Merge branch 'master' into master

README.md

@@ -155,14 +155,15 @@ The documentation was moved from this README over to the project's [wiki](https:
 - Swin2SR - https://github.com/mv-lab/swin2sr
 - LDSR - https://github.com/Hafiidz/latent-diffusion
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
-- Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
-- InvokeAI, lstein - Cross Attention layer optimization - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
-- Rinon Gal - Textual Inversion - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
+- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
+- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
+- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
 - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
 - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
 - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
 - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
 - xformers - https://github.com/facebookresearch/xformers
 - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
+- Security advice - RyotaK
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)

javascript/edit-attention.js

 addEventListener('keydown', (event) => {
 	let target = event.originalTarget || event.composedPath()[0];
-	if (!target.hasAttribute("placeholder")) return;
-	if (!target.placeholder.toLowerCase().includes("prompt")) return;
+	if (!target.matches("#toprow textarea.gr-text-input[placeholder]")) return;
 	if (! (event.metaKey || event.ctrlKey)) return;
 
 

launch.py

@@ -142,7 +142,7 @@ def prepare_enviroment():
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
     taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
-    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
+    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "60e5042ca0da89c14d1dd59d73883280f8fce991")
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
@@ -238,12 +238,15 @@ def tests(argv):
     proc.kill()
 
 
-def start_webui():
-    print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")
+def start():
+    print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
     import webui
-    webui.webui()
+    if '--nowebui' in sys.argv:
+        webui.api_only()
+    else:
+        webui.webui()
 
 
 if __name__ == "__main__":
     prepare_enviroment()
-    start_webui()
+    start()

