AUTOMATIC1111
GitHub
commit
fac92610d2
@ -82,8 +82,8 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
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- Use VAEs
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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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- 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 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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@ -9,7 +9,7 @@ contextMenuInit = function(){
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function showContextMenu(event,element,menuEntries){
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let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
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let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
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let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
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let oldMenu = gradioApp().querySelector('#context-menu')
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if(oldMenu){
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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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currentItems = menuSpecs.get(targetEmementSelector)
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function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
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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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@ -97,7 +97,7 @@ contextMenuInit = function(){
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if(source.id && source.id.indexOf('check_progress')>-1){
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return
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}
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let oldMenu = gradioApp().querySelector('#context-menu')
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if(oldMenu){
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oldMenu.remove()
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@ -117,7 +117,7 @@ contextMenuInit = function(){
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})
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});
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eventListenerApplied=true
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}
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return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
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@ -152,8 +152,8 @@ addContextMenuEventListener = initResponse[2];
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generateOnRepeat('#img2img_generate','#img2img_interrupt');
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})
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let cancelGenerateForever = function(){
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clearInterval(window.generateOnRepeatInterval)
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let cancelGenerateForever = function(){
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clearInterval(window.generateOnRepeatInterval)
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}
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appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
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@ -162,7 +162,7 @@ addContextMenuEventListener = initResponse[2];
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appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
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appendContextMenuOption('#roll','Roll three',
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function(){
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function(){
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let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
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setTimeout(function(){rollbutton.click()},100)
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setTimeout(function(){rollbutton.click()},200)
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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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@ -20,21 +20,21 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
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var skip = id_skip ? gradioApp().getElementById(id_skip) : null
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var interrupt = gradioApp().getElementById(id_interrupt)
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if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
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if(progressbar.innerText){
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let newtitle = '[' + progressbar.innerText.trim() + '] Stable Diffusion';
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if(document.title != newtitle){
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document.title = newtitle;
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document.title = newtitle;
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}
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}else{
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let newtitle = 'Stable Diffusion'
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if(document.title != newtitle){
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document.title = newtitle;
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document.title = newtitle;
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}
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}
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}
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if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){
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global_progressbars[id_progressbar] = progressbar
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@ -63,7 +63,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
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skip.style.display = "none"
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}
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interrupt.style.display = "none"
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//disconnect observer once generation finished, so user can close selected image if they want
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if (galleryObservers[id_gallery]) {
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galleryObservers[id_gallery].disconnect();
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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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@ -239,7 +239,7 @@ class Api:
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def interrogateapi(self, interrogatereq: InterrogateRequest):
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image_b64 = interrogatereq.image
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if image_b64 is None:
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raise HTTPException(status_code=404, detail="Image not found")
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raise HTTPException(status_code=404, detail="Image not found")
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img = decode_base64_to_image(image_b64)
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img = img.convert('RGB')
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@ -252,7 +252,7 @@ class Api:
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processed = deepbooru.model.tag(img)
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else:
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raise HTTPException(status_code=404, detail="Model not found")
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return InterrogateResponse(caption=processed)
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def interruptapi(self):
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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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@ -438,7 +438,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
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basename (`str`):
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The base filename which will be applied to `filename pattern`.
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seed, prompt, short_filename,
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seed, prompt, short_filename,
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extension (`str`):
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Image file extension, default is `png`.
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pngsectionname (`str`):
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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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@ -150,11 +150,11 @@ class StableDiffusionProcessing():
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# The "masked-image" in this case will just be all zeros since the entire image is masked.
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image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
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image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
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image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
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# Add the fake full 1s mask to the first dimension.
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image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
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image_conditioning = image_conditioning.to(x.dtype)
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image_conditioning = image_conditioning.to(x.dtype)
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return image_conditioning
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@ -202,7 +202,7 @@ class StableDiffusionProcessing():
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source_image * (1.0 - conditioning_mask),
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getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
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)
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# Encode the new masked image using first stage of network.
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
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@ -540,7 +540,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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for n in range(p.n_iter):
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if state.skipped:
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state.skipped = False
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if state.interrupted:
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break
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@ -615,7 +615,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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image.info["parameters"] = text
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output_images.append(image)
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del x_samples_ddim
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del x_samples_ddim
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devices.torch_gc()
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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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@ -723,7 +723,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
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# Avoid making the inpainting conditioning unless necessary as
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# Avoid making the inpainting conditioning unless necessary as
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# this does need some extra compute to decode / encode the image again.
