch from Directory": "从目录进行批量处理",
+    "Source": "来源",
+    "Show result images": "显示输出图像",
+    "Scale by": "等比缩放",
+    "Scale to": "指定尺寸缩放",
+    "Resize": "缩放",
+    "Crop to fit": "裁剪以适应",
+    "Upscaler 2 visibility": "放大算法 2 (Upscaler 2) 可见度",
+    "GFPGAN visibility": "GFPGAN 可见度",
+    "CodeFormer visibility": "CodeFormer 可见度",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
+    "Upscale Before Restoring Faces": "放大后再进行面部修复",
+    "Send to txt2img": ">> 文生图",
+    "A merger of the two checkpoints will be generated in your": "合并后的模型(ckpt)会生成在你的",
+    "checkpoint": "模型(ckpt)",
+    "directory.": "目录",
+    "Primary model (A)": "主要模型 (A)",
+    "Secondary model (B)": "第二模型 (B)",
+    "Tertiary model (C)": "第三模型 (C)",
+    "Custom Name (Optional)": "自定义名称 (可选)",
+    "Multiplier (M) - set to 0 to get model A": "倍率 (M) - 设为 0 等价于模型 A",
+    "Interpolation Method": "插值方法",
+    "Weighted sum": "加权和",
+    "Add difference": "添加差分",
+    "Save as float16": "以 float16 储存",
+    "See": "查看",
+    "wiki": "wiki文档",
+    "for detailed explanation.": "以了解详细说明",
+    "Create embedding": "生成 embedding",
+    "Create hypernetwork": "生成 hypernetwork",
+    "Preprocess images": "图像预处理",
+    "Name": "名称",
+    "Initialization text": "初始化文字",
+    "Number of vectors per token": "每个 token 的向量数",
+    "Overwrite Old Embedding": "覆写旧的 Embedding",
+    "Modules": "模块",
+    "Enter hypernetwork layer structure": "输入 hypernetwork 层结构",
+    "Select activation function of hypernetwork": "选择 hypernetwork 的激活函数",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "阈值",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "选择初始化层权重的方案. 类relu - Kaiming, 类sigmoid - Xavier 都是比较推荐的选项",
+    "Normal": "正态",
+    "KaimingUniform": "Kaiming 均匀",
+    "KaimingNormal": "Kaiming 正态",
+    "XavierUniform": "Xavier 均匀",
+    "XavierNormal": "Xavier 正态",
+    "Add layer normalization": "添加层标准化",
+    "Use dropout": "采用 dropout 防止过拟合",
+    "Overwrite Old Hypernetwork": "覆写旧的 Hypernetwork",
+    "Source directory": "源目录",
+    "Destination directory": "目标目录",
+    "Existing Caption txt Action": "对已有的 txt 说明文字的行为",
+    "ignore": "无视",
+    "copy": "复制",
+    "prepend": "放前面",
+    "append": "放后面",
+    "Create flipped copies": "生成镜像副本",
+    "Split oversized images": "分割过大的图像",
+    "Auto focal point crop": "自动焦点裁切",
+    "Use BLIP for caption": "使用 BLIP 生成说明文字(自然语言描述)",
+    "Use deepbooru for caption": "使用 deepbooru 生成说明文字(tags)",
+    "Split image threshold": "图像分割阈值",
+    "Split image overlap ratio": "分割图像重叠的比率",
+    "Focal point face weight": "焦点面部权重",
+    "Focal point entropy weight": "焦点熵权重",
+    "Focal point edges weight": "焦点线条权重",
+    "Create debug image": "生成调试(debug)图片",
+    "Preprocess": "预处理",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "训练 embedding 或者 hypernetwork； 必须指定一组具有 1:1 比例图像的目录",
+    "[wiki]": "[wiki文档]",
+    "Embedding": "Embedding",
+    "Embedding Learning rate": "Embedding 学习率",
+    "Hypernetwork Learning rate": "Hypernetwork 学习率",
+    "Dataset directory": "数据集目录",
+    "Log directory": "日志目录",
+    "Prompt template file": "提示词模版文件",
+    "Max steps": "最大迭代步数",
+    "Save an image to log directory every N steps, 0 to disable": "每 N 步保存一个图像到日志目录，0 表示禁用",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步将 embedding 的副本保存到日志目录，0 表示禁用",
+    "Save images with embedding in PNG chunks": "保存图像，并在 PNG 图片文件中嵌入 embedding 文件",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "进行预览时，从文生图选项卡中读取参数（提示词等）",
+    "Train Hypernetwork": "训练 Hypernetwork",
+    "Train Embedding": "训练 Embedding",
+    "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
+    "Create images embedding": "生成图集 embedding",
+    "Favorites": "收藏夹(已保存)",
+    "Others": "其他",
+    "Images directory": "图像目录",
+    "Dropdown": "下拉列表",
+    "First Page": "首页",
+    "Prev Page": "上一页",
+    "Page Index": "页数",
+    "Next Page": "下一页",
+    "End Page": "尾页",
+    "delete next": "删除下一张",
+    "Delete": "删除",
+    "sort by": "排序方式",
+    "path name": "路径名",
+    "date": "日期",
+    "keyword": "搜索",
+    "Generate Info": "生成信息",
+    "File Name": "文件名",
+    "Move to favorites": "移动到收藏夹(保存)",
+    "Renew Page": "刷新页面",
+    "Number": "数量",
+    "set_index": "设置索引",
+    "load_switch": "载入开关",
+    "turn_page_switch": "翻页开关",
+    "Checkbox": "勾选框",
+    "Apply settings": "保存设置",
+    "Saving images/grids": "保存图像/宫格图",
+    "Always save all generated images": "始终保存所有生成的图像",
+    "File format for images": "图像的文件格式",
+    "Images filename pattern": "图像文件名格式",
+    "Add number to filename when saving": "储存的时候在文件名里添加数字",
+    "Always save all generated image grids": "始终保存所有生成的宫格图",
+    "File format for grids": "宫格图的文件格式",
+    "Add extended info (seed, prompt) to filename when saving grid": "保存宫格图时，将扩展信息（随机种子、提示词）添加到文件名",
+    "Do not save grids consisting of one picture": "只有一张图片时不要保存宫格图",
+    "Prevent empty spots in grid (when set to autodetect)": "（启用自动检测时）防止宫格图中出现空位",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "宫格图行数； 使用 -1 进行自动检测，使用 0 使其与每批数量相同",
+    "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息，作为块保存到 png 图片文件中",
+    "Create a text file next to every image with generation parameters.": "保存图像时，在每个图像旁边创建一个文本文件储存生成参数",
+    "Save a copy of image before doing face restoration.": "在进行面部修复之前保存图像副本",
+    "Save a copy of image before applying highres fix.": "在做高分辨率修复之前保存初始图像副本",
+    "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
+    "Quality for saved jpeg images": "保存的 jpeg 图像的质量",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 图像大于 4MB 或宽高大于 4000，则缩小并保存副本为 JPG 图片",
+    "Use original name for output filename during batch process in extras tab": "在更多选项卡中的批量处理过程中，使用原始名称作为输出文件名",
+    "When using 'Save' button, only save a single selected image": "使用“保存”按钮时，只保存一个选定的图像",
+    "Do not add watermark to images": "不要给图像加水印",
+    "Paths for saving": "保存路径",
+    "Output directory for images; if empty, defaults to three directories below": "图像的输出目录； 如果为空，则默认为以下三个目录",
+    "Output directory for txt2img images": "文生图的输出目录",
+    "Output directory for img2img images": "图生图的输出目录",
+    "Output directory for images from extras tab": "更多选项卡的输出目录",
+    "Output directory for grids; if empty, defaults to two directories below": "宫格图的输出目录； 如果为空，则默认为以下两个目录",
+    "Output directory for txt2img grids": "文生图宫格的输出目录",
+    "Output directory for img2img grids": "图生图宫格的输出目录",
+    "Directory for saving images using the Save button": "使用“保存”按钮保存图像的目录",
+    "Saving to a directory": "保存到目录",
+    "Save images to a subdirectory": "将图像保存到子目录",
+    "Save grids to a subdirectory": "将宫格图保存到子目录",
+    "When using \"Save\" button, save images to a subdirectory": "使用“保存”按钮时，将图像保存到子目录",
+    "Directory name pattern": "目录名称格式",
+    "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示词数量",
+    "Upscaling": "放大",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的图块尺寸(Tile size)。0 = 不分块(no tiling)",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 的图块重叠(Tile overlap)像素。低值 = 可见接缝",
+    "Tile size for all SwinIR.": "适用所有 SwinIR 系算法的图块尺寸(Tile size)",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR 的图块重叠(Tile overlap)像素。低值 = 可见接缝",
+    "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。更低 = 更快",
+    "Upscaler for img2img": "图生图的放大算法",
+    "Upscale latent space image when doing hires. fix": "做高分辨率修复时，也放大潜空间图像",
+    "Face restoration": "面部修复",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 权重参数； 0 = 最大效果； 1 = 最小效果",
+    "Move face restoration model from VRAM into RAM after processing": "面部修复处理完成后，将面部修复模型从显存(VRAM)移至内存(RAM)",
+    "System": "系统",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成图像时，每秒轮询显存(VRAM)使用情况的次数。设置为 0 以禁用",
+    "Always print all generation info to standard output": "始终将所有生成信息输出到 standard output (一般为控制台)",
+    "Add a second progress bar to the console that shows progress for an entire job.": "向控制台添加第二个进度条，显示整个作业的进度",
+    "Training": "训练",
+    "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)如果可行的话，节省显存(VRAM)",
+    "Filename word regex": "文件名用词的正则表达式",
+    "Filename join string": "文件名连接用字符串",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每个 epoch 中单个输入图像的重复次数； 仅用于显示 epoch 数",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一个包含 loss 的 csv 表格到日志目录，0 表示禁用",
+    "Use cross attention optimizations while training": "训练时开启 cross attention 优化",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "缓存在内存(RAM)中的模型(ckpt)",
+    "SD VAE": "模型的 VAE (SD VAE)",
+    "auto": "自动",
+    "Hypernetwork strength": "Hypernetwork 强度",
+    "Inpainting conditioning mask strength": "局部重绘时图像调节的蒙版屏蔽强度",
+    "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情况下更弱的重绘幅度需要更少的迭代步数）",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器 批量生成与生成单个图像时，产出相同的图像",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填补来提高一致性",
+    "Filter NSFW content": "过滤成人内容(NSFW)",
+    "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下 (Clip skip)",
+    "Interrogate Options": "反推提示词选项",
+    "Interrogate: keep models in VRAM": "反推: 将模型保存在显存(VRAM)中",
+    "Interrogate: use artists from artists.csv": "反推: 使用 artists.csv 中的艺术家",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推: 在生成结果中包含与模型标签(tags)相匹配的等级（对基于生成自然语言描述的反推没有影响）",
+    "Interrogate: num_beams for BLIP": "反推: BLIP 的 num_beams",
+    "Interrogate: minimum description length (excluding artists, etc..)": "反推: 最小描述长度（不包括艺术家, 等…）",
+    "Interrogate: maximum description length": "反推: 最大描述长度",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: 文本文件中的最大行数（0 = 无限制）",
+    "Interrogate: deepbooru score threshold": "反推: deepbooru 分数阈值",
+    "Interrogate: deepbooru sort alphabetically": "反推: deepbooru 按字母顺序排序",
+    "use spaces for tags in deepbooru": "在 deepbooru 中为标签使用空格",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用转义 (\\) 括号（因此它们用作文字括号而不是强调符号）",
+    "User interface": "用户界面",
+    "Show progressbar": "显示进度条",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 个采样迭代步数显示图像生成进度。设置 0 禁用",
+    "Show previews of all images generated in a batch as a grid": "以网格的形式，预览批量生成的所有图像",
+    "Show grid in results for web": "在网页的结果中显示宫格图",
+    "Do not show any images in results for web": "不在网页的结果中显示任何图像",
+    "Add model hash to generation information": "将模型的哈希值添加到生成信息",
+    "Add model name to generation information": "将模型名称添加到生成信息",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "从文本读取生成参数到用户界面（从 PNG 图片信息或粘贴文本）时，不要更改选定的模型(ckpt)",
+    "Send seed when sending prompt or image to other interface": "将提示词或者图片发送到 >> 其他界面时，把随机种子也传送过去",
+    "Font for image grids that have text": "有文字的宫格图使用的字体",
+    "Enable full page image viewer": "启用整页图像查看器",
+    "Show images zoomed in by default in full page image viewer": "在整页图像查看器中，默认放大显示图像",
+    "Show generation progress in window title.": "在窗口标题中显示生成进度",
+    "Quicksettings list": "快速设置列表",
+    "Localization (requires restart)": "本地化翻译（需要保存设置，并重启Gradio）",
+    "Sampler parameters": "采样器参数",
+    "Hide samplers in user interface (requires restart)": "在用户界面中隐藏采样器（需要重新启动）",
+    "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪声乘数) ",
+    "eta (noise multiplier) for ancestral samplers": "ancestral 采样器的 eta (噪声乘数)",
+    "img2img DDIM discretize": "图生图 DDIM 离散化",
+    "uniform": "均匀",
+    "quad": "二阶",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "最小(tmin) sigma",
+    "sigma noise": "sigma 噪声",
+    "Eta noise seed delta": "Eta 噪声种子偏移(ENSD - Eta noise seed delta)",
+    "Images Browser": "图库浏览器",
+    "Preload images at startup": "在启动时预加载图像",
+    "Number of columns on the page": "每页列数",
+    "Number of rows on the page": "每页行数",
+    "Minimum number of pages per load": "每次加载的最小页数",
+    "Use same seed for all images": "为所有图像使用同一个随机种子",
+    "Request browser notifications": "请求浏览器通知",
+    "Download localization template": "下载本地化模板",
+    "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无用户界面更新，无重启）",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重启 Gradio 及刷新组件（仅限自定义脚本、ui.py、js 和 css）",
+    "Available": "可用",
+    "Install from URL": "从网址安装",
+    "Apply and restart UI": "应用并重启用户界面",
+    "Check for updates": "检查更新",
+    "Extension": "扩展",
+    "URL": "网址",
+    "Update": "更新",
+    "a1111-sd-webui-tagcomplete": "Tag自动补全",
+    "unknown": "未知",
+    "deforum-for-automatic1111-webui": "Deforum",
+    "sd-dynamic-prompting": "动态提示词",
+    "stable-diffusion-webui-aesthetic-gradients": "美术风格梯度",
+    "stable-diffusion-webui-aesthetic-image-scorer": "美术风格评分",
+    "stable-diffusion-webui-artists-to-study": "艺术家图库",
+    "stable-diffusion-webui-dataset-tag-editor": "数据集标签编辑器",
+    "stable-diffusion-webui-images-browser": "图库浏览器",
+    "stable-diffusion-webui-inspiration": "灵感",
+    "stable-diffusion-webui-wildcards": "通配符",
+    "Load from:": "加载自",
+    "Extension index URL": "扩展列表链接",
+    "URL for extension's git repository": "扩展的 git 仓库链接",
+    "Local directory name": "本地路径名",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nPrompt",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nNegative prompt",
+    "Stop processing current image and continue processing.": "停止处理当前图像，并继续处理下一个",
+    "Stop processing images and return any results accumulated so far.": "停止处理图像，并返回迄今为止累积的任何结果",
+    "Do not do anything special": "什么都不做",
+    "Which algorithm to use to produce the image": "使用哪种算法生成图像",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长局部重绘",
+    "Produce an image that can be tiled.": "生成可用于平铺(tiled)的图像",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候，以较小的分辨率生成初步图像、接着放大图像，然后在不更改构图的情况下改进其中的细节",
+    "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.": "决定算法对图像内容的影响程度。设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。\n值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
+    "How many batches of images to create": "创建多少批次的图像",
+    "How many image to create in a single batch": "每批创建多少图像",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 图像应在多大程度上服从提示词 - 较低的值会产生更有创意的结果",
+    "Seed of a different picture to be mixed into the generation.": "将要参与生成的另一张图的随机种子",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。设为 0 时，将没有效果。设为 1 时，你将获得完全产自差异随机种子的图像（ancestral 采样器除外，你只是单纯地生成了一些东西）",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同随机种子生成的图像相似的图片",
+    "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转图集 embeddings 的特征空间",
+    "Separate values for X axis using commas.": "使用逗号分隔 X 轴的值",
+    "Separate values for Y axis using commas.": "使用逗号分隔 Y 轴的值",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "将图像写入目录（默认 - log/images）并将生成参数写入 csv 表格文件",
+    "How much to blur the mask before processing, in pixels.": "处理前要对蒙版进行多强的模糊，以像素为单位",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
+    "fill it with colors of the image": "用图像的颜色(高强度模糊)填充它",
+    "keep whatever was there originally": "保留原来的图像，不进行预处理",
+    "fill it with latent space noise": "于潜空间填充噪声",
+    "fill it with latent space zeroes": "于潜空间填零",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域（包括预留像素长度的缓冲区域）放大到目标分辨率，进行局部重绘。\n然后缩小并粘贴回原始图像中",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
+    "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像并用作下次迭代输入的次数",
+    "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.": "在回送模式下，在每个循环中，重绘幅度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块(Tiles)之间需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
+    "A directory on the same machine where the server is running.": "与服务器主机上的目录",
+    "Leave blank to save images to the default path.": "留空以将图像保存到默认路径",
+    "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "结果 = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "第一个和最后一个数字必须是 1。例:'1, 2, 1'",
+    "Path to directory with input images": "带有输入图像的路径",
+    "Path to directory where to write outputs": "进行输出的路径",
+    "Input images directory": "输入图像目录",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; 默认请留空",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果启用此选项，水印将不会添加到生成出来的图像中。警告：如果你不添加水印，你的行为可能是不符合专业操守的",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和宫格图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; 默认请留空",
+    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
+    "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于局部重绘专用的模型（模型后缀为 inpainting.ckpt 的模型）。决定了蒙版在局部重绘以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽原图，这是默认行为。0.0 表示完全不屏蔽让原图进行图像调节。较低的值将有助于保持原图的整体构图，但很难遇到较大的变化",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置项名称的列表，以逗号分隔，该设置会移动到顶部的快速访问栏，而不是默认的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
+    "Leave empty for auto": "留空时自动生成",
+    "Autocomplete options": "自动补全选项",
+    "Enable Autocomplete": "开启Tag补全",
+    "Append commas": "附加逗号",
+    "latest": "最新",
+    "behind": "落后",
+    "Roll three": "抽三位出来",
+    "Generate forever": "无限生成",
+    "Cancel generate forever": "停止无限生成",
+    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
+    "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "迭代改进生成的图像多少次；更高的值需要更长的时间；非常低的值会产生不好的结果",
+    "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt": "在图像上画一个蒙版，脚本会根据提示重新生成蒙版区域的内容",
+    "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back": "正常放大图像，将结果分割成图块（tiles），用图生图改进每个图块（tiles），最后将整个图像合并回来",
+    "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "创建一个网格，图像将有不同的参数。使用下面的输入来指定哪些参数将由列和行共享",
+    "Run Python code. Advanced user only. Must run program with --allow-code for this to work": "运行 Python 代码。仅限老手使用。必须以 --allow-code 来开启程序，才能使其运行",
+    "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "以逗号分割的单词列表，第一个单词将被用作关键词：脚本将在提示词中搜索这个单词，并用其他单词替换它",
+    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "以逗号分割的单词列表，脚本会排列出这些单词的所有排列方式，并加入提示词各生成一次",
+    "Reconstruct prompt from existing image and put it into the prompt field.": "从现有的图像中重构出提示词，并将其放入提示词的输入文本框",
+    "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": "设置在[prompt_words]选项中要使用的最大字数；注意：如果字数太长，可能会超过系统可处理的文件路径的最大长度",
+    "Process an image, use it as an input, repeat.": "处理一张图像，将其作为输入，并重复",
+    "Insert selected styles into prompt fields": "在提示词中插入选定的模版风格",
+    "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.": "将当前的提示词保存为模版风格。如果你在文本中添加{prompt}标记，那么将来你使用该模版风格时，你现有的提示词会替换模版风格中的{prompt}",
+    "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在生成图像之前从模型(ckpt)中加载权重。你可以使用哈希值或文件名的一部分（如设置中所示）作为模型(ckpt)名称。建议用在Y轴上以减少过程中模型的切换",
+    "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active: 在生成过程中，Torch使用的显存(VRAM)峰值，不包括缓存的数据。\nTorch reserved: Torch 分配的显存(VRAM)的峰值量，包括所有活动和缓存数据。\nSys VRAM: 所有应用程序分配的显存(VRAM)的峰值量 / GPU 的总显存(VRAM)（峰值利用率%）",
+    "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潜空间中的图像。而另一种方法是，从潜变量表达中直接解码并生成完整的图像，接着放大它，然后再将其编码回潜空间",
+    "Start drawing": "开始绘制",
+    "Description": "描述",
+    "Action": "行动",
+    "Aesthetic Gradients": "美术风格梯度",
+    "aesthetic-gradients": "美术风格梯度",
+    "Wildcards": "通配符",
+    "Dynamic Prompts": "动态提示词",
+    "Image browser": "图库浏览器",
+    "images-browser": "图库浏览器",
+    "Inspiration": "灵感",
+    "Deforum": "Deforum",
+    "Artists to study": "艺术家图库",
+    "Aesthetic Image Scorer": "美术风格评分",
+    "Dataset Tag Editor": "数据集标签编辑器",
+
+
+    "----not work----": "----以下内容无法被翻译，Bug----",
+    "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
+    "Save style": "储存为模版风格",
+    "Apply selected styles to current prompt": "将所选模板风格，应用于当前提示词",
+    "Set seed to -1, which will cause a new random number to be used every time": "将随机种子设置为-1，则每次都会使用一个新的随机数",
+    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的随机种子，如果想要固定结果就会很有用",
+    "Open images output directory": "打开图像输出目录",
+    "Upscaler 1": "放大算法 1",
+    "Upscaler 2": "放大算法 2",
+    "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "用竖线分隔符(|)将提示词分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了被分割的第一部分，所有的组合都会包含这部分）",
+    "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些 Real-ESRGAN 模型显示在网页用户界面。(需要重新启动)",
+    "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
+    "Face restoration model": "面部修复模型",
+    "Install": "安装",
+    "Installing...": "安装中...",
+    "Installed": "已安装",
+
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要使用的模版风格； 模版风格包含正向和反向提示词，并应用于两者\n\ud83c\udfa8    随机添加一个艺术家到提示词中\n \u2199\ufe0f     从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面\n\ud83d\udcbe    将当前的提示词保存为模版风格(保存在styles.csv)\n\ud83d\udccb    将所选模板风格，应用于当前提示词\n如果你在文本中添加{prompt}标记，并保存为模版风格\n那么将来你使用该模版风格时，你现有的提示词会替换模版风格中的{prompt}",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和随机种子生成的图像会得到相同的结果\n\ud83c\udfb2    将随机种子设置为-1，则每次都会使用一个新的随机数\n\u267b\ufe0f    重用上一次使用的随机种子，如果想要固定输出结果就会很有用",
+
+
+    "----deprecated----": "----以下内容在webui新版本已移除----",
+    "▼": "▼",
+    "History": "历史记录",
+    "Show Textbox": "显示文本框",
+    "File with inputs": "含输入内容的文件",
+    "Prompts": "提示词",
+    "Disabled when launched with --hide-ui-dir-config.": "启动 --hide-ui-dir-config 时禁用",
+    "Open output directory": "打开输出目录",
+    "Create aesthetic images embedding": "生成美术风格图集 embedding",
+    "Split oversized images into two": "将过大的图像分为两份",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "训练 embedding； 必须指定一组具有 1:1 比例图像的目录",
+    "Learning rate": "学习率",
+    "txt2img history": "文生图历史记录",
+    "img2img history": "图生图历史记录",
+    "extras history": "更多选项卡的历史记录",
+    "extras": "更多",
+    "custom fold": "自定义文件夹",
+    "Load": "载入",
+    "Prev batch": "上一批",
+    "Next batch": "下一批",
+    "number of images to delete consecutively next": "接下来要连续删除的图像数",
+    "Date to": "日期至",
+    "Refresh page": "刷新页面",
+    "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
+    "Number of pictures displayed on each page": "每页显示的图像数量",
+    "Number of grids in each row": "每行显示多少格",
+    "favorites": "收藏夹(已保存)",
+    "others": "其他",
+    "Collect": "收藏(保存)",
+
+
+    "--------": "--------"
+}