localizations/it_IT.json

@@ -104,6 +104,7 @@
     "Seed travel": "Interpolazione semi",
     "Shift attention": "Sposta l'attenzione",
     "Text to Vector Graphics": "Da testo a grafica vettoriale",
+    "Unprompted": "Unprompted",
     "X/Y plot": "Grafico X/Y",
     "X/Y/Z plot": "Grafico X/Y/Z",
     "Dynamic Prompting v0.13.6": "Prompt dinamici v0.13.6",
@@ -259,6 +260,7 @@
     "Save results as video": "Salva i risultati come video",
     "Frames per second": "Fotogrammi al secondo",
     "Iterate seed every line": "Iterare il seme per ogni riga",
+    "Use same random seed for all lines": "Usa lo stesso seme casuale per tutte le righe",
     "List of prompt inputs": "Elenco di prompt di input",
     "Upload prompt inputs": "Carica un file contenente i prompt di input",
     "n": "Esegui n volte",
@@ -294,6 +296,13 @@
     "Transparent PNG": "PNG trasparente",
     "Noise Tolerance": "Tolleranza al rumore",
     "Quantize": "Quantizzare",
+    "Dry Run": "Esecuzione a vuoto (Debug)",
+    "NEW!": "NUOVO!",
+    "Premium Fantasy Card Template": "Premium Fantasy Card Template",
+    "is now available.": "è ora disponibile.",
+    "Generate a wide variety of creatures and characters in the style of a fantasy card game. Perfect for heroes, animals, monsters, and even crazy hybrids.": "Genera un'ampia varietà di creature e personaggi nello stile di un gioco di carte fantasy. Perfetto per eroi, animali, mostri e persino ibridi incredibili.",
+    "Learn More ➜": "Per saperne di più ➜",
+    "Purchases help fund the continued development of Unprompted. Thank you for your support!": "Gli acquisti aiutano a finanziare il continuo sviluppo di Unprompted. Grazie per il vostro sostegno!",
     "X type": "Parametro asse X",
     "Nothing": "Niente",
     "Var. seed": "Seme della variazione",
@@ -424,6 +433,7 @@
     "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l'immagine",
     "Tile size": "Dimensione piastrella",
     "Tile overlap": "Sovrapposizione piastrella",
+    "New seed for each tile": "Nuovo seme per ogni piastrella",
     "alternate img2img imgage": "Immagine alternativa per img2img",
     "interpolation values": "Valori di interpolazione",
     "Refinement loops": "Cicli di affinamento",
@@ -455,8 +465,9 @@
     "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l'immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
     "Upscaler": "Ampliamento immagine",
     "Lanczos": "Lanczos",
+    "Nearest": "Nearest",
     "LDSR": "LDSR",
-    "ESRGAN_4x": "ESRGAN_4x",
+    "BSRGAN": "BSRGAN",
     "ScuNET GAN": "ScuNET GAN",
     "ScuNET PSNR": "ScuNET PSNR",
     "SwinIR 4x": "SwinIR 4x",
@@ -808,6 +819,7 @@
     "image_path": "Percorso immagine",
     "mp4_path": "Percorso MP4",
     "Click here after the generation to show the video": "Clicca qui dopo la generazione per mostrare il video",
+    "NOTE: If the 'Generate' button doesn't work, go in Settings and click 'Restart Gradio and Refresh...'.": "NOTA: se il pulsante 'Genera' non funziona, vai in Impostazioni e fai clic su 'Riavvia Gradio e Aggiorna...'.",
     "Save Settings": "Salva le impostazioni",
     "Load Settings": "Carica le impostazioni",
     "Path relative to the webui folder." : "Percorso relativo alla cartella webui.",
@@ -922,8 +934,8 @@
     "Renew Page": "Aggiorna la pagina",
     "Number": "Numero",
     "set_index": "Imposta indice",
-    "load_switch": "load_switch",
-    "turn_page_switch": "turn_page_switch",
+    "load_switch": "Carica",
+    "turn_page_switch": "Volta pagina",
     "Checkbox": "Casella di controllo",						   
     "Checkbox Group": "Seleziona immagini per",
     "artists": "Artisti",
@@ -956,6 +968,8 @@
     "Save text information about generation parameters as chunks to png files": "Salva le informazioni di testo dei parametri di generazione come blocchi nel file png",
     "Create a text file next to every image with generation parameters.": "Crea un file di testo assieme a ogni immagine con i parametri di generazione.",
     "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di eseguire il restauro dei volti.",
+    "Save a copy of image before applying highres fix.": "Salva una copia dell'immagine prima di applicare la correzione ad alta risoluzione.",
+    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
     "Quality for saved jpeg images": "Qualità delle immagini salvate in formato JPEG",
     "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
     "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l'elaborazione a lotti nella scheda 'Extra'",
@@ -997,12 +1011,14 @@
     "Filename join string": "Stringa per unire le parole estratte dal nome del file",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un file CSV contenente la perdita nella cartella di registrazione ogni N passaggi, 0 per disabilitare",
+    "Use cross attention optimizations while training": "Usa le ottimizzazioni di controllo dell'attenzione incrociato durante l'allenamento",
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "Checkpoint da memorizzare nella RAM",
+    "SD VAE": "SD VAE",
+    "auto": "auto",
     "Hypernetwork strength": "Forza della Iperrete",
     "Inpainting conditioning mask strength": "Forza della maschera di condizionamento del Inpainting",
     "Apply color correction to img2img results to match original colors.": "Applica la correzione del colore ai risultati di img2img in modo che corrispondano ai colori originali.",
-    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare i semi esistenti. Richiede il riavvio per applicare la modifica.",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: utilizzare (testo) per fare in modo che il modello presti maggiore attenzione al testo e [testo] per fargli prestare meno attenzione",
@@ -1194,8 +1210,8 @@
     "Hue:0": "Hue:0",
     "S:0": "S:0",
     "L:0": "L:0",
-    "Load Canvas": "Carica Tela",
-    "saveCanvas": "Salva Tela",
+    "Load Canvas": "Carica Canvas",
+    "Save Canvas": "Salva Canvas",
     "latest": "aggiornato",
     "behind": "da aggiornare",
     "Description": "Descrizione",

localizations/pt_BR.json

@@ -17,7 +17,7 @@
     "Checkpoint Merger": "Fusão de Checkpoint",
     "Train": "Treinar",
     "Settings": "Configurações",
-    "Extensions": "Extensions",
+    "Extensions": "Extensões",
     "Prompt": "Prompt",
     "Negative prompt": "Prompt negativo",
     "Run": "Executar",
@@ -57,7 +57,7 @@
     "Highres. fix": "Ajuste de alta resolução",
     "Firstpass width": "Primeira Passagem da largura",
     "Firstpass height": "Primeira Passagem da altura",
-    "Denoising strength": "Denoising strength",
+    "Denoising strength": "Força do denoise",
     "Batch count": "Quantidade por lote",
     "Batch size": "Quantidade de lotes",
     "CFG Scale": "Escala CFG",
@@ -480,6 +480,6 @@
     "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."
+    "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"
 }

