diff options
| author | AUTOMATIC1111 <16777216c@gmail.com> | 2023-07-08 16:50:23 +0300 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-07-08 16:50:23 +0300 |
| commit | 993dd9a8927407de8d19142cacb07e6f76686a67 (patch) | |
| tree | 89df31c33ecf054c9cb70aaf38fc3382f306f450 /modules | |
| parent | ff6acd35d0807a4e0c3ee86cdb1520a4a3a11cdd (diff) | |
| parent | d7d6e8cfc8b85a99a48f82975ee213d487783c28 (diff) | |
Merge branch 'dev' into patch-1
Diffstat (limited to 'modules')
97 files changed, 4197 insertions, 1935 deletions
diff --git a/modules/Roboto-Regular.ttf b/modules/Roboto-Regular.ttf Binary files differnew file mode 100644 index 00000000..500b1045 --- /dev/null +++ b/modules/Roboto-Regular.ttf diff --git a/modules/api/api.py b/modules/api/api.py index 9bb95dfd..224bbfc6 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -14,32 +14,31 @@ from fastapi.encoders import jsonable_encoder from secrets import compare_digest import modules.shared as shared -from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing -from modules.api.models import * +from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart +from modules.api import models +from modules.shared import opts from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.textual_inversion.textual_inversion import create_embedding, train_embedding from modules.textual_inversion.preprocess import preprocess from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork from PIL import PngImagePlugin,Image -from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights +from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights, checkpoint_alisases +from modules.sd_vae import vae_dict from modules.sd_models_config import find_checkpoint_config_near_filename from modules.realesrgan_model import get_realesrgan_models from modules import devices -from typing import List +from typing import Dict, List, Any import piexif import piexif.helper +from contextlib import closing -def upscaler_to_index(name: str): - try: - return [x.name.lower() for x in shared.sd_upscalers].index(name.lower()) - except: - raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in sd_upscalers])}") def script_name_to_index(name, scripts): try: return [script.title().lower() for script in scripts].index(name.lower()) - except: - raise HTTPException(status_code=422, detail=f"Script '{name}' not found") + except Exception as e: + raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e + def validate_sampler_name(name): config = sd_samplers.all_samplers_map.get(name, None) @@ -48,20 +47,23 @@ def validate_sampler_name(name): return name + def setUpscalers(req: dict): reqDict = vars(req) reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None) reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None) return reqDict + def decode_base64_to_image(encoding): if encoding.startswith("data:image/"): encoding = encoding.split(";")[1].split(",")[1] try: image = Image.open(BytesIO(base64.b64decode(encoding))) return image - except Exception as err: - raise HTTPException(status_code=500, detail="Invalid encoded image") + except Exception as e: + raise HTTPException(status_code=500, detail="Invalid encoded image") from e + def encode_pil_to_base64(image): with io.BytesIO() as output_bytes: @@ -76,6 +78,8 @@ def encode_pil_to_base64(image): image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality) elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"): + if image.mode == "RGBA": + image = image.convert("RGB") parameters = image.info.get('parameters', None) exif_bytes = piexif.dump({ "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") } @@ -92,6 +96,7 @@ def encode_pil_to_base64(image): return base64.b64encode(bytes_data) + def api_middleware(app: FastAPI): rich_available = True try: @@ -99,8 +104,7 @@ def api_middleware(app: FastAPI): import starlette # importing just so it can be placed on silent list from rich.console import Console console = Console() - except: - import traceback + except Exception: rich_available = False @app.middleware("http") @@ -131,11 +135,12 @@ def api_middleware(app: FastAPI): "errors": str(e), } if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions - print(f"API error: {request.method}: {request.url} {err}") + message = f"API error: {request.method}: {request.url} {err}" if rich_available: + print(message) console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200])) else: - traceback.print_exc() + errors.report(message, exc_info=True) return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err)) @app.middleware("http") @@ -157,7 +162,7 @@ def api_middleware(app: FastAPI): class Api: def __init__(self, app: FastAPI, queue_lock: Lock): if shared.cmd_opts.api_auth: - self.credentials = dict() + self.credentials = {} for auth in shared.cmd_opts.api_auth.split(","): user, password = auth.split(":") self.credentials[user] = password @@ -166,36 +171,44 @@ class Api: self.app = app self.queue_lock = queue_lock api_middleware(self.app) - self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse) - self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse) - self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse) - self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse) - self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse) - self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse) + self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=models.TextToImageResponse) + self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=models.ImageToImageResponse) + self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=models.ExtrasSingleImageResponse) + self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ExtrasBatchImagesResponse) + self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=models.PNGInfoResponse) + self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=models.ProgressResponse) self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"]) self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"]) self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"]) - self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel) + self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel) self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"]) - self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel) - self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem]) - self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem]) - self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem]) - 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/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem]) - self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem]) - self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse) + self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel) + self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem]) + self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem]) + self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem]) + self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem]) + self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem]) + self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem]) + self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem]) + self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem]) + self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem]) + self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse) self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"]) - self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse) - self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse) - self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse) - self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse) - self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse) - self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse) + self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse) + self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse) + self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse) + self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse) + self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse) + self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse) self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"]) self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"]) - self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList) + self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList) + self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo]) + + if shared.cmd_opts.api_server_stop: + self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"]) + self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"]) + self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"]) self.default_script_arg_txt2img = [] self.default_script_arg_img2img = [] @@ -219,17 +232,25 @@ class Api: script_idx = script_name_to_index(script_name, script_runner.selectable_scripts) script = script_runner.selectable_scripts[script_idx] return script, script_idx - + def get_scripts_list(self): - t2ilist = [str(title.lower()) for title in scripts.scripts_txt2img.titles] - i2ilist = [str(title.lower()) for title in scripts.scripts_img2img.titles] + t2ilist = [script.name for script in scripts.scripts_txt2img.scripts if script.name is not None] + i2ilist = [script.name for script in scripts.scripts_img2img.scripts if script.name is not None] + + return models.ScriptsList(txt2img=t2ilist, img2img=i2ilist) + + def get_script_info(self): + res = [] - return ScriptsList(txt2img = t2ilist, img2img = i2ilist) + for script_list in [scripts.scripts_txt2img.scripts, scripts.scripts_img2img.scripts]: + res += [script.api_info for script in script_list if script.api_info is not None] + + return res def get_script(self, script_name, script_runner): if script_name is None or script_name == "": return None, None - + script_idx = script_name_to_index(script_name, script_runner.scripts) return script_runner.scripts[script_idx] @@ -261,14 +282,14 @@ class Api: script_args[0] = selectable_idx + 1 # Now check for always on scripts - if request.alwayson_scripts and (len(request.alwayson_scripts) > 0): + if request.alwayson_scripts: for alwayson_script_name in request.alwayson_scripts.keys(): alwayson_script = self.get_script(alwayson_script_name, script_runner) - if alwayson_script == None: + if alwayson_script is None: raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found") # Selectable script in always on script param check - if alwayson_script.alwayson == False: - raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params") + if alwayson_script.alwayson is False: + raise HTTPException(status_code=422, detail="Cannot have a selectable script in the always on scripts params") # always on script with no arg should always run so you don't really need to add them to the requests if "args" in request.alwayson_scripts[alwayson_script_name]: # min between arg length in scriptrunner and arg length in the request @@ -276,7 +297,7 @@ class Api: script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx] return script_args - def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): + def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI): script_runner = scripts.scripts_txt2img if not script_runner.scripts: script_runner.initialize_scripts(False) @@ -304,25 +325,25 @@ class Api: args.pop('save_images', None) with self.queue_lock: - p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args) - p.scripts = script_runner - p.outpath_grids = opts.outdir_txt2img_grids - p.outpath_samples = opts.outdir_txt2img_samples - - shared.state.begin() - if selectable_scripts != None: - p.script_args = script_args - processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here - else: - p.script_args = tuple(script_args) # Need