Merge pull request #5753 from calvinballing/master

Fix various typos
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AUTOMATIC1111 2022-12-24 09:58:28 +03:00 committed by GitHub
commit fac92610d2
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16 changed files with 68 additions and 68 deletions

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@ -82,8 +82,8 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
- Use VAEs - Use VAEs
- Estimated completion time in progress bar - Estimated completion time in progress bar
- API - API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
- 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)) - 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))
- [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 - [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
## Installation and Running ## Installation and Running

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@ -9,7 +9,7 @@ contextMenuInit = function(){
function showContextMenu(event,element,menuEntries){ function showContextMenu(event,element,menuEntries){
let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft; let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
let oldMenu = gradioApp().querySelector('#context-menu') let oldMenu = gradioApp().querySelector('#context-menu')
if(oldMenu){ if(oldMenu){
@ -61,15 +61,15 @@ contextMenuInit = function(){
} }
function appendContextMenuOption(targetEmementSelector,entryName,entryFunction){ function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
currentItems = menuSpecs.get(targetEmementSelector) currentItems = menuSpecs.get(targetElementSelector)
if(!currentItems){ if(!currentItems){
currentItems = [] currentItems = []
menuSpecs.set(targetEmementSelector,currentItems); menuSpecs.set(targetElementSelector,currentItems);
} }
let newItem = {'id':targetEmementSelector+'_'+uid(), let newItem = {'id':targetElementSelector+'_'+uid(),
'name':entryName, 'name':entryName,
'func':entryFunction, 'func':entryFunction,
'isNew':true} 'isNew':true}
@ -97,7 +97,7 @@ contextMenuInit = function(){
if(source.id && source.id.indexOf('check_progress')>-1){ if(source.id && source.id.indexOf('check_progress')>-1){
return return
} }
let oldMenu = gradioApp().querySelector('#context-menu') let oldMenu = gradioApp().querySelector('#context-menu')
if(oldMenu){ if(oldMenu){
oldMenu.remove() oldMenu.remove()
@ -117,7 +117,7 @@ contextMenuInit = function(){
}) })
}); });
eventListenerApplied=true eventListenerApplied=true
} }
return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener] return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
@ -152,8 +152,8 @@ addContextMenuEventListener = initResponse[2];
generateOnRepeat('#img2img_generate','#img2img_interrupt'); generateOnRepeat('#img2img_generate','#img2img_interrupt');
}) })
let cancelGenerateForever = function(){ let cancelGenerateForever = function(){
clearInterval(window.generateOnRepeatInterval) clearInterval(window.generateOnRepeatInterval)
} }
appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever) appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
@ -162,7 +162,7 @@ addContextMenuEventListener = initResponse[2];
appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever) appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
appendContextMenuOption('#roll','Roll three', appendContextMenuOption('#roll','Roll three',
function(){ function(){
let rollbutton = get_uiCurrentTabContent().querySelector('#roll'); let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
setTimeout(function(){rollbutton.click()},100) setTimeout(function(){rollbutton.click()},100)
setTimeout(function(){rollbutton.click()},200) setTimeout(function(){rollbutton.click()},200)

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@ -3,7 +3,7 @@ global_progressbars = {}
galleries = {} galleries = {}
galleryObservers = {} galleryObservers = {}
// this tracks laumnches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running // this tracks launches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
timeoutIds = {} timeoutIds = {}
function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
@ -20,21 +20,21 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
var skip = id_skip ? gradioApp().getElementById(id_skip) : null var skip = id_skip ? gradioApp().getElementById(id_skip) : null
var interrupt = gradioApp().getElementById(id_interrupt) var interrupt = gradioApp().getElementById(id_interrupt)
if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){ if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
if(progressbar.innerText){ if(progressbar.innerText){
let newtitle = '[' + progressbar.innerText.trim() + '] Stable Diffusion'; let newtitle = '[' + progressbar.innerText.trim() + '] Stable Diffusion';
if(document.title != newtitle){ if(document.title != newtitle){
document.title = newtitle; document.title = newtitle;
} }
}else{ }else{
let newtitle = 'Stable Diffusion' let newtitle = 'Stable Diffusion'
if(document.title != newtitle){ if(document.title != newtitle){
document.title = newtitle; document.title = newtitle;
} }
} }
} }
if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){ if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){
global_progressbars[id_progressbar] = progressbar global_progressbars[id_progressbar] = progressbar
@ -63,7 +63,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
skip.style.display = "none" skip.style.display = "none"
} }
interrupt.style.display = "none" interrupt.style.display = "none"
//disconnect observer once generation finished, so user can close selected image if they want //disconnect observer once generation finished, so user can close selected image if they want
if (galleryObservers[id_gallery]) { if (galleryObservers[id_gallery]) {
galleryObservers[id_gallery].disconnect(); galleryObservers[id_gallery].disconnect();