modules/api/api.py

@@ -2,15 +2,18 @@ import base64
 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
+from threading import Lock
+from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
+from fastapi import APIRouter, Depends, FastAPI, HTTPException
 import modules.shared as shared
-from modules import devices
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
-
+from PIL import PngImagePlugin
+from modules.sd_models import checkpoints_list
+from modules.realesrgan_model import get_realesrgan_models
+from typing import List
 
 def upscaler_to_index(name: str):
     try:
@@ -32,13 +35,25 @@ def setUpscalers(req: dict):
 
 
 def encode_pil_to_base64(image):
-    buffer = io.BytesIO()
-    image.save(buffer, format="png")
-    return base64.b64encode(buffer.getvalue())
+    with io.BytesIO() as output_bytes:
+
+        # Copy any text-only metadata
+        use_metadata = False
+        metadata = PngImagePlugin.PngInfo()
+        for key, value in image.info.items():
+            if isinstance(key, str) and isinstance(value, str):
+                metadata.add_text(key, value)
+                use_metadata = True
+
+        image.save(
+            output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
+        )
+        bytes_data = output_bytes.getvalue()
+    return base64.b64encode(bytes_data)
 
 
 class Api:
-    def __init__(self, app, queue_lock):
+    def __init__(self, app: FastAPI, queue_lock: Lock):
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
@@ -49,6 +64,18 @@ class Api:
         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"])
+        self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
+        self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
+        self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
+        self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
+        self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
+        self.app.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
+        self.app.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
+        self.app.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
+        self.app.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
+        self.app.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
+        self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -179,6 +206,8 @@ class Api:
 
         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)
@@ -190,6 +219,70 @@ class Api:
 
         return {}
 
+    def get_config(self):
+        options = {}
+        for key in shared.opts.data.keys():
+            metadata = shared.opts.data_labels.get(key)
+            if(metadata is not None):
+                options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)})
+            else:
+                options.update({key: shared.opts.data.get(key, None)})
+
+        return options
+
+    def set_config(self, req: OptionsModel):
+        # currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will
+        # overwrite all options with default values.
+        raise RuntimeError('Setting options via API is not supported')
+
+        reqDict = vars(req)
+        for o in reqDict:
+            setattr(shared.opts, o, reqDict[o])
+
+        shared.opts.save(shared.config_filename)
+        return
+
+    def get_cmd_flags(self):
+        return vars(shared.cmd_opts)
+
+    def get_samplers(self):
+        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
+
+    def get_upscalers(self):
+        upscalers = []
+
+        for upscaler in shared.sd_upscalers:
+            u = upscaler.scaler
+            upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url})
+
+        return upscalers
+
+    def get_sd_models(self):
+        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": x.config} for x in checkpoints_list.values()]
+
+    def get_hypernetworks(self):
+        return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
+
+    def get_face_restorers(self):
+        return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers]
+
+    def get_realesrgan_models(self):
+        return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
+
+    def get_promp_styles(self):
+        styleList = []
+        for k in shared.prompt_styles.styles:
+            style = shared.prompt_styles.styles[k]
+            styleList.append({"name":style[0], "prompt": style[1], "negative_prompr": style[2]})
+
+        return styleList
+
+    def get_artists_categories(self):
+        return shared.artist_db.cats
+
+    def get_artists(self):
+        return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
+
     def launch(self, server_name, port):
         self.app.include_router(self.router)
         uvicorn.run(self.app, host=server_name, port=port)

modules/api/models.py

 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
+from modules.shared import sd_upscalers, opts, parser
+from typing import Dict, List
 
 API_NOT_ALLOWED = [
     "self",
@@ -109,12 +109,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
 ).generate_model()
 
 class TextToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    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.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: dict
     info: str
 