localizations/zh_TW.json

@@ -0,0 +1,598 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "載入中…",
+    "view": "檢視",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "基於 Gradio 構建",
+    "Stable Diffusion checkpoint": "Stable Diffusion 模型權重存檔點",
+    "txt2img": "文生圖",
+    "img2img": "圖生圖",
+    "Extras": "更多",
+    "PNG Info": "圖片資訊",
+    "Checkpoint Merger": "模型權重存檔點合併",
+    "Train": "訓練",
+    "Create aesthetic embedding": "生成美術風格",
+    "Image Browser": "圖庫瀏覽器",
+    "History": "歷史記錄",
+    "Settings": "設定",
+    "Extensions": "擴充",
+    "Prompt": "提示詞",
+    "Negative prompt": "反向提示詞",
+    "Run": "執行",
+    "Skip": "跳過",
+    "Interrupt": "中止",
+    "Generate": "生成",
+    "Style 1": "模版風格 1",
+    "Style 2": "模版風格 2",
+    "Label": "標記",
+    "File": "檔案",
+    "Drop File Here": "拖曳檔案到此",
+    "-": "-",
+    "or": "或",
+    "Click to Upload": "點擊上傳",
+    "Image": "圖像",
+    "Check progress": "檢視進度",
+    "Check progress (first)": "（首次）檢視進度",
+    "Sampling Steps": "採樣疊代步數",
+    "Sampling method": "採樣方法",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "寬度",
+    "Height": "高度",
+    "Restore faces": "面部修復",
+    "Tiling": "可平鋪",
+    "Highres. fix": "高解析度修復",
+    "Firstpass width": "第一遍的寬度",
+    "Firstpass height": "第一遍的高度",
+    "Denoising strength": "重繪幅度",
+    "Batch count": "生成批次",
+    "Batch size": "每批數量",
+    "CFG Scale": "提示詞相關性（CFG）",
+    "Seed": "隨機種子",
+    "Extra": "▼",
+    "Variation seed": "差異隨機種子",
+    "Variation strength": "差異強度",
+    "Resize seed from width": "自寬度縮放隨機種子",
+    "Resize seed from height": "自高度縮放隨機種子",
+    "Open for Clip Aesthetic!": "打開美術風格 Clip!",
+    "▼": "▼",
+    "Aesthetic weight": "美術風格權重",
+    "Aesthetic steps": "美術風格疊代步數",
+    "Aesthetic learning rate": "美術風格學習率",
+    "Slerp interpolation": "Slerp 插值",
+    "Aesthetic imgs embedding": "美術風格圖集 embedding",
+    "None": "無",
+    "Aesthetic text for imgs": "該圖集的美術風格描述",
+    "Slerp angle": "Slerp 角度",
+    "Is negative text": "是反向提示詞",
+    "Script": "指令碼",
+    "Embedding to Shareable PNG": "將 Embedding 轉換為可分享的 PNG 圖片檔案",
+    "Prompt matrix": "提示詞矩陣",
+    "Prompts from file or textbox": "從文字方塊或檔案載入提示詞",
+    "X/Y plot": "X/Y 圖表",
+    "Source embedding to convert": "用於轉換的源 Embedding",
+    "Embedding token": "Embedding 的關鍵詞",
+    "Put variable parts at start of prompt": "把變量部分放在提示詞文本的開頭",
+    "Show Textbox": "顯示文字方塊",
+    "File with inputs": "含輸入內容的檔案",
+    "Prompts": "提示詞",
+    "Iterate seed every line": "每行輸入都換一個種子",
+    "Use same random seed for all lines": "每行輸入都使用同一個隨機種子",
+    "List of prompt inputs": "提示詞輸入列表",
+    "Upload prompt inputs": "上傳提示詞輸入檔案",
+    "X type": "X軸類型",
+    "Nothing": "無",
+    "Var. seed": "差異隨機種子",
+    "Var. strength": "差異強度",
+    "Steps": "疊代步數",
+    "Prompt S/R": "提示詞替換",
+    "Prompt order": "提示詞順序",
+    "Sampler": "採樣器",
+    "Checkpoint name": "模型權重存檔點的名稱",
+    "Hypernetwork": "超網路",
+    "Hypernet str.": "超網路強度",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "最小 Sigma",
+    "Sigma max": "最大 Sigma",
+    "Sigma noise": "Sigma noise",
+    "Eta": "Eta",
+    "Clip skip": "Clip 跳過",
+    "Denoising": "去噪",
+    "Cond. Image Mask Weight": "圖像調節屏蔽度",
+    "X values": "X軸數值",
+    "Y type": "Y軸類型",
+    "Y values": "Y軸數值",
+    "Draw legend": "在圖表中包括軸標題",
+    "Include Separate Images": "包括獨立的圖像",
+    "Keep -1 for seeds": "保持隨機種子為-1",
+    "Drop Image Here": "拖曳圖像到此",
+    "Save": "儲存",
+    "Send to img2img": ">> 圖生圖",
+    "Send to inpaint": ">> 局部重繪",
+    "Send to extras": ">> 更多",
+    "Make Zip when Save?": "儲存時生成ZIP壓縮檔案？",
+    "Textbox": "文字方塊",
+    "Interrogate\nCLIP": "CLIP\n反推提示詞",
+    "Interrogate\nDeepBooru": "DeepBooru\n反推提示詞",
+    "Inpaint": "局部重繪",
+    "Batch img2img": "批量圖生圖",
+    "Image for img2img": "圖生圖的圖像",
+    "Image for inpainting with mask": "用於局部重繪並手動畫蒙版的圖像",
+    "Mask": "蒙版",
+    "Mask blur": "蒙版模糊",
+    "Mask mode": "蒙版模式",
+    "Draw mask": "繪製蒙版",
+    "Upload mask": "上傳蒙版",
+    "Masking mode": "蒙版模式",
+    "Inpaint masked": "重繪蒙版內容",
+    "Inpaint not masked": "重繪非蒙版內容",
+    "Masked content": "蒙版蒙住的內容",
+    "fill": "填充",
+    "original": "原圖",
+    "latent noise": "潛空間噪聲",
+    "latent nothing": "潛空間數值零",
+    "Inpaint at full resolution": "全解析度局部重繪",
+    "Inpaint at full resolution padding, pixels": "預留畫素",
+    "Process images in a directory on the same machine where the server is running.": "使用伺服器主機上的一個目錄，作為輸入目錄處理圖像",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "使用一個空的資料夾作為輸出目錄，而不是使用預設的 output 資料夾作為輸出目錄",
+    "Disabled when launched with --hide-ui-dir-config.": "啟動 --hide-ui-dir-config 時禁用",
+    "Input directory": "輸入目錄",
+    "Output directory": "輸出目錄",
+    "Resize mode": "縮放模式",
+    "Just resize": "拉伸",
+    "Crop and resize": "裁剪",
+    "Resize and fill": "填充",
+    "img2img alternative test": "圖生圖的另一種測試",
+    "Loopback": "回送",
+    "Outpainting mk2": "向外繪製第二版",
+    "Poor man's outpainting": "效果稍差的向外繪製",
+    "SD upscale": "使用 SD 放大",
+    "should be 2 or lower.": "必須小於等於2",
+    "Override `Sampling method` to Euler?(this method is built for it)": "覆寫「採樣方法」為 Euler？（這個方法就是為這樣做設計的）",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆寫「提示詞」為「初始提示詞」？（包括「反向提示詞」）",
+    "Original prompt": "初始提示詞",
+    "Original negative prompt": "初始反向提示詞",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "覆寫「採樣疊代步數」為「解碼疊代步數」？",
+    "Decode steps": "解碼疊代步數",
+    "Override `Denoising strength` to 1?": "覆寫「重繪幅度」為1?",
+    "Decode CFG scale": "解碼提示詞相關性（CFG）",
+    "Randomness": "隨機度",
+    "Sigma adjustment for finding noise for image": "為尋找圖中噪點的 Sigma 調整",
+    "Loops": "疊代次數",
+    "Denoising strength change factor": "重繪幅度的調整係數",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推薦設定：採樣疊代步數：80-100，採樣器：Euler a，重繪幅度：0.8",
+    "Pixels to expand": "拓展的畫素數",
+    "Outpainting direction": "向外繪製的方向",
+    "left": "左",
+    "right": "右",
+    "up": "上",
+    "down": "下",
+    "Fall-off exponent (lower=higher detail)": "衰減指數（越低細節越好）",
+    "Color variation": "色彩變化",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "將圖像放大到兩倍尺寸； 使用寬度和高度滑塊設定圖塊尺寸",
+    "Tile overlap": "圖塊重疊的畫素",
+    "Upscaler": "放大演算法",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "BSRGAN 4x": "BSRGAN 4x",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR_4x": "SwinIR 4x",
+    "Single Image": "單個圖像",
+    "Batch Process": "批量處理",
+    "Batch from Directory": "從目錄進行批量處理",
+    "Source": "來源",
+    "Show result images": "顯示輸出圖像",
+    "Scale by": "等比縮放",
+    "Scale to": "指定尺寸縮放",
+    "Resize": "縮放",
+    "Crop to fit": "裁剪以適應",
+    "Upscaler 2": "放大演算法 2",
+    "Upscaler 2 visibility": "放大演算法 2 可見度",
+    "GFPGAN visibility": "GFPGAN 可見度",
+    "CodeFormer visibility": "CodeFormer 可見度",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 權重 （0 = 最大效果， 1 = 最小效果）",
+    "Open output directory": "打開輸出目錄",
+    "Upscale Before Restoring Faces": "放大後再進行面部修復",
+    "Send to txt2img": ">> 文生圖",
+    "A merger of the two checkpoints will be generated in your": "合併後的模型權重存檔點會生成在你的",
+    "checkpoint": "模型權重存檔點",
+    "directory.": "目錄",
+    "Primary model (A)": "主要模型 (A)",
+    "Secondary model (B)": "第二模型 (B)",
+    "Tertiary model (C)": "第三模型 (C)",
+    "Custom Name (Optional)": "自訂名稱 （可選）",
+    "Multiplier (M) - set to 0 to get model A": "倍率 (M) - 設為 0 等價於模型 A",
+    "Interpolation Method": "插值方法",
+    "Weighted sum": "加權和",
+    "Add difference": "加入差分",
+    "Save as float16": "以 float16 儲存",
+    "See": "檢視",
+    "wiki": "wiki文件",
+    "for detailed explanation.": "以了解詳細說明",
+    "Create embedding": "生成 embedding",
+    "Create aesthetic images embedding": "生成美術風格圖集 embedding",
+    "Create hypernetwork": "生成 hypernetwork",
+    "Preprocess images": "圖像預處理",
+    "Name": "名稱",
+    "Initialization text": "初始化文字",
+    "Number of vectors per token": "每個 token 的向量數",
+    "Overwrite Old Embedding": "覆寫舊的 Embedding",
+    "Modules": "模組",
+    "Enter hypernetwork layer structure": "輸入 hypernetwork 層結構",
+    "Select activation function of hypernetwork": "選擇 hypernetwork 的激活函數",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "閾值",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "挑選初始化層權重的方案. 類relu - Kaiming, 類sigmoid - Xavier 都是比較推薦的選項",
+    "Normal": "正態",
+    "KaimingUniform": "Kaiming 均勻",
+    "KaimingNormal": "Kaiming 正態",
+    "XavierUniform": "Xavier 均勻",
+    "XavierNormal": "Xavier 正態",
+    "Add layer normalization": "加入層標準化",
+    "Use dropout": "採用 dropout 防止過擬合",
+    "Overwrite Old Hypernetwork": "覆寫舊的 Hypernetwork",
+    "Source directory": "來源目錄",
+    "Destination directory": "目標目錄",
+    "Existing Caption txt Action": "對已有的TXT說明文字的行為",
+    "ignore": "無視",
+    "copy": "複製",
+    "prepend": "放前面",
+    "append": "放後面",
+    "Create flipped copies": "生成鏡像副本",
+    "Split oversized images into two": "將過大的圖像分為兩份",
+    "Split oversized images": "分割過大的圖像",
+    "Auto focal point crop": "自動焦點裁切",
+    "Use BLIP for caption": "使用 BLIP 生成說明文字（自然語言描述）",
+    "Use deepbooru for caption": "使用 deepbooru 生成說明文字（標記）",
+    "Split image threshold": "圖像分割閾值",
+    "Split image overlap ratio": "分割圖像重疊的比率",
+    "Focal point face weight": "焦點面部權重",
+    "Focal point entropy weight": "焦點熵權重",
+    "Focal point edges weight": "焦點線條權重",
+    "Create debug image": "生成除錯圖片",
+    "Preprocess": "預處理",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "訓練 embedding； 必須指定一組具有 1:1 比例圖像的目錄",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "訓練 embedding 或者 hypernetwork； 必須指定一組具有 1:1 比例圖像的目錄",
+    "[wiki]": "[wiki]",
+    "Embedding": "Embedding",
+    "Embedding Learning rate": "Embedding 學習率",
+    "Hypernetwork Learning rate": "Hypernetwork 學習率",
+    "Learning rate": "學習率",
+    "Dataset directory": "資料集目錄",
+    "Log directory": "日誌目錄",
+    "Prompt template file": "提示詞模版檔案",
+    "Max steps": "最大疊代步數",
+    "Save an image to log directory every N steps, 0 to disable": "每 N 步儲存一個圖像到日誌目錄，0 表示禁用",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步將 embedding 的副本儲存到日誌目錄，0 表示禁用",
+    "Save images with embedding in PNG chunks": "儲存圖像，並在 PNG 圖片檔案中嵌入 embedding 檔案",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "進行預覽時，從文生圖頁籤中讀取參數（提示詞等）",
+    "Train Hypernetwork": "訓練 Hypernetwork",
+    "Train Embedding": "訓練 Embedding",
+    "Create an aesthetic embedding out of any number of images": "從任意數量的圖像中建立美術風格 embedding",
+    "Create images embedding": "生成圖集 embedding",
+    "txt2img history": "文生圖歷史記錄",
+    "img2img history": "圖生圖歷史記錄",
+    "extras history": "後處理歷史記錄",
+    "extras": "後處理",
+    "favorites": "收藏夾",
+    "Favorites": "收藏夾",
+    "Others": "其他",
+    "custom fold": "自訂資料夾",
+    "Load": "載入",
+    "Images directory": "圖像目錄",
+    "Prev batch": "上一批",
+    "Next batch": "下一批",
+    "Dropdown": "下拉式清單",
+    "First Page": "首頁",
+    "Prev Page": "上一頁",
+    "Page Index": "頁數",
+    "Next Page": "下一頁",
+    "End Page": "尾頁",
+    "number of images to delete consecutively next": "接下來要連續刪除的圖像數",
+    "delete next": "刪除下一張",
+    "Delete": "刪除",
+    "sort by": "排序方式",
+    "path name": "路徑名",
+    "date": "日期",
+    "keyword": "搜尋",
+    "Generate Info": "生成資訊",
+    "File Name": "檔案名",
+    "Collect": "收藏",
+    "Refresh page": "刷新頁面",
+    "Date to": "日期至",
+    "Move to favorites": "移動到收藏夾",
+    "Renew Page": "刷新頁面",
+    "Number": "數量",
+    "set_index": "設定索引",
+    "load_switch": "載入開關",
+    "turn_page_switch": "翻頁開關",
+    "Checkbox": "核取方塊",
+    "Apply settings": "儲存設定",
+    "Saving images/grids": "儲存圖像／宮格圖",
+    "Always save all generated images": "始終儲存所有生成的圖像",
+    "File format for images": "圖像的檔案格式",
+    "Images filename pattern": "圖像檔案名格式",
+    "Add number to filename when saving": "儲存的時候在檔案名里加入數字",
+    "Always save all generated image grids": "始終儲存所有生成的宮格圖",
+    "File format for grids": "宮格圖的檔案格式",
+    "Add extended info (seed, prompt) to filename when saving grid": "儲存宮格圖時，將擴展資訊（隨機種子，提示詞）加入到檔案名",
+    "Do not save grids consisting of one picture": "只有一張圖片時不要儲存宮格圖",
+    "Prevent empty spots in grid (when set to autodetect)": "（啟用自動偵測時）防止宮格圖中出現空位",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "宮格圖行數； 使用 -1 進行自動檢測，使用 0 使其與每批數量相同",
+    "Save text information about generation parameters as chunks to png files": "將有關生成參數的文本資訊，作為塊儲存到PNG圖片檔案中",
+    "Create a text file next to every image with generation parameters.": "儲存圖像時，在每個圖像旁邊建立一個文本檔案儲存生成參數",
+    "Save a copy of image before doing face restoration.": "在進行面部修復之前儲存圖像副本",
+    "Save a copy of image before applying highres fix.": "在做高解析度修復之前儲存初始圖像副本",
+    "Save a copy of image before applying color correction to img2img results": "在對圖生圖結果套用顏色校正之前儲存圖像副本",
+    "Quality for saved jpeg images": "儲存的JPEG圖像的品質",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 圖像大於 4MB 或寬高大於 4000，則縮小並儲存副本為 JPG 圖片",
+    "Use original name for output filename during batch process in extras tab": "在更多頁籤中的批量處理過程中，使用原始名稱作為輸出檔案名",
+    "When using 'Save' button, only save a single selected image": "使用「儲存」按鈕時，只儲存一個選定的圖像",
+    "Do not add watermark to images": "不要給圖像加浮水印",
+    "Paths for saving": "儲存路徑",
+    "Output directory for images; if empty, defaults to three directories below": "圖像的輸出目錄； 如果為空，則預設為以下三個目錄",
+    "Output directory for txt2img images": "文生圖的輸出目錄",
+    "Output directory for img2img images": "圖生圖的輸出目錄",
+    "Output directory for images from extras tab": "更多頁籤的輸出目錄",
+    "Output directory for grids; if empty, defaults to two directories below": "宮格圖的輸出目錄； 如果為空，則預設為以下兩個目錄",
+    "Output directory for txt2img grids": "文生圖宮格的輸出目錄",
+    "Output directory for img2img grids": "圖生圖宮格的輸出目錄",
+    "Directory for saving images using the Save button": "使用「儲存」按鈕儲存圖像的目錄",
+    "Saving to a directory": "儲存到目錄",
+    "Save images to a subdirectory": "將圖像儲存到子目錄",
+    "Save grids to a subdirectory": "將宮格圖儲存到子目錄",
+    "When using \"Save\" button, save images to a subdirectory": "使用「儲存」按鈕時，將圖像儲存到子目錄",
+    "Directory name pattern": "目錄名稱格式",
+    "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示詞數量",
+    "Upscaling": "放大",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的圖塊尺寸。0 = 不分塊",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 的圖塊重疊畫素。低值 = 可見接縫",
+    "Tile size for all SwinIR.": "適用所有 SwinIR 系演算法的圖塊尺寸",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR 的圖塊重疊畫素。低值 = 可見接縫",
+    "LDSR processing steps. Lower = faster": "LDSR 處理疊代步數。更低 = 更快",
+    "Upscaler for img2img": "圖生圖的放大演算法",
+    "Upscale latent space image when doing hires. fix": "做高解析度修復時，也放大潛空間圖像",
+    "Face restoration": "面部修復",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 權重參數； 0 = 最大效果； 1 = 最小效果",
+    "Move face restoration model from VRAM into RAM after processing": "面部修復處理完成後，將面部修復模型從顯存（VRAM）移至內存（RAM）",
+    "System": "系統",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成圖像時每秒輪詢顯存（VRAM）使用情況的次數。設定為 0 以禁用",
+    "Always print all generation info to standard output": "始終將所有生成資訊輸出到 standard output （一般為控制台）",
+    "Add a second progress bar to the console that shows progress for an entire job.": "向控制台加入第二個進度列，顯示整個作業的進度",
+    "Training": "訓練",
+    "Unload VAE and CLIP from VRAM when training": "訓練時從顯存（VRAM）中取消 VAE 和 CLIP 的載入",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "訓練時將 VAE 和 CLIP 從顯存（VRAM）移放到內存（RAM），節省顯存（VRAM）",
+    "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "訓練時將 VAE 和 CLIP 從顯存（VRAM）移放到內存（RAM）如果可行的話，節省顯存（VRAM）",
+    "Filename word regex": "檔案名用詞的正則表達式",
+    "Filename join string": "檔案名連接用字串",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每個 epoch 中單個輸入圖像的重複次數； 僅用於顯示 epoch 數",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步儲存一個包含 loss 的CSV表格到日誌目錄，0 表示禁用",
+    "Use cross attention optimizations while training": "訓練時開啟 cross attention 最佳化",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "快取在內存（RAM）中的模型權重存檔點",
+    "SD VAE": "模型的VAE",
+    "auto": "自動",
+    "Hypernetwork strength": "Hypernetwork 強度",
+    "Inpainting conditioning mask strength": "局部重繪時圖像調節的蒙版屏蔽強度",
+    "Apply color correction to img2img results to match original colors.": "對圖生圖結果套用顏色校正以匹配原始顏色",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在進行圖生圖的時候，確切地執行滑塊指定的疊代步數（正常情況下更弱的重繪幅度需要更少的疊代步數）",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 採樣器中啟用量化以獲得更清晰，更清晰的結果。這可能會改變現有的隨機種子。需要重新啟動才能套用",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "強調符：使用 (文字) 使模型更關注該文本，使用 [文字] 使其減少關注",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用舊的強調符實作。可用於復現舊隨機種子",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 採樣器批量生成與生成單個圖像時，產出相同的圖像",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "當使用超過 75 個 token 時，通過從 n 個 token 中的最後一個逗號填補來提高一致性",
+    "Filter NSFW content": "過濾成人內容",
+    "Stop At last layers of CLIP model": "在 CLIP 模型的最後哪一層停下",
+    "Interrogate Options": "反推提示詞選項",
+    "Interrogate: keep models in VRAM": "反推： 將模型儲存在顯存（VRAM）中",
+    "Interrogate: use artists from artists.csv": "反推： 使用 artists.csv 中的藝術家",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推： 在生成結果中包含與模型標記相匹配的等級（對基於生成自然語言描述的反推沒有影響）",
+    "Interrogate: num_beams for BLIP": "反推： BLIP 的 num_beams",
+    "Interrogate: minimum description length (excluding artists, etc..)": "反推： 最小描述長度（不包括藝術家，等…）",
+    "Interrogate: maximum description length": "反推： 最大描述長度",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP： 文本檔案中的最大行數（0 = 無限制）",
+    "Interrogate: deepbooru score threshold": "反推： deepbooru 分數閾值",
+    "Interrogate: deepbooru sort alphabetically": "反推： deepbooru 按字母順序排序",
+    "use spaces for tags in deepbooru": "在 deepbooru 中為標記使用空格",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用轉義 (\\) 括號（因此它們用作文字括號而不是強調符號）",
+    "User interface": "使用者介面",
+    "Show progressbar": "顯示進度列",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 個採樣疊代步數顯示圖像生成進度。設定 0 禁用",
+    "Show previews of all images generated in a batch as a grid": "以網格的形式，預覽批量生成的所有圖像",
+    "Show grid in results for web": "在網頁的結果中顯示宮格圖",
+    "Do not show any images in results for web": "不在網頁的結果中顯示任何圖像",
+    "Add model hash to generation information": "將模型的雜湊值加入到生成資訊",
+    "Add model name to generation information": "將模型名稱加入到生成資訊",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "從文本讀取生成參數到使用者介面（從 PNG 圖片資訊或粘貼文本）時，不要更改選定的模型權重存檔點",
+    "Send seed when sending prompt or image to other interface": "將提示詞或者圖片發送到 >> 其他界面時，把隨機種子也傳送過去",
+    "Font for image grids that have text": "有文字的宮格圖使用的字體",
+    "Enable full page image viewer": "啟用整頁圖像檢視器",
+    "Show images zoomed in by default in full page image viewer": "在整頁圖像檢視器中，預設放大顯示圖像",
+    "Show generation progress in window title.": "在視窗標題中顯示生成進度",
+    "Quicksettings list": "快速設定列表",
+    "Localization (requires restart)": "本地化翻譯（需要儲存設定，並重啟Gradio）",
+    "Sampler parameters": "採樣器參數",
+    "Hide samplers in user interface (requires restart)": "在使用者介面中隱藏採樣器（需要重新啟動）",
+    "eta (noise multiplier) for DDIM": "DDIM 的 eta （噪聲乘數）",
+    "eta (noise multiplier) for ancestral samplers": "ancestral 採樣器的 eta （噪聲乘數）",
+    "img2img DDIM discretize": "圖生圖 DDIM 離散化",
+    "uniform": "均勻",
+    "quad": "二階",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "最小(tmin) sigma",
+    "sigma noise": "sigma 噪聲",
+    "Eta noise seed delta": "Eta 噪聲種子偏移（ENSD）",
+    "Images Browser": "圖庫瀏覽器",
+    "Preload images at startup": "在啟動時預加載圖像",
+    "Number of columns on the page": "每頁列數",
+    "Number of rows on the page": "每頁行數",
+    "Number of pictures displayed on each page": "每頁顯示的圖像數量",
+    "Minimum number of pages per load": "每次載入的最小頁數",
+    "Number of grids in each row": "每行顯示多少格",
+    "Wildcards": "萬用字元",
+    "Use same seed for all images": "為所有圖像使用同一個隨機種子",
+    "Request browser notifications": "請求瀏覽器通知",
+    "Download localization template": "下載本地化模板",
+    "Reload custom script bodies (No ui updates, No restart)": "重新載入自訂指令碼主體（無使用者介面更新，無重啟）",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重啟 Gradio 及刷新組件（僅限自訂指令碼，ui.py，JS 和 CSS）",
+    "Available": "可用",
+    "Install from URL": "從網址安裝",
+    "Apply and restart UI": "應用並重啟使用者介面",
+    "Check for updates": "檢查更新",
+    "Extension": "擴充",
+    "URL": "網址",
+    "Update": "更新",
+    "unknown": "未知",
+    "Load from:": "載入自",
+    "Extension index URL": "擴充清單連結",
+    "URL for extension's git repository": "擴充的 git 倉庫連結",
+    "Local directory name": "本地路徑名",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示詞（按 Ctrl+Enter 或 Alt+Enter 生成）",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示詞（按 Ctrl+Enter 或 Alt+Enter 生成）",
+    "Add a random artist to the prompt.": "隨機加入一個藝術家到提示詞中",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "從提示詞中讀取生成參數，如果提示詞為空，則讀取上一次的生成參數到使用者介面",
+    "Save style": "存儲為模板風格",
+    "Apply selected styles to current prompt": "將所選樣式套用於當前提示",
+    "Stop processing current image and continue processing.": "停止處理當前圖像，並繼續處理下一個",
+    "Stop processing images and return any results accumulated so far.": "停止處理圖像，並返回迄今為止累積的任何結果",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要套用的模版風格； 模版風格包含正向和反向提示詞，並套用於兩者",
+    "Do not do anything special": "什麼都不做",
+    "Which algorithm to use to produce the image": "使用哪種演算法生成圖像",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有創意，可以根據疊代步數獲得完全不同的圖像，將疊代步數設定為高於 30-40 不會有正面作用",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅長局部重繪",
+    "Produce an image that can be tiled.": "生成可用於平舖的圖像",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用兩步處理的時候，以較小的解析度生成初步圖像，接著放大圖像，然後在不更改構圖的情況下改進其中的細節",
+    "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.": "決定演算法對圖像內容的影響程度。設定 0 時，什麼都不會改變，而在 1 時，你將獲得不相關的圖像。\n值低於 1.0 時，處理的疊代步數將少於「採樣疊代步數」滑塊指定的步數",
+    "How many batches of images to create": "建立多少批次的圖像",
+    "How many image to create in a single batch": "每批建立多少圖像",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 圖像應在多大程度上服從提示詞 - 較低的值會產生更有創意的結果",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一個固定隨機數生成器輸出的值 — 以相同參數和隨機種子生成的圖像會得到相同的結果",
+    "Set seed to -1, which will cause a new random number to be used every time": "將隨機種子設定為-1，則每次都會使用一個新的隨機數",
+    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的隨機種子，如果想要固定結果就會很有用",
+    "Seed of a different picture to be mixed into the generation.": "將要參與生成的另一張圖的隨機種子",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要產生多強烈的變化。設為 0 時，將沒有效果。設為 1 時，你將獲得完全產自差異隨機種子的圖像（ancestral 採樣器除外，你只是單純地生成了一些東西）",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "嘗試生成與在指定解析度下使用相同隨機種子生成的圖像相似的圖片",
+    "This text is used to rotate the feature space of the imgs embs": "此文本用於旋轉圖集 embeddings 的特徵空間",
+    "Separate values for X axis using commas.": "使用逗號分隔 X 軸的值",
+    "Separate values for Y axis using commas.": "使用逗號分隔 Y 軸的值",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "將圖像寫入目錄（預設 — log/images）並將生成參數寫入CSV表格檔案",
+    "Open images output directory": "打開圖像輸出目錄",
+    "How much to blur the mask before processing, in pixels.": "處理前要對蒙版進行多強的模糊，以畫素為單位",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 處理蒙版區域之前要在蒙版區域內放置什麼",
+    "fill it with colors of the image": "用圖像的顏色（高強度模糊）填充它",
+    "keep whatever was there originally": "保留原來的圖像，不進行預處理",
+    "fill it with latent space noise": "用潛空間的噪聲填充它",
+    "fill it with latent space zeroes": "用潛空間的零填充它",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "將蒙版區域（包括預留畫素長度的緩衝區域）放大到目標解析度，進行局部重繪。\n然後縮小並粘貼回原始圖像中",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "將圖像大小調整為目標解析度。除非高度和寬度匹配，否則你將獲得不正確的縱橫比",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "調整圖像大小，使整個目標解析度都被圖像填充。裁剪多出來的部分",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "調整圖像大小，使整個圖像在目標解析度內。用圖像的顏色填充空白區域",
+    "How many times to repeat processing an image and using it as input for the next iteration": "重複處理圖像並用作下次疊代輸入的次數",
+    "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.": "在回送模式下，在每個循環中，重繪幅度都會乘以該值。<1 表示減少多樣性，因此你的這一組圖將集中在固定的圖像上。>1 意味著增加多樣性，因此你的這一組圖將變得越來越混亂",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大時，圖塊之間應該有多少畫素重疊。圖塊之間需要重疊才可以讓它們在合併回一張圖像時，沒有清晰可見的接縫",
+    "A directory on the same machine where the server is running.": "與伺服器主機上的目錄",
+    "Leave blank to save images to the default path.": "留空以將圖像儲存到預設路徑",
+    "Result = A * (1 - M) + B * M": "結果 = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "結果 = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "第一個和最後一個數字必須是 1。例：'1, 2, 1'",
+    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "訓練應該多快。低值將需要更長的時間來訓練，高值可能無法收斂（無法產生準確的結果）以及／也許可能會破壞 embedding（如果你在訓練資訊文字方塊中看到 Loss: nan 就會發生這種情況。如果發生這種情況，你需要從較舊的未損壞的備份手動恢復 embedding）\n\n你可以使用以下語法設定單個數值或多個學習率：\n\n   率1:步限1, 率2:步限2, …\n\n如：   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步將以 0.005 的速率訓練，接著直到 1000 步為止以 1e-3 訓練，然後剩餘所有步以 1e-5 訓練",
+    "Path to directory with input images": "帶有輸入圖像的路徑",
+    "Path to directory where to write outputs": "進行輸出的路徑",
+    "Input images directory": "輸入圖像目錄",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下標記定義如何選擇圖像的檔案名： [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]； 預設請留空",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果啟用此選項，浮水印將不會加入到生成出來的圖像中。警告：如果你不加入浮水印，你的行為可能是不符合道德操守的",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下標記定義如何選擇圖像和宮格圖的子目錄： [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]； 預設請留空",
+    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神經網路修復低品質面部",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正則表達式將用於從檔案名中提取單詞，並將使用以下選項將它們接合到用於訓練的標記文本中。留空以保持檔案名文本不變",
+    "This string will be used to join split words into a single line if the option above is enabled.": "如果啟用了上述選項，則此處的字元會用於將拆分的單詞接合為同一行",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "僅適用於局部重繪專用的模型（模型後綴為 inpainting.ckpt 的模型）。決定了蒙版在局部重繪以及圖生圖中屏蔽原圖內容的強度。 1.0 表示完全屏蔽原圖，這是預設行為。 0.0 表示完全不屏蔽讓原圖進行圖像調節。較低的值將有助於保持原圖的整體構圖，但很難遇到較大的變化",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "設定項名稱列表，以逗號分隔，該設定會移動到頂部的快速存取列，而不是預設的設定頁籤。有關設定名稱，請參見 modules/shared.py。需要重新啟動才能套用",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果這個值不為零，它將被加入到隨機種子中，並在使用帶有 Eta 的採樣器時用於初始化隨機噪聲。你可以使用它來產生更多的圖像變化，或者你可以使用它來模仿其他軟體生成的圖像，如果你知道你在做什麼",
+    "Leave empty for auto": "留空時自動生成",
+    "Autocomplete options": "自動補全選項",
+    "Enable Autocomplete": "開啟Tag補全",
+    "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "選擇哪些 Real-ESRGAN 模型顯示在網頁使用者介面。（需要重新啟動）",
+    "Allowed categories for random artists selection when using the Roll button": "使用抽選藝術家按鈕時將會隨機的藝術家類別",
+    "Append commas": "附加逗號",
+    "Roll three": "抽三位出來",
+    "Generate forever": "無限生成",
+    "Cancel generate forever": "停止無限生成",
+    "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "疊代改進生成的圖像多少次；更高的值需要更長的時間；非常低的值會產生不好的結果",
+    "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt": "在圖像上畫一個蒙版，指令碼會根據提示重新生成蒙版區域的內容",
+    "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back": "正常放大圖像，將結果分割成圖塊，用圖生圖改進每個圖塊，最後將整個圖像合併回來",
+    "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "創建一個網格，圖像將有不同的參數。使用下面的輸入來指定哪些參數將由列和行共享",
+    "Run Python code. Advanced user only. Must run program with --allow-code for this to work": "執行 Python 程式碼。僅限老手使用。必須以 --allow-code 來開啟程式，才能使其執行",
+    "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "以逗號分割的單詞列表，第一個單詞將被用作關鍵詞：指令碼將在提示詞中搜尋這個單詞，並用其他單詞替換它",
+    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "以逗號分割的單詞列表，指令碼會排列出這些單詞的所有排列方式，並加入提示詞各生成一次",
+    "Reconstruct prompt from existing image and put it into the prompt field.": "從現有的圖像中重構出提示詞，並將其放入提示詞的輸入文字方塊",
+    "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": "設定在[prompt_words]選項中要使用的最大字數；注意：如果字數太長，可能會超過系統可處理的檔案路徑的最大長度",
+    "Process an image, use it as an input, repeat.": "處理一張圖像，將其作為輸入，並重複",
+    "Insert selected styles into prompt fields": "在提示詞中插入選定的模版風格",
+    "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.": "將當前的提示詞儲存為模版風格。如果你在文本中加入{prompt}標記，那麼將來你使用該模版風格時，你現有的提示詞會替換模版風格中的{prompt}",
+    "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在生成圖像之前從模型權重存檔點中載入權重。你可以使用哈希值或檔案名的一部分（如設定中所示）作為模型權重存檔點名稱。建議用在Y軸上以減少過程中模型的切換",
+    "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active: 在生成過程中，Torch使用的顯存(VRAM)峰值，不包括快取的數據。\nTorch reserved: Torch 分配的顯存(VRAM)的峰值量，包括所有活動和快取數據。\nSys VRAM: 所有應用程式分配的顯存(VRAM)的峰值量 / GPU 的總顯存(VRAM)（峰值利用率%）",
+    "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潛空間中的圖像。而另一種方法是，從潛變量表達中直接解碼並生成完整的圖像，接著放大它，然後再將其編碼回潛空間",
+    "Start drawing": "開始繪製",
+    "Description": "描述",
+    "Action": "行動",
+    "Aesthetic Gradients": "美術風格",
+    "aesthetic-gradients": "美術風格",
+    "stable-diffusion-webui-wildcards": "萬用字元",
+    "Dynamic Prompts": "動態提示",
+    "images-browser": "圖庫瀏覽器",
+    "Inspiration": "靈感",
+    "Deforum": "Deforum",
+    "Artists to study": "藝術家圖庫",
+    "Aesthetic Image Scorer": "美術風格評分",
+    "Dataset Tag Editor": "數據集標記編輯器",
+    "Face restoration model": "面部修復模型",
+    "Install": "安裝",
+    "Installing...": "安裝中…",
+    "Installed": "已安裝"
+}