to pass args as tuple here - processed = process_images(p) - shared.state.end() + with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p: + p.scripts = script_runner + p.outpath_grids = opts.outdir_txt2img_grids + p.outpath_samples = opts.outdir_txt2img_samples + + shared.state.begin(job="scripts_txt2img") + if selectable_scripts is not None: + p.script_args = script_args + processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here + else: + p.script_args = tuple(script_args) # Need to pass args as tuple here + processed = process_images(p) + shared.state.end() b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] - return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js()) + return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js()) - def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI): + def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI): init_images = img2imgreq.init_images if init_images is None: raise HTTPException(status_code=404, detail="Init image not found") @@ -360,20 +381,20 @@ class Api: args.pop('save_images', None) with self.queue_lock: - p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args) - p.init_images = [decode_base64_to_image(x) for x in init_images] - p.scripts = script_runner - p.outpath_grids = opts.outdir_img2img_grids - p.outpath_samples = opts.outdir_img2img_samples - - shared.state.begin() - if selectable_scripts != None: - p.script_args = script_args - processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here - else: - p.script_args = tuple(script_args) # Need to pass args as tuple here - processed = process_images(p) - shared.state.end() + with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p: + p.init_images = [decode_base64_to_image(x) for x in init_images] + p.scripts = script_runner + p.outpath_grids = opts.outdir_img2img_grids + p.outpath_samples = opts.outdir_img2img_samples + + shared.state.begin(job="scripts_img2img") + if selectable_scripts is not None: + p.script_args = script_args + processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here + else: + p.script_args = tuple(script_args) # Need to pass args as tuple here + processed = process_images(p) + shared.state.end() b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] @@ -381,9 +402,9 @@ class Api: img2imgreq.init_images = None img2imgreq.mask = None - return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js()) + return models.ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js()) - def extras_single_image_api(self, req: ExtrasSingleImageRequest): + def extras_single_image_api(self, req: models.ExtrasSingleImageRequest): reqDict = setUpscalers(req) reqDict['image'] = decode_base64_to_image(reqDict['image']) @@ -391,9 +412,9 @@ class Api: with self.queue_lock: result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict) - return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1]) + return models.ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1]) - def extras_batch_images_api(self, req: ExtrasBatchImagesRequest): + def extras_batch_images_api(self, req: models.ExtrasBatchImagesRequest): reqDict = setUpscalers(req) image_list = reqDict.pop('imageList', []) @@ -402,15 +423,15 @@ class Api: with self.queue_lock: result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict) - return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) + return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) - def pnginfoapi(self, req: PNGInfoRequest): + def pnginfoapi(self, req: models.PNGInfoRequest): if(not req.image.strip()): - return PNGInfoResponse(info="") + return models.PNGInfoResponse(info="") image = decode_base64_to_image(req.image.strip()) if image is None: - return PNGInfoResponse(info="") + return models.PNGInfoResponse(info="") geninfo, items = images.read_info_from_image(image) if geninfo is None: @@ -418,13 +439,13 @@ class Api: items = {**{'parameters': geninfo}, **items} - return PNGInfoResponse(info=geninfo, items=items) + return models.PNGInfoResponse(info=geninfo, items=items) - def progressapi(self, req: ProgressRequest = Depends()): + def progressapi(self, req: models.ProgressRequest = Depends()): # copy from check_progress_call of ui.py if shared.state.job_count == 0: - return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo) + return models.ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo) # avoid dividing zero progress = 0.01 @@ -446,9 +467,9 @@ class Api: if shared.state.current_image and not req.skip_current_image: current_image = encode_pil_to_base64(shared.state.current_image) - return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo) + return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo) - def interrogateapi(self, interrogatereq: InterrogateRequest): + def interrogateapi(self, interrogatereq: models.InterrogateRequest): image_b64 = interrogatereq.image if image_b64 is None: raise HTTPException(status_code=404, detail="Image not found") @@ -465,7 +486,7 @@ class Api: else: raise HTTPException(status_code=404, detail="Model not found") - return InterrogateResponse(caption=processed) + return models.InterrogateResponse(caption=processed) def interruptapi(self): shared.state.interrupt() @@ -497,6 +518,10 @@ class Api: return options def set_config(self, req: Dict[str, Any]): + checkpoint_name = req.get("sd_model_checkpoint", None) + if checkpoint_name is not None and checkpoint_name not in checkpoint_alisases: + raise RuntimeError(f"model {checkpoint_name!r} not found") + for k, v in req.items(): shared.opts.set(k, v) @@ -521,9 +546,20 @@ class Api: for upscaler in shared.sd_upscalers ] + def get_latent_upscale_modes(self): + return [ + { + "name": upscale_mode, + } + for upscale_mode in [*(shared.latent_upscale_modes or {})] + ] + def get_sd_models(self): return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()] + def get_sd_vaes(self): + return [{"model_name": x, "filename": vae_dict[x]} for x in vae_dict.keys()] + def get_hypernetworks(self): return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks] @@ -566,44 +602,42 @@ class Api: def create_embedding(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="create_embedding") filename = create_embedding(**args) # create empty embedding sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used - shared.state.end() - return CreateResponse(info=f"create embedding filename: {filename}") + return models.CreateResponse(info=f"create embedding filename: {filename}") except AssertionError as e: + return models.TrainResponse(info=f"create embedding error: {e}") + finally: shared.state.end() - return TrainResponse(info=f"create embedding error: {e}") + def create_hypernetwork(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="create_hypernetwork") filename = create_hypernetwork(**args) # create empty embedding - shared.state.end() - return CreateResponse(info=f"create hypernetwork filename: {filename}") + return models.CreateResponse(info=f"create hypernetwork filename: {filename}") except AssertionError as e: + return models.TrainResponse(info=f"create hypernetwork error: {e}") + finally: shared.state.end() - return TrainResponse(info=f"create hypernetwork error: {e}") def preprocess(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="preprocess") preprocess(**args) # quick operation unless blip/booru interrogation is enabled shared.state.end() - return PreprocessResponse(info = 'preprocess complete') + return models.PreprocessResponse(info='preprocess complete') except KeyError as e: + return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}") + except Exception as e: + return models.PreprocessResponse(info=f"preprocess error: {e}") + finally: shared.state.end() - return PreprocessResponse(info=f"preprocess error: invalid token: {e}") - except AssertionError as e: - shared.state.end() - return PreprocessResponse(info=f"preprocess error: {e}") - except FileNotFoundError as e: - shared.state.end() - return PreprocessResponse(info=f'preprocess error: {e}') def train_embedding(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="train_embedding") apply_optimizations = shared.opts.training_xattention_optimizations error = None filename = '' @@ -616,15 +650,15 @@ class Api: finally: if not apply_optimizations: sd_hijack.apply_optimizations() - shared.state.end() - return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") - except AssertionError as msg: + return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") + except Exception as msg: + return models.TrainResponse(info=f"train embedding error: {msg}") + finally: shared.state.end() - return TrainResponse(info=f"train embedding error: {msg}") def train_hypernetwork(self, args: dict): try: - shared.state.begin() + shared.state.begin(job="train_hypernetwork") shared.loaded_hypernetworks = [] apply_optimizations = shared.opts.training_xattention_optimizations error = None @@ -641,14 +675,16 @@ class Api: if not apply_optimizations: sd_hijack.apply_optimizations() shared.state.end() - return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") - except AssertionError as msg: + return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") + except Exception as exc: + return models.TrainResponse(info=f"train embedding error: {exc}") + finally: shared.state.end() - return TrainResponse(info=f"train embedding error: {error}") def get_memory(self): try: - import os, psutil + import os + import psutil process = psutil.Process(os.getpid()) res = process.memory_info() # only rss is cross-platform guaranteed so we dont rely on other values ram_total = 100 * res.rss / process.memory_percent() # and total memory is calculated as actual value is not cross-platform safe @@ -675,11 +711,23 @@ class Api: 'events': warnings, } else: - cuda = { 'error': 'unavailable' } + cuda = {'error': 'unavailable'} except Exception as err: - cuda = { 'error': f'{err}' } - return MemoryResponse(ram = ram, cuda = cuda) + cuda = {'error': f'{err}'} + return models.MemoryResponse(ram=ram, cuda=cuda) def launch(self, server_name, port): self.app.include_router(self.router) - uvicorn.run(self.app, host=server_name, port=port) + uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=0) + + def kill_webui(self): + restart.stop_program() + + def restart_webui(self): + if restart.is_restartable(): + restart.restart_program() + return Response(status_code=501) + + def stop_webui(request): + shared.state.server_command = "stop" + return Response("Stopping.") diff --git a/modules/api/models.py b/modules/api/models.py index 4a70f440..b5683071 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -223,8 +223,9 @@ for key in _options: if(_options[key].dest != 'help'): flag = _options[key] _type = str - if _options[key].default is not None: _type = type(_options[key].default) - flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))}) + if _options[key].default is not None: + _type = type(_options[key].default) + flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))}) FlagsModel = create_model("Flags", **flags) @@ -240,6 +241,9 @@ class UpscalerItem(BaseModel): model_url: Optional[str] = Field(title="URL") scale: Optional[float] = Field(title="Scale") +class LatentUpscalerModeItem(BaseModel): + name: str = Field(title="Name") + class SDModelItem(BaseModel): title: str = Field(title="Title") model_name: str = Field(title="Model Name") @@ -248,6 +252,10 @@ class SDModelItem(BaseModel): filename: str = Field(title="Filename") config: Optional[str] = Field(title="Config file") +class SDVaeItem(BaseModel): + model_name: str = Field(title="Model Name") + filename: str = Field(title="Filename") + class HypernetworkItem(BaseModel): name: str = Field(title="Name") path: Optional[str] = Field(title="Path") @@ -266,10 +274,6 @@ class PromptStyleItem(BaseModel): prompt: Optional[str] = Field(title="Prompt") negative_prompt: Optional[str] = Field(title="Negative Prompt") -class ArtistItem(BaseModel): - name: str = Field(title="Name") - score: float = Field(title="Score") - category: str = Field(title="Category") class EmbeddingItem(BaseModel): step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available") @@ -286,6 +290,23 @@ class MemoryResponse(BaseModel): ram: dict = Field(title="RAM", description="System memory stats") cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats") + class ScriptsList(BaseModel): - txt2img: list = Field(default=None,title="Txt2img", description="Titles of scripts (txt2img)") - img2img: list = Field(default=None,title="Img2img", description="Titles of scripts (img2img)")
