AUTOMATIC1111
GitHub
commit
fac92610d2
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@ -83,7 +83,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
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- Estimated completion time in progress bar
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- API
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- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
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- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
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- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
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- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
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## Installation and Running
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@ -61,15 +61,15 @@ contextMenuInit = function(){
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}
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function appendContextMenuOption(targetEmementSelector,entryName,entryFunction){
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function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
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currentItems = menuSpecs.get(targetEmementSelector)
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currentItems = menuSpecs.get(targetElementSelector)
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if(!currentItems){
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currentItems = []
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menuSpecs.set(targetEmementSelector,currentItems);
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menuSpecs.set(targetElementSelector,currentItems);
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}
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let newItem = {'id':targetEmementSelector+'_'+uid(),
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let newItem = {'id':targetElementSelector+'_'+uid(),
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'name':entryName,
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'func':entryFunction,
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'isNew':true}
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@ -3,7 +3,7 @@ global_progressbars = {}
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galleries = {}
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galleryObservers = {}
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// this tracks laumnches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
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// this tracks launches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
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timeoutIds = {}
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function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
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@ -100,7 +100,7 @@ function create_submit_args(args){
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// As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
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// This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
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// I don't know why gradio is seding outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
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// I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
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// If gradio at some point stops sending outputs, this may break something
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if(Array.isArray(res[res.length - 3])){
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res[res.length - 3] = null
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@ -67,10 +67,10 @@ def encode_pil_to_base64(image):
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class Api:
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def __init__(self, app: FastAPI, queue_lock: Lock):
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if shared.cmd_opts.api_auth:
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self.credenticals = dict()
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self.credentials = dict()
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for auth in shared.cmd_opts.api_auth.split(","):
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user, password = auth.split(":")
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self.credenticals[user] = password
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self.credentials[user] = password
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self.router = APIRouter()
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self.app = app
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@ -93,7 +93,7 @@ class Api:
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self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
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self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
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self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
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self.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
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self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
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self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
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self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
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@ -102,9 +102,9 @@ class Api:
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return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
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return self.app.add_api_route(path, endpoint, **kwargs)
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def auth(self, credenticals: HTTPBasicCredentials = Depends(HTTPBasic())):
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if credenticals.username in self.credenticals:
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if compare_digest(credenticals.password, self.credenticals[credenticals.username]):
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def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())):
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if credentials.username in self.credentials:
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if compare_digest(credentials.password, self.credentials[credentials.username]):
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return True
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raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
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@ -308,7 +308,7 @@ class Api:
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def get_realesrgan_models(self):
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return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
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def get_promp_styles(self):
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def get_prompt_styles(self):
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styleList = []
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for k in shared.prompt_styles.styles:
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style = shared.prompt_styles.styles[k]
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@ -128,7 +128,7 @@ class ExtrasBaseRequest(BaseModel):
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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.")
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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.")
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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.")
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upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?")
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upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
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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])}")
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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])}")
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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.")
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@ -599,7 +599,7 @@ def read_info_from_image(image):
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Negative prompt: {json_info["uc"]}
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Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
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except Exception:
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print(f"Error parsing NovelAI iamge generation parameters:", file=sys.stderr)
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print(f"Error parsing NovelAI image generation parameters:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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return geninfo, items
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@ -707,7 +707,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
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"""saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images"""
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"""saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
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def save_intermediate(image, index):
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if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
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return
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@ -108,7 +108,7 @@ def load(filename, *args, **kwargs):
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def load_with_extra(filename, extra_handler=None, *args, **kwargs):
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"""
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this functon is intended to be used by extensions that want to load models with
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this function is intended to be used by extensions that want to load models with
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some extra classes in them that the usual unpickler would find suspicious.
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Use the extra_handler argument to specify a function that takes module and field name as text,
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@ -36,7 +36,7 @@ class Script:
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def ui(self, is_img2img):
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"""this function should create gradio UI elements. See https://gradio.app/docs/#components
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The return value should be an array of all components that are used in processing.
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Values of those returned componenbts will be passed to run() and process() functions.
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Values of those returned components will be passed to run() and process() functions.
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"""
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pass
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@ -47,7 +47,7 @@ class Script:
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This function should return:
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- False if the script should not be shown in UI at all
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- True if the script should be shown in UI if it's scelected in the scripts drowpdown
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- True if the script should be shown in UI if it's selected in the scripts dropdown
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- script.AlwaysVisible if the script should be shown in UI at all times
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"""
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@ -325,7 +325,7 @@ def should_hijack_inpainting(checkpoint_info):
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def do_inpainting_hijack():
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# most of this stuff seems to no longer be needed because it is already included into SD2.0
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# p_sample_plms is needed because PLMS can't work with dicts as conditionings
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# this file should be cleaned up later if weverything tuens out to work fine
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# this file should be cleaned up later if everything turns out to work fine
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# ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
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# ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
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@ -4,7 +4,7 @@ import torch
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class TorchHijackForUnet:
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"""
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This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
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this makes it possible to create pictures with dimensions that are muliples of 8 rather than 64
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this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
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"""
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def __getattr__(self, item):
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@ -57,7 +57,7 @@ class PersonalizedBase(Dataset):
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print("Preparing dataset...")
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for path in tqdm.tqdm(self.image_paths):
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if shared.state.interrupted:
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raise Exception("inturrupted")
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raise Exception("interrupted")
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try:
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image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
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except Exception:
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@ -133,7 +133,7 @@ class EmbeddingDatabase:
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process_file(fullfn, fn)
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except Exception:
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print(f"Error loading emedding {fn}:", file=sys.stderr)
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print(f"Error loading embedding {fn}:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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continue
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@ -18,7 +18,7 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
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ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
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hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
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first_pocessed = None
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first_processed = None
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state.job_count = len(xs) * len(ys)
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@ -27,17 +27,17 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
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state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
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processed = cell(x, y)
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if first_pocessed is None:
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first_pocessed = processed
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if first_processed is None:
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first_processed = processed
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res.append(processed.images[0])
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grid = images.image_grid(res, rows=len(ys))
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grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
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first_pocessed.images = [grid]
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first_processed.images = [grid]
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return first_pocessed
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return first_processed
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class Script(scripts.Script):
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4
webui.py
4
webui.py
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@ -154,8 +154,8 @@ def webui():
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# gradio uses a very open CORS policy via app.user_middleware, which makes it possible for
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# an attacker to trick the user into opening a malicious HTML page, which makes a request to the
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# running web ui and do whatever the attcker wants, including installing an extension and
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# runnnig its code. We disable this here. Suggested by RyotaK.
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# running web ui and do whatever the attacker wants, including installing an extension and
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# running its code. We disable this here. Suggested by RyotaK.
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app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
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setup_cors(app)
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