modules/api/api.py

@@ -1,29 +1,39 @@
-from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
-from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, 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
-from typing import List
-import json
-import io
 import base64
-from PIL import Image
+import io
+import time
+import uvicorn
+from gradio.processing_utils import decode_base64_to_file, decode_base64_to_image
+from fastapi import APIRouter, Depends, HTTPException
+import modules.shared as shared
+from modules.api.models import *
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid
+from modules.extras import run_extras, run_pnginfo
+
+
+def upscaler_to_index(name: str):
+    try:
+        return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
+    except:
+        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
+
 
 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 ImageToImageResponse(BaseModel):
-    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: Json
-    info: Json
+def setUpscalers(req: dict):
+    reqDict = vars(req)
+    reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
+    reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2)
+    reqDict.pop('upscaler_1')
+    reqDict.pop('upscaler_2')
+    return reqDict
+
+
+def encode_pil_to_base64(image):
+    buffer = io.BytesIO()
+    image.save(buffer, format="png")
+    return base64.b64encode(buffer.getvalue())
 
 
 class Api:
@@ -31,25 +41,22 @@ class Api:
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
-        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
-        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
-
-    def __base64_to_image(self, base64_string):
-        # if has a comma, deal with prefix
-        if "," in base64_string:
-            base64_string = base64_string.split(",")[1]
-        imgdata = base64.b64decode(base64_string)
-        # convert base64 to PIL image
-        return Image.open(io.BytesIO(imgdata))
+        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
+        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
+        self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
+        self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
+        self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
+        self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
+        self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
-        
+
         if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found") 
-        
+            raise HTTPException(status_code=404, detail="Sampler not found")
+
         populate = txt2imgreq.copy(update={ # Override __init__ params
-            "sd_model": shared.sd_model, 
+            "sd_model": shared.sd_model,
             "sampler_index": sampler_index[0],
             "do_not_save_samples": True,
             "do_not_save_grid": True
@@ -57,40 +64,39 @@ class Api:
         )
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
         # Override object param
+
+        shared.state.begin()
+
         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=processed.js())
-        
-        
+        shared.state.end()
+
+        b64images = list(map(encode_pil_to_base64, processed.images))
+
+        return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
         sampler_index = sampler_to_index(img2imgreq.sampler_index)
-        
+
         if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found") 
+            raise HTTPException(status_code=404, detail="Sampler not found")
 
 
         init_images = img2imgreq.init_images
         if init_images is None:
-            raise HTTPException(status_code=404, detail="Init image not found") 
+            raise HTTPException(status_code=404, detail="Init image not found")
 
         mask = img2imgreq.mask
         if mask:
-            mask = self.__base64_to_image(mask)
+            mask = decode_base64_to_image(mask)
+
 
-        
         populate = img2imgreq.copy(update={ # Override __init__ params
-            "sd_model": shared.sd_model, 
+            "sd_model": shared.sd_model,
             "sampler_index": sampler_index[0],
             "do_not_save_samples": True,
-            "do_not_save_grid": True, 
+            "do_not_save_grid": True,
             "mask": mask
             }
         )
@@ -98,31 +104,92 @@ class Api:
 
         imgs = []
         for img in init_images:
-            img = self.__base64_to_image(img)
+            img = decode_base64_to_image(img)
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
-        # Override object param
+
+        shared.state.begin()
+
         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()))
+
+        shared.state.end()
+
+        b64images = list(map(encode_pil_to_base64, processed.images))
 
         if (not img2imgreq.include_init_images):
             img2imgreq.init_images = None
             img2imgreq.mask = None
 
-        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js())
+        return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
 
-    def extrasapi(self):
-        raise NotImplementedError
+    def extras_single_image_api(self, req: ExtrasSingleImageRequest):
+        reqDict = setUpscalers(req)
 
-    def pnginfoapi(self):
-        raise NotImplementedError
+        reqDict['image'] = decode_base64_to_image(reqDict['image'])
+
+        with self.queue_lock:
+            result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", **reqDict)
+
+        return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
+
+    def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
+        reqDict = setUpscalers(req)
+
+        def prepareFiles(file):
+            file = decode_base64_to_file(file.data, file_path=file.name)
+            file.orig_name = file.name
+            return file
+
+        reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
+        reqDict.pop('imageList')
+
+        with self.queue_lock:
+            result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict)
+
+        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
+
+    def pnginfoapi(self, req: PNGInfoRequest):
+        if(not req.image.strip()):
+            return PNGInfoResponse(info="")
+
+        result = run_pnginfo(decode_base64_to_image(req.image.strip()))
+
+        return PNGInfoResponse(info=result[1])
+
+    def progressapi(self, req: ProgressRequest = Depends()):
+        # copy from check_progress_call of ui.py
+
+        if shared.state.job_count == 0:
+            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict())
+
+        # avoid dividing zero
+        progress = 0.01
+
+        if shared.state.job_count > 0:
+            progress += shared.state.job_no / shared.state.job_count
+        if shared.state.sampling_steps > 0:
+            progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
+
+        time_since_start = time.time() - shared.state.time_start
+        eta = (time_since_start/progress)
+        eta_relative = eta-time_since_start
+
+        progress = min(progress, 1)
+
+        shared.state.set_current_image()
+
+        current_image = None
+        if shared.state.current_image and not req.skip_current_image:
+            current_image = encode_pil_to_base64(shared.state.current_image)
+
+        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
+
+    def interruptapi(self):
+        shared.state.interrupt()
+
+        return {}
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)

modules/api/models.py

@@ -1,10 +1,11 @@
-from array import array
-from inflection import underscore
-from typing import Any, Dict, Optional
-from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 import inspect
-
+from click import prompt
+from pydantic import BaseModel, Field, create_model
+from typing import Any, Optional
+from typing_extensions import Literal
+from inflection import underscore
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
+from modules.shared import sd_upscalers
 