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@ -100,7 +100,7 @@ function create_submit_args(args){
// 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. // 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.
// This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate. // This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
// 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. // 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.
// If gradio at some point stops sending outputs, this may break something // If gradio at some point stops sending outputs, this may break something
if(Array.isArray(res[res.length - 3])){ if(Array.isArray(res[res.length - 3])){
res[res.length - 3] = null res[res.length - 3] = null

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@ -67,10 +67,10 @@ def encode_pil_to_base64(image):
class Api: class Api:
def __init__(self, app: FastAPI, queue_lock: Lock): def __init__(self, app: FastAPI, queue_lock: Lock):
if shared.cmd_opts.api_auth: if shared.cmd_opts.api_auth:
self.credenticals = dict() self.credentials = dict()
for auth in shared.cmd_opts.api_auth.split(","): for auth in shared.cmd_opts.api_auth.split(","):
user, password = auth.split(":") user, password = auth.split(":")
self.credenticals[user] = password self.credentials[user] = password
self.router = APIRouter() self.router = APIRouter()
self.app = app self.app = app
@ -93,7 +93,7 @@ class Api:
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem]) self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem]) self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem]) self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
self.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem]) self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str]) self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem]) self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
@ -102,9 +102,9 @@ class Api:
return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs) return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
return self.app.add_api_route(path, endpoint, **kwargs) return self.app.add_api_route(path, endpoint, **kwargs)
def auth(self, credenticals: HTTPBasicCredentials = Depends(HTTPBasic())): def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())):
if credenticals.username in self.credenticals: if credentials.username in self.credentials:
if compare_digest(credenticals.password, self.credenticals[credenticals.username]): if compare_digest(credentials.password, self.credentials[credentials.username]):
return True return True
raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"}) raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
@ -239,7 +239,7 @@ class Api:
def interrogateapi(self, interrogatereq: InterrogateRequest): def interrogateapi(self, interrogatereq: InterrogateRequest):
image_b64 = interrogatereq.image image_b64 = interrogatereq.image
if image_b64 is None: if image_b64 is None:
raise HTTPException(status_code=404, detail="Image not found") raise HTTPException(status_code=404, detail="Image not found")
img = decode_base64_to_image(image_b64) img = decode_base64_to_image(image_b64)
img = img.convert('RGB') img = img.convert('RGB')
@ -252,7 +252,7 @@ class Api:
processed = deepbooru.model.tag(img) processed = deepbooru.model.tag(img)
else: else:
raise HTTPException(status_code=404, detail="Model not found") raise HTTPException(status_code=404, detail="Model not found")
return InterrogateResponse(caption=processed) return InterrogateResponse(caption=processed)
def interruptapi(self): def interruptapi(self):
@ -308,7 +308,7 @@ class Api:
def get_realesrgan_models(self): def get_realesrgan_models(self):
return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)] return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
def get_promp_styles(self): def get_prompt_styles(self):
styleList = [] styleList = []
for k in shared.prompt_styles.styles: for k in shared.prompt_styles.styles:
style = shared.prompt_styles.styles[k] style = shared.prompt_styles.styles[k]

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@ -128,7 +128,7 @@ class ExtrasBaseRequest(BaseModel):
upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.") upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.") upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.") upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?") upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") upscaler_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])}") 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.") 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
The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory. The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
basename (`str`): basename (`str`):
The base filename which will be applied to `filename pattern`. The base filename which will be applied to `filename pattern`.
seed, prompt, short_filename, seed, prompt, short_filename,
extension (`str`): extension (`str`):
Image file extension, default is `png`. Image file extension, default is `png`.
pngsectionname (`str`): pngsectionname (`str`):
@ -599,7 +599,7 @@ def read_info_from_image(image):
Negative prompt: {json_info["uc"]} Negative prompt: {json_info["uc"]}
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""" 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"""
except Exception: except Exception:
print(f"Error parsing NovelAI iamge generation parameters:", file=sys.stderr) print(f"Error parsing NovelAI image generation parameters:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr) print(traceback.format_exc(), file=sys.stderr)
return geninfo, items return geninfo, items