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if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
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image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
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@ -80,7 +80,7 @@ def check_pt(filename, extra_handler):
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# new pytorch format is a zip file
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with zipfile.ZipFile(filename) as z:
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check_zip_filenames(filename, z.namelist())
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# find filename of data.pkl in zip file: '<directory name>/data.pkl'
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data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
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if len(data_pkl_filenames) == 0:
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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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@ -209,7 +209,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
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else:
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x_in = torch.cat([x] * 2)
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t_in = torch.cat([t] * 2)
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if isinstance(c, dict):
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assert isinstance(unconditional_conditioning, dict)
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c_in = dict()
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@ -278,7 +278,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
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x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
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return x_prev, pred_x0, e_t
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# =================================================================================================
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# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
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# Adapted from:
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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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@ -28,9 +28,9 @@ class DatasetEntry:
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class PersonalizedBase(Dataset):
|
||||
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):
|
||||
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):
|
||||
re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
|
||||
|
||||
|
||||
self.placeholder_token = placeholder_token
|
||||
|
||||
self.width = width
|
||||
@ -50,14 +50,14 @@ class PersonalizedBase(Dataset):
|
||||
|
||||
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
|
||||
|
||||
|
||||
|
||||
self.shuffle_tags = shuffle_tags
|
||||
self.tag_drop_out = tag_drop_out
|
||||
|
||||
print("Preparing dataset...")
|
||||
for path in tqdm.tqdm(self.image_paths):
|
||||
if shared.state.interrupted:
|
||||
raise Exception("inturrupted")
|
||||
raise Exception("interrupted")
|
||||
try:
|
||||
image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
|
||||
except Exception:
|
||||
@ -144,7 +144,7 @@ class PersonalizedDataLoader(DataLoader):
|
||||
self.collate_fn = collate_wrapper_random
|
||||
else:
|
||||
self.collate_fn = collate_wrapper
|
||||
|
||||
|
||||
|
||||
class BatchLoader:
|
||||
def __init__(self, data):
|
||||
|
||||
@ -133,7 +133,7 @@ class EmbeddingDatabase:
|
||||
|
||||
process_file(fullfn, fn)
|
||||
except Exception:
|
||||
print(f"Error loading emedding {fn}:", file=sys.stderr)
|
||||
print(f"Error loading embedding {fn}:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
continue
|
||||
|
||||
@ -194,7 +194,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
|
||||
csv_writer.writeheader()
|
||||
|
||||
epoch = (step - 1) // epoch_len
|
||||
epoch_step = (step - 1) % epoch_len
|
||||
epoch_step = (step - 1) % epoch_len
|
||||
|
||||
csv_writer.writerow({
|
||||
"step": step,
|
||||
@ -270,9 +270,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
|
||||
# dataset loading may take a while, so input validations and early returns should be done before this
|
||||
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
|
||||
old_parallel_processing_allowed = shared.parallel_processing_allowed
|
||||
|
||||
|
||||
pin_memory = shared.opts.pin_memory
|
||||
|
||||
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
|
||||
|
||||
latent_sampling_method = ds.latent_sampling_method
|
||||
@ -295,12 +295,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
|
||||
loss_step = 0
|
||||
_loss_step = 0 #internal
|
||||
|
||||
|
||||
|
||||
last_saved_file = "<none>"
|
||||
last_saved_image = "<none>"
|
||||
forced_filename = "<none>"
|
||||
embedding_yet_to_be_embedded = False
|
||||
|
||||
|
||||
pbar = tqdm.tqdm(total=steps - initial_step)
|
||||
try:
|
||||
for i in range((steps-initial_step) * gradient_step):
|
||||
@ -327,10 +327,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
|
||||
c = shared.sd_model.cond_stage_model(batch.cond_text)
|
||||
loss = shared.sd_model(x, c)[0] / gradient_step
|
||||
del x
|
||||
|
||||
|
||||
_loss_step += loss.item()
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
|
||||
# go back until we reach gradient accumulation steps
|
||||
if (j + 1) % gradient_step != 0:
|
||||
continue
|
||||
|
||||
@ -18,7 +18,7 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
|
||||
ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
|
||||
hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
|
||||
|
||||
first_pocessed = None
|
||||
first_processed = None
|
||||
|
||||
state.job_count = len(xs) * len(ys)
|
||||
|
||||
@ -27,17 +27,17 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
|
||||
state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
|
||||
|
||||
processed = cell(x, y)
|
||||
if first_pocessed is None:
|
||||
first_pocessed = processed
|
||||
if first_processed is None:
|
||||
first_processed = processed
|
||||
|
||||
res.append(processed.images[0])
|
||||
|
||||
grid = images.image_grid(res, rows=len(ys))
|
||||
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
|
||||
|
||||
first_pocessed.images = [grid]
|
||||
first_processed.images = [grid]
|
||||
|
||||
return first_pocessed
|
||||
return first_processed
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
|
||||
4
webui.py
4
webui.py
@ -154,8 +154,8 @@ def webui():
|
||||
|
||||
# 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.
|
||||
# running web ui and do whatever the attacker wants, including installing an extension and
|
||||
# running 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)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user