 API_NOT_ALLOWED = [
     "self",
@@ -51,17 +52,17 @@ class PydanticModelGenerator:
             # 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 = [
@@ -73,11 +74,11 @@ class PydanticModelGenerator:
             )
             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=underscore(fields["key"]),
+                field_alias=fields["key"],
                 field_type=fields["type"],
                 field_value=fields["default"],
                 field_exclude=fields["exclude"] if "exclude" in fields else False))
@@ -94,15 +95,74 @@ class PydanticModelGenerator:
         DynamicModel.__config__.allow_population_by_field_name = True
         DynamicModel.__config__.allow_mutation = True
         return DynamicModel
-    
+
 StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingTxt2Img", 
+    "StableDiffusionProcessingTxt2Img",
     StableDiffusionProcessingTxt2Img,
     [{"key": "sampler_index", "type": str, "default": "Euler"}]
 ).generate_model()
 
 StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingImg2Img", 
+    "StableDiffusionProcessingImg2Img",
     StableDiffusionProcessingImg2Img,
     [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
-).generate_model()
+).generate_model()
+
+class TextToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: dict
+    info: str
+
+class ImageToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: dict
+    info: str
+
+class ExtrasBaseRequest(BaseModel):
+    resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
+    show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?")
+    gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
+    codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
+    codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
+    upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
+    upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
+    upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
+    upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?")
+    upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
+    upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
+    extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
+    upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?")
+
+class ExtraBaseResponse(BaseModel):
+    html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
+
+class ExtrasSingleImageRequest(ExtrasBaseRequest):
+    image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+
+class ExtrasSingleImageResponse(ExtraBaseResponse):
+    image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
+
+class FileData(BaseModel):
+    data: str = Field(title="File data", description="Base64 representation of the file")
+    name: str = Field(title="File name")
+
+class ExtrasBatchImagesRequest(ExtrasBaseRequest):
+    imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+
+class ExtrasBatchImagesResponse(ExtraBaseResponse):
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
+
+class PNGInfoRequest(BaseModel):
+    image: str = Field(title="Image", description="The base64 encoded PNG image")
+
+class PNGInfoResponse(BaseModel):
+    info: str = Field(title="Image info", description="A string with all the info the image had")
+
+class ProgressRequest(BaseModel):
+    skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
+
+class ProgressResponse(BaseModel):
+    progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
+    eta_relative: float = Field(title="ETA in secs")
+    state: dict = Field(title="State", description="The current state snapshot")
+    current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")

modules/esrgan_model.py

@@ -50,6 +50,7 @@ def mod2normal(state_dict):
 def resrgan2normal(state_dict, nb=23):
     # this code is copied from https://github.com/victorca25/iNNfer
     if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
+        re8x = 0
         crt_net = {}
         items = []
         for k, v in state_dict.items():
@@ -75,10 +76,18 @@ def resrgan2normal(state_dict, nb=23):
         crt_net['model.3.bias'] = state_dict['conv_up1.bias']
         crt_net['model.6.weight'] = state_dict['conv_up2.weight']
         crt_net['model.6.bias'] = state_dict['conv_up2.bias']
-        crt_net['model.8.weight'] = state_dict['conv_hr.weight']
-        crt_net['model.8.bias'] = state_dict['conv_hr.bias']
-        crt_net['model.10.weight'] = state_dict['conv_last.weight']
-        crt_net['model.10.bias'] = state_dict['conv_last.bias']
+
+        if 'conv_up3.weight' in state_dict:
+            # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
+            re8x = 3
+            crt_net['model.9.weight'] = state_dict['conv_up3.weight']
+            crt_net['model.9.bias'] = state_dict['conv_up3.bias']
+
+        crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
+        crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
+        crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
+        crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
+
         state_dict = crt_net
     return state_dict
 

modules/extensions.py

@@ -0,0 +1,83 @@
+import os
+import sys
+import traceback
+
+import git
+
+from modules import paths, shared
+
+
+extensions = []
+extensions_dir = os.path.join(paths.script_path, "extensions")
+
+
+def active():
+    return [x for x in extensions if x.enabled]
+
+
+class Extension:
+    def __init__(self, name, path, enabled=True):
+        self.name = name
+        self.path = path
+        self.enabled = enabled
+        self.status = ''
+        self.can_update = False
+
+        repo = None
+        try:
+            if os.path.exists(os.path.join(path, ".git")):
+                repo = git.Repo(path)
+        except Exception:
+            print(f"Error reading github repository info from {path}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+        if repo is None or repo.bare:
+            self.remote = None
+        else:
+            self.remote = next(repo.remote().urls, None)
+            self.status = 'unknown'
+
+    def list_files(self, subdir, extension):
+        from modules import scripts
+
+        dirpath = os.path.join(self.path, subdir)
+        if not os.path.isdir(dirpath):
+            return []
+
+        res = []
+        for filename in sorted(os.listdir(dirpath)):
+            res.append(scripts.ScriptFile(self.path, filename, os.path.join(dirpath, filename)))
+
+        res = [x for x in res if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
+
+        return res
+
+    def check_updates(self):
+        repo = git.Repo(self.path)
+        for fetch in repo.remote().fetch("--dry-run"):
+            if fetch.flags != fetch.HEAD_UPTODATE:
+                self.can_update = True
+                self.status = "behind"
+                return
+
+        self.can_update = False
+        self.status = "latest"
+
+    def pull(self):
+        repo = git.Repo(self.path)
+        repo.remotes.origin.pull()
+
+
+def list_extensions():
+    extensions.clear()
+
+    if not os.path.isdir(extensions_dir):
+        return
+
+    for dirname in sorted(os.listdir(extensions_dir)):
+        path = os.path.join(extensions_dir, dirname)
+        if not os.path.isdir(path):
+            continue
+
+        extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions)
+        extensions.append(extension)

modules/extras.py

@@ -1,3 +1,4 @@
+from __future__ import annotations
 import math
 import os
 
@@ -7,6 +8,10 @@ from PIL import Image
 import torch
 import tqdm
 
+from typing import Callable, List, OrderedDict, Tuple
+from functools import partial
+from dataclasses import dataclass
+
 from modules import processing, shared, images, devices, sd_models
 from modules.shared import opts
 import modules.gfpgan_model
@@ -17,10 +22,38 @@ import piexif.helper
 import gradio as gr
 
 
-cached_images = {}
+class LruCache(OrderedDict):
+    @dataclass(frozen=True)
+    class Key:
+        image_hash: int
+        info_hash: int
+        args_hash: int
+
+    @dataclass
+    class Value:
+        image: Image.Image
+        info: str
+
+    def __init__(self, max_size: int = 5, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._max_size = max_size
+
+    def get(self, key: LruCache.Key) -> LruCache.Value:
+        ret = super().get(key)
+        if ret is not None:
+            self.move_to_end(key)  # Move to end of eviction list
+        return ret
+
+    def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
+        self[key] = value
+        while len(self) > self._max_size:
+            self.popitem(last=False)
 
 
-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):
+cached_images: LruCache = LruCache(max_size=5)
+
+
+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, upscale_first: bool):
     devices.torch_gc()
 
     imageArr = []
@@ -39,7 +72,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         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 sorted(os.listdir(input_dir))] if os.path.isfile(file)]
+        image_list = shared.listfiles(input_dir)
         for img in image_list:
             try:
                 image = Image.open(img)
@@ -56,7 +89,91 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     else:
         outpath = opts.outdir_samples or opts.outdir_extras_samples
 
-    
+    # Extra operation definitions
+
+    def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
+        res = Image.fromarray(restored_img)
+
+        if gfpgan_visibility < 1.0:
+            res = Image.blend(image, res, gfpgan_visibility)
+
+        info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
+        return (res, info)
+
+    def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
+        res = Image.fromarray(restored_img)
+
+        if codeformer_visibility < 1.0:
+            res = Image.blend(image, res, codeformer_visibility)
+
+        info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
+        return (res, info)
+
+    def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
+        upscaler = shared.sd_upscalers[scaler_index]
+        res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+        if mode == 1 and crop:
+            cropped = Image.new("RGB", (resize_w, resize_h))
+            cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))
+            res = cropped
+        return res
+
+    def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        # Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
+        nonlocal upscaling_resize
+        if resize_mode == 1:
+            upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
+            crop_info = " (crop)" if upscaling_crop else ""
+            info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
+        return (image, info)
+
+    @dataclass
+    class UpscaleParams:
+        upscaler_idx: int
+        blend_alpha: float
+
+    def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        blended_result: Image.Image = None
+        for upscaler in params:
+            upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
+                            upscaling_resize_w, upscaling_resize_h, upscaling_crop)
+            cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
+                                     info_hash=hash(info),
+                                     args_hash=hash((upscale_args, upscale_first)))
+            cached_entry = cached_images.get(cache_key)
+            if cached_entry is None:
+                res = upscale(image, *upscale_args)
+                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
+                cached_images.put(cache_key, LruCache.Value(image=res, info=info))
+            else:
+                res, info = cached_entry.image, cached_entry.info
+
+            if blended_result is None:
+                blended_result = res
+            else:
+                blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)
+        return (blended_result, info)
+
+    # Build a list of operations to run
+    facefix_ops: List[Callable] = []
+    facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []
+    facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []
+
+    upscale_ops: List[Callable] = []
+    upscale_ops += [run_prepare_crop] if resize_mode == 1 else []
+
+    if upscaling_resize != 0:
+        step_params: List[UpscaleParams] = []
+        step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))
+        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
+            step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))
+
+        upscale_ops.append(partial(run_upscalers_blend, step_params))
+
+    extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
+
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
@@ -64,64 +181,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         image = image.convert("RGB")
         info = ""
+        # Run each operation on each image
+        for op in extras_ops:
+            image, info = op(image, info)
 
-        if gfpgan_visibility > 0:
-            restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
-            res = Image.fromarray(restored_img)
-
-            if gfpgan_visibility < 1.0:
-                res = Image.blend(image, res, gfpgan_visibility)
-
-            info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
-            image = res
-
-        if codeformer_visibility > 0:
-            restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
-            res = Image.fromarray(restored_img)
-
-            if codeformer_visibility < 1.0:
-                res = Image.blend(image, res, codeformer_visibility)
-
-            info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
-            image = res
-
-        if resize_mode == 1:
-            upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
-            crop_info = " (crop)" if upscaling_crop else ""
-            info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
-
-        if upscaling_resize != 1.0:
-            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, 
-                       resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
-
-                c = cached_images.get(key)
-                if c is None:
-                    upscaler = shared.sd_upscalers[scaler_index]
-                    c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
-                    if mode == 1 and crop:
-                        cropped = Image.new("RGB", (resize_w, resize_h))
-                        cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
-                        c = cropped
-                    cached_images[key] = c
-
-                return c
-
-            info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
-            res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-
-            if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-                res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
-                res = Image.blend(res, res2, extras_upscaler_2_visibility)
-
-            image = res
-
-        while len(cached_images) > 2:
-            del cached_images[next(iter(cached_images.keys()))]
-        
         if opts.use_original_name_batch and image_name != None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
@@ -141,6 +204,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     return outputs, plaintext_to_html(info), ''
 
+def clear_cache():
+    cached_images.clear()
+
 
 def run_pnginfo(image):
     if image is None:

modules/generation_parameters_copypaste.py

@@ -1,14 +1,25 @@
+import base64
+import io
 import os
 import re
 import gradio as gr
 from modules.shared import script_path
 from modules import shared
+import tempfile
+from PIL import Image
 
 re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 type_of_gr_update = type(gr.update())
+paste_fields = {}
+bind_list = []
+
+
+def reset():
+    paste_fields.clear()
+    bind_list.clear()
 
 
 def quote(text):
@@ -20,6 +31,111 @@ def quote(text):
     text = text.replace('"', '\\"')
     return f'"{text}"'
 
+
+def image_from_url_text(filedata):
+    if type(filedata) == dict and filedata["is_file"]:
+        filename = filedata["name"]
+        tempdir = os.path.normpath(tempfile.gettempdir())
+        normfn = os.path.normpath(filename)
+        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
+
+        return Image.open(filename)
+
+    if type(filedata) == list:
+        if len(filedata) == 0:
+            return None
+
+        filedata = filedata[0]
+
+    if filedata.startswith("data:image/png;base64,"):
+        filedata = filedata[len("data:image/png;base64,"):]
+
+    filedata = base64.decodebytes(filedata.encode('utf-8'))
+    image = Image.open(io.BytesIO(filedata))
+    return image
+
+
+def add_paste_fields(tabname, init_img, fields):
+    paste_fields[tabname] = {"init_img": init_img, "fields": fields}
+
+    # backwards compatibility for existing extensions
+    import modules.ui
+    if tabname == 'txt2img':
+        modules.ui.txt2img_paste_fields = fields
+    elif tabname == 'img2img':
+        modules.ui.img2img_paste_fields = fields
+
+
+def integrate_settings_paste_fields(component_dict):
+    from modules import ui
+
+    settings_map = {
+        'sd_hypernetwork': 'Hypernet',
+        'sd_hypernetwork_strength': 'Hypernet strength',
+        'CLIP_stop_at_last_layers': 'Clip skip',
+        'sd_model_checkpoint': 'Model hash',
+    }
+    settings_paste_fields = [
+        (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))
+        for k, v in settings_map.items()
+    ]
+
+    for tabname, info in paste_fields.items():
+        if info["fields"] is not None:
+            info["fields"] += settings_paste_fields
+
+
+def create_buttons(tabs_list):
+    buttons = {}
+    for tab in tabs_list:
+        buttons[tab] = gr.Button(f"Send to {tab}")
+    return buttons
+
+
+#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab
+def bind_buttons(buttons, send_image, send_generate_info):
+    bind_list.append([buttons, send_image, send_generate_info])
+
+
+def run_bind():
+    for buttons, send_image, send_generate_info in bind_list:
+        for tab in buttons:
+            button = buttons[tab]
+            if send_image and paste_fields[tab]["init_img"]:
+                if type(send_image) == gr.Gallery:
+                    button.click(
+                        fn=lambda x: image_from_url_text(x),
+                        _js="extract_image_from_gallery",
+                        inputs=[send_image],
+                        outputs=[paste_fields[tab]["init_img"]],
+                    )
+                else:
+                    button.click(
+                        fn=lambda x: x,
+                        inputs=[send_image],
+                        outputs=[paste_fields[tab]["init_img"]],
+                    )
+
+            if send_generate_info and paste_fields[tab]["fields"] is not None:
+                if send_generate_info in paste_fields:
+                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2'] + (["Seed"] if shared.opts.send_seed else [])
+
+                    button.click(
+                        fn=lambda *x: x,
+                        inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
+                        outputs=[field for field, name in paste_fields[tab]["fields"] if name in paste_field_names],
+                    )
+                else:
+                    connect_paste(button, paste_fields[tab]["fields"], send_generate_info)
+
+            button.click(
+                fn=None,
+                _js=f"switch_to_{tab}",
+                inputs=None,
+                outputs=None,
+            )
+
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
@@ -68,7 +184,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     return res
 
 
-def connect_paste(button, paste_fields, input_comp, js=None):
+def connect_paste(button, paste_fields, input_comp, jsfunc=None):
     def paste_func(prompt):
         if not prompt and not shared.cmd_opts.hide_ui_dir_config:
             filename = os.path.join(script_path, "params.txt")
@@ -106,7 +222,9 @@ def connect_paste(button, paste_fields, input_comp, js=None):
 
     button.click(
         fn=paste_func,
-        _js=js,
+        _js=jsfunc,
         inputs=[input_comp],
         outputs=[x[0] for x in paste_fields],
     )
+
+

modules/hypernetworks/hypernetwork.py

@@ -25,6 +25,7 @@ from statistics import stdev, mean
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
     activation_dict = {
+        "linear": torch.nn.Identity,
         "relu": torch.nn.ReLU,
         "leakyrelu": torch.nn.LeakyReLU,
         "elu": torch.nn.ELU,
@@ -220,13 +221,16 @@ def list_hypernetworks(path):
     res = {}
     for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
         name = os.path.splitext(os.path.basename(filename))[0]
-        res[name] = filename
+        # Prevent a hypothetical "None.pt" from being listed.
+        if name != "None":
+            res[name] = filename
     return res
 
 
 def load_hypernetwork(filename):
     path = shared.hypernetworks.get(filename, None)
-    if path is not None:
+    # Prevent any file named "None.pt" from being loaded.
+    if path is not None and filename != "None":
         print(f"Loading hypernetwork {filename}")
         try:
             shared.loaded_hypernetwork = Hypernetwork()
@@ -343,7 +347,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
-    assert hypernetwork_name, 'hypernetwork not selected'
+    save_hypernetwork_every = save_hypernetwork_every or 0
+    create_image_every = create_image_every or 0
+    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
@@ -369,39 +375,44 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:
         images_dir = None
 
+    hypernetwork = shared.loaded_hypernetwork
+    checkpoint = sd_models.select_checkpoint()
+
+    ititial_step = hypernetwork.step or 0
+    if ititial_step >= steps:
+        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        return hypernetwork, filename
+
+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+    
+    # dataset loading may take a while, so input validations and early returns should be done before this
     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=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)
         shared.sd_model.first_stage_model.to(devices.cpu)
 
-    hypernetwork = shared.loaded_hypernetwork
-    weights = hypernetwork.weights()
-    for weight in weights:
-        weight.requires_grad = True
-
     size = len(ds.indexes)
     loss_dict = defaultdict(lambda : deque(maxlen = 1024))
     losses = torch.zeros((size,))
     previous_mean_losses = [0]
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
-
-    last_saved_file = "<none>"
-    last_saved_image = "<none>"
-    forced_filename = "<none>"
-
-    ititial_step = hypernetwork.step or 0
-    if ititial_step > steps:
-        return hypernetwork, filename
-
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+    
+    weights = hypernetwork.weights()
+    for weight in weights:
+        weight.requires_grad = True
     # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
     optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     steps_without_grad = 0
 
+    last_saved_file = "<none>"
+    last_saved_image = "<none>"
+    forced_filename = "<none>"
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
@@ -440,7 +451,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             optimizer.step()
 
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
+        steps_done = hypernetwork.step + 1
+
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
             raise RuntimeError("Loss diverged.")
         
         if len(previous_mean_losses) > 1:
@@ -450,19 +463,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
         pbar.set_description(dataset_loss_info)
 
-        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
+        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
             # Before saving, change name to match current checkpoint.
-            hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
-            hypernetwork.save(last_saved_file)
+            hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+            save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{previous_mean_loss:.7f}",
             "learn_rate": scheduler.learn_rate
         })
 
-        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-            forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{hypernetwork_name}-{steps_done}'
             last_saved_image = os.path.join(images_dir, forced_filename)
 
             optimizer.zero_grad()
@@ -499,7 +512,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             if image is not None:
                 shared.state.current_image = image
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename)
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                 last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
@@ -515,13 +528,23 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 """
         
     report_statistics(loss_dict)
-    checkpoint = sd_models.select_checkpoint()
 
-    hypernetwork.sd_checkpoint = checkpoint.hash
-    hypernetwork.sd_checkpoint_name = checkpoint.model_name
-    # Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).
-    hypernetwork.name = hypernetwork_name
-    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt')
-    hypernetwork.save(filename)
+    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+    save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
 
     return hypernetwork, filename
+
+def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
+    old_hypernetwork_name = hypernetwork.name
+    old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
+    old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None
+    try:
+        hypernetwork.sd_checkpoint = checkpoint.hash
+        hypernetwork.sd_checkpoint_name = checkpoint.model_name
+        hypernetwork.name = hypernetwork_name
+        hypernetwork.save(filename)
+    except:
+        hypernetwork.sd_checkpoint = old_sd_checkpoint
+        hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
+        hypernetwork.name = old_hypernetwork_name
+        raise

modules/hypernetworks/ui.py

@@ -8,7 +8,8 @@ import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
-keys = list(hypernetwork.HypernetworkModule.activation_dict.keys())
+not_available = ["hardswish", "multiheadattention"]
+keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
     # Remove illegal characters from name.

modules/images.py

@@ -300,8 +300,8 @@ class FilenameGenerator:
         'seed': lambda self: self.seed if self.seed is not None else '',
         'steps': lambda self:  self.p and self.p.steps,
         'cfg': lambda self: self.p and self.p.cfg_scale,
-        'width': lambda self: self.p and self.p.width,
-        'height': lambda self: self.p and self.p.height,
+        'width': lambda self: self.image.width,
+        'height': lambda self: self.image.height,
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
@@ -315,10 +315,11 @@ class FilenameGenerator:
     }
     default_time_format = '%Y%m%d%H%M%S'
 
-    def __init__(self, p, seed, prompt):
+    def __init__(self, p, seed, prompt, image):
         self.p = p
         self.seed = seed
         self.prompt = prompt
+        self.image = image
 
     def prompt_no_style(self):
         if self.p is None or self.prompt is None:
@@ -449,18 +450,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         txt_fullfn (`str` or None):
             If a text file is saved for this image, this will be its full path. Otherwise None.
     """
-    namegen = FilenameGenerator(p, seed, prompt)
-
-    if extension == 'png' and opts.enable_pnginfo and info is not None:
-        pnginfo = PngImagePlugin.PngInfo()
-
-        if existing_info is not None:
-            for k, v in existing_info.items():
-                pnginfo.add_text(k, str(v))
-
-        pnginfo.add_text(pnginfo_section_name, info)
-    else:
-        pnginfo = None
+    namegen = FilenameGenerator(p, seed, prompt, image)
 
     if save_to_dirs is None:
         save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
@@ -489,19 +479,27 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         if add_number:
             basecount = get_next_sequence_number(path, basename)
             fullfn = None
-            fullfn_without_extension = None
             for i in range(500):
                 fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
                 fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
-                fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
                 if not os.path.exists(fullfn):
                     break
         else:
             fullfn = os.path.join(path, f"{file_decoration}.{extension}")
-            fullfn_without_extension = os.path.join(path, file_decoration)
     else:
         fullfn = os.path.join(path, f"{forced_filename}.{extension}")
-        fullfn_without_extension = os.path.join(path, forced_filename)
+
+    pnginfo = existing_info or {}
+    if info is not None:
+        pnginfo[pnginfo_section_name] = info
+
+    params = script_callbacks.ImageSaveParams(image, p, fullfn, pnginfo)
+    script_callbacks.before_image_saved_callback(params)
+
+    image = params.image
+    fullfn = params.filename
+    info = params.pnginfo.get(pnginfo_section_name, None)
+    fullfn_without_extension, extension = os.path.splitext(params.filename)
 
     def exif_bytes():
         return piexif.dump({
@@ -510,12 +508,21 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             },
         })
 