\ No newline at end of file + txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)") + img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)") + + +class ScriptArg(BaseModel): + label: str = Field(default=None, title="Label", description="Name of the argument in UI") + value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument") + minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI") + maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI") + step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI") + choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument") + + +class ScriptInfo(BaseModel): + name: str = Field(default=None, title="Name", description="Script name") + is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script") + is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script") + args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments") diff --git a/modules/call_queue.py b/modules/call_queue.py index 447bb764..3b94f8a4 100644 --- a/modules/call_queue.py +++ b/modules/call_queue.py @@ -1,10 +1,9 @@ +from functools import wraps
import html
-import sys
import threading
-import traceback
import time
-from modules import shared, progress
+from modules import shared, progress, errors
queue_lock = threading.Lock()
@@ -20,17 +19,18 @@ def wrap_queued_call(func): def wrap_gradio_gpu_call(func, extra_outputs=None):
+ @wraps(func)
def f(*args, **kwargs):
# if the first argument is a string that says "task(...)", it is treated as a job id
- if len(args) > 0 and type(args[0]) == str and args[0][0:5] == "task(" and args[0][-1] == ")":
+ if args and type(args[0]) == str and args[0].startswith("task(") and args[0].endswith(")"):
id_task = args[0]
progress.add_task_to_queue(id_task)
else:
id_task = None
with queue_lock:
- shared.state.begin()
+ shared.state.begin(job=id_task)
progress.start_task(id_task)
try:
@@ -47,6 +47,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None): def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+ @wraps(func)
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
if run_memmon:
@@ -56,16 +57,14 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False): try:
res = list(func(*args, **kwargs))
except Exception as e:
- # When printing out our debug argument list, do not print out more than a MB of text
- max_debug_str_len = 131072 # (1024*1024)/8
-
- print("Error completing request", file=sys.stderr)
- argStr = f"Arguments: {args} {kwargs}"
- print(argStr[:max_debug_str_len], file=sys.stderr)
- if len(argStr) > max_debug_str_len:
- print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
- print(traceback.format_exc(), file=sys.stderr)
+ # When printing out our debug argument list,
+ # do not print out more than a 100 KB of text
+ max_debug_str_len = 131072
+ message = "Error completing request"
+ arg_str = f"Arguments: {args} {kwargs}"[:max_debug_str_len]
+ if len(arg_str) > max_debug_str_len:
+ arg_str += f" (Argument list truncated at {max_debug_str_len}/{len(arg_str)} characters)"
+ errors.report(f"{message}\n{arg_str}", exc_info=True)
shared.state.job = ""
shared.state.job_count = 0
@@ -108,4 +107,3 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False): return tuple(res)
return f
-
diff --git a/modules/cmd_args.py b/modules/cmd_args.py index d906a571..278a605e 100644 --- a/modules/cmd_args.py +++ b/modules/cmd_args.py @@ -1,6 +1,7 @@ import argparse
+import json
import os
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401
parser = argparse.ArgumentParser()
@@ -10,9 +11,9 @@ parser.add_argument("--skip-python-version-check", action='store_true', help="la parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
-parser.add_argument("--update-check", action='store_true', help="launch.py argument: chck for updates at startup")
-parser.add_argument("--tests", type=str, default=None, help="launch.py argument: run tests in the specified directory")
-parser.add_argument("--no-tests", action='store_true', help="launch.py argument: do not run tests even if --tests option is specified")
+parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup")
+parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
+parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
@@ -39,7 +40,8 @@ parser.add_argument("--precision", type=str, help="evaluate at this precision", parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
-parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
+parser.add_argument("--ngrok-region", type=str, help="does not do anything.", default="")
+parser.add_argument("--ngrok-options", type=json.loads, help='The options to pass to ngrok in JSON format, e.g.: \'{"authtoken_from_env":true, "basic_auth":"user:password", "oauth_provider":"google", "oauth_allow_emails":"user@asdf.com"}\'', default=dict())
parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
@@ -51,16 +53,16 @@ parser.add_argument("--xformers", action='store_true', help="enable xformers for parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
-parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
+parser.add_argument("--opt-split-attention", action='store_true', help="prefer Doggettx's cross-attention layer optimization for automatic choice of optimization")
+parser.add_argument("--opt-sub-quad-attention", action='store_true', help="prefer memory efficient sub-quadratic cross-attention layer optimization for automatic choice of optimization")
parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
-parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
-parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-parser.add_argument("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization; requires PyTorch 2.*")
-parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization without memory efficient attention, makes image generation deterministic; requires PyTorch 2.*")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
+parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="prefer InvokeAI's cross-attention layer optimization for automatic choice of optimization")
+parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization")
+parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")
+parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
@@ -75,6 +77,7 @@ parser.add_argument("--gradio-auth", type=str, help='set gradio authentication l parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
+parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it")
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
@@ -102,4 +105,5 @@ parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gra parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
-parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
\ No newline at end of file +parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
+parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
diff --git a/modules/codeformer/codeformer_arch.py b/modules/codeformer/codeformer_arch.py index 11dcc3ee..12db6814 100644 --- a/modules/codeformer/codeformer_arch.py +++ b/modules/codeformer/codeformer_arch.py @@ -1,14 +1,12 @@ # this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py import math -import numpy as np import torch from torch import nn, Tensor import torch.nn.functional as F -from typing import Optional, List +from typing import Optional -from modules.codeformer.vqgan_arch import * -from basicsr.utils import get_root_logger +from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock from basicsr.utils.registry import ARCH_REGISTRY def calc_mean_std(feat, eps=1e-5): @@ -121,7 +119,7 @@ class TransformerSALayer(nn.Module): tgt_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None, query_pos: Optional[Tensor] = None): - + # self attention tgt2 = self.norm1(tgt) q = k = self.with_pos_embed(tgt2, query_pos) @@ -161,10 +159,10 @@ class Fuse_sft_block(nn.Module): @ARCH_REGISTRY.register() class CodeFormer(VQAutoEncoder): - def __init__(self, dim_embd=512, n_head=8, n_layers=9, + def __init__(self, dim_embd=512, n_head=8, n_layers=9, codebook_size=1024, latent_size=256, - connect_list=['32', '64', '128', '256'], - fix_modules=['quantize','generator']): + connect_list=('32', '64', '128', '256'), + fix_modules=('quantize', 'generator')): super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size) if fix_modules is not None: @@ -181,14 +179,14 @@ class CodeFormer(VQAutoEncoder): self.feat_emb = nn.Linear(256, self.dim_embd) # transformer - self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) + self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) for _ in range(self.n_layers)]) # logits_predict head self.idx_pred_layer = nn.Sequential( nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False)) - + self.channels = { '16': 512, '32': 256, @@ -223,7 +221,7 @@ class CodeFormer(VQAutoEncoder): enc_feat_dict = {} out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] for i, block in enumerate(self.encoder.blocks): - x = block(x) + x = block(x) if i in out_list: enc_feat_dict[str(x.shape[-1])] = x.clone() @@ -268,11 +266,11 @@ class CodeFormer(VQAutoEncoder): fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] for i, block in enumerate(self.generator.blocks): - x = block(x) + x = block(x) if i in fuse_list: # fuse after i-th block f_size = str(x.shape[-1]) if w>0: x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w) out = x # logits doesn't need softmax before cross_entropy loss - return out, logits, lq_feat
\ No newline at end of file + return out, logits, lq_feat diff --git a/modules/codeformer/vqgan_arch.py b/modules/codeformer/vqgan_arch.py index e7293683..09ee6660 100644 --- a/modules/codeformer/vqgan_arch.py +++ b/modules/codeformer/vqgan_arch.py @@ -5,17 +5,15 @@ VQGAN code, adapted from the original created by the Unleashing Transformers aut https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py ''' -import numpy as np import torch import torch.nn as nn import torch.nn.functional as F -import copy from basicsr.utils import get_root_logger from basicsr.utils.registry import ARCH_REGISTRY def normalize(in_channels): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - + @torch.jit.script def swish(x): @@ -212,15 +210,15 @@ class AttnBlock(nn.Module): # compute attention b, c, h, w = q.shape q = q.reshape(b, c, h*w) - q = q.permute(0, 2, 1) + q = q.permute(0, 2, 1) k = k.reshape(b, c, h*w) - w_ = torch.bmm(q, k) + w_ = torch.bmm(q, k) w_ = w_ * (int(c)**(-0.5)) w_ = F.softmax(w_, dim=2) # attend to values v = v.reshape(b, c, h*w) - w_ = w_.permute(0, 2, 1) + w_ = w_.permute(0, 2, 1) h_ = torch.bmm(v, w_) h_ = h_.reshape(b, c, h, w) @@ -272,18 +270,18 @@ class Encoder(nn.Module): def forward(self, x): for block in self.blocks: x = block(x) - + return x class Generator(nn.Module): def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions): super().__init__() - self.nf = nf - self.ch_mult = ch_mult + self.nf = nf + self.ch_mult = ch_mult self.num_resolutions = len(self.ch_mult) self.num_res_blocks = res_blocks - self.resolution = img_size + self.resolution = img_size self.attn_resolutions = attn_resolutions self.in_channels = emb_dim self.out_channels = 3 @@ -317,29 +315,29 @@ class Generator(nn.Module): blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1)) self.blocks = nn.ModuleList(blocks) - + def forward(self, x): for block in self.blocks: x = block(x) - + return x - + @ARCH_REGISTRY.register() class VQAutoEncoder(nn.Module): - def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256, + def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256, beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None): super().__init__() logger = get_root_logger() - self.in_channels = 3 - self.nf = nf - self.n_blocks = res_blocks + self.in_channels = 3 + self.nf = nf + self.n_blocks = res_blocks self.codebook_size = codebook_size self.embed_dim = emb_dim self.ch_mult = ch_mult self.resolution = img_size - self.attn_resolutions = attn_resolutions + self.attn_resolutions = attn_resolutions or [16] self.quantizer_type = quantizer self.encoder = Encoder( self.in_channels, @@ -365,11 +363,11 @@ class VQAutoEncoder(nn.Module): self.kl_weight ) self.generator = Generator( - self.nf, + self.nf, self.embed_dim, - self.ch_mult, - self.n_blocks, - self.resolution, + self.ch_mult, + self.n_blocks, + self.resolution, self.attn_resolutions ) @@ -434,4 +432,4 @@ class VQGANDiscriminator(nn.Module): raise ValueError('Wrong params!') def forward(self, x): - return self.main(x)