@@ -131,6 +131,7 @@ class ExtrasBaseRequest(BaseModel):
     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.")
@@ -146,10 +147,10 @@ class FileData(BaseModel):
     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")
+    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.")
+    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")
@@ -165,3 +166,68 @@ class ProgressResponse(BaseModel):
     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.")
+
+fields = {}
+for key, value in opts.data.items():
+    metadata = opts.data_labels.get(key)
+    optType = opts.typemap.get(type(value), type(value))
+
+    if (metadata is not None):
+        fields.update({key: (Optional[optType], Field(
+            default=metadata.default ,description=metadata.label))})
+    else:
+        fields.update({key: (Optional[optType], Field())})
+
+OptionsModel = create_model("Options", **fields)
+
+flags = {}
+_options = vars(parser)['_option_string_actions']
+for key in _options:
+    if(_options[key].dest != 'help'):
+        flag = _options[key]
+        _type = str
+        if _options[key].default is not None: _type = type(_options[key].default)
+        flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
+
+FlagsModel = create_model("Flags", **flags)
+
+class SamplerItem(BaseModel):
+    name: str = Field(title="Name")
+    aliases: List[str] = Field(title="Aliases")
+    options: Dict[str, str] = Field(title="Options")
+
+class UpscalerItem(BaseModel):
+    name: str = Field(title="Name")
+    model_name: Optional[str] = Field(title="Model Name")
+    model_path: Optional[str] = Field(title="Path")
+    model_url: Optional[str] = Field(title="URL")
+
+class SDModelItem(BaseModel):
+    title: str = Field(title="Title")
+    model_name: str = Field(title="Model Name")
+    hash: str = Field(title="Hash")
+    filename: str = Field(title="Filename")
+    config: str = Field(title="Config file")
+
+class HypernetworkItem(BaseModel):
+    name: str = Field(title="Name")
+    path: Optional[str] = Field(title="Path")
+
+class FaceRestorerItem(BaseModel):
+    name: str = Field(title="Name")
+    cmd_dir: Optional[str] = Field(title="Path")
+
+class RealesrganItem(BaseModel):
+    name: str = Field(title="Name")
+    path: Optional[str] = Field(title="Path")
+    scale: Optional[int] = Field(title="Scale")
+
+class PromptStyleItem(BaseModel):
+    name: str = Field(title="Name")
+    prompt: Optional[str] = Field(title="Prompt")
+    negative_prompt: Optional[str] = Field(title="Negative Prompt")
+
+class ArtistItem(BaseModel):
+    name: str = Field(title="Name")
+    score: float = Field(title="Score")
+    category: str = Field(title="Category")

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

@@ -34,8 +34,11 @@ class Extension:
         if repo is None or repo.bare:
             self.remote = None
         else:
-            self.remote = next(repo.remote().urls, None)
-            self.status = 'unknown'
+            try:
+                self.remote = next(repo.remote().urls, None)
+                self.status = 'unknown'
+            except Exception:
+                self.remote = None
 
     def list_files(self, subdir, extension):
         from modules import scripts

modules/extras.py

@@ -136,12 +136,13 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
         blended_result: Image.Image = None
+        image_hash: str = hash(np.array(image.getdata()).tobytes())
         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()),
+            cache_key = LruCache.Key(image_hash=image_hash,
                                      info_hash=hash(info),
-                                     args_hash=hash((upscale_args, upscale_first)))
+                                     args_hash=hash(upscale_args))
             cached_entry = cached_images.get(cache_key)
             if cached_entry is None:
                 res = upscale(image, *upscale_args)

modules/hypernetworks/ui.py

@@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
 not_available = ["hardswish", "multiheadattention"]
-keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = 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/img2img.py

@@ -81,7 +81,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         mask = None
 
     # Use the EXIF orientation of photos taken by smartphones.
-    image = ImageOps.exif_transpose(image) 
+    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]'
 

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

@@ -134,11 +134,7 @@ class StableDiffusionProcessing():
             # 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 torch.zeros(
-                x.shape[0], 5, 1, 1, 
-                dtype=x.dtype, 
-                device=x.device
-            )
+            return x.new_zeros(x.shape[0], 5, 1, 1)
 
         height = height or self.height
         width = width or self.width
@@ -156,11 +152,7 @@ class StableDiffusionProcessing():
     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 torch.zeros(
-                latent_image.shape[0], 5, 1, 1,
-                dtype=latent_image.dtype,
-                device=latent_image.device
-            )
+            return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
 