-    if extension.lower() in ("jpg", "jpeg", "webp"):
+    if extension.lower() == '.png':
+        pnginfo_data = PngImagePlugin.PngInfo()
+        if opts.enable_pnginfo:
+            for k, v in params.pnginfo.items():
+                pnginfo_data.add_text(k, str(v))
+
+        image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
+
+    elif extension.lower() in (".jpg", ".jpeg", ".webp"):
         image.save(fullfn, quality=opts.jpeg_quality)
+
         if opts.enable_pnginfo and info is not None:
             piexif.insert(exif_bytes(), fullfn)
     else:
-        image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
+        image.save(fullfn, quality=opts.jpeg_quality)
 
     target_side_length = 4000
     oversize = image.width > target_side_length or image.height > target_side_length
@@ -538,7 +545,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     else:
         txt_fullfn = None
 
-    script_callbacks.image_saved_callback(image, p, fullfn, txt_fullfn)
+    script_callbacks.image_saved_callback(params)
+
     return fullfn, txt_fullfn
 
 

modules/img2img.py

@@ -19,7 +19,7 @@ import modules.scripts
 def process_batch(p, input_dir, output_dir, args):
     processing.fix_seed(p)
 
-    images = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+    images = shared.listfiles(input_dir)
 
     print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
 
@@ -39,6 +39,8 @@ def process_batch(p, input_dir, output_dir, args):
             break
 
         img = Image.open(image)
+        # Use the EXIF orientation of photos taken by smartphones.
+        img = ImageOps.exif_transpose(img) 
         p.init_images = [img] * p.batch_size
 
         proc = modules.scripts.scripts_img2img.run(p, *args)
@@ -53,6 +55,7 @@ def process_batch(p, input_dir, output_dir, args):
                 filename = f"{left}-{n}{right}"
 
             if not save_normally:
+                os.makedirs(output_dir, exist_ok=True)
                 processed_image.save(os.path.join(output_dir, filename))
 
 
@@ -61,19 +64,26 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     is_batch = mode == 2
 
     if is_inpaint:
+        # Drawn mask
         if mask_mode == 0:
             image = init_img_with_mask['image']
             mask = init_img_with_mask['mask']
             alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
             mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
             image = image.convert('RGB')
+        # Uploaded mask
         else:
             image = init_img_inpaint
             mask = init_mask_inpaint
+    # No mask
     else:
         image = init_img
         mask = None
 
+    # Use the EXIF orientation of photos taken by smartphones.
+    if image is not None:
+        image = ImageOps.exif_transpose(image) 
+
     assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
 
     p = StableDiffusionProcessingImg2Img(
@@ -128,6 +138,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         if processed is None:
             processed = process_images(p)
 
+    p.close()
+
     shared.total_tqdm.clear()
 
     generation_info_js = processed.js()

modules/interrogate.py

@@ -56,9 +56,9 @@ class InterrogateModels:
         import clip
 
         if self.running_on_cpu:
-            model, preprocess = clip.load(clip_model_name, device="cpu")
+            model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.cmd_opts.clip_models_path)
         else:
-            model, preprocess = clip.load(clip_model_name)
+            model, preprocess = clip.load(clip_model_name, download_root=shared.cmd_opts.clip_models_path)
 
         model.eval()
         model = model.to(devices.device_interrogate)

modules/lowvram.py

@@ -38,13 +38,18 @@ def setup_for_low_vram(sd_model, use_medvram):
     # see below for register_forward_pre_hook;
     # first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
     # useless here, and we just replace those methods
-    def first_stage_model_encode_wrap(self, encoder, x):
-        send_me_to_gpu(self, None)
-        return encoder(x)
 
-    def first_stage_model_decode_wrap(self, decoder, z):
-        send_me_to_gpu(self, None)
-        return decoder(z)
+    first_stage_model = sd_model.first_stage_model
+    first_stage_model_encode = sd_model.first_stage_model.encode
+    first_stage_model_decode = sd_model.first_stage_model.decode
+
+    def first_stage_model_encode_wrap(x):
+        send_me_to_gpu(first_stage_model, None)
+        return first_stage_model_encode(x)
+
+    def first_stage_model_decode_wrap(z):
+        send_me_to_gpu(first_stage_model, None)
+        return first_stage_model_decode(z)
 
     # remove three big modules, cond, first_stage, and unet from the model and then
     # send the model to GPU. Then put modules back. the modules will be in CPU.
@@ -56,8 +61,8 @@ def setup_for_low_vram(sd_model, use_medvram):
     # register hooks for those the first two models
     sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
     sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
-    sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x)
-    sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z)
+    sd_model.first_stage_model.encode = first_stage_model_encode_wrap
+    sd_model.first_stage_model.decode = first_stage_model_decode_wrap
     parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
     if use_medvram:

modules/masking.py

@@ -49,7 +49,7 @@ def expand_crop_region(crop_region, processing_width, processing_height, image_w
     ratio_processing = processing_width / processing_height
 
     if ratio_crop_region > ratio_processing:
-        desired_height = (x2 - x1) * ratio_processing
+        desired_height = (x2 - x1) / ratio_processing
         desired_height_diff = int(desired_height - (y2-y1))
         y1 -= desired_height_diff//2
         y2 += desired_height_diff - desired_height_diff//2

modules/modelloader.py

@@ -85,6 +85,9 @@ def cleanup_models():
     src_path = os.path.join(root_path, "ESRGAN")
     dest_path = os.path.join(models_path, "ESRGAN")
     move_files(src_path, dest_path)
+    src_path = os.path.join(models_path, "BSRGAN")
+    dest_path = os.path.join(models_path, "ESRGAN")
+    move_files(src_path, dest_path, ".pth")
     src_path = os.path.join(root_path, "gfpgan")
     dest_path = os.path.join(models_path, "GFPGAN")
     move_files(src_path, dest_path)

modules/processing.py

@@ -77,9 +77,8 @@ def get_correct_sampler(p):
 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):
+    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 = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -109,13 +108,14 @@ class StableDiffusionProcessing():
         self.do_not_reload_embeddings = do_not_reload_embeddings
         self.paste_to = None
         self.color_corrections = None
-        self.denoising_strength: float = 0
+        self.denoising_strength: float = denoising_strength
         self.sampler_noise_scheduler_override = None
-        self.ddim_discretize = opts.ddim_discretize
+        self.ddim_discretize = ddim_discretize or opts.ddim_discretize
         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
+        self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -129,13 +129,75 @@ class StableDiffusionProcessing():
         self.all_seeds = None
         self.all_subseeds = None
 
+    def txt2img_image_conditioning(self, x, width=None, height=None):
+        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
+            # Dummy zero conditioning if we're not using inpainting model.
+            # Still takes up a bit of memory, but no encoder call.
+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+            return x.new_zeros(x.shape[0], 5, 1, 1)
+
+        height = height or self.height
+        width = width or self.width
+
+        # The "masked-image" in this case will just be all zeros since the entire image is masked.
+        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+
+        # Add the fake full 1s mask to the first dimension.
+        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+        image_conditioning = image_conditioning.to(x.dtype)            
+
+        return image_conditioning
+
+    def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
+        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
+            # Dummy zero conditioning if we're not using inpainting model.
+            return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+
+        # Handle the different mask inputs
+        if image_mask is not None:
+            if torch.is_tensor(image_mask):
+                conditioning_mask = image_mask
+            else:
+                conditioning_mask = np.array(image_mask.convert("L"))
+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+                conditioning_mask = torch.round(conditioning_mask)
+        else:
+            conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
+
+        # Create another latent image, this time with a masked version of the original input.
+        # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
+        conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)
+        conditioning_image = torch.lerp(
+            source_image,
+            source_image * (1.0 - conditioning_mask),
+            getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
+        )
+        
+        # Encode the new masked image using first stage of network.
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+        # Create the concatenated conditioning tensor to be fed to `c_concat`
+        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
+        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+        image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+        image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
+        return image_conditioning
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         raise NotImplementedError()
 
+    def close(self):
+        self.sd_model = None
+        self.sampler = None
+
 
 class Processed:
     def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
@@ -330,6 +392,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
         "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
+        "Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
@@ -351,6 +414,22 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
 
 def process_images(p: StableDiffusionProcessing) -> Processed:
+    stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
+
+    try:
+        for k, v in p.override_settings.items():
+            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model, hypernet impossible
+
+        res = process_images_inner(p)
+
+    finally:
+        for k, v in stored_opts.items():
+            opts.data[k] = v
+
+    return res
+
+
+def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
     if type(p.prompt) == list:
@@ -396,7 +475,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     if p.scripts is not None:
-        p.scripts.run_alwayson_scripts(p)
+        p.scripts.process(p)
 
     infotexts = []
     output_images = []
@@ -419,7 +498,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
             subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
-            if (len(prompts) == 0):
+            if len(prompts) == 0:
                 break
 
             with devices.autocast():
@@ -434,7 +513,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             with devices.autocast():
-                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
 
             samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
@@ -508,7 +587,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+
+    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+
+    if p.scripts is not None:
+        p.scripts.postprocess(p, res)
+
+    return res
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
@@ -556,43 +641,41 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-    def create_dummy_mask(self, x, width=None, height=None):
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            height = height or self.height
-            width = width or self.width
-
-            # The "masked-image" in this case will just be all zeros since the entire image is masked.
-            image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-            image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
-
-            # Add the fake full 1s mask to the first dimension.
-            image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-            image_conditioning = image_conditioning.to(x.dtype)
-
-        else:
-            # Dummy zero conditioning if we're not using inpainting model.
-            # Still takes up a bit of memory, but no encoder call.
-            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
-
-        return image_conditioning
-
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height))
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
+        """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images"""
+        def save_intermediate(image, index):
+            if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
+                return
+
+            if not isinstance(image, Image.Image):
+                image = sd_samplers.sample_to_image(image, index)
+
+            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
+
         if opts.use_scale_latent_for_hires_fix:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            
+            # Avoid making the inpainting conditioning unless necessary as 
+            # this does need some extra compute to decode / encode the image again.
+            if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
+                image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
+            else:
+                image_conditioning = self.txt2img_image_conditioning(samples)
 
+            for i in range(samples.shape[0]):
+                save_intermediate(samples, i)
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
@@ -602,6 +685,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
                 image = Image.fromarray(x_sample)
+
+                save_intermediate(image, i)
+
                 image = images.resize_image(0, image, self.width, self.height)
                 image = np.array(image).astype(np.float32) / 255.0
                 image = np.moveaxis(image, 2, 0)
@@ -613,6 +699,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
+            image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
+
         shared.state.nextjob()
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -623,7 +711,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples))
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
 
         return samples
 
@@ -755,36 +843,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            if self.image_mask is not None:
-                conditioning_mask = np.array(self.image_mask.convert("L"))
-                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
 
-                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-                conditioning_mask = torch.round(conditioning_mask)
-            else:
-                conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
-
-            # Create another latent image, this time with a masked version of the original input.
-            conditioning_mask = conditioning_mask.to(image.device)
-            conditioning_image = image * (1.0 - conditioning_mask)
-            conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-
-            # Create the concatenated conditioning tensor to be fed to `c_concat`
-            conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
-            conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-            self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-            self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
-        else:
-            self.image_conditioning = torch.zeros(
-                self.init_latent.shape[0], 5, 1, 1,
-                dtype=self.init_latent.dtype,
-                device=self.init_latent.device
-            )
-
-
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
@@ -795,4 +856,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         del x
         devices.torch_gc()
 
-        return samples
+        return samples

modules/safe.py

@@ -32,7 +32,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             return getattr(collections, name)
         if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
             return getattr(torch._utils, name)
-        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
+        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage']:
             return getattr(torch, name)
         if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
             return getattr(torch.nn.modules.container, name)

modules/script_callbacks.py

@@ -2,23 +2,73 @@ import sys
 import traceback
 from collections import namedtuple
 import inspect
+from typing import Optional
 
+from fastapi import FastAPI
+from gradio import Blocks
 
 def report_exception(c, job):
     print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
     print(traceback.format_exc(), file=sys.stderr)
 
 
+class ImageSaveParams:
+    def __init__(self, image, p, filename, pnginfo):
+        self.image = image
+        """the PIL image itself"""
+
+        self.p = p
+        """p object with processing parameters; either StableDiffusionProcessing or an object with same fields"""
+
+        self.filename = filename
+        """name of file that the image would be saved to"""
+
+        self.pnginfo = pnginfo
+        """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
+
+
+class CFGDenoiserParams:
+    def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
+        self.x = x
+        """Latent image representation in the process of being denoised"""
+        
+        self.image_cond = image_cond
+        """Conditioning image"""
+        
+        self.sigma = sigma
+        """Current sigma noise step value"""
+        
+        self.sampling_step = sampling_step
+        """Current Sampling step number"""
+        
+        self.total_sampling_steps = total_sampling_steps
+        """Total number of sampling steps planned"""
+
+
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
+callbacks_app_started = []
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
+callbacks_before_image_saved = []
 callbacks_image_saved = []
+callbacks_cfg_denoiser = []
+
 
 def clear_callbacks():
     callbacks_model_loaded.clear()
     callbacks_ui_tabs.clear()
+    callbacks_ui_settings.clear()
+    callbacks_before_image_saved.clear()
     callbacks_image_saved.clear()
+    callbacks_cfg_denoiser.clear()
+
+def app_started_callback(demo: Optional[Blocks], app: FastAPI):
+    for c in callbacks_app_started:
+        try:
+            c.callback(demo, app)
+        except Exception:
+            report_exception(c, 'app_started_callback')
 
 
 def model_loaded_callback(sd_model):
@@ -49,14 +99,30 @@ def ui_settings_callback():
             report_exception(c, 'ui_settings_callback')
 
 
-def image_saved_callback(image, p, fullfn, txt_fullfn):
+def before_image_saved_callback(params: ImageSaveParams):
+    for c in callbacks_before_image_saved:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'before_image_saved_callback')
+
+
+def image_saved_callback(params: ImageSaveParams):
     for c in callbacks_image_saved:
         try:
-            c.callback(image, p, fullfn, txt_fullfn)
+            c.callback(params)
         except Exception:
             report_exception(c, 'image_saved_callback')
 
 
+def cfg_denoiser_callback(params: CFGDenoiserParams):
+    for c in callbacks_cfg_denoiser:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'cfg_denoiser_callback')
+
+
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -64,6 +130,11 @@ def add_callback(callbacks, fun):
     callbacks.append(ScriptCallback(filename, fun))
 
 
+def on_app_started(callback):
+    """register a function to be called when the webui started, the gradio `Block` component and
+    fastapi `FastAPI` object are passed as the arguments"""
+    add_callback(callbacks_app_started, callback)
+
 
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
@@ -90,11 +161,26 @@ def on_ui_settings(callback):
     add_callback(callbacks_ui_settings, callback)
 
 
-def on_save_imaged(callback):
-    """register a function to be called after modules.images.save_image is called.
-    The callback is called with three arguments:
-        - p - procesing object (or a dummy object with same fields if the image is saved using save button)
-        - fullfn - image filename
-        - txt_fullfn - text file with parameters; may be None
+def on_before_image_saved(callback):
+    """register a function to be called before an image is saved to a file.
+    The callback is called with one argument:
+        - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
+    """
+    add_callback(callbacks_before_image_saved, callback)
+
+
+def on_image_saved(callback):
+    """register a function to be called after an image is saved to a file.
+    The callback is called with one argument:
+        - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
     """
     add_callback(callbacks_image_saved, callback)
+
+
+def on_cfg_denoiser(callback):
+    """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
+    The callback is called with one argument:
+        - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
+    """
+    add_callback(callbacks_cfg_denoiser, callback)
+

modules/scripts.py

@@ -7,7 +7,7 @@ import modules.ui as ui
 import gradio as gr
 
 from modules.processing import StableDiffusionProcessing
-from modules import shared, paths, script_callbacks
+from modules import shared, paths, script_callbacks, extensions
 
 AlwaysVisible = object()
 
@@ -18,6 +18,9 @@ class Script:
     args_to = None
     alwayson = False
 
+    """A gr.Group component that has all script's UI inside it"""
+    group = None
+
     infotext_fields = None
     """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
     parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
@@ -64,7 +67,16 @@ class Script:
     def process(self, p, *args):
         """
         This function is called before processing begins for AlwaysVisible scripts.
-        scripts. You can modify the processing object (p) here, inject hooks, etc.
+        You can modify the processing object (p) here, inject hooks, etc.
+        args contains all values returned by components from ui()
+        """
+
+        pass
+
+    def postprocess(self, p, processed, *args):
+        """
+        This function is called after processing ends for AlwaysVisible scripts.
+        args contains all values returned by components from ui()
         """
 
         pass
@@ -98,17 +110,8 @@ def list_scripts(scriptdirname, extension):
         for filename in sorted(os.listdir(basedir)):
             scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
 
-    extdir = os.path.join(paths.script_path, "extensions")
-    if os.path.exists(extdir):
-        for dirname in sorted(os.listdir(extdir)):
-            dirpath = os.path.join(extdir, dirname)
-            scriptdirpath = os.path.join(dirpath, scriptdirname)
-
-            if not os.path.isdir(scriptdirpath):
-                continue
-
-            for filename in sorted(os.listdir(scriptdirpath)):
-                scripts_list.append(ScriptFile(dirpath, filename, os.path.join(scriptdirpath, filename)))
+    for ext in extensions.active():
+        scripts_list += ext.list_files(scriptdirname, extension)
 
     scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
 
@@ -118,11 +121,7 @@ def list_scripts(scriptdirname, extension):
 def list_files_with_name(filename):
     res = []
 
-    dirs = [paths.script_path]
-
-    extdir = os.path.join(paths.script_path, "extensions")
-    if os.path.exists(extdir):
-        dirs += [os.path.join(extdir, d) for d in sorted(os.listdir(extdir))]
+    dirs = [paths.script_path] + [ext.path for ext in extensions.active()]
 
     for dirpath in dirs:
         if not os.path.isdir(dirpath):
@@ -222,8 +221,6 @@ class ScriptRunner:
 
             for control in controls:
                 control.custom_script_source = os.path.basename(script.filename)
-                if not script.alwayson:
-                    control.visible = False
 
             if script.infotext_fields is not None:
                 self.infotext_fields += script.infotext_fields
@@ -233,40 +230,41 @@ class ScriptRunner:
             script.args_to = len(inputs)
 
         for script in self.alwayson_scripts:
-            with gr.Group():
+            with gr.Group() as group:
                 create_script_ui(script, inputs, inputs_alwayson)
 
-        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
+            script.group = group
+
+        dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
         dropdown.save_to_config = True
         inputs[0] = dropdown
 
         for script in self.selectable_scripts:
-            create_script_ui(script, inputs, inputs_alwayson)
+            with gr.Group(visible=False) as group:
+                create_script_ui(script, inputs, inputs_alwayson)
+
+            script.group = group
 
         def select_script(script_index):
-            if 0 < script_index <= len(self.selectable_scripts):
-                script = self.selectable_scripts[script_index-1]
-                args_from = script.args_from
-                args_to = script.args_to
-            else:
-                args_from = 0
-                args_to = 0
+            selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
 
-            return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]
+            return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
 
         def init_field(title):
+            """called when an initial value is set from ui-config.json to show script's UI components"""
+
             if title == 'None':
                 return
+
             script_index = self.titles.index(title)
-            script = self.selectable_scripts[script_index]
-            for i in range(script.args_from, script.args_to):
-                inputs[i].visible = True
+            self.selectable_scripts[script_index].group.visible = True
 
         dropdown.init_field = init_field
+
         dropdown.change(
             fn=select_script,
             inputs=[dropdown],
-            outputs=inputs
+            outputs=[script.group for script in self.selectable_scripts]
         )
 
         return inputs
@@ -289,13 +287,22 @@ class ScriptRunner:
 
         return processed
 
-    def run_alwayson_scripts(self, p):
+    def process(self, p):
         for script in self.alwayson_scripts:
             try:
                 script_args = p.script_args[script.args_from:script.args_to]
                 script.process(p, *script_args)
             except Exception:
-                print(f"Error running alwayson script: {script.filename}", file=sys.stderr)
+                print(f"Error running process: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
+    def postprocess(self, p, processed):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess(p, processed, *script_args)
+            except Exception:
+                print(f"Error running postprocess: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
     def reload_sources(self, cache):

modules/sd_hijack.py

@@ -94,6 +94,10 @@ class StableDiffusionModelHijack:
         if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
             model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
 
+        self.layers = None
+        self.circular_enabled = False
+        self.clip = None
+
     def apply_circular(self, enable):
         if self.circular_enabled == enable:
             return

modules/sd_models.py

@@ -1,13 +1,15 @@
 import collections
 import os.path
 import sys
+import gc
 from collections import namedtuple
 import torch
+import re
 from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices, script_callbacks
+from modules import shared, modelloader, devices, script_callbacks, sd_vae
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
@@ -35,8 +37,10 @@ def setup_model():
     list_models()
 
 
-def checkpoint_tiles():
-    return sorted([x.title for x in checkpoints_list.values()])
+def checkpoint_tiles(): 
+    convert = lambda name: int(name) if name.isdigit() else name.lower() 
+    alphanumeric_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)] 
+    return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
 
 
 def list_models():
@@ -155,14 +159,15 @@ def get_state_dict_from_checkpoint(pl_sd):
     return pl_sd
 
 
-vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
-
-
-def load_model_weights(model, checkpoint_info):
+def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    if checkpoint_info not in checkpoints_loaded:
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+
+    checkpoint_key = checkpoint_info
+
+    if checkpoint_key not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
         pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
@@ -170,42 +175,47 @@ def load_model_weights(model, checkpoint_info):
             print(f"Global Step: {pl_sd['global_step']}")
 
         sd = get_state_dict_from_checkpoint(pl_sd)
-        missing, extra = model.load_state_dict(sd, strict=False)
+        del pl_sd
+        model.load_state_dict(sd, strict=False)
+        del sd
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)
 
         if not shared.cmd_opts.no_half:
+            vae = model.first_stage_model
+
+            # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
+            if shared.cmd_opts.no_half_vae:
+                model.first_stage_model = None
+
             model.half()
+            model.first_stage_model = vae
 
         devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
         devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
-        vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
-
-        if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
-            vae_file = shared.cmd_opts.vae_path
-
-        if os.path.exists(vae_file):
-            print(f"Loading VAE weights from: {vae_file}")
-            vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-            model.first_stage_model.load_state_dict(vae_dict)
-
         model.first_stage_model.to(devices.dtype_vae)
 