\ No newline at end of file + return self.main(x) diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index 8d84bbc9..f293acf5 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -1,13 +1,11 @@ import os
-import sys
-import traceback
import cv2
import torch
import modules.face_restoration
import modules.shared
-from modules import shared, devices, modelloader
+from modules import shared, devices, modelloader, errors
from modules.paths import models_path
# codeformer people made a choice to include modified basicsr library to their project which makes
@@ -17,14 +15,11 @@ model_dir = "Codeformer" model_path = os.path.join(models_path, model_dir)
model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-have_codeformer = False
codeformer = None
def setup_model(dirname):
- global model_path
- if not os.path.exists(model_path):
- os.makedirs(model_path)
+ os.makedirs(model_path, exist_ok=True)
path = modules.paths.paths.get("CodeFormer", None)
if path is None:
@@ -33,11 +28,9 @@ def setup_model(dirname): try:
from torchvision.transforms.functional import normalize
from modules.codeformer.codeformer_arch import CodeFormer
- from basicsr.utils.download_util import load_file_from_url
- from basicsr.utils import imwrite, img2tensor, tensor2img
+ from basicsr.utils import img2tensor, tensor2img
from facelib.utils.face_restoration_helper import FaceRestoreHelper
from facelib.detection.retinaface import retinaface
- from modules.shared import cmd_opts
net_class = CodeFormer
@@ -96,7 +89,7 @@ def setup_model(dirname): self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
self.face_helper.align_warp_face()
- for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
+ for cropped_face in self.face_helper.cropped_faces:
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
@@ -107,8 +100,8 @@ def setup_model(dirname): restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
del output
torch.cuda.empty_cache()
- except Exception as error:
- print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
+ except Exception:
+ errors.report('Failed inference for CodeFormer', exc_info=True)
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
restored_face = restored_face.astype('uint8')
@@ -129,15 +122,11 @@ def setup_model(dirname): return restored_img
- global have_codeformer
- have_codeformer = True
-
global codeformer
codeformer = FaceRestorerCodeFormer(dirname)
shared.face_restorers.append(codeformer)
except Exception:
- print("Error setting up CodeFormer:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report("Error setting up CodeFormer", exc_info=True)
# sys.path = stored_sys_path
diff --git a/modules/config_states.py b/modules/config_states.py index 2ea00929..6f1ab53f 100644 --- a/modules/config_states.py +++ b/modules/config_states.py @@ -3,8 +3,6 @@ Supports saving and restoring webui and extensions from a known working set of c """ import os -import sys -import traceback import json import time import tqdm @@ -13,8 +11,8 @@ from datetime import datetime from collections import OrderedDict import git -from modules import shared, extensions -from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path, config_states_dir +from modules import shared, extensions, errors +from modules.paths_internal import script_path, config_states_dir all_config_states = OrderedDict() @@ -35,7 +33,7 @@ def list_config_states(): j["filepath"] = path config_states.append(j) - config_states = list(sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)) + config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True) for cs in config_states: timestamp = time.asctime(time.gmtime(cs["created_at"])) @@ -53,8 +51,7 @@ def get_webui_config(): if os.path.exists(os.path.join(script_path, ".git")): webui_repo = git.Repo(script_path) except Exception: - print(f"Error reading webui git info from {script_path}:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) + errors.report(f"Error reading webui git info from {script_path}", exc_info=True) webui_remote = None webui_commit_hash = None @@ -83,6 +80,8 @@ def get_extension_config(): ext_config = {} for ext in extensions.extensions: + ext.read_info_from_repo() + entry = { "name": ext.name, "path": ext.path, @@ -132,8 +131,7 @@ def restore_webui_config(config): if os.path.exists(os.path.join(script_path, ".git")): webui_repo = git.Repo(script_path) except Exception: - print(f"Error reading webui git info from {script_path}:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) + errors.report(f"Error reading webui git info from {script_path}", exc_info=True) return try: @@ -141,8 +139,7 @@ def restore_webui_config(config): webui_repo.git.reset(webui_commit_hash, hard=True) print(f"* Restored webui to commit {webui_commit_hash}.") except Exception: - print(f"Error restoring webui to commit {webui_commit_hash}:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) + errors.report(f"Error restoring webui to commit{webui_commit_hash}") def restore_extension_config(config): diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 122fce7f..547e1b4c 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -2,7 +2,6 @@ import os import re import torch -from PIL import Image import numpy as np from modules import modelloader, paths, deepbooru_model, devices, images, shared @@ -79,7 +78,7 @@ class DeepDanbooru: res = [] - filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")]) + filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")} for tag in [x for x in tags if x not in filtertags]: probability = probability_dict[tag] diff --git a/modules/devices.py b/modules/devices.py index c705a3cb..620ed1a6 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -1,5 +1,7 @@ import sys import contextlib +from functools import lru_cache + import torch from modules import errors @@ -13,13 +15,6 @@ def has_mps() -> bool: else: return mac_specific.has_mps -def extract_device_id(args, name): - for x in range(len(args)): - if name in args[x]: - return args[x + 1] - - return None - def get_cuda_device_string(): from modules import shared @@ -65,7 +60,7 @@ def enable_tf32(): # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407 - if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]): + if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())): torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True @@ -154,3 +149,19 @@ def test_for_nans(x, where): message += " Use --disable-nan-check commandline argument to disable this check." raise NansException(message) + + +@lru_cache +def first_time_calculation(): + """ + just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and + spends about 2.7 seconds doing that, at least wih NVidia. + """ + + x = torch.zeros((1, 1)).to(device, dtype) + linear = torch.nn.Linear(1, 1).to(device, dtype) + linear(x) + + x = torch.zeros((1, 1, 3, 3)).to(device, dtype) + conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype) + conv2d(x) diff --git a/modules/errors.py b/modules/errors.py index f6b80dbb..5271a9fe 100644 --- a/modules/errors.py +++ b/modules/errors.py @@ -1,8 +1,42 @@ import sys
+import textwrap
import traceback
+exception_records = []
+
+
+def record_exception():
+ _, e, tb = sys.exc_info()
+ if e is None:
+ return
+
+ if exception_records and exception_records[-1] == e:
+ return
+
+ exception_records.append((e, tb))
+
+ if len(exception_records) > 5:
+ exception_records.pop(0)
+
+
+def report(message: str, *, exc_info: bool = False) -> None:
+ """
+ Print an error message to stderr, with optional traceback.
+ """
+
+ record_exception()
+
+ for line in message.splitlines():
+ print("***", line, file=sys.stderr)
+ if exc_info:
+ print(textwrap.indent(traceback.format_exc(), " "), file=sys.stderr)
+ print("---", file=sys.stderr)
+
+
def print_error_explanation(message):
+ record_exception()
+
lines = message.strip().split("\n")
max_len = max([len(x) for x in lines])
@@ -12,9 +46,15 @@ def print_error_explanation(message): print('=' * max_len, file=sys.stderr)
-def display(e: Exception, task):
+def display(e: Exception, task, *, full_traceback=False):
+ record_exception()
+
print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ te = traceback.TracebackException.from_exception(e)
+ if full_traceback:
+ # include frames leading up to the try-catch block
+ te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack)
+ print(*te.format(), sep="", file=sys.stderr)
message = str(e)
if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
@@ -28,6 +68,8 @@ already_displayed = {} def display_once(e: Exception, task):
+ record_exception()
+
if task in already_displayed:
return
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index f4369257..02a1727d 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -1,24 +1,20 @@ -import os
+import sys
import numpy as np
import torch
from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
import modules.esrgan_model_arch as arch
-from modules import shared, modelloader, images, devices
-from modules.upscaler import Upscaler, UpscalerData
+from modules import modelloader, images, devices
from modules.shared import opts
-
+from modules.upscaler import Upscaler, UpscalerData
def mod2normal(state_dict):
# this code is copied from https://github.com/victorca25/iNNfer
if 'conv_first.weight' in state_dict:
crt_net = {}
- items = []
- for k, v in state_dict.items():
- items.append(k)
+ items = list(state_dict)
crt_net['model.0.weight'] = state_dict['conv_first.weight']
crt_net['model.0.bias'] = state_dict['conv_first.bias']
@@ -52,9 +48,7 @@ def resrgan2normal(state_dict, nb=23): if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
re8x = 0
crt_net = {}
- items = []
- for k, v in state_dict.items():
- items.append(k)
+ items = list(state_dict)
crt_net['model.0.weight'] = state_dict['conv_first.weight']
crt_net['model.0.bias'] = state_dict['conv_first.bias']
@@ -138,7 +132,7 @@ class UpscalerESRGAN(Upscaler): scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
scalers.append(scaler_data)
for file in model_paths:
- if "http" in file:
+ if file.startswith("http"):
name = self.model_name
else:
name = modelloader.friendly_name(file)
@@ -147,26 +141,25 @@ class UpscalerESRGAN(Upscaler): self.scalers.append(scaler_data)
def do_upscale(self, img, selected_model):
- model = self.load_model(selected_model)
- if model is None:
+ try:
+ model = self.load_model(selected_model)
+ except Exception as e:
+ print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
return img
model.to(devices.device_esrgan)
img = esrgan_upscale(model, img)
return img
def load_model(self, path: str):
- if "http" in path:
- filename = load_file_from_url(
+ if path.startswith("http"):
+ # TODO: this doesn't use `path` at all?