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@ -150,11 +150,11 @@ class StableDiffusionProcessing():
# The "masked-image" in this case will just be all zeros since the entire image is masked. # The "masked-image" in this case will just be all zeros since the entire image is masked.
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
# Add the fake full 1s mask to the first dimension. # Add the fake full 1s mask to the first dimension.
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
image_conditioning = image_conditioning.to(x.dtype) image_conditioning = image_conditioning.to(x.dtype)
return image_conditioning return image_conditioning
@ -202,7 +202,7 @@ class StableDiffusionProcessing():
source_image * (1.0 - conditioning_mask), source_image * (1.0 - conditioning_mask),
getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
) )
# Encode the new masked image using first stage of network. # Encode the new masked image using first stage of network.
conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
@ -540,7 +540,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
for n in range(p.n_iter): for n in range(p.n_iter):
if state.skipped: if state.skipped:
state.skipped = False state.skipped = False
if state.interrupted: if state.interrupted:
break break
@ -615,7 +615,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
image.info["parameters"] = text image.info["parameters"] = text
output_images.append(image) output_images.append(image)
del x_samples_ddim del x_samples_ddim
devices.torch_gc() devices.torch_gc()
@ -707,7 +707,7 @@ 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] 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""" """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
def save_intermediate(image, index): def save_intermediate(image, index):
if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix: if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
return return
@ -723,7 +723,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") 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 # Avoid making the inpainting conditioning unless necessary as
# this does need some extra compute to decode / encode the image again. # 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: 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) 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):
# new pytorch format is a zip file # new pytorch format is a zip file
with zipfile.ZipFile(filename) as z: with zipfile.ZipFile(filename) as z:
check_zip_filenames(filename, z.namelist()) check_zip_filenames(filename, z.namelist())
# find filename of data.pkl in zip file: '<directory name>/data.pkl' # find filename of data.pkl in zip file: '<directory name>/data.pkl'
data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)] data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
if len(data_pkl_filenames) == 0: if len(data_pkl_filenames) == 0:
@ -108,7 +108,7 @@ def load(filename, *args, **kwargs):
def load_with_extra(filename, extra_handler=None, *args, **kwargs): def load_with_extra(filename, extra_handler=None, *args, **kwargs):
""" """
this functon is intended to be used by extensions that want to load models with this function is intended to be used by extensions that want to load models with
some extra classes in them that the usual unpickler would find suspicious. some extra classes in them that the usual unpickler would find suspicious.
Use the extra_handler argument to specify a function that takes module and field name as text, 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:
def ui(self, is_img2img): def ui(self, is_img2img):
"""this function should create gradio UI elements. See https://gradio.app/docs/#components """this function should create gradio UI elements. See https://gradio.app/docs/#components
The return value should be an array of all components that are used in processing. The return value should be an array of all components that are used in processing.
Values of those returned componenbts will be passed to run() and process() functions. Values of those returned components will be passed to run() and process() functions.
""" """
pass pass
@ -47,7 +47,7 @@ class Script:
This function should return: This function should return:
- False if the script should not be shown in UI at all - False if the script should not be shown in UI at all
- True if the script should be shown in UI if it's scelected in the scripts drowpdown - True if the script should be shown in UI if it's selected in the scripts dropdown
- script.AlwaysVisible if the script should be shown in UI at all times - script.AlwaysVisible if the script should be shown in UI at all times
""" """

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@ -209,7 +209,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
else: else:
x_in = torch.cat([x] * 2) x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2) t_in = torch.cat([t] * 2)
if isinstance(c, dict): if isinstance(c, dict):
assert isinstance(unconditional_conditioning, dict) assert isinstance(unconditional_conditioning, dict)
c_in = dict() c_in = dict()
@ -278,7 +278,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
return x_prev, pred_x0, e_t return x_prev, pred_x0, e_t
# ================================================================================================= # =================================================================================================
# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config. # Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
# Adapted from: # Adapted from:
@ -325,7 +325,7 @@ def should_hijack_inpainting(checkpoint_info):
def do_inpainting_hijack(): def do_inpainting_hijack():
# most of this stuff seems to no longer be needed because it is already included into SD2.0 # most of this stuff seems to no longer be needed because it is already included into SD2.0
# p_sample_plms is needed because PLMS can't work with dicts as conditionings # p_sample_plms is needed because PLMS can't work with dicts as conditionings
# this file should be cleaned up later if weverything tuens out to work fine # this file should be cleaned up later if everything turns out to work fine
# ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
# ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion # ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion

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@ -4,7 +4,7 @@ import torch
class TorchHijackForUnet: class TorchHijackForUnet:
""" """
This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match; This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
this makes it possible to create pictures with dimensions that are muliples of 8 rather than 64 this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
""" """
def __getattr__(self, item): def __getattr__(self, item):

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@ -28,9 +28,9 @@ class DatasetEntry:
class PersonalizedBase(Dataset): 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 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.placeholder_token = placeholder_token
self.width = width 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.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
self.shuffle_tags = shuffle_tags self.shuffle_tags = shuffle_tags
self.tag_drop_out = tag_drop_out self.tag_drop_out = tag_drop_out
print("Preparing dataset...") print("Preparing dataset...")
for path in tqdm.tqdm(self.image_paths): for path in tqdm.tqdm(self.image_paths):
if shared.state.interrupted: if shared.state.interrupted:
raise Exception("inturrupted") raise Exception("interrupted")
try: try:
image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC) image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
except Exception: except Exception:
@ -144,7 +144,7 @@ class PersonalizedDataLoader(DataLoader):
self.collate_fn = collate_wrapper_random self.collate_fn = collate_wrapper_random
else: else:
self.collate_fn = collate_wrapper self.collate_fn = collate_wrapper
class BatchLoader: class BatchLoader:
def __init__(self, data): def __init__(self, data):

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@ -133,7 +133,7 @@ class EmbeddingDatabase:
process_file(fullfn, fn) process_file(fullfn, fn)
except Exception: 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) print(traceback.format_exc(), file=sys.stderr)
continue continue
@ -194,7 +194,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
csv_writer.writeheader() csv_writer.writeheader()
epoch = (step - 1) // epoch_len epoch = (step - 1) // epoch_len
epoch_step = (step - 1) % epoch_len epoch_step = (step - 1) % epoch_len
csv_writer.writerow({ csv_writer.writerow({
"step": step, "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 # 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)}..." shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
old_parallel_processing_allowed = shared.parallel_processing_allowed old_parallel_processing_allowed = shared.parallel_processing_allowed
pin_memory = shared.opts.pin_memory 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) 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 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
_loss_step = 0 #internal _loss_step = 0 #internal
last_saved_file = "<none>" last_saved_file = "<none>"
last_saved_image = "<none>" last_saved_image = "<none>"
forced_filename = "<none>" forced_filename = "<none>"
embedding_yet_to_be_embedded = False embedding_yet_to_be_embedded = False
pbar = tqdm.tqdm(total=steps - initial_step) pbar = tqdm.tqdm(total=steps - initial_step)
try: try:
for i in range((steps-initial_step) * gradient_step): 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) c = shared.sd_model.cond_stage_model(batch.cond_text)
loss = shared.sd_model(x, c)[0] / gradient_step loss = shared.sd_model(x, c)[0] / gradient_step
del x del x
_loss_step += loss.item() _loss_step += loss.item()
scaler.scale(loss).backward() scaler.scale(loss).backward()
# go back until we reach gradient accumulation steps # go back until we reach gradient accumulation steps
if (j + 1) % gradient_step != 0: if (j + 1) % gradient_step != 0:
continue continue

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@ -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] ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs] hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
first_pocessed = None first_processed = None
state.job_count = len(xs) * len(ys) 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)}" state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
processed = cell(x, y) processed = cell(x, y)
if first_pocessed is None: if first_processed is None:
first_pocessed = processed first_processed = processed
res.append(processed.images[0]) res.append(processed.images[0])
grid = images.image_grid(res, rows=len(ys)) grid = images.image_grid(res, rows=len(ys))
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts) 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): class Script(scripts.Script):

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@ -154,8 +154,8 @@ def webui():
# gradio uses a very open CORS policy via app.user_middleware, which makes it possible for # 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 # 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 # running web ui and do whatever the attacker wants, including installing an extension and
# runnnig its code. We disable this here. Suggested by RyotaK. # 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'] app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
setup_cors(app) setup_cors(app)