-        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
-            checkpoints_loaded.popitem(last=False)  # LRU
+        if shared.opts.sd_checkpoint_cache > 0:
+            # if PR #4035 were to get merged, restore base VAE first before caching
+            checkpoints_loaded[checkpoint_key] = model.state_dict().copy()
+            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+                checkpoints_loaded.popitem(last=False)  # LRU
+
     else:
-        print(f"Loading weights [{sd_model_hash}] from cache")
-        checkpoints_loaded.move_to_end(checkpoint_info)
-        model.load_state_dict(checkpoints_loaded[checkpoint_info])
+        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
+        vae_message = f" with {vae_name} VAE" if vae_name else ""
+        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
+        checkpoints_loaded.move_to_end(checkpoint_key)
+        model.load_state_dict(checkpoints_loaded[checkpoint_key])
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    sd_vae.load_vae(model, vae_file)
+
 
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
@@ -214,6 +224,12 @@ def load_model(checkpoint_info=None):
     if checkpoint_info.config != shared.cmd_opts.config:
         print(f"Loading config from: {checkpoint_info.config}")
 
+    if shared.sd_model:
+        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+        shared.sd_model = None
+        gc.collect()
+        devices.torch_gc()
+
     sd_config = OmegaConf.load(checkpoint_info.config)
     
     if should_hijack_inpainting(checkpoint_info):
@@ -227,6 +243,7 @@ def load_model(checkpoint_info=None):
         checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
 
     do_inpainting_hijack()
+
     sd_model = instantiate_from_config(sd_config.model)
     load_model_weights(sd_model, checkpoint_info)
 
@@ -246,14 +263,18 @@ def load_model(checkpoint_info=None):
     return sd_model
 
 
-def reload_model_weights(sd_model, info=None):
+def reload_model_weights(sd_model=None, info=None):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
+ 
+    if not sd_model:
+        sd_model = shared.sd_model
 
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+        del sd_model
         checkpoints_loaded.clear()
         load_model(checkpoint_info)
         return shared.sd_model

modules/sd_samplers.py

@@ -1,5 +1,6 @@
 from collections import namedtuple
 import numpy as np
+from math import floor
 import torch
 import tqdm
 from PIL import Image
@@ -11,6 +12,7 @@ from modules import prompt_parser, devices, processing, images
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
@@ -91,8 +93,8 @@ def single_sample_to_image(sample):
     return Image.fromarray(x_sample)
 
 
-def sample_to_image(samples):
-    return single_sample_to_image(samples[0])
+def sample_to_image(samples, index=0):
+    return single_sample_to_image(samples[index])
 
 
 def samples_to_image_grid(samples):
@@ -205,17 +207,22 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None
         self.nmask = p.nmask if hasattr(p, 'nmask') else None
 
+
+    def adjust_steps_if_invalid(self, p, num_steps):
+        if  (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
+            valid_step = 999 / (1000 // num_steps)
+            if valid_step == floor(valid_step):
+                return int(valid_step) + 1
+        
+        return num_steps
+
+
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
-
+        steps = self.adjust_steps_if_invalid(p, steps)
         self.initialize(p)
 
-        # existing code fails with certain step counts, like 9
-        try:
-            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-        except Exception:
-            self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-
+        self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
 
         self.init_latent = x
@@ -239,18 +246,14 @@ class VanillaStableDiffusionSampler:
         self.last_latent = x
         self.step = 0
 
-        steps = steps or p.steps
+        steps = self.adjust_steps_if_invalid(p, steps or p.steps)
 
         # Wrap the conditioning models with additional image conditioning for inpainting model
         if image_conditioning is not None:
             conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
 
-        # existing code fails with certain step counts, like 9
-        try:
-            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.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])
+        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])
 
         return samples_ddim
 
@@ -278,6 +281,12 @@ class CFGDenoiser(torch.nn.Module):
         image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
+        cfg_denoiser_callback(denoiser_params)
+        x_in = denoiser_params.x
+        image_cond_in = denoiser_params.image_cond
+        sigma_in = denoiser_params.sigma
+
         if tensor.shape[1] == uncond.shape[1]:
             cond_in = torch.cat([tensor, uncond])
 

modules/sd_vae.py

@@ -0,0 +1,207 @@
+import torch
+import os
+from collections import namedtuple
+from modules import shared, devices, script_callbacks
+from modules.paths import models_path
+import glob
+
+
+model_dir = "Stable-diffusion"
+model_path = os.path.abspath(os.path.join(models_path, model_dir))
+vae_dir = "VAE"
+vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
+
+
+vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+
+
+default_vae_dict = {"auto": "auto", "None": "None"}
+default_vae_list = ["auto", "None"]
+
+
+default_vae_values = [default_vae_dict[x] for x in default_vae_list]
+vae_dict = dict(default_vae_dict)
+vae_list = list(default_vae_list)
+first_load = True
+
+
+base_vae = None
+loaded_vae_file = None
+checkpoint_info = None
+
+
+def get_base_vae(model):
+    if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
+        return base_vae
+    return None
+
+
+def store_base_vae(model):
+    global base_vae, checkpoint_info
+    if checkpoint_info != model.sd_checkpoint_info:
+        base_vae = model.first_stage_model.state_dict().copy()
+        checkpoint_info = model.sd_checkpoint_info
+
+
+def delete_base_vae():
+    global base_vae, checkpoint_info
+    base_vae = None
+    checkpoint_info = None
+
+
+def restore_base_vae(model):
+    global base_vae, checkpoint_info
+    if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
+        load_vae_dict(model, base_vae)
+    delete_base_vae()
+
+
+def get_filename(filepath):
+    return os.path.splitext(os.path.basename(filepath))[0]
+
+
+def refresh_vae_list(vae_path=vae_path, model_path=model_path):
+    global vae_dict, vae_list
+    res = {}
+    candidates = [
+        *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
+        *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True)
+    ]
+    if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
+        candidates.append(shared.cmd_opts.vae_path)
+    for filepath in candidates:
+        name = get_filename(filepath)
+        res[name] = filepath
+    vae_list.clear()
+    vae_list.extend(default_vae_list)
+    vae_list.extend(list(res.keys()))
+    vae_dict.clear()
+    vae_dict.update(res)
+    vae_dict.update(default_vae_dict)
+    return vae_list
+
+
+def resolve_vae(checkpoint_file, vae_file="auto"):
+    global first_load, vae_dict, vae_list
+
+    # if vae_file argument is provided, it takes priority, but not saved
+    if vae_file and vae_file not in default_vae_list:
+        if not os.path.isfile(vae_file):
+            vae_file = "auto"
+            print("VAE provided as function argument doesn't exist")
+    # for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
+    if first_load and shared.cmd_opts.vae_path is not None:
+        if os.path.isfile(shared.cmd_opts.vae_path):
+            vae_file = shared.cmd_opts.vae_path
+            shared.opts.data['sd_vae'] = get_filename(vae_file)
+        else:
+            print("VAE provided as command line argument doesn't exist")
+    # else, we load from settings
+    if vae_file == "auto" and shared.opts.sd_vae is not None:
+        # if saved VAE settings isn't recognized, fallback to auto
+        vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
+        # if VAE selected but not found, fallback to auto
+        if vae_file not in default_vae_values and not os.path.isfile(vae_file):
+            vae_file = "auto"
+            print("Selected VAE doesn't exist")
+    # vae-path cmd arg takes priority for auto
+    if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
+        if os.path.isfile(shared.cmd_opts.vae_path):
+            vae_file = shared.cmd_opts.vae_path
+            print("Using VAE provided as command line argument")
+    # if still not found, try look for ".vae.pt" beside model
+    model_path = os.path.splitext(checkpoint_file)[0]
+    if vae_file == "auto":
+        vae_file_try = model_path + ".vae.pt"
+        if os.path.isfile(vae_file_try):
+            vae_file = vae_file_try
+            print("Using VAE found beside selected model")
+    # if still not found, try look for ".vae.ckpt" beside model
+    if vae_file == "auto":
+        vae_file_try = model_path + ".vae.ckpt"
+        if os.path.isfile(vae_file_try):
+            vae_file = vae_file_try
+            print("Using VAE found beside selected model")
+    # No more fallbacks for auto
+    if vae_file == "auto":
+        vae_file = None
+    # Last check, just because
+    if vae_file and not os.path.exists(vae_file):
+        vae_file = None
+
+    return vae_file
+
+
+def load_vae(model, vae_file=None):
+    global first_load, vae_dict, vae_list, loaded_vae_file
+    # save_settings = False
+
+    if vae_file:
+        print(f"Loading VAE weights from: {vae_file}")
+        vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
+        vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
+        load_vae_dict(model, vae_dict_1)
+
+        # If vae used is not in dict, update it
+        # It will be removed on refresh though
+        vae_opt = get_filename(vae_file)
+        if vae_opt not in vae_dict:
+            vae_dict[vae_opt] = vae_file
+            vae_list.append(vae_opt)
+
+    loaded_vae_file = vae_file
+
+    """
+    # Save current VAE to VAE settings, maybe? will it work?
+    if save_settings:
+        if vae_file is None:
+            vae_opt = "None"
+
+        # shared.opts.sd_vae = vae_opt
+    """
+
+    first_load = False
+
+
+# don't call this from outside
+def load_vae_dict(model, vae_dict_1=None):
+    if vae_dict_1:
+        store_base_vae(model)
+        model.first_stage_model.load_state_dict(vae_dict_1)
+    else:
+        restore_base_vae()
+    model.first_stage_model.to(devices.dtype_vae)
+
+
+def reload_vae_weights(sd_model=None, vae_file="auto"):
+    from modules import lowvram, devices, sd_hijack
+
+    if not sd_model:
+        sd_model = shared.sd_model
+
+    checkpoint_info = sd_model.sd_checkpoint_info
+    checkpoint_file = checkpoint_info.filename
+    vae_file = resolve_vae(checkpoint_file, vae_file=vae_file)
+
+    if loaded_vae_file == vae_file:
+        return
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.send_everything_to_cpu()
+    else:
+        sd_model.to(devices.cpu)
+
+    sd_hijack.model_hijack.undo_hijack(sd_model)
+
+    load_vae(sd_model, vae_file)
+
+    sd_hijack.model_hijack.hijack(sd_model)
+    script_callbacks.model_loaded_callback(sd_model)
+
+    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+        sd_model.to(devices.device)
+
+    print(f"VAE Weights loaded.")
+    return sd_model

modules/shared.py

@@ -4,6 +4,7 @@ import json
 import os
 import sys
 from collections import OrderedDict
+import time
 
 import gradio as gr
 import tqdm
@@ -14,7 +15,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models, localization
+from modules import sd_samplers, sd_models, localization, sd_vae
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -40,7 +41,7 @@ parser.add_argument("--lowram", action='store_true', help="load stable diffusion
 parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 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("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
 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'))
@@ -51,6 +52,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
 parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
+parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
 parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
@@ -82,9 +84,10 @@ parser.add_argument("--api", action='store_true', help="use api=True to launch t
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
+parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
 
 cmd_opts = parser.parse_args()
-restricted_opts = [
+restricted_opts = {
     "samples_filename_pattern",
     "directories_filename_pattern",
     "outdir_samples",
@@ -94,7 +97,9 @@ restricted_opts = [
     "outdir_grids",
     "outdir_txt2img_grids",
     "outdir_save",
-]
+}
+
+cmd_opts.disable_extension_access = cmd_opts.share or cmd_opts.listen
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, 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', 'swinir', 'esrgan', 'scunet', 'codeformer'])
@@ -131,6 +136,8 @@ class State:
     current_image = None
     current_image_sampling_step = 0
     textinfo = None
+    time_start = None
+    need_restart = False
 
     def skip(self):
         self.skipped = True
@@ -143,8 +150,52 @@ class State:
         self.sampling_step = 0
         self.current_image_sampling_step = 0
 
-    def get_job_timestamp(self):
-        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
+    def dict(self):
+        obj = {
+            "skipped": self.skipped,
+            "interrupted": self.skipped,
+            "job": self.job,
+            "job_count": self.job_count,
+            "job_no": self.job_no,
+            "sampling_step": self.sampling_step,
+            "sampling_steps": self.sampling_steps,
+        }
+
+        return obj
+
+    def begin(self):
+        self.sampling_step = 0
+        self.job_count = -1
+        self.job_no = 0
+        self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+        self.current_latent = None
+        self.current_image = None
+        self.current_image_sampling_step = 0
+        self.skipped = False
+        self.interrupted = False
+        self.textinfo = None
+        self.time_start = time.time()
+
+        devices.torch_gc()
+
+    def end(self):
+        self.job = ""
+        self.job_count = 0
+
+        devices.torch_gc()
+
+    """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
+    def set_current_image(self):
+        if not parallel_processing_allowed:
+            return
+
+        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and self.current_latent is not None:
+            if opts.show_progress_grid:
+                self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
+            else:
+                self.current_image = sd_samplers.sample_to_image(self.current_latent)
+
+            self.current_image_sampling_step = self.sampling_step
 
 
 state = State()
@@ -204,6 +255,8 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
     "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
     "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"),
     "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
 
@@ -255,20 +308,22 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
     "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
+    "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
+    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
@@ -303,6 +358,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "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."),
+    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "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"),
@@ -322,6 +378,12 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
+options_templates.update(options_section((None, "Hidden options"), {
+    "disabled_extensions": OptionInfo([], "Disable those extensions"),
+}))
+
+options_templates.update()
+
 
 class Options:
     data = None
@@ -333,8 +395,9 @@ class Options:
 
     def __setattr__(self, key, value):
         if self.data is not None:
-            if key in self.data:
+            if key in self.data or key in self.data_labels:
                 self.data[key] = value
+                return
 
         return super(Options, self).__setattr__(key, value)
 
@@ -375,11 +438,12 @@ class Options:
         if bad_settings > 0:
             print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
 
-    def onchange(self, key, func):
+    def onchange(self, key, func, call=True):
         item = self.data_labels.get(key)
         item.onchange = func
 
-        func()
+        if call:
+            func()
 
     def dumpjson(self):
         d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
@@ -449,3 +513,8 @@ total_tqdm = TotalTQDM()
 
 mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
 mem_mon.start()
+
+
+def listfiles(dirname):
+    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
+    return [file for file in filenames if os.path.isfile(file)]

modules/textual_inversion/dataset.py

@@ -42,6 +42,8 @@ class PersonalizedBase(Dataset):
         self.lines = lines
 
         assert data_root, 'dataset directory not specified'
+        assert os.path.isdir(data_root), "Dataset directory doesn't exist"
+        assert os.listdir(data_root), "Dataset directory is empty"
 
         cond_model = shared.sd_model.cond_stage_model
 
@@ -86,12 +88,12 @@ class PersonalizedBase(Dataset):
         assert len(self.dataset) > 0, "No images have been found in the dataset."
         self.length = len(self.dataset) * repeats // batch_size
 
-        self.initial_indexes = np.arange(len(self.dataset))
+        self.dataset_length = len(self.dataset)
         self.indexes = None
         self.shuffle()
 
     def shuffle(self):
-        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()]
+        self.indexes = np.random.permutation(self.dataset_length)
 
     def create_text(self, filename_text):
         text = random.choice(self.lines)

modules/textual_inversion/learn_schedule.py

@@ -4,30 +4,37 @@ import tqdm
 class LearnScheduleIterator:
     def __init__(self, learn_rate, max_steps, cur_step=0):
         """
-        specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000
+        specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000
         """
 
         pairs = learn_rate.split(',')
         self.rates = []
         self.it = 0
         self.maxit = 0
-        for i, pair in enumerate(pairs):
-            tmp = pair.split(':')
-            if len(tmp) == 2:
-                step = int(tmp[1])
-                if step > cur_step:
-                    self.rates.append((float(tmp[0]), min(step, max_steps)))
-                    self.maxit += 1
-                    if step > max_steps:
+        try:
+            for i, pair in enumerate(pairs):
+                if not pair.strip():
+                    continue
+                tmp = pair.split(':')
+                if len(tmp) == 2:
+                    step = int(tmp[1])
+                    if step > cur_step:
+                        self.rates.append((float(tmp[0]), min(step, max_steps)))
+                        self.maxit += 1
+                        if step > max_steps:
+                            return
+                    elif step == -1:
+                        self.rates.append((float(tmp[0]), max_steps))
+                        self.maxit += 1
                         return
-                elif step == -1:
+                else:
                     self.rates.append((float(tmp[0]), max_steps))
                     self.maxit += 1
                     return
-            else:
-                self.rates.append((float(tmp[0]), max_steps))
-                self.maxit += 1
-                return
+            assert self.rates
+        except (ValueError, AssertionError):
+            raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.')
+
 
     def __iter__(self):
         return self
@@ -52,7 +59,7 @@ class LearnRateScheduler:
         self.finished = False
 
     def apply(self, optimizer, step_number):
-        if step_number <= self.end_step:
+        if step_number < self.end_step:
             return
 
         try:

modules/textual_inversion/textual_inversion.py

@@ -10,7 +10,7 @@ import csv
 
 from PIL import Image, PngImagePlugin
 
-from modules import shared, devices, sd_hijack, processing, sd_models
+from modules import shared, devices, sd_hijack, processing, sd_models, images
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -119,7 +119,7 @@ class EmbeddingDatabase:
             vec = emb.detach().to(devices.device, dtype=torch.float32)
             embedding = Embedding(vec, name)
             embedding.step = data.get('step', None)
-            embedding.sd_checkpoint = data.get('hash', None)
+            embedding.sd_checkpoint = data.get('sd_checkpoint', None)
             embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
             self.register_embedding(embedding, shared.sd_model)
 
@@ -167,6 +167,8 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     for i in range(num_vectors_per_token):
         vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
 
+    # Remove illegal characters from name.
+    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
     fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
     if not overwrite_old:
         assert not os.path.exists(fn), f"file {fn} already exists"
@@ -182,9 +184,8 @@ def write_loss(log_directory, filename, step, epoch_len, values):
     if shared.opts.training_write_csv_every == 0:
         return
 
-    if step % shared.opts.training_write_csv_every != 0:
+    if (step + 1) % shared.opts.training_write_csv_every != 0:
         return
-
     write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
 
     with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
@@ -194,18 +195,39 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             csv_writer.writeheader()
 
         epoch = step // epoch_len
-        epoch_step = step - epoch * epoch_len
+        epoch_step = step % epoch_len 
 
         csv_writer.writerow({
             "step": step + 1,
-            "epoch": epoch + 1,
+            "epoch": epoch,
             "epoch_step": epoch_step + 1,
             **values,
         })
 
+def validate_train_inputs(model_name, learn_rate, batch_size, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
+    assert model_name, f"{name} not selected"
+    assert learn_rate, "Learning rate is empty or 0"
+    assert isinstance(batch_size, int), "Batch size must be integer"
+    assert batch_size > 0, "Batch size must be positive"
+    assert data_root, "Dataset directory is empty"
+    assert os.path.isdir(data_root), "Dataset directory doesn't exist"
+    assert os.listdir(data_root), "Dataset directory is empty"
+    assert template_file, "Prompt template file is empty"
+    assert os.path.isfile(template_file), "Prompt template file doesn't exist"
+    assert steps, "Max steps is empty or 0"
+    assert isinstance(steps, int), "Max steps must be integer"
+    assert steps > 0 , "Max steps must be positive"
+    assert isinstance(save_model_every, int), "Save {name} must be integer"
+    assert save_model_every >= 0 , "Save {name} must be positive or 0"
+    assert isinstance(create_image_every, int), "Create image must be integer"
+    assert create_image_every >= 0 , "Create image must be positive or 0"
+    if save_model_every or create_image_every:
+        assert log_directory, "Log directory is empty"
 
 def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
-    assert embedding_name, 'embedding not selected'
+    save_embedding_every = save_embedding_every or 0
+    create_image_every = create_image_every or 0
+    validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
 
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
@@ -213,6 +235,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
 
     log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
+    unload = shared.opts.unload_models_when_training
 
     if save_embedding_every > 0:
         embedding_dir = os.path.join(log_directory, "embeddings")
@@ -231,31 +254,38 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         os.makedirs(images_embeds_dir, exist_ok=True)
     else:
         images_embeds_dir = None
-        
-    cond_model = shared.sd_model.cond_stage_model
 
-    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=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+    cond_model = shared.sd_model.cond_stage_model
 
     hijack = sd_hijack.model_hijack
 
     embedding = hijack.embedding_db.word_embeddings[embedding_name]
+    checkpoint = sd_models.select_checkpoint()
+
+    ititial_step = embedding.step or 0
+    if ititial_step >= steps:
+        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        return embedding, filename
+
+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+
+    # dataset loading may take a while, so input validations and early returns should be done before this
+    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=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+    if unload:
+        shared.sd_model.first_stage_model.to(devices.cpu)
+
     embedding.vec.requires_grad = True
+    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
     losses = torch.zeros((32,))
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
+    forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
 
-    ititial_step = embedding.step or 0
-    if ititial_step > steps:
-        return embedding, filename
-
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
-
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, entries in pbar:
         embedding.step = i + ititial_step
@@ -279,15 +309,18 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             loss.backward()
             optimizer.step()
 
+        steps_done = embedding.step + 1
 
         epoch_num = embedding.step // len(ds)
-        epoch_step = embedding.step - (epoch_num * len(ds)) + 1
+        epoch_step = embedding.step % len(ds)
 
-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
 
-        if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
-            last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
-            embedding.save(last_saved_file)
+        if embedding_dir is not None and steps_done % save_embedding_every == 0:
+            # Before saving, change name to match current checkpoint.
+            embedding_name_every = f'{embedding_name}-{steps_done}'
+            last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
+            save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
             embedding_yet_to_be_embedded = True
 
         write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
@@ -295,8 +328,11 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             "learn_rate": scheduler.learn_rate
         })
 
-        if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
-            last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{embedding_name}-{steps_done}'
+            last_saved_image = os.path.join(images_dir, forced_filename)
+
+            shared.sd_model.first_stage_model.to(devices.device)
 