         # Handle the different mask inputs
         if image_mask is not None:
@@ -174,11 +166,11 @@ class StableDiffusionProcessing():
                 # 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, *source_image.shape[-2:])
+            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)
+        conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)
         conditioning_image = torch.lerp(
             source_image,
             source_image * (1.0 - conditioning_mask),
@@ -199,7 +191,7 @@ class StableDiffusionProcessing():
     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):
@@ -426,13 +418,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     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
+            setattr(opts, 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
+            setattr(opts, k, v)
 
     return res
 
@@ -509,6 +501,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if len(prompts) == 0:
                 break
 
+            if p.scripts is not None:
+                p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
+
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
@@ -521,7 +516,7 @@ def process_images_inner(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)
@@ -649,7 +644,7 @@ 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 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:
@@ -662,9 +657,28 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         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:
+            for i in range(samples.shape[0]):
+                save_intermediate(samples, i)
+
             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)
         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)
@@ -674,6 +688,9 @@ 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)
@@ -685,14 +702,14 @@ 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)
 
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
-        image_conditioning = self.txt2img_image_conditioning(x)
-
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()
@@ -831,8 +848,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
 
-
-    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)
@@ -843,4 +859,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         del x
         devices.torch_gc()
 
-        return samples
\ No newline at end of file
+        return samples

modules/script_callbacks.py

@@ -2,6 +2,7 @@ import sys
 import traceback
 from collections import namedtuple
 import inspect
+from typing import Optional
 
 from fastapi import FastAPI
 from gradio import Blocks
@@ -45,25 +46,23 @@ class CFGDenoiserParams:
 
 
 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 = []
+callback_map = dict(
+    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: Blocks, app: FastAPI):
-    for c in callbacks_app_started:
+    for callback_list in callback_map.values():
+        callback_list.clear()
+
+def app_started_callback(demo: Optional[Blocks], app: FastAPI):
+    for c in callback_map['callbacks_app_started']:
         try:
             c.callback(demo, app)
         except Exception:
@@ -71,7 +70,7 @@ def app_started_callback(demo: Blocks, app: FastAPI):
 
 
 def model_loaded_callback(sd_model):
-    for c in callbacks_model_loaded:
+    for c in callback_map['callbacks_model_loaded']:
         try:
             c.callback(sd_model)
         except Exception:
@@ -81,7 +80,7 @@ def model_loaded_callback(sd_model):
 def ui_tabs_callback():
     res = []
     
-    for c in callbacks_ui_tabs:
+    for c in callback_map['callbacks_ui_tabs']:
         try:
             res += c.callback() or []
         except Exception:
@@ -91,7 +90,7 @@ def ui_tabs_callback():
 
 
 def ui_settings_callback():
-    for c in callbacks_ui_settings:
+    for c in callback_map['callbacks_ui_settings']:
         try:
             c.callback()
         except Exception:
@@ -99,7 +98,7 @@ def ui_settings_callback():
 
 
 def before_image_saved_callback(params: ImageSaveParams):
-    for c in callbacks_before_image_saved:
+    for c in callback_map['callbacks_before_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -107,7 +106,7 @@ def before_image_saved_callback(params: ImageSaveParams):
 
 
 def image_saved_callback(params: ImageSaveParams):
-    for c in callbacks_image_saved:
+    for c in callback_map['callbacks_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -115,7 +114,7 @@ def image_saved_callback(params: ImageSaveParams):
 
 
 def cfg_denoiser_callback(params: CFGDenoiserParams):
-    for c in callbacks_cfg_denoiser:
+    for c in callback_map['callbacks_cfg_denoiser']:
         try:
             c.callback(params)
         except Exception:
@@ -128,17 +127,33 @@ def add_callback(callbacks, fun):
 
     callbacks.append(ScriptCallback(filename, fun))
 
+    
+def remove_current_script_callbacks():
+    stack = [x for x in inspect.stack() if x.filename != __file__]
+    filename = stack[0].filename if len(stack) > 0 else 'unknown file'
+    if filename == 'unknown file':
+        return
+    for callback_list in callback_map.values():
+        for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
+            callback_list.remove(callback_to_remove)
+
+
+def remove_callbacks_for_function(callback_func):
+    for callback_list in callback_map.values():
+        for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
+            callback_list.remove(callback_to_remove)
+
 