+ filename = modelloader.load_file_from_url(
url=self.model_url,
- model_dir=self.model_path,
+ model_dir=self.model_download_path,
file_name=f"{self.model_name}.pth",
- progress=True,
)
else:
filename = path
- if not os.path.exists(filename) or filename is None:
- print(f"Unable to load {self.model_path} from {filename}")
- return None
state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py index 6071fea7..2b9888ba 100644 --- a/modules/esrgan_model_arch.py +++ b/modules/esrgan_model_arch.py @@ -2,7 +2,6 @@ from collections import OrderedDict
import math
-import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
@@ -106,7 +105,7 @@ class ResidualDenseBlock_5C(nn.Module): Modified options that can be used:
- "Partial Convolution based Padding" arXiv:1811.11718
- "Spectral normalization" arXiv:1802.05957
- - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
+ - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
{Rakotonirina} and A. {Rasoanaivo}
"""
@@ -171,7 +170,7 @@ class GaussianNoise(nn.Module): scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
x = x + sampled_noise
- return x
+ return x
def conv1x1(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
@@ -438,9 +437,11 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias= padding = padding if pad_type == 'zero' else 0
if convtype=='PartialConv2D':
+ from torchvision.ops import PartialConv2d # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer
c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, bias=bias, groups=groups)
elif convtype=='DeformConv2D':
+ from torchvision.ops import DeformConv2d # not tested
c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, bias=bias, groups=groups)
elif convtype=='Conv3D':
diff --git a/modules/extensions.py b/modules/extensions.py index 34d9d654..abc6e2b1 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -1,18 +1,13 @@ import os
-import sys
-import traceback
+import threading
-import time
-from datetime import datetime
-import git
-
-from modules import shared
-from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path
+from modules import shared, errors
+from modules.gitpython_hack import Repo
+from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401
extensions = []
-if not os.path.exists(extensions_dir):
- os.makedirs(extensions_dir)
+os.makedirs(extensions_dir, exist_ok=True)
def active():
@@ -25,6 +20,8 @@ def active(): class Extension:
+ lock = threading.Lock()
+
def __init__(self, name, path, enabled=True, is_builtin=False):
self.name = name
self.path = path
@@ -43,15 +40,19 @@ class Extension: if self.is_builtin or self.have_info_from_repo:
return
- self.have_info_from_repo = True
+ with self.lock:
+ if self.have_info_from_repo:
+ return
+ self.do_read_info_from_repo()
+
+ def do_read_info_from_repo(self):
repo = None
try:
if os.path.exists(os.path.join(self.path, ".git")):
- repo = git.Repo(self.path)
+ repo = Repo(self.path)
except Exception:
- print(f"Error reading github repository info from {self.path}:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error reading github repository info from {self.path}", exc_info=True)
if repo is None or repo.bare:
self.remote = None
@@ -59,18 +60,19 @@ class Extension: try:
self.status = 'unknown'
self.remote = next(repo.remote().urls, None)
- head = repo.head.commit
- self.commit_date = repo.head.commit.committed_date
- ts = time.asctime(time.gmtime(self.commit_date))
+ commit = repo.head.commit
+ self.commit_date = commit.committed_date
if repo.active_branch:
self.branch = repo.active_branch.name
- self.commit_hash = head.hexsha
- self.version = f'{self.commit_hash[:8]} ({ts})'
+ self.commit_hash = commit.hexsha
+ self.version = self.commit_hash[:8]
- except Exception as ex:
- print(f"Failed reading extension data from Git repository ({self.name}): {ex}", file=sys.stderr)
+ except Exception:
+ errors.report(f"Failed reading extension data from Git repository ({self.name})", exc_info=True)
self.remote = None
+ self.have_info_from_repo = True
+
def list_files(self, subdir, extension):
from modules import scripts
@@ -87,7 +89,7 @@ class Extension: return res
def check_updates(self):
- repo = git.Repo(self.path)
+ repo = Repo(self.path)
for fetch in repo.remote().fetch(dry_run=True):
if fetch.flags != fetch.HEAD_UPTODATE:
self.can_update = True
@@ -109,7 +111,7 @@ class Extension: self.status = "latest"
def fetch_and_reset_hard(self, commit='origin'):
- repo = git.Repo(self.path)
+ repo = Repo(self.path)
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
repo.git.fetch(all=True)
diff --git a/modules/extra_networks.py b/modules/extra_networks.py index 1978673d..41799b0a 100644 --- a/modules/extra_networks.py +++ b/modules/extra_networks.py @@ -14,9 +14,26 @@ def register_extra_network(extra_network): extra_network_registry[extra_network.name] = extra_network
+def register_default_extra_networks():
+ from modules.extra_networks_hypernet import ExtraNetworkHypernet
+ register_extra_network(ExtraNetworkHypernet())
+
+
class ExtraNetworkParams:
def __init__(self, items=None):
self.items = items or []
+ self.positional = []
+ self.named = {}
+
+ for item in self.items:
+ parts = item.split('=', 2) if isinstance(item, str) else [item]
+ if len(parts) == 2:
+ self.named[parts[0]] = parts[1]
+ else:
+ self.positional.append(item)
+
+ def __eq__(self, other):
+ return self.items == other.items
class ExtraNetwork:
@@ -86,12 +103,15 @@ def activate(p, extra_network_data): except Exception as e:
errors.display(e, f"activating extra network {extra_network_name}")
+ if p.scripts is not None:
+ p.scripts.after_extra_networks_activate(p, batch_number=p.iteration, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds, extra_network_data=extra_network_data)
+
def deactivate(p, extra_network_data):
"""call deactivate for extra networks in extra_network_data in specified order, then call
deactivate for all remaining registered networks"""
- for extra_network_name, extra_network_args in extra_network_data.items():
+ for extra_network_name in extra_network_data:
extra_network = extra_network_registry.get(extra_network_name, None)
if extra_network is None:
continue
diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py index 04f27c9f..b6a6dc0e 100644 --- a/modules/extra_networks_hypernet.py +++ b/modules/extra_networks_hypernet.py @@ -1,4 +1,4 @@ -from modules import extra_networks, shared, extra_networks
+from modules import extra_networks, shared
from modules.hypernetworks import hypernetwork
@@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork): def activate(self, p, params_list):
additional = shared.opts.sd_hypernetwork
- if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+ if additional != "None" and additional in shared.hypernetworks and not any(x for x in params_list if x.items[0] == additional):
hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
@@ -17,7 +17,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork): names = []
multipliers = []
for params in params_list:
- assert len(params.items) > 0
+ assert params.items
names.append(params.items[0])
multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
diff --git a/modules/extras.py b/modules/extras.py index ff4e9c4e..e9c0263e 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -73,8 +73,7 @@ def to_half(tensor, enable): def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
- shared.state.begin()
- shared.state.job = 'model-merge'
+ shared.state.begin(job="model-merge")
def fail(message):
shared.state.textinfo = message
@@ -136,14 +135,14 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_ result_is_instruct_pix2pix_model = False
if theta_func2:
- shared.state.textinfo = f"Loading B"
+ shared.state.textinfo = "Loading B"
print(f"Loading {secondary_model_info.filename}...")
theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
else:
theta_1 = None
if theta_func1:
- shared.state.textinfo = f"Loading C"
+ shared.state.textinfo = "Loading C"
print(f"Loading {tertiary_model_info.filename}...")
theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
@@ -199,7 +198,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_ result_is_inpainting_model = True
else:
theta_0[key] = theta_func2(a, b, multiplier)
-
+
theta_0[key] = to_half(theta_0[key], save_as_half)
shared.state.sampling_step += 1
@@ -242,9 +241,11 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_ shared.state.textinfo = "Saving"
print(f"Saving to {output_modelname}...")
- metadata = {"format": "pt", "sd_merge_models": {}, "sd_merge_recipe": None}
+ metadata = None
if save_metadata:
+ metadata = {"format": "pt"}
+
merge_recipe = {
"type": "webui", # indicate this model was merged with webui's built-in merger
"primary_model_hash": primary_model_info.sha256,
@@ -262,15 +263,17 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_ }
metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
+ sd_merge_models = {}
+
def add_model_metadata(checkpoint_info):
checkpoint_info.calculate_shorthash()
- metadata["sd_merge_models"][checkpoint_info.sha256] = {
+ sd_merge_models[checkpoint_info.sha256] = {
"name": checkpoint_info.name,
"legacy_hash": checkpoint_info.hash,
"sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
}
- metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
+ sd_merge_models.update(checkpoint_info.metadata.get("sd_merge_models", {}))
add_model_metadata(primary_model_info)
if secondary_model_info:
@@ -278,7 +281,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_ if tertiary_model_info:
add_model_metadata(tertiary_model_info)
- metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
+ metadata["sd_merge_models"] = json.dumps(sd_merge_models)
_, extension = os.path.splitext(output_modelname)
if extension.lower() == ".safetensors":
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index fe8b18b2..a3448be9 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -1,15 +1,12 @@ import base64
-import html
import io
-import math
+import json
import os
import re
-from pathlib import Path
import gradio as gr
from modules.paths import data_path
from modules import shared, ui_tempdir, script_callbacks
-import tempfile
from PIL import Image
re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
@@ -23,14 +20,14 @@ registered_param_bindings = [] class ParamBinding:
- def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=[]):
+ def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
self.paste_button = paste_button
self.tabname = tabname
self.source_text_component = source_text_component
self.source_image_component = source_image_component
self.source_tabname = source_tabname
self.override_settings_component = override_settings_component
- self.paste_field_names = paste_field_names
+ self.paste_field_names = paste_field_names or []
def reset():
@@ -38,20 +35,27 @@ def reset(): def quote(text):
- if ',' not in str(text):
+ if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
return text
- text = str(text)
- text = text.replace('\\', '\\\\')
- text = text.replace('"', '\\"')
- return f'"{text}"'
+ return json.dumps(text, ensure_ascii=False)
+
+
+def unquote(text):
+ if len(text) == 0 or text[0] != '"' or text[-1] != '"':
+ return text
+
+ try:
+ return json.loads(text)
+ except Exception:
+ return text
def image_from_url_text(filedata):
if filedata is None:
return None
- if type(filedata) == list and len(filedata) > 0 and type(filedata[0]) == dict and filedata[0].get("is_file", False):
+ if type(filedata) == list and filedata and type(filedata[0]) == dict and filedata[0].get("is_file", False):
filedata = filedata[0]
if type(filedata) == dict and filedata.get("is_file", False):
@@ -170,31 +174,6 @@ def send_image_and_dimensions(x): return img, w, h
-
-def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
- """Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
-
- Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config
- parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to.
-
- If the infotext has no hash, then a hypernet with the same name will be selected instead.
- """
- hypernet_name = hypernet_name.lower()
- if hypernet_hash is not None:
- # Try to match the hash in the name
- for hypernet_key in shared.hypernetworks.keys():
- result = re_hypernet_hash.search(hypernet_key)
- if result is not None and result[1] == hypernet_hash:
- return hypernet_key
- else:
- # Fall back to a hypernet with the same name
- for hypernet_key in shared.hypernetworks.keys():
- if hypernet_key.lower().startswith(hypernet_name):
- return hypernet_key
-
- return None
-
-
def restore_old_hires_fix_params(res):
"""for infotexts that specify old First pass size parameter, convert it into
width, height, and hr scale"""
@@ -251,28 +230,40 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model lines.append(lastline)
lastline = ''
- for i, line in enumerate(lines):
+ for line in lines:
line = line.strip()
if line.startswith("Negative prompt:"):
done_with_prompt = True
line = line[16:].strip()
-
if done_with_prompt:
negative_prompt += ("" if negative_prompt == "" else "\n") + line
else:
prompt += ("" if prompt == "" else "\n") + line
+ if shared.opts.infotext_styles != "Ignore":
+ found_styles, prompt, negative_prompt = shared.prompt_styles.extract_styles_from_prompt(prompt, negative_prompt)
+
+ if shared.opts.infotext_styles == "Apply":
+ res["Styles array"] = found_styles
+ elif shared.opts.infotext_styles == "Apply if any" and found_styles:
+ res["Styles array"] = found_styles
+
res["Prompt"] = prompt
res["Negative prompt"] = negative_prompt
for k, v in re_param.findall(lastline):
- v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
- m = re_imagesize.match(v)
- if m is not None:
- res[f"{k}-1"] = m.group(1)
- res[f"{k}-2"] = m.group(2)
- else:
- res[k] = v
+ try:
+ if v[0] == '"' and v[-1] == '"':
+ v = unquote(v)
+
+ m = re_imagesize.match(v)
+ if m is not None:
+ res[f"{k}-1"] = m.group(1)
+ res[f"{k}-2"] = m.group(2)
+ else:
+ res[k] = v
+ except Exception:
+ print(f"Error parsing \"{k}: {v}\"")
# Missing CLIP skip means it was set to 1 (the default)
if "Clip skip" not in res:
@@ -286,17 +277,34 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model res["Hires resize-1"] = 0
res["Hires resize-2"] = 0
+ if "Hires sampler" not in res:
+ res["Hires sampler"] = "Use same sampler"
+
+ if "Hires prompt" not in res:
+ res["Hires prompt"] = ""
+
+ if "Hires negative prompt" not in res:
+ res["Hires negative prompt"] = ""
+
restore_old_hires_fix_params(res)
# Missing RNG means the default was set, which is GPU RNG
if "RNG" not in res:
res["RNG"] = "GPU"
- return res
+ if "Schedule type" not in res:
+ res["Schedule type"] = "Automatic"
+
+ if "Schedule max sigma" not in res:
+ res["Schedule max sigma"] = 0
+ if "Schedule min sigma" not in res:
+ res["Schedule min sigma"] = 0
-settings_map = {}
+ if "Schedule rho" not in res:
+ res["Schedule rho"] = 0
+ return res
infotext_to_setting_name_mapping = [
@@ -304,6 +312,10 @@ infotext_to_setting_name_mapping = [ ('Conditional mask weight', 'inpainting_mask_weight'),
('Model hash', 'sd_model_checkpoint'),
('ENSD', 'eta_noise_seed_delta'),
+ ('Schedule type', 'k_sched_type'),
+ ('Schedule max sigma', 'sigma_max'),
+ ('Schedule min sigma', 'sigma_min'),
+ ('Schedule rho', 'rho'),
('Noise multiplier', 'initial_noise_multiplier'),
('Eta', 'eta_ancestral'),
('Eta DDIM', 'eta_ddim'),
@@ -312,8 +324,11 @@ infotext_to_setting_name_mapping = [ ('UniPC skip type', 'uni_pc_skip_type'),
('UniPC order', 'uni_pc_order'),
('UniPC lower order final', 'uni_pc_lower_order_final'),
+ ('Token merging ratio', 'token_merging_ratio'),
+ ('Token merging ratio hr', 'token_merging_ratio_hr'),
('RNG', 'randn_source'),
('NGMS', 's_min_uncond'),
+ ('Pad conds', 'pad_cond_uncond'),
]
@@ -405,7 +420,7 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component, vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
- return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=len(vals_pairs) > 0)
+ return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=bool(vals_pairs))
paste_fields = paste_fields + [(override_settings_component, paste_settings)]
@@ -422,5 +437,3 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component, outputs=[],
show_progress=False,
)
-
-
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py index fbe6215a..8e0f13bd 100644 --- a/modules/gfpgan_model.py +++ b/modules/gfpgan_model.py @@ -1,12 +1,10 @@ import os
-import sys
-import traceback
import facexlib
import gfpgan
import modules.face_restoration
-from modules import paths, shared, devices, modelloader
+from modules import paths, shared, devices, modelloader, errors
model_dir = "GFPGAN"
user_path = None
@@ -27,7 +25,7 @@ def gfpgann(): return None
models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
- if len(models) == 1 and "http" in models[0]:
+ if len(models) == 1 and models[0].startswith("http"):
model_file = models[0]
elif len(models) != 0:
latest_file = max(models, key=os.path.getctime)
@@ -72,13 +70,10 @@ gfpgan_constructor = None def setup_model(dirname):
- global model_path
- if not os.path.exists(model_path):
- os.makedirs(model_path)
-
try:
+ os.makedirs(model_path, exist_ok=True)
from gfpgan import GFPGANer
- from facexlib import detection, parsing
+ from facexlib import detection, parsing # noqa: F401
global user_path
global have_gfpgan
global gfpgan_constructor
@@ -112,5 +107,4 @@ def setup_model(dirname): shared.face_restorers.append(FaceRestorerGFPGAN())
except Exception:
- print("Error setting up GFPGAN:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report("Error setting up GFPGAN", exc_info=True)
diff --git a/modules/gitpython_hack.py b/modules/gitpython_hack.py new file mode 100644 index 00000000..e537c1df --- /dev/null +++ b/modules/gitpython_hack.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +import io +import subprocess + +import git + + +class Git(git.Git): + """ + Git subclassed to never use persistent processes. + """ + + def _get_persistent_cmd(self, attr_name, cmd_name, *args, **kwargs): + raise NotImplementedError(f"Refusing to use persistent process: {attr_name} ({cmd_name} {args} {kwargs})") + + def get_object_header(self, ref: str | bytes) -> tuple[str, str, int]: + ret = subprocess.check_output( + [self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch-check"], + input=self._prepare_ref(ref), + cwd=self._working_dir, + timeout=2, + ) + return self._parse_object_header(ret) + + def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]: + # Not really streaming, per se; this buffers the entire object in memory. + # Shouldn't be a problem for our use case, since we're only using this for + # object headers (commit objects). + ret = subprocess.check_output( + [self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch"], + input=self._prepare_ref(ref), + cwd=self._working_dir, + timeout=30, + ) + bio = io.BytesIO(ret) + hexsha, typename, size = self._parse_object_header(bio.readline()) + return (hexsha, typename, size, self.CatFileContentStream(size, bio)) + + +class Repo(git.Repo): + GitCommandWrapperType = Git diff --git a/modules/hashes.py b/modules/hashes.py index 032120f4..8b7ea0ac 100644 --- a/modules/hashes.py +++ b/modules/hashes.py @@ -46,8 +46,8 @@ def calculate_sha256(filename): return hash_sha256.hexdigest()