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
@@ -325,11 +361,14 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             processed = processing.process_images(p)
             image = processed.images[0]
 
+            if unload:
+                shared.sd_model.first_stage_model.to(devices.cpu)
+
             shared.state.current_image = image
 
             if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
 
-                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
+                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
 
                 info = PngImagePlugin.PngInfo()
                 data = torch.load(last_saved_file)
@@ -345,7 +384,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}v {}s'.format(vectorSize, embedding.step)
+                footer_right = '{}v {}s'.format(vectorSize, steps_done)
 
                 captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                 captioned_image = insert_image_data_embed(captioned_image, data)
@@ -353,8 +392,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
                 embedding_yet_to_be_embedded = False
 
-            image.save(last_saved_image)
-
+            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
             last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = embedding.step
@@ -369,11 +407,27 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
 
-    checkpoint = sd_models.select_checkpoint()
-
-    embedding.sd_checkpoint = checkpoint.hash
-    embedding.sd_checkpoint_name = checkpoint.model_name
-    embedding.cached_checksum = None
-    embedding.save(filename)
+    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+    save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+    shared.sd_model.first_stage_model.to(devices.device)
 
     return embedding, filename
+
+def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True):
+    old_embedding_name = embedding.name
+    old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
+    old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
+    old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None
+    try:
+        embedding.sd_checkpoint = checkpoint.hash
+        embedding.sd_checkpoint_name = checkpoint.model_name
+        if remove_cached_checksum:
+            embedding.cached_checksum = None
+        embedding.name = embedding_name
+        embedding.save(filename)
+    except:
+        embedding.sd_checkpoint = old_sd_checkpoint
+        embedding.sd_checkpoint_name = old_sd_checkpoint_name
+        embedding.name = old_embedding_name
+        embedding.cached_checksum = old_cached_checksum
+        raise

modules/textual_inversion/ui.py

@@ -25,8 +25,10 @@ def train_embedding(*args):
 
     assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
 
+    apply_optimizations = shared.opts.training_xattention_optimizations
     try:
-        sd_hijack.undo_optimizations()
+        if not apply_optimizations:
+            sd_hijack.undo_optimizations()
 
         embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
 
@@ -38,5 +40,6 @@ Embedding saved to {html.escape(filename)}
     except Exception:
         raise
     finally:
-        sd_hijack.apply_optimizations()
+        if not apply_optimizations:
+            sd_hijack.apply_optimizations()
 

modules/txt2img.py

@@ -47,6 +47,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     if processed is None:
         processed = process_images(p)
 
+    p.close()
+
     shared.total_tqdm.clear()
 
     generation_info_js = processed.js()

modules/ui.py

@@ -1,6 +1,4 @@
-import base64
 import html
-import io
 import json
 import math
 import mimetypes
@@ -18,15 +16,10 @@ import gradio as gr
 import gradio.routes
 import gradio.utils
 import numpy as np
-import piexif
-import torch
 from PIL import Image, PngImagePlugin
 
-import gradio as gr
-import gradio.utils
-import gradio.routes
 
-from modules import sd_hijack, sd_models, localization, script_callbacks
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
 from modules.paths import script_path
 
 from modules.shared import opts, cmd_opts, restricted_opts
@@ -35,7 +28,7 @@ if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
 
 import modules.codeformer_model
-import modules.generation_parameters_copypaste
+import modules.generation_parameters_copypaste as parameters_copypaste
 import modules.gfpgan_model
 import modules.hypernetworks.ui
 import modules.ldsr_model
@@ -49,13 +42,11 @@ from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
-txt2img_paste_fields = []
-img2img_paste_fields = []
-
 
 if not cmd_opts.share and not cmd_opts.listen:
     # fix gradio phoning home
@@ -98,37 +89,11 @@ def plaintext_to_html(text):
     text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
     return text
 
-
-def image_from_url_text(filedata):
-    if type(filedata) == dict and filedata["is_file"]:
-        filename = filedata["name"]
-        tempdir = os.path.normpath(tempfile.gettempdir())
-        normfn = os.path.normpath(filename)
-        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
-
-        return Image.open(filename)
-
-    if type(filedata) == list:
-        if len(filedata) == 0:
-            return None
-
-        filedata = filedata[0]
-
-    if filedata.startswith("data:image/png;base64,"):
-        filedata = filedata[len("data:image/png;base64,"):]
-
-    filedata = base64.decodebytes(filedata.encode('utf-8'))
-    image = Image.open(io.BytesIO(filedata))
-    return image
-
-
 def send_gradio_gallery_to_image(x):
     if len(x) == 0:
         return None
-
     return image_from_url_text(x[0])
 
-
 def save_files(js_data, images, do_make_zip, index):
     import csv
     filenames = []
@@ -192,7 +157,6 @@ def save_files(js_data, images, do_make_zip, index):
 
     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
-
 def save_pil_to_file(pil_image, dir=None):
     use_metadata = False
     metadata = PngImagePlugin.PngInfo()
@@ -313,15 +277,7 @@ def check_progress_call(id_part):
     preview_visibility = gr_show(False)
 
     if opts.show_progress_every_n_steps > 0:
-        if shared.parallel_processing_allowed:
-
-            if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:
-                if opts.show_progress_grid:
-                    shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent)
-                else:
-                    shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
-                shared.state.current_image_sampling_step = shared.state.sampling_step
-
+        shared.state.set_current_image()
         image = shared.state.current_image
 
         if image is None:
@@ -626,10 +582,90 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
     return refresh_button
 
 
+def create_output_panel(tabname, outdir):
+    def open_folder(f):
+        if not os.path.exists(f):
+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+            return
+        elif not os.path.isdir(f):
+            print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+            return
+
+        if not shared.cmd_opts.hide_ui_dir_config:
+            path = os.path.normpath(f)
+            if platform.system() == "Windows":
+                os.startfile(path)
+            elif platform.system() == "Darwin":
+                sp.Popen(["open", path])
+            else:
+                sp.Popen(["xdg-open", path])
+
+    with gr.Column(variant='panel'):
+            with gr.Group():
+                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+
+            generation_info = None
+            with gr.Column():
+                with gr.Row():
+                    if tabname != "extras":
+                        save = gr.Button('Save', elem_id=f'save_{tabname}')
+
+                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
+
+                open_folder_button.click(
+                    fn=lambda: open_folder(opts.outdir_samples or outdir),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                if tabname != "extras":
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
+
+                        save.click(
+                            fn=wrap_gradio_call(save_files),
+                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+                            inputs=[
+                                generation_info,
+                                result_gallery,
+                                do_make_zip,
+                                html_info,
+                            ],
+                            outputs=[
+                                download_files,
+                                html_info,
+                                html_info,
+                                html_info,
+                            ]
+                        )
+                else:
+                    html_info_x = gr.HTML()
+                    html_info = gr.HTML()
+                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+
+
 def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
+    reload_javascript()
+
+    parameters_copypaste.reset()
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
@@ -675,30 +711,8 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
-            with gr.Column(variant='panel'):
-
-                with gr.Group():
-                    txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
-                    txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
-
-                with gr.Column():
-                    with gr.Row():
-                        save = gr.Button('Save')
-                        send_to_img2img = gr.Button('Send to img2img')
-                        send_to_inpaint = gr.Button('Send to inpaint')
-                        send_to_extras = gr.Button('Send to extras')
-                        button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                        open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
-
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
+            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -756,23 +770,6 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[hr_options],
             )
 
-            save.click(
-                fn=wrap_gradio_call(save_files),
-                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                inputs=[
-                    generation_info,
-                    txt2img_gallery,
-                    do_make_zip,
-                    html_info,
-                ],
-                outputs=[
-                    download_files,
-                    html_info,
-                    html_info,
-                    html_info,
-                ]
-            )
-
             roll.click(
                 fn=roll_artist,
                 _js="update_txt2img_tokens",
@@ -784,7 +781,6 @@ def create_ui(wrap_gradio_gpu_call):
                 ]
             )
 
-            global txt2img_paste_fields 
             txt2img_paste_fields = [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
@@ -807,6 +803,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_height, "First pass size-2"),
                 *modules.scripts.scripts_txt2img.infotext_fields
             ]
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
 
             txt2img_preview_params = [
                 txt2img_prompt,
@@ -893,30 +890,8 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
 
-            with gr.Column(variant='panel'):
-
-                with gr.Group():
-                    img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
-                    img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4)
-
-                with gr.Column():
-                    with gr.Row():
-                        save = gr.Button('Save')
-                        img2img_send_to_img2img = gr.Button('Send to img2img')
-                        img2img_send_to_inpaint = gr.Button('Send to inpaint')
-                        img2img_send_to_extras = gr.Button('Send to extras')
-                        button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                        open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
-
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
+            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
+            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -1003,25 +978,9 @@ def create_ui(wrap_gradio_gpu_call):
                     fn=interrogate_deepbooru,
                     inputs=[init_img],
                     outputs=[img2img_prompt],
-                )
-
-            save.click(
-                fn=wrap_gradio_call(save_files),
-                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                inputs=[
-                    generation_info,
-                    img2img_gallery,
-                    do_make_zip,
-                    html_info,
-                ],
-                outputs=[
-                    download_files,
-                    html_info,
-                    html_info,
-                    html_info,
-                ]
             )
 
+
             roll.click(
                 fn=roll_artist,
                 _js="update_img2img_tokens",
@@ -1055,7 +1014,8 @@ def create_ui(wrap_gradio_gpu_call):
                     outputs=[prompt, negative_prompt, style1, style2],
                 )
 
-            global img2img_paste_fields
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+
             img2img_paste_fields = [
                 (img2img_prompt, "Prompt"),
                 (img2img_negative_prompt, "Negative prompt"),
@@ -1074,7 +1034,8 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 *modules.scripts.scripts_img2img.infotext_fields
             ]
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
@@ -1087,26 +1048,24 @@ def create_ui(wrap_gradio_gpu_call):
                         image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
 
                     with gr.TabItem('Batch from Directory'):
-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs,
-                            placeholder="A directory on the same machine where the server is running."
-                        )
-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs,
-                            placeholder="Leave blank to save images to the default path."
-                        )
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
                         show_extras_results = gr.Checkbox(label='Show result images', value=True)
 
+                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
                 with gr.Tabs(elem_id="extras_resize_mode"):
                     with gr.TabItem('Scale by'):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
                     with gr.TabItem('Scale to'):
                         with gr.Group():
                             with gr.Row():
                                 upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
                                 upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-                
+
                 with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") 
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
@@ -1119,17 +1078,10 @@ def create_ui(wrap_gradio_gpu_call):
                     codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
                     codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
 
-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
-            with gr.Column(variant='panel'):
-                result_images = gr.Gallery(label="Result", show_label=False)
-                html_info_x = gr.HTML()
-                html_info = gr.HTML()
-                extras_send_to_img2img = gr.Button('Send to img2img')
-                extras_send_to_inpaint = gr.Button('Send to inpaint')
-                button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''
-                open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
+                with gr.Group():
+                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
 
+            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
             fn=wrap_gradio_gpu_call(modules.extras.run_extras),
@@ -1152,6 +1104,7 @@ def create_ui(wrap_gradio_gpu_call):
                 extras_upscaler_1,
                 extras_upscaler_2,
                 extras_upscaler_2_visibility,
+                upscale_before_face_fix,
             ],
             outputs=[
                 result_images,
@@ -1159,19 +1112,11 @@ def create_ui(wrap_gradio_gpu_call):
                 html_info,
             ]
         )
+        parameters_copypaste.add_paste_fields("extras", extras_image, None)
 
-        extras_send_to_img2img.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_img2img",
-            inputs=[result_images],
-            outputs=[init_img],
-        )
-
-        extras_send_to_inpaint.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_inpaint",
-            inputs=[result_images],
-            outputs=[init_img_with_mask],
+        extras_image.change(
+            fn=modules.extras.clear_cache,
+            inputs=[], outputs=[]
         )
 
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
@@ -1183,17 +1128,16 @@ def create_ui(wrap_gradio_gpu_call):
                 html = gr.HTML()
                 generation_info = gr.Textbox(visible=False)
                 html2 = gr.HTML()
-
                 with gr.Row():
-                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+                parameters_copypaste.bind_buttons(buttons, image, generation_info)
 
         image.change(
             fn=wrap_gradio_call(modules.extras.run_pnginfo),
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
-   
+
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
@@ -1238,8 +1182,8 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Normal is default, for experiments, relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
                     overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
@@ -1491,28 +1435,6 @@ def create_ui(wrap_gradio_gpu_call):
     script_callbacks.ui_settings_callback()
     opts.reorder()
 
-    def open_folder(f):
-        if not os.path.exists(f):
-            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
-            return
-        elif not os.path.isdir(f):
-            print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
-            return
-
-        if not shared.cmd_opts.hide_ui_dir_config:
-            path = os.path.normpath(f)
-            if platform.system() == "Windows":
-                os.startfile(path)
-            elif platform.system() == "Darwin":
-                sp.Popen(["open", path])
-            else:
-                sp.Popen(["xdg-open", path])
-
     def run_settings(*args):
         changed = 0
 
@@ -1584,8 +1506,9 @@ Requested path was: {f}
         column = None
         with gr.Row(elem_id="settings").style(equal_height=False):
             for i, (k, item) in enumerate(opts.data_labels.items()):
+                section_must_be_skipped = item.section[0] is None
 
-                if previous_section != item.section:
+                if previous_section != item.section and not section_must_be_skipped:
                     if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
                         if column is not None:
                             column.__exit__()
@@ -1604,6 +1527,8 @@ Requested path was: {f}
                 if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
                     quicksettings_list.append((i, k, item))
                     components.append(dummy_component)
+                elif section_must_be_skipped:
+                    components.append(dummy_component)
                 else:
                     component = create_setting_component(k)
                     component_dict[k] = component
@@ -1639,19 +1564,19 @@ Requested path was: {f}
         reload_script_bodies.click(
             fn=reload_scripts,
             inputs=[],
-            outputs=[],
-            _js='function(){}'
+            outputs=[]
         )
 
         def request_restart():
             shared.state.interrupt()
-            settings_interface.gradio_ref.do_restart = True
+            shared.state.need_restart = True
 
         restart_gradio.click(
+
             fn=request_restart,
             inputs=[],
             outputs=[],
-            _js='function(){restart_reload()}'
+            _js='restart_reload'
         )
 
         if column is not None:
@@ -1666,10 +1591,6 @@ Requested path was: {f}
         (train_interface, "Train", "ti"),
     ]
 
-    interfaces += script_callbacks.ui_tabs_callback()
-
-    interfaces += [(settings_interface, "Settings", "settings")]
-
     css = ""
 
     for cssfile in modules.scripts.list_files_with_name("style.css"):
@@ -1686,13 +1607,20 @@ Requested path was: {f}
     if not cmd_opts.no_progressbar_hiding:
         css += css_hide_progressbar
 
+    interfaces += script_callbacks.ui_tabs_callback()
+    interfaces += [(settings_interface, "Settings", "settings")]
+
+    extensions_interface = ui_extensions.create_ui()
+    interfaces += [(extensions_interface, "Extensions", "extensions")]
+
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings"):
             for i, k, item in quicksettings_list:
                 component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
-        settings_interface.gradio_ref = demo
+        parameters_copypaste.integrate_settings_paste_fields(component_dict)
+        parameters_copypaste.run_bind()
 
         with gr.Tabs(elem_id="tabs") as tabs:
             for interface, label, ifid in interfaces:
@@ -1747,85 +1675,6 @@ Requested path was: {f}
                 component_dict['sd_model_checkpoint'],
             ]
         )
-        paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2']
-        txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names]
-        img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names]
-        send_to_img2img.click(
-            fn=lambda img, *args: (image_from_url_text(img),*args),
-            _js="(gallery, ...args) => [extract_image_from_gallery_img2img(gallery), ...args]",
-            inputs=[txt2img_gallery] + txt2img_fields,
-            outputs=[init_img] + img2img_fields,
-        )
-
-        send_to_inpaint.click(
-            fn=lambda x, *args: (image_from_url_text(x), *args),
-            _js="(gallery, ...args) => [extract_image_from_gallery_inpaint(gallery), ...args]",
-            inputs=[txt2img_gallery] + txt2img_fields,
-            outputs=[init_img_with_mask] + img2img_fields,
-        )
-
-        img2img_send_to_img2img.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_img2img",
-            inputs=[img2img_gallery],
-            outputs=[init_img],
-        )
-
-        img2img_send_to_inpaint.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_inpaint",
-            inputs=[img2img_gallery],
-            outputs=[init_img_with_mask],
-        )
-
-        send_to_extras.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_extras",
-            inputs=[txt2img_gallery],
-            outputs=[extras_image],
-        )
-
-        open_txt2img_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_txt2img_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        open_img2img_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_img2img_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        open_extras_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_extras_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        img2img_send_to_extras.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_extras",
-            inputs=[img2img_gallery],
-            outputs=[extras_image],
-        )
-
-        settings_map = {
-            'sd_hypernetwork': 'Hypernet',
-            'CLIP_stop_at_last_layers': 'Clip skip',
-            'sd_model_checkpoint': 'Model hash',
-        }
-
-        settings_paste_fields = [
-            (component_dict[k], lambda d, k=k, v=v: apply_setting(k, d.get(v, None)))
-            for k, v in settings_map.items()
-        ]
-
-        modules.generation_parameters_copypaste.connect_paste(txt2img_paste, txt2img_paste_fields + settings_paste_fields, txt2img_prompt)
-        modules.generation_parameters_copypaste.connect_paste(img2img_paste, img2img_paste_fields + settings_paste_fields, img2img_prompt)
-
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_txt2img')
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_img2img_img2img')
 
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
@@ -1845,7 +1694,7 @@ Requested path was: {f}
         def apply_field(obj, field, condition=None, init_field=None):
             key = path + "/" + field
 
-            if getattr(obj,'custom_script_source',None) is not None:
+            if getattr(obj, 'custom_script_source', None) is not None:
               key = 'customscript/' + obj.custom_script_source + '/' + key
 
             if getattr(obj, 'do_not_save_to_config', False):
@@ -1905,7 +1754,7 @@ def load_javascript(raw_response):
         javascript = f'<script>{jsfile.read()}</script>'
 
     scripts_list = modules.scripts.list_scripts("javascript", ".js")
-    
+
     for basedir, filename, path in scripts_list:
         with open(path, "r", encoding="utf8") as jsfile:
             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
@@ -1926,4 +1775,3 @@ def load_javascript(raw_response):
 
 
 reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)
-reload_javascript()

modules/ui_extensions.py

@@ -0,0 +1,268 @@
+import json
+import os.path
+import shutil
+import sys
+import time
+import traceback
+
+import git
+
+import gradio as gr
+import html
+
+from modules import extensions, shared, paths
+
+
+available_extensions = {"extensions": []}
+
+
+def check_access():
+    assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
+
+
+def apply_and_restart(disable_list, update_list):
+    check_access()
+
+    disabled = json.loads(disable_list)
+    assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
+
+    update = json.loads(update_list)
+    assert type(update) == list, f"wrong update_list data for apply_and_restart: {update_list}"
+
+    update = set(update)
+
+    for ext in extensions.extensions:
+        if ext.name not in update:
+            continue
+
+        try:
+            ext.pull()
+        except Exception:
+            print(f"Error pulling updates for {ext.name}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    shared.opts.disabled_extensions = disabled
+    shared.opts.save(shared.config_filename)
+
+    shared.state.interrupt()
+    shared.state.need_restart = True
+
+
+def check_updates():
+    check_access()
+
+    for ext in extensions.extensions:
+        if ext.remote is None:
+            continue
+
+        try:
+            ext.check_updates()
+        except Exception:
+            print(f"Error checking updates for {ext.name}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    return extension_table()
+
+
+def extension_table():
+    code = f"""<!-- {time.time()} -->
+    <table id="extensions">
+        <thead>
+            <tr>
+                <th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
+                <th>URL</th>
+                <th><abbr title="Use checkbox to mark the extension for update; it will be updated when you click apply button">Update</abbr></th>
+            </tr>
+        </thead>
+        <tbody>
+    """
+
+    for ext in extensions.extensions:
+        if ext.can_update:
+            ext_status = f"""<label><input class="gr-check-radio gr-checkbox" name="update_{html.escape(ext.name)}" checked="checked" type="checkbox">{html.escape(ext.status)}</label>"""
+        else:
+            ext_status = ext.status
+
+        code += f"""
+            <tr>
+                <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
+                <td><a href="{html.escape(ext.remote or '')}">{html.escape(ext.remote or '')}</a></td>
+                <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
+            </tr>
+    """
+
+    code += """
+        </tbody>
+    </table>
+    """
+
+    return code
+
+
+def normalize_git_url(url):
+    if url is None:
+        return ""
+
+    url = url.replace(".git", "")
+    return url
+
+
+def install_extension_from_url(dirname, url):
+    check_access()
+
+    assert url, 'No URL specified'
+
+    if dirname is None or dirname == "":
+        *parts, last_part = url.split('/')
+        last_part = normalize_git_url(last_part)
+
+        dirname = last_part
+
+    target_dir = os.path.join(extensions.extensions_dir, dirname)
+    assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
+
+    normalized_url = normalize_git_url(url)
+    assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
+
+    tmpdir = os.path.join(paths.script_path, "tmp", dirname)
+
+    try:
+        shutil.rmtree(tmpdir, True)
+
+        repo = git.Repo.clone_from(url, tmpdir)
+        repo.remote().fetch()
+
+        os.rename(tmpdir, target_dir)
+
+        extensions.list_extensions()
+        return [extension_table(), html.escape(f"Installed into {target_dir}. Use Installed tab to restart.")]
+    finally:
+        shutil.rmtree(tmpdir, True)
+
+
+def install_extension_from_index(url):
+    ext_table, message = install_extension_from_url(None, url)
+
+    return refresh_available_extensions_from_data(), ext_table, message
+
+
+def refresh_available_extensions(url):
+    global available_extensions
+
+    import urllib.request
+    with urllib.request.urlopen(url) as response:
+        text = response.read()
+
+    available_extensions = json.loads(text)
+
+    return url, refresh_available_extensions_from_data(), ''
+
+
+def refresh_available_extensions_from_data():
+    extlist = available_extensions["extensions"]
+    installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
+
+    code = f"""<!-- {time.time()} -->
+    <table id="available_extensions">
+        <thead>
+            <tr>
+                <th>Extension</th>
+                <th>Description</th>
+                <th>Action</th>
+            </tr>
+        </thead>
+        <tbody>
+    """
+
+    for ext in extlist:
+        name = ext.get("name", "noname")
+        url = ext.get("url", None)
+        description = ext.get("description", "")
+
+        if url is None:
+            continue
+
+        existing = installed_extension_urls.get(normalize_git_url(url), None)
+
+        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">"""
+
+        code += f"""
+            <tr>
+                <td><a href="{html.escape(url)}">{html.escape(name)}</a></td>
+                <td>{html.escape(description)}</td>
+                <td>{install_code}</td>
+            </tr>
+    """
+
+    code += """
+        </tbody>
+    </table>
+    """
+
+    return code
+
+
+def create_ui():
+    import modules.ui
+
+    with gr.Blocks(analytics_enabled=False) as ui:
+        with gr.Tabs(elem_id="tabs_extensions") as tabs:
+            with gr.TabItem("Installed"):
+
+                with gr.Row():
+                    apply = gr.Button(value="Apply and restart UI", variant="primary")
+                    check = gr.Button(value="Check for updates")
+                    extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
+                    extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
+
+                extensions_table = gr.HTML(lambda: extension_table())
+
+                apply.click(
+                    fn=apply_and_restart,
+                    _js="extensions_apply",
+                    inputs=[extensions_disabled_list, extensions_update_list],
+                    outputs=[],
+                )
+
+                check.click(
+                    fn=check_updates,
+                    _js="extensions_check",
+                    inputs=[],
+                    outputs=[extensions_table],
+                )
+
+            with gr.TabItem("Available"):
+                with gr.Row():
+                    refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
+                    available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/wiki/AUTOMATIC1111/stable-diffusion-webui/Extensions-index.md", label="Extension index URL").style(container=False)
+                    extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
+                    install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
+
+                install_result = gr.HTML()
+                available_extensions_table = gr.HTML()
+
+                refresh_available_extensions_button.click(
+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update()]),
+                    inputs=[available_extensions_index],
+                    outputs=[available_extensions_index, available_extensions_table, install_result],
+                )
+
+                install_extension_button.click(
+                    fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
+                    inputs=[extension_to_install],
+                    outputs=[available_extensions_table, extensions_table, install_result],
+                )
+
+            with gr.TabItem("Install from URL"):
+                install_url = gr.Text(label="URL for extension's git repository")
+                install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
+                install_button = gr.Button(value="Install", variant="primary")
+                install_result = gr.HTML(elem_id="extension_install_result")
+
+                install_button.click(
+                    fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
+                    inputs=[install_dirname, install_url],
+                    outputs=[extensions_table, install_result],
+                )
+
+    return ui

modules/upscaler.py

@@ -10,6 +10,7 @@ import modules.shared
 from modules import modelloader, shared
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST)
 from modules.paths import models_path
 
 
@@ -57,7 +58,7 @@ class Upscaler:
         dest_w = img.width * scale
         dest_h = img.height * scale
         for i in range(3):
-            if img.width >= dest_w and img.height >= dest_h:
+            if img.width > dest_w and img.height > dest_h:
                 break
             img = self.do_upscale(img, selected_model)
         if img.width != dest_w or img.height != dest_h:
@@ -120,3 +121,17 @@ class UpscalerLanczos(Upscaler):
         self.name = "Lanczos"
         self.scalers = [UpscalerData("Lanczos", None, self)]
 
+
+class UpscalerNearest(Upscaler):
+    scalers = []
+
+    def do_upscale(self, img, selected_model=None):
+        return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=NEAREST)
+
+    def load_model(self, _):
+        pass
+
+    def __init__(self, dirname=None):
+        super().__init__(False)
+        self.name = "Nearest"
+        self.scalers = [UpscalerData("Nearest", None, self)]

requirements.txt

@@ -4,7 +4,7 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.5
+gradio==3.8
 invisible-watermark
 numpy
 omegaconf
@@ -12,7 +12,7 @@ opencv-python
 requests
 piexif
 Pillow
-pytorch_lightning
+pytorch_lightning==1.7.7
 realesrgan
 scikit-image>=0.19
 timm==0.4.12
@@ -26,3 +26,4 @@ torchdiffeq
 kornia
 lark
 inflection
+GitPython

requirements_versions.txt

@@ -2,7 +2,7 @@ transformers==4.19.2
 diffusers==0.3.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.5
+gradio==3.8
 numpy==1.23.3
 Pillow==9.2.0
 realesrgan==0.3.0
@@ -23,3 +23,4 @@ torchdiffeq==0.2.3
 kornia==0.6.7
 lark==1.1.2
 inflection==0.5.1
+GitPython==3.1.27

scripts/custom_code.py

@@ -14,7 +14,7 @@ class Script(scripts.Script):
         return cmd_opts.allow_code
 
     def ui(self, is_img2img):
-        code = gr.Textbox(label="Python code", visible=False, lines=1)
+        code = gr.Textbox(label="Python code", lines=1)
 
         return [code]
 

scripts/img2imgalt.py

@@ -166,8 +166,7 @@ class Script(scripts.Script):
         if override_strength:
             p.denoising_strength = 1.0
 
-
-        def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+        def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
             lat = (p.init_latent.cpu().numpy() * 10).astype(int)
 
             same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \

scripts/outpainting_mk_2.py

@@ -132,7 +132,7 @@ class Script(scripts.Script):
         info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>")
 
         pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128)
-        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, visible=False)
+        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8)
         direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'])
         noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0)
         color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05)

scripts/poor_mans_outpainting.py

@@ -22,8 +22,8 @@ class Script(scripts.Script):
             return None
 
         pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128)
-        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
-        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
+        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index")
         direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'])
 
         return [pixels, mask_blur, inpainting_fill, direction]

scripts/prompts_from_file.py

@@ -1,6 +1,7 @@
 import copy
 import math
 import os
+import random
 import sys
 import traceback
 import shlex
@@ -81,32 +82,36 @@ def cmdargs(line):
     return res
 
 
+def load_prompt_file(file):
+    if file is None:
+        lines = []
+    else:
+        lines = [x.strip() for x in file.decode('utf8', errors='ignore').split("\n")]
+
+    return None, "\n".join(lines), gr.update(lines=7)
+
+
 class Script(scripts.Script):
     def title(self):
         return "Prompts from file or textbox"
 
     def ui(self, is_img2img):
-        # This checkbox would look nicer as two tabs, but there are two problems:
-        # 1) There is a bug in Gradio 3.3 that prevents visibility from working on Tabs
-        # 2) Even with Gradio 3.3.1, returning a control (like Tabs) that can't be used as input
-        #    causes a AttributeError: 'Tabs' object has no attribute 'preprocess' assert,
-        #    due to the way Script assumes all controls returned can be used as inputs.
-        # Therefore, there's no good way to use grouping components right now,
-        # so we will use a checkbox! :)
-        checkbox_txt = gr.Checkbox(label="Show Textbox", value=False)
-        file = gr.File(label="File with inputs", type='bytes')
-        prompt_txt = gr.TextArea(label="Prompts")
-        checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
-        return [checkbox_txt, file, prompt_txt]
+        checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False)
+        checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False)
 
-    def on_show(self, checkbox_txt, file, prompt_txt):
-        return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
+        prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1)
+        file = gr.File(label="Upload prompt inputs", type='bytes')
 
-    def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
-        if checkbox_txt:
-            lines = [x.strip() for x in prompt_txt.splitlines()]
-        else:
-            lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
+        file.change(fn=load_prompt_file, inputs=[file], outputs=[file, prompt_txt, prompt_txt])
+
+        # We start at one line. When the text changes, we jump to seven lines, or two lines if no \n.
+        # We don't shrink back to 1, because that causes the control to ignore [enter], and it may
+        # be unclear to the user that shift-enter is needed.
+        prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt])
+        return [checkbox_iterate, checkbox_iterate_batch, prompt_txt]
+
+    def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_txt: str):
+        lines = [x.strip() for x in prompt_txt.splitlines()]
         lines = [x for x in lines if len(x) > 0]
 
         p.do_not_save_grid = True
@@ -134,6 +139,9 @@ class Script(scripts.Script):
             jobs.append(args)
 
         print(f"Will process {len(lines)} lines in {job_count} jobs.")
+        if (checkbox_iterate or checkbox_iterate_batch) and p.seed == -1:
+            p.seed = int(random.randrange(4294967294))
+
         state.job_count = job_count
 
         images = []
@@ -146,5 +154,8 @@ class Script(scripts.Script):
 
             proc = process_images(copy_p)
             images += proc.images
+            
+            if checkbox_iterate:
+                p.seed = p.seed + (p.batch_size * p.n_iter)
 
         return Processed(p, images, p.seed, "")

scripts/sd_upscale.py

@@ -18,8 +18,8 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):
         info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image to twice the dimensions; use width and height sliders to set tile size</p>")
-        overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, visible=False)
-        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", visible=False)
+        overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
+        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
         return [info, overlap, upscaler_index]
 

scripts/xy_grid.py

@@ -153,7 +153,6 @@ def str_permutations(x):
     """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
     return x
 
-
 AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])
 AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])
 
@@ -178,6 +177,7 @@ axis_options = [
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
     AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
     AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
+    AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),
 ]
 
 
@@ -263,12 +263,12 @@ class Script(scripts.Script):
         current_axis_options = [x for x in axis_options if type(x) == AxisOption or type(x) == AxisOptionImg2Img and is_img2img]
 
         with gr.Row():
-            x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, visible=False, type="index", elem_id="x_type")
-            x_values = gr.Textbox(label="X values", visible=False, lines=1)
+            x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, type="index", elem_id="x_type")
+            x_values = gr.Textbox(label="X values", lines=1)
 
         with gr.Row():
-            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, visible=False, type="index", elem_id="y_type")
-            y_values = gr.Textbox(label="Y values", visible=False, lines=1)
+            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, type="index", elem_id="y_type")
+            y_values = gr.Textbox(label="Y values", lines=1)
         
         draw_legend = gr.Checkbox(label='Draw legend', value=True)
         include_lone_images = gr.Checkbox(label='Include Separate Images', value=False)

style.css

@@ -260,6 +260,16 @@ input[type="range"]{
 #txt2img_negative_prompt, #img2img_negative_prompt{
 }
 
+/* gradio 3.8 adds opacity to progressbar which makes it blink; disable it here */
+.transition.opacity-20 {
+  opacity: 1 !important;
+}
+
+/* more gradio's garbage cleanup */
+.min-h-\[4rem\] {
+  min-height: unset !important;
+}
+
 #txt2img_progressbar, #img2img_progressbar, #ti_progressbar{
   position: absolute;
   z-index: 1000;
@@ -314,8 +324,8 @@ input[type="range"]{
 
 .modalControls {
     display: grid;
-    grid-template-columns: 32px auto 1fr 32px;
-    grid-template-areas: "zoom tile space close";
+    grid-template-columns: 32px 32px 32px 1fr 32px;
+    grid-template-areas: "zoom tile save space close";
     position: absolute;
     top: 0;
     left: 0;
@@ -333,6 +343,10 @@ input[type="range"]{
     grid-area: zoom;
 }
 
+.modalSave {
+    grid-area: save;
+}
+
 .modalTileImage {
     grid-area: tile;
 }
@@ -346,8 +360,18 @@ input[type="range"]{
   cursor: pointer;
 }
 
+.modalSave {
+    color: white;
+    font-size: 28px;
+    margin-top: 8px;
+    font-weight: bold;
+    cursor: pointer;
+}
+
 .modalClose:hover,
 .modalClose:focus,
+.modalSave:hover,
+.modalSave:focus,
 .modalZoom:hover,
 .modalZoom:focus {
   color: #999;
@@ -477,7 +501,7 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
+#refresh_sd_model_checkpoint, #refresh_sd_vae, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
     max-width: 2.5em;
     min-width: 2.5em;
     height: 2.4em;
@@ -516,24 +540,52 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
     min-height: 480px !important;
 }
 
+/* Extensions */
+
+#tab_extensions table{
+    border-collapse: collapse;
+}
+
+#tab_extensions table td, #tab_extensions table th{
+    border: 1px solid #ccc;
+    padding: 0.25em 0.5em;
+}
+
+#tab_extensions table input[type="checkbox"]{
+    margin-right: 0.5em;
+}
+
+#tab_extensions button{
+    max-width: 16em;
+}
+
+#tab_extensions input[disabled="disabled"]{
+    opacity: 0.5;
+}
+
 /* The following handles localization for right-to-left (RTL) languages like Arabic.
 The rtl media type will only be activated by the logic in javascript/localization.js.
 If you change anything above, you need to make sure it is RTL compliant by just running
 your changes through converters like https://cssjanus.github.io/ or https://rtlcss.com/.
 Then, you will need to add the RTL counterpart only if needed in the rtl section below.*/
 @media rtl {
-    /* this part was manualy added */
+    /* this part was added manually */
     :host {
         direction: rtl;
     }
-    .output-html:has(.performance), .gr-text-input {
+    select, .file-preview, .gr-text-input, .output-html:has(.performance), #ti_progress {
         direction: ltr;
     }
+    #script_list > label > select,
+    #x_type > label > select,
+    #y_type > label > select {
+        direction: rtl;
+    }
     .gr-radio, .gr-checkbox{
         margin-left: 0.25em;
     }
 
-    /* this part was automatically generated with few manual modifications */
+    /* automatically generated with few manual modifications */
     .performance .time {
         margin-right: unset;
         margin-left: 0;
@@ -588,4 +640,4 @@ Then, you will need to add the RTL counterpart only if needed in the rtl section
         right: unset;
         left: 0.5em;
     }
-}
+}

test/extras_test.py

@@ -0,0 +1,29 @@
+import unittest
+
+
+class TestExtrasWorking(unittest.TestCase):
+    def setUp(self):
+        self.url_img2img = "http://localhost:7860/sdapi/v1/extra-single-image"
+        self.simple_extras = {
+            "resize_mode": 0,
+            "show_extras_results": True,
+            "gfpgan_visibility": 0,
+            "codeformer_visibility": 0,
+            "codeformer_weight": 0,
+            "upscaling_resize": 2,
+            "upscaling_resize_w": 512,
+            "upscaling_resize_h": 512,
+            "upscaling_crop": True,
+            "upscaler_1": "None",
+            "upscaler_2": "None",
+            "extras_upscaler_2_visibility": 0,
+            "image": ""
+            }
+
+
+class TestExtrasCorrectness(unittest.TestCase):
+    pass
+
+
+if __name__ == "__main__":
+    unittest.main()

test/img2img_test.py

@@ -0,0 +1,59 @@
+import unittest
+import requests
+from gradio.processing_utils import encode_pil_to_base64
+from PIL import Image
+
+
+class TestImg2ImgWorking(unittest.TestCase):
+    def setUp(self):
+        self.url_img2img = "http://localhost:7860/sdapi/v1/img2img"
+        self.simple_img2img = {
+            "init_images": [encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))],
+            "resize_mode": 0,
+            "denoising_strength": 0.75,
+            "mask": None,
+            "mask_blur": 4,
+            "inpainting_fill": 0,
+            "inpaint_full_res": False,
+            "inpaint_full_res_padding": 0,
+            "inpainting_mask_invert": 0,
+            "prompt": "example prompt",
+            "styles": [],
+            "seed": -1,
+            "subseed": -1,
+            "subseed_strength": 0,
+            "seed_resize_from_h": -1,
+            "seed_resize_from_w": -1,
+            "batch_size": 1,
+            "n_iter": 1,
+            "steps": 3,
+            "cfg_scale": 7,
+            "width": 64,
+            "height": 64,
+            "restore_faces": False,
+            "tiling": False,
+            "negative_prompt": "",
+            "eta": 0,
+            "s_churn": 0,
+            "s_tmax": 0,
+            "s_tmin": 0,
+            "s_noise": 1,
+            "override_settings": {},
+            "sampler_index": "Euler a",
+            "include_init_images": False
+            }
+
+    def test_img2img_simple_performed(self):
+        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
+
+    def test_inpainting_masked_performed(self):
+        self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
+        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
+
+
+class TestImg2ImgCorrectness(unittest.TestCase):
+    pass
+
+
+if __name__ == "__main__":
+    unittest.main()

test/server_poll.py

@@ -0,0 +1,19 @@
+import unittest
+import requests
+import time
+
+
+def run_tests():
+    timeout_threshold = 240
+    start_time = time.time()
+    while time.time()-start_time < timeout_threshold:
+        try:
+            requests.head("http://localhost:7860/")
+            break
+        except requests.exceptions.ConnectionError:
+            pass
+    if time.time()-start_time < timeout_threshold:
+        suite = unittest.TestLoader().discover('', pattern='*_test.py')
+        result = unittest.TextTestRunner(verbosity=2).run(suite)
+    else:
+        print("Launch unsuccessful")

test/txt2img_test.py

@@ -0,0 +1,74 @@
+import unittest
+import requests
+
+
+class TestTxt2ImgWorking(unittest.TestCase):
+    def setUp(self):
+        self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
+        self.simple_txt2img = {
+            "enable_hr": False,
+            "denoising_strength": 0,
+            "firstphase_width": 0,
+            "firstphase_height": 0,
+            "prompt": "example prompt",
+            "styles": [],
+            "seed": -1,
+            "subseed": -1,
+            "subseed_strength": 0,
+            "seed_resize_from_h": -1,
+            "seed_resize_from_w": -1,
+            "batch_size": 1,
+            "n_iter": 1,
+            "steps": 3,
+            "cfg_scale": 7,
+            "width": 64,
+            "height": 64,
+            "restore_faces": False,
+            "tiling": False,
+            "negative_prompt": "",
+            "eta": 0,
+            "s_churn": 0,
+            "s_tmax": 0,
+            "s_tmin": 0,
+            "s_noise": 1,
+            "sampler_index": "Euler a"
+        }
+
+    def test_txt2img_simple_performed(self):
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_with_negative_prompt_performed(self):
+        self.simple_txt2img["negative_prompt"] = "example negative prompt"
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_not_square_image_performed(self):
+        self.simple_txt2img["height"] = 128
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_with_hrfix_performed(self):
+        self.simple_txt2img["enable_hr"] = True
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_with_restore_faces_performed(self):
+        self.simple_txt2img["restore_faces"] = True
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_with_tiling_faces_performed(self):
+        self.simple_txt2img["tiling"] = True
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_with_vanilla_sampler_performed(self):
+        self.simple_txt2img["sampler_index"] = "PLMS"
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_multiple_batches_performed(self):
+        self.simple_txt2img["n_iter"] = 2
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+
+class TestTxt2ImgCorrectness(unittest.TestCase):
+    pass
+
+
+if __name__ == "__main__":
+    unittest.main()

webui.py

@@ -9,7 +9,7 @@ from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
 
-from modules import devices, sd_samplers, upscaler
+from modules import devices, sd_samplers, upscaler, extensions
 import modules.codeformer_model as codeformer
 import modules.extras
 import modules.face_restoration
@@ -21,8 +21,10 @@ import modules.paths
 import modules.scripts
 import modules.sd_hijack
 import modules.sd_models
+import modules.sd_vae
 import modules.shared as shared
 import modules.txt2img
+import modules.script_callbacks
 
 import modules.ui
 from modules import devices
@@ -46,26 +48,13 @@ def wrap_queued_call(func):
 
 def wrap_gradio_gpu_call(func, extra_outputs=None):
     def f(*args, **kwargs):
-        devices.torch_gc()
 
-        shared.state.sampling_step = 0
-        shared.state.job_count = -1
-        shared.state.job_no = 0
-        shared.state.job_timestamp = shared.state.get_job_timestamp()
-        shared.state.current_latent = None
-        shared.state.current_image = None
-        shared.state.current_image_sampling_step = 0
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.textinfo = None
+        shared.state.begin()
 
         with queue_lock:
             res = func(*args, **kwargs)
 
-        shared.state.job = ""
-        shared.state.job_count = 0
-
-        devices.torch_gc()
+        shared.state.end()
 
         return res
 
@@ -73,6 +62,8 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
 
 def initialize():
+    extensions.list_extensions()
+
     if cmd_opts.ui_debug_mode:
         shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
         modules.scripts.load_scripts()
@@ -87,8 +78,10 @@ def initialize():
 
     modules.scripts.load_scripts()
 
+    modules.sd_vae.refresh_vae_list()
     modules.sd_models.load_model()
-    shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
+    shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
+    shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     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)
 
@@ -105,15 +98,18 @@ def create_api(app):
     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):
+        if shared.state.need_restart:
+            shared.state.need_restart = False
             time.sleep(0.5)
             demo.close()
             time.sleep(0.5)
             break
 
+
 def api_only():
     initialize()
 
@@ -121,6 +117,8 @@ def api_only():
     app.add_middleware(GZipMiddleware, minimum_size=1000)
     api = create_api(app)
 
+    modules.script_callbacks.app_started_callback(None, 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)
 
 
@@ -145,14 +143,18 @@ def webui():
 
         app.add_middleware(GZipMiddleware, minimum_size=1000)
 
-        if (launch_api):
+        if launch_api:
             create_api(app)
 
+        modules.script_callbacks.app_started_callback(demo, app)
+
         wait_on_server(demo)
 
         sd_samplers.set_samplers()
 
-        print('Reloading Custom Scripts')
+        print('Reloading extensions')
+        extensions.list_extensions()
+        print('Reloading custom scripts')
         modules.scripts.reload_scripts()
         print('Reloading modules: modules.ui')
         importlib.reload(modules.ui)
@@ -161,8 +163,6 @@ def webui():
         print('Restarting Gradio')
 
 
-
-task = []
 if __name__ == "__main__":
     if cmd_opts.nowebui:
         api_only()

webui.sh

@@ -102,15 +102,14 @@ then
     exit 1
 fi
 
-printf "\n%s\n" "${delimiter}"
-printf "Clone or update stable-diffusion-webui"
-printf "\n%s\n" "${delimiter}"
 cd "${install_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/, aborting...\e[0m" "${install_dir}"; exit 1; }
 if [[ -d "${clone_dir}" ]]
 then
     cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
-    "${GIT}" pull
 else
+    printf "\n%s\n" "${delimiter}"
+    printf "Clone stable-diffusion-webui"
+    printf "\n%s\n" "${delimiter}"
     "${GIT}" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git "${clone_dir}"
     cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
 fi