 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)
+    add_callback(callback_map['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
     passed as an argument"""
-    add_callback(callbacks_model_loaded, callback)
+    add_callback(callback_map['callbacks_model_loaded'], callback)
 
 
 def on_ui_tabs(callback):
@@ -151,13 +166,13 @@ def on_ui_tabs(callback):
     title is tab text displayed to user in the UI
     elem_id is HTML id for the tab
     """
-    add_callback(callbacks_ui_tabs, callback)
+    add_callback(callback_map['callbacks_ui_tabs'], callback)
 
 
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
-    add_callback(callbacks_ui_settings, callback)
+    add_callback(callback_map['callbacks_ui_settings'], callback)
 
 
 def on_before_image_saved(callback):
@@ -165,7 +180,7 @@ def on_before_image_saved(callback):
     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)
+    add_callback(callback_map['callbacks_before_image_saved'], callback)
 
 
 def on_image_saved(callback):
@@ -173,7 +188,7 @@ def on_image_saved(callback):
     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)
+    add_callback(callback_map['callbacks_image_saved'], callback)
 
 
 def on_cfg_denoiser(callback):
@@ -181,5 +196,4 @@ def on_cfg_denoiser(callback):
     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)
-
+    add_callback(callback_map['callbacks_cfg_denoiser'], callback)

modules/scripts.py

@@ -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
@@ -70,6 +73,19 @@ class Script:
 
         pass
 
+    def process_batch(self, p, *args, **kwargs):
+        """
+        Same as process(), but called for every batch.
+
+        **kwargs will have those items:
+          - batch_number - index of current batch, from 0 to number of batches-1
+          - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+          - seeds - list of seeds for current batch
+          - subseeds - list of subseeds for current batch
+        """
+
+        pass
+
     def postprocess(self, p, processed, *args):
         """
         This function is called after processing ends for AlwaysVisible scripts.
@@ -218,8 +234,6 @@ 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
@@ -229,40 +243,41 @@ 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)
 
+            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
@@ -294,6 +309,15 @@ class ScriptRunner:
                 print(f"Error running process: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
+    def process_batch(self, p, **kwargs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.process_batch(p, *script_args, **kwargs)
+            except Exception:
+                print(f"Error running process_batch: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
     def postprocess(self, p, processed):
         for script in self.alwayson_scripts:
             try:

modules/sd_models.py

@@ -9,7 +9,7 @@ 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
 
@@ -159,13 +159,16 @@ 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 shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
+        sd_vae.restore_base_vae(model)
+        checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
+
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+
     if checkpoint_info not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
@@ -182,37 +185,36 @@ def load_model_weights(model, checkpoint_info):
             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)
 
-        if shared.opts.sd_checkpoint_cache > 0:
-            checkpoints_loaded[checkpoint_info] = 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)
+        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")
         model.load_state_dict(checkpoints_loaded[checkpoint_info])
 
+    if shared.opts.sd_checkpoint_cache > 0:
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+            checkpoints_loaded.popitem(last=False)  # LRU
+
     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
@@ -263,7 +265,7 @@ def load_model(checkpoint_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
 

modules/sd_samplers.py

@@ -24,11 +24,15 @@ samplers_k_diffusion = [
     ('Heun', 'sample_heun', ['k_heun'], {}),
     ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
     ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
 
 samplers_data_k_diffusion = [
@@ -93,8 +97,8 @@ 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):

modules/sd_vae.py

+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/ui.py

@@ -276,16 +276,8 @@ def check_progress_call(id_part):
     image = gr_show(False)
     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
-
+    if opts.show_progress_every_n_steps != 0:
+        shared.state.set_current_image()
         image = shared.state.current_image
 
         if image is None:
@@ -1060,9 +1052,11 @@ def create_ui(wrap_gradio_gpu_call):
                         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():
@@ -1087,8 +1081,6 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
 
-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
             result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
@@ -1190,8 +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="linear", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. 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")
@@ -1446,48 +1438,39 @@ def create_ui(wrap_gradio_gpu_call):
     def run_settings(*args):
         changed = 0
 
-        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
-
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
-                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
+            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
             if comp == dummy_component:
                 continue
 