-def sha256_from_cache(filename, title):
- hashes = cache("hashes")
+def sha256_from_cache(filename, title, use_addnet_hash=False):
+ hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
ondisk_mtime = os.path.getmtime(filename)
if title not in hashes:
@@ -62,10 +62,10 @@ def sha256_from_cache(filename, title): return cached_sha256
-def sha256(filename, title):
- hashes = cache("hashes")
+def sha256(filename, title, use_addnet_hash=False):
+ hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
- sha256_value = sha256_from_cache(filename, title)
+ sha256_value = sha256_from_cache(filename, title, use_addnet_hash)
if sha256_value is not None:
return sha256_value
@@ -73,7 +73,11 @@ def sha256(filename, title): return None
print(f"Calculating sha256 for {filename}: ", end='')
- sha256_value = calculate_sha256(filename)
+ if use_addnet_hash:
+ with open(filename, "rb") as file:
+ sha256_value = addnet_hash_safetensors(file)
+ else:
+ sha256_value = calculate_sha256(filename)
print(f"{sha256_value}")
hashes[title] = {
@@ -86,6 +90,19 @@ def sha256(filename, title): return sha256_value
+def addnet_hash_safetensors(b):
+ """kohya-ss hash for safetensors from https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py"""
+ hash_sha256 = hashlib.sha256()
+ blksize = 1024 * 1024
+ b.seek(0)
+ header = b.read(8)
+ n = int.from_bytes(header, "little")
+ offset = n + 8
+ b.seek(offset)
+ for chunk in iter(lambda: b.read(blksize), b""):
+ hash_sha256.update(chunk)
+
+ return hash_sha256.hexdigest()
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 1fc49537..51941c11 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -1,10 +1,7 @@ -import csv
import datetime
import glob
import html
import os
-import sys
-import traceback
import inspect
import modules.textual_inversion.dataset
@@ -12,13 +9,13 @@ import torch import tqdm
from einops import rearrange, repeat
from ldm.util import default
-from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint
+from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
from modules.textual_inversion import textual_inversion, logging
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from torch import einsum
from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
-from collections import defaultdict, deque
+from collections import deque
from statistics import stdev, mean
@@ -178,34 +175,34 @@ class Hypernetwork: def weights(self):
res = []
- for k, layers in self.layers.items():
+ for layers in self.layers.values():
for layer in layers:
res += layer.parameters()
return res
def train(self, mode=True):
- for k, layers in self.layers.items():
+ for layers in self.layers.values():
for layer in layers:
layer.train(mode=mode)
for param in layer.parameters():
param.requires_grad = mode
def to(self, device):
- for k, layers in self.layers.items():
+ for layers in self.layers.values():
for layer in layers:
layer.to(device)
return self
def set_multiplier(self, multiplier):
- for k, layers in self.layers.items():
+ for layers in self.layers.values():
for layer in layers:
layer.multiplier = multiplier
return self
def eval(self):
- for k, layers in self.layers.items():
+ for layers in self.layers.values():
for layer in layers:
layer.eval()
for param in layer.parameters():
@@ -326,17 +323,14 @@ def load_hypernetwork(name): if path is None:
return None
- hypernetwork = Hypernetwork()
-
try:
+ hypernetwork = Hypernetwork()
hypernetwork.load(path)
+ return hypernetwork
except Exception:
- print(f"Error loading hypernetwork {path}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error loading hypernetwork {path}", exc_info=True)
return None
- return hypernetwork
-
def load_hypernetworks(names, multipliers=None):
already_loaded = {}
@@ -359,17 +353,6 @@ def load_hypernetworks(names, multipliers=None): shared.loaded_hypernetworks.append(hypernetwork)
-def find_closest_hypernetwork_name(search: str):
- if not search:
- return None
- search = search.lower()
- applicable = [name for name in shared.hypernetworks if search in name.lower()]
- if not applicable:
- return None
- applicable = sorted(applicable, key=lambda name: len(name))
- return applicable[0]
-
-
def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None)
@@ -404,7 +387,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None): k = self.to_k(context_k)
v = self.to_v(context_v)
- q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+ q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
@@ -452,18 +435,6 @@ def statistics(data): return total_information, recent_information
-def report_statistics(loss_info:dict):
- keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
- for key in keys:
- try:
- print("Loss statistics for file " + key)
- info, recent = statistics(list(loss_info[key]))
- print(info)
- print(recent)
- except Exception as e:
- print(e)
-
-
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
# Remove illegal characters from name.
name = "".join( x for x in name if (x.isalnum() or x in "._- "))
@@ -541,7 +512,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi return hypernetwork, filename
scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
-
+
clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None
if clip_grad:
clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
@@ -594,7 +565,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi print(e)
scaler = torch.cuda.amp.GradScaler()
-
+
batch_size = ds.batch_size
gradient_step = ds.gradient_step
# n steps = batch_size * gradient_step * n image processed
@@ -620,7 +591,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi try:
sd_hijack_checkpoint.add()
- for i in range((steps-initial_step) * gradient_step):
+ for _ in range((steps-initial_step) * gradient_step):
if scheduler.finished:
break
if shared.state.interrupted:
@@ -637,7 +608,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi if clip_grad:
clip_grad_sched.step(hypernetwork.step)
-
+
with devices.autocast():
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
if use_weight:
@@ -658,14 +629,14 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi _loss_step += loss.item()
scaler.scale(loss).backward()
-
+
# go back until we reach gradient accumulation steps
if (j + 1) % gradient_step != 0:
continue
loss_logging.append(_loss_step)
if clip_grad:
clip_grad(weights, clip_grad_sched.learn_rate)
-
+
scaler.step(optimizer)
scaler.update()
hypernetwork.step += 1
@@ -675,7 +646,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi _loss_step = 0
steps_done = hypernetwork.step + 1
-
+
epoch_num = hypernetwork.step // steps_per_epoch
epoch_step = hypernetwork.step % steps_per_epoch
@@ -771,12 +742,11 @@ Last saved image: {html.escape(last_saved_image)}<br/> </p>
"""
except Exception:
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report("Exception in training hypernetwork", exc_info=True)
finally:
pbar.leave = False
pbar.close()
hypernetwork.eval()
- #report_statistics(loss_dict)
sd_hijack_checkpoint.remove()
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 76599f5a..8b6255e2 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -1,19 +1,17 @@ import html
-import os
-import re
import gradio as gr
import modules.hypernetworks.hypernetwork
from modules import devices, sd_hijack, shared
not_available = ["hardswish", "multiheadattention"]
-keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = [x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict if x not in not_available]
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure)
- return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {filename}", ""
+ return gr.Dropdown.update(choices=sorted(shared.hypernetworks)), f"Created: {filename}", ""
def train_hypernetwork(*args):
diff --git a/modules/images.py b/modules/images.py index a41965ab..b5412548 100644 --- a/modules/images.py +++ b/modules/images.py @@ -1,6 +1,6 @@ +from __future__ import annotations
+
import datetime
-import sys
-import traceback
import pytz
import io
@@ -12,18 +12,27 @@ import re import numpy as np
import piexif
import piexif.helper
-from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
-from fonts.ttf import Roboto
+from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin
import string
import json
import hashlib
from modules import sd_samplers, shared, script_callbacks, errors
-from modules.shared import opts, cmd_opts
+from modules.paths_internal import roboto_ttf_file
+from modules.shared import opts
+
+import modules.sd_vae as sd_vae
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+def get_font(fontsize: int):
+ try:
+ return ImageFont.truetype(opts.font or roboto_ttf_file, fontsize)
+ except Exception:
+ return ImageFont.truetype(roboto_ttf_file, fontsize)
+
+
def image_grid(imgs, batch_size=1, rows=None):
if rows is None:
if opts.n_rows > 0:
@@ -132,6 +141,11 @@ class GridAnnotation: def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
+
+ color_active = ImageColor.getcolor(opts.grid_text_active_color, 'RGB')
+ color_inactive = ImageColor.getcolor(opts.grid_text_inactive_color, 'RGB')
+ color_background = ImageColor.getcolor(opts.grid_background_color, 'RGB')
+
def wrap(drawing, text, font, line_length):
lines = ['']
for word in text.split():
@@ -142,14 +156,8 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): lines.append(word)
return lines
- def get_font(fontsize):
- try:
- return ImageFont.truetype(opts.font or Roboto, fontsize)
- except Exception:
- return ImageFont.truetype(Roboto, fontsize)
-
def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
- for i, line in enumerate(lines):
+ for line in lines:
fnt = initial_fnt
fontsize = initial_fontsize
while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
@@ -167,9 +175,6 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): fnt = get_font(fontsize)
- color_active = (0, 0, 0)
- color_inactive = (153, 153, 153)
-
pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4
cols = im.width // width
@@ -178,7 +183,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}'
assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}'
- calc_img = Image.new("RGB", (1, 1), "white")
+ calc_img = Image.new("RGB", (1, 1), color_background)
calc_d = ImageDraw.Draw(calc_img)
for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)):
@@ -199,7 +204,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2
- result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), "white")
+ result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), color_background)
for row in range(rows):
for col in range(cols):
@@ -335,8 +340,20 @@ def sanitize_filename_part(text, replace_spaces=True): class FilenameGenerator:
+ def get_vae_filename(self): #get the name of the VAE file.
+ if sd_vae.loaded_vae_file is None:
+ return "NoneType"
+ file_name = os.path.basename(sd_vae.loaded_vae_file)
+ split_file_name = file_name.split('.')
+ if len(split_file_name) > 1 and split_file_name[0] == '':
+ return split_file_name[1] # if the first character of the filename is "." then [1] is obtained.
+ else:
+ return split_file_name[0]
+
replacements = {
'seed': lambda self: self.seed if self.seed is not None else '',
+ 'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0],
+ 'seed_last': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.all_seeds[-1],
'steps': lambda self: self.p and self.p.steps,
'cfg': lambda self: self.p and self.p.cfg_scale,
'width': lambda self: self.image.width,
@@ -353,20 +370,24 @@ class FilenameGenerator: 'prompt_no_styles': lambda self: self.prompt_no_style(),
'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
'prompt_words': lambda self: self.prompt_words(),
- 'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.batch_index + 1,
- 'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
+ 'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 or self.zip else self.p.batch_index + 1,
+ 'batch_size': lambda self: self.p.batch_size,
+ 'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if (self.p.n_iter == 1 and self.p.batch_size == 1) or self.zip else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt<prompt1|default><prompt2>..]
'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
+ 'user': lambda self: self.p.user,
+ 'vae_filename': lambda self: self.get_vae_filename(),
}
default_time_format = '%Y%m%d%H%M%S'
- def __init__(self, p, seed, prompt, image):
+ def __init__(self, p, seed, prompt, image, zip=False):
self.p = p
self.seed = seed
self.prompt = prompt
self.image = image
-
+ self.zip = zip
+
def hasprompt(self, *args):
lower = self.prompt.lower()
if self.p is None or self.prompt is None:
@@ -389,7 +410,7 @@ class FilenameGenerator: prompt_no_style = self.prompt
for style in shared.prompt_styles.get_style_prompts(self.p.styles):
- if len(style) > 0:
+ if style:
for part in style.split("{prompt}"):
prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
@@ -398,7 +419,7 @@ class FilenameGenerator: return sanitize_filename_part(prompt_no_style, replace_spaces=False)
def prompt_words(self):
- words = [x for x in re_nonletters.split(self.prompt or "") if len(x) > 0]
+ words = [x for x in re_nonletters.split(self.prompt or "") if x]
if len(words) == 0:
words = ["empty"]
return sanitize_filename_part(" ".join(words[0:opts.directories_max_prompt_words]), replace_spaces=False)
@@ -406,16 +427,16 @@ class FilenameGenerator: def datetime(self, *args):
time_datetime = datetime.datetime.now()
- time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
+ time_format = args[0] if (args and args[0] != "") else self.default_time_format
try:
time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
- except pytz.exceptions.UnknownTimeZoneError as _:
+ except pytz.exceptions.UnknownTimeZoneError:
time_zone = None
time_zone_time = time_datetime.astimezone(time_zone)
try:
formatted_time = time_zone_time.strftime(time_format)
- except (ValueError, TypeError) as _:
+ except (ValueError, TypeError):
formatted_time = time_zone_time.strftime(self.default_time_format)
return sanitize_filename_part(formatted_time, replace_spaces=False)
@@ -445,8 +466,7 @@ class FilenameGenerator: replacement = fun(self, *pattern_args)
except Exception:
replacement = None
- print(f"Error adding [{pattern}] to filename", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error adding [{pattern}] to filename", exc_info=True)
if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
continue
@@ -472,15 +492,61 @@ def get_next_sequence_number(path, basename): prefix_length = len(basename)
for p in os.listdir(path):
if p.startswith(basename):
- l = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
+ parts = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
try:
- result = max(int(l[0]), result)
+ result = max(int(parts[0]), result)
except ValueError:
pass
return result + 1
+def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None, pnginfo_section_name='parameters'):
+ """
+ Saves image to filename, including geninfo as text information for generation info.
+ For PNG images, geninfo is added to existing pnginfo dictionary using the pnginfo_section_name argument as key.
+ For JPG images, there's no dictionary and geninfo just replaces the EXIF description.
+ """
+
+ if extension is None:
+ extension = os.path.splitext(filename)[1]
+
+ image_format = Image.registered_extensions()[extension]
+
+ if extension.lower() == '.png':
+ existing_pnginfo = existing_pnginfo or {}
+ if opts.enable_pnginfo:
+ existing_pnginfo[pnginfo_section_name] = geninfo
+
+ if opts.enable_pnginfo:
+ pnginfo_data = PngImagePlugin.PngInfo()
+ for k, v in (existing_pnginfo or {}).items():
+ pnginfo_data.add_text(k, str(v))
+ else:
+ pnginfo_data = None
+
+ image.save(filename, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
+
+ elif extension.lower() in (".jpg", ".jpeg", ".webp"):
+ if image.mode == 'RGBA':
+ image = image.convert("RGB")
+ elif image.mode == 'I;16':
+ image = image.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
+
+ image.save(filename, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
+
+ if opts.enable_pnginfo and geninfo is not None:
+ exif_bytes = piexif.dump({
+ "Exif": {
+ piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(geninfo or "", encoding="unicode")
+ },
+ })
+
+ piexif.insert(exif_bytes, filename)
+ else:
+ image.save(filename, format=image_format, quality=opts.jpeg_quality)
+
+
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
"""Save an image.
@@ -565,38 +631,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i info = params.pnginfo.get(pnginfo_section_name, None)
def _atomically_save_image(image_to_save, filename_without_extension, extension):
- # save image with .tmp extension to avoid race condition when another process detects new image in the directory
+ """
+ save image with .tmp extension to avoid race condition when another process detects new image in the directory
+ """
temp_file_path = f"{filename_without_extension}.tmp"
- image_format = Image.registered_extensions()[extension]
- if extension.lower() == '.png':
- pnginfo_data = PngImagePlugin.PngInfo()
- if opts.enable_pnginfo:
- for k, v in params.pnginfo.items():
- pnginfo_data.add_text(k, str(v))
+ save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name)
- image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
-
- elif extension.lower() in (".jpg", ".jpeg", ".webp"):
- if image_to_save.mode == 'RGBA':
- image_to_save = image_to_save.convert("RGB")
- elif image_to_save.mode == 'I;16':
- image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
-
- image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
-
- if opts.enable_pnginfo and info is not None:
- exif_bytes = piexif.dump({
- "Exif": {
- piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode")
- },
- })
-
- piexif.insert(exif_bytes, temp_file_path)
- else:
- image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
-
- # atomically rename the file with correct extension
os.replace(temp_file_path, filename_without_extension + extension)
fullfn_without_extension, extension = os.path.splitext(params.filename)
@@ -612,12 +653,18 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
ratio = image.width / image.height
-
+ resize_to = None
if oversize and ratio > 1:
- image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS)
+ resize_to = round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)
elif oversize:
- image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS)
+ resize_to = round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)
+ if resize_to is not None:
+ try:
+ # Resizing image with LANCZOS could throw an exception if e.g. image mode is I;16
+ image = image.resize(resize_to, LANCZOS)
+ except Exception:
+ image = image.resize(resize_to)
try:
_atomically_save_image(image, fullfn_without_extension, ".jpg")
except Exception as e:
@@ -635,8 +682,15 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i return fullfn, txt_fullfn
-def read_info_from_image(image):
- items = image.info or {}
+IGNORED_INFO_KEYS = {
+ 'jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
+ 'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression',
+ 'icc_profile', 'chromaticity', 'photoshop',
+}
+
+
+def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
+ items = (image.info or {}).copy()
geninfo = items.pop('parameters', None)
@@ -652,9 +706,8 @@ def read_info_from_image(image): items['exif comment'] = exif_comment
geninfo = exif_comment
- for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
- 'loop', 'background', 'timestamp', 'duration']:
- items.pop(field, None)
+ for field in IGNORED_INFO_KEYS:
+ items.pop(field, None)
if items.get("Software", None) == "NovelAI":
try:
@@ -665,8 +718,7 @@ def read_info_from_image(image): 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"""
except Exception:
- print("Error parsing NovelAI image generation parameters:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report("Error parsing NovelAI image generation parameters", exc_info=True)
return geninfo, items
diff --git a/modules/img2img.py b/modules/img2img.py index d1872bed..3b83814b 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -1,23 +1,21 @@ -import math
import os
-import sys
-import traceback
+from pathlib import Path
import numpy as np
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
+import gradio as gr
-from modules import devices, sd_samplers
-from modules.generation_parameters_copypaste import create_override_settings_dict
+from modules import sd_samplers, images as imgutil
+from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
import modules.shared as shared
import modules.processing as processing
from modules.ui import plaintext_to_html
-import modules.images as images
import modules.scripts
-def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
+def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
processing.fix_seed(p)
images = []
@@ -31,9 +29,10 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): is_inpaint_batch = False
if inpaint_mask_dir:
inpaint_masks = shared.listfiles(inpaint_mask_dir)
- is_inpaint_batch = len(inpaint_masks) > 0
- if is_inpaint_batch:
- print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
+ is_inpaint_batch = bool(inpaint_masks)
+
+ if is_inpaint_batch:
+ print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
@@ -44,6 +43,14 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): state.job_count = len(images) * p.n_iter
+ # extract "default" params to use in case getting png info fails
+ prompt = p.prompt
+ negative_prompt = p.negative_prompt
+ seed = p.seed
+ cfg_scale = p.cfg_scale
+ sampler_name = p.sampler_name
+ steps = p.steps
+
for i, image in enumerate(images):
state.job = f"{i+1} out of {len(images)}"
if state.skipped:
@@ -59,23 +66,59 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): continue
# Use the EXIF orientation of photos taken by smartphones.
img = ImageOps.exif