-            comp_args = opts.data_labels[key].component_args
-            if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
-                continue
-
-            if cmd_opts.hide_ui_dir_config and key in restricted_opts:
-                continue
-
             oldval = opts.data.get(key, None)
-            opts.data[key] = value
-
+            try:
+                setattr(opts, key, value)
+            except RuntimeError:
+                continue
             if oldval != value:
                 if opts.data_labels[key].onchange is not None:
                     opts.data_labels[key].onchange()
 
                 changed += 1
-
-        opts.save(shared.config_filename)
-
-        return f'{changed} settings changed.', opts.dumpjson()
+        try:
+            opts.save(shared.config_filename)
+        except RuntimeError:
+            return opts.dumpjson(), f'{changed} settings changed without save.'
+        return opts.dumpjson(), f'{changed} settings changed.'
 
     def run_settings_single(value, key):
-        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
-
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
         oldval = opts.data.get(key, None)
-        if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+        try:
+            setattr(opts, key, value)
+        except Exception:
             return gr.update(value=oldval), opts.dumpjson()
 
-        opts.data[key] = value
-
         if oldval != value:
             if opts.data_labels[key].onchange is not None:
                 opts.data_labels[key].onchange()
@@ -1640,9 +1623,9 @@ def create_ui(wrap_gradio_gpu_call):
 
         text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
         settings_submit.click(
-            fn=run_settings,
+            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
             inputs=components,
-            outputs=[result, text_settings],
+            outputs=[text_settings, result],
         )
 
         for i, k, item in quicksettings_list:

modules/ui_extensions.py

@@ -86,7 +86,7 @@ def extension_table():
         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><a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape(ext.remote or '')}</a></td>
                 <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
             </tr>
     """
@@ -188,7 +188,7 @@ def refresh_available_extensions_from_data():
 
         code += f"""
             <tr>
-                <td><a href="{html.escape(url)}">{html.escape(name)}</a></td>
+                <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a></td>
                 <td>{html.escape(description)}</td>
                 <td>{install_code}</td>
             </tr>

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
 
 
@@ -56,10 +57,18 @@ class Upscaler:
         self.scale = scale
         dest_w = img.width * scale
         dest_h = img.height * scale
+
         for i in range(3):
+            shape = (img.width, img.height)
+
+            img = self.do_upscale(img, selected_model)
+
+            if shape == (img.width, img.height):
+                break
+
             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:
             img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)
 
@@ -120,3 +129,17 @@ 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)]
\ No newline at end of file

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

@@ -83,13 +83,14 @@ def cmdargs(line):
 
 
 def load_prompt_file(file):
-    if (file is None):
+    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"
@@ -107,9 +108,9 @@ class Script(scripts.Script):
         # 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, file, prompt_txt]
+        return [checkbox_iterate, checkbox_iterate_batch, prompt_txt]
 
-    def run(self, p, checkbox_iterate, checkbox_iterate_batch, file, prompt_txt: str):
+    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]
 
@@ -157,5 +158,4 @@ class Script(scripts.Script):
             if checkbox_iterate:
                 p.seed = p.seed + (p.batch_size * p.n_iter)
 
-
-        return Processed(p, images, p.seed, "")
\ No newline at end of file
+        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

@@ -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

@@ -501,7 +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;

test/utils_test.py

+import unittest
+import requests
+
+class UtilsTests(unittest.TestCase):
+  def setUp(self):
+    self.url_options = "http://localhost:7860/sdapi/v1/options"
+    self.url_cmd_flags = "http://localhost:7860/sdapi/v1/cmd-flags"
+    self.url_samplers = "http://localhost:7860/sdapi/v1/samplers"
+    self.url_upscalers = "http://localhost:7860/sdapi/v1/upscalers"
+    self.url_sd_models = "http://localhost:7860/sdapi/v1/sd-models"
+    self.url_hypernetworks = "http://localhost:7860/sdapi/v1/hypernetworks"
+    self.url_face_restorers = "http://localhost:7860/sdapi/v1/face-restorers"
+    self.url_realesrgan_models = "http://localhost:7860/sdapi/v1/realesrgan-models"
+    self.url_prompt_styles = "http://localhost:7860/sdapi/v1/prompt-styles"
+    self.url_artist_categories = "http://localhost:7860/sdapi/v1/artist-categories"
+    self.url_artists = "http://localhost:7860/sdapi/v1/artists"
+
+  def test_options_get(self):
+    self.assertEqual(requests.get(self.url_options).status_code, 200)
+
+  def test_options_write(self):
+    response = requests.get(self.url_options)
+    self.assertEqual(response.status_code, 200)
+    
+    pre_value = response.json()["send_seed"]
+
+    self.assertEqual(requests.post(self.url_options, json={"send_seed":not pre_value}).status_code, 200)
+
+    response = requests.get(self.url_options)
+    self.assertEqual(response.status_code, 200)
+    self.assertEqual(response.json()["send_seed"], not pre_value)
+
+    requests.post(self.url_options, json={"send_seed": pre_value})
+
+  def test_cmd_flags(self):
+    self.assertEqual(requests.get(self.url_cmd_flags).status_code, 200)
+
+  def test_samplers(self):
+    self.assertEqual(requests.get(self.url_samplers).status_code, 200)
+
+  def test_upscalers(self):
+    self.assertEqual(requests.get(self.url_upscalers).status_code, 200)
+
+  def test_sd_models(self):
+    self.assertEqual(requests.get(self.url_sd_models).status_code, 200)
+
+  def test_hypernetworks(self):
+    self.assertEqual(requests.get(self.url_hypernetworks).status_code, 200)
+
+  def test_face_restorers(self):
+    self.assertEqual(requests.get(self.url_face_restorers).status_code, 200)
+  
+  def test_realesrgan_models(self):
+    self.assertEqual(requests.get(self.url_realesrgan_models).status_code, 200)
+  
+  def test_prompt_styles(self):
+    self.assertEqual(requests.get(self.url_prompt_styles).status_code, 200)
+  
+  def test_artist_categories(self):
+    self.assertEqual(requests.get(self.url_artist_categories).status_code, 200)
+
+  def test_artists(self):
+    self.assertEqual(requests.get(self.url_artists).status_code, 200)
\ No newline at end of file

webui.py

@@ -5,6 +5,7 @@ import importlib
 import signal
 import threading
 from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
@@ -21,6 +22,7 @@ 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
@@ -33,7 +35,7 @@ from modules.shared import cmd_opts
 import modules.hypernetworks.hypernetwork
 
 queue_lock = threading.Lock()
-
+server_name = "0.0.0.0" if cmd_opts.listen else cmd_opts.server_name
 
 def wrap_queued_call(func):
     def f(*args, **kwargs):
@@ -77,11 +79,27 @@ 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.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)
 
+    if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:
+
+        try:
+            if not os.path.exists(cmd_opts.tls_keyfile):
+                print("Invalid path to TLS keyfile given")
+            if not os.path.exists(cmd_opts.tls_certfile):
+                print(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
+        except TypeError:
+            cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None
+            print("TLS setup invalid, running webui without TLS")
+        else:
+            print("Running with TLS")
+
+
     # make the program just exit at ctrl+c without waiting for anything
     def sigint_handler(sig, frame):
         print(f'Interrupted with signal {sig} in {frame}')
@@ -90,6 +108,11 @@ def initialize():
     signal.signal(signal.SIGINT, sigint_handler)
 
 
+def setup_cors(app):
+    if cmd_opts.cors_allow_origins:
+        app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_methods=['*'])
+
+
 def create_api(app):
     from modules.api.api import Api
     api = Api(app, queue_lock)
@@ -111,9 +134,12 @@ def api_only():
     initialize()
 
     app = FastAPI()
+    setup_cors(app)
     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)
 
 
@@ -126,8 +152,10 @@ def webui():
 
         app, local_url, share_url = demo.launch(
             share=cmd_opts.share,
-            server_name="0.0.0.0" if cmd_opts.listen else None,
+            server_name=server_name,
             server_port=cmd_opts.port,
+            ssl_keyfile=cmd_opts.tls_keyfile,
+            ssl_certfile=cmd_opts.tls_certfile,
             debug=cmd_opts.gradio_debug,
             auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
             inbrowser=cmd_opts.autolaunch,
@@ -136,6 +164,14 @@ def webui():
         # after initial launch, disable --autolaunch for subsequent restarts
         cmd_opts.autolaunch = False
 
+        # gradio uses a very open CORS policy via app.user_middleware, which makes it possible for
+        # an attacker to trick the user into opening a malicious HTML page, which makes a request to the
+        # running web ui and do whatever the attcker wants, including installing an extension and
+        # runnnig its code. We disable this here. Suggested by RyotaK.
+        app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
+
+        setup_cors(app)
+
         app.add_middleware(GZipMiddleware, minimum_size=1000)
 
         if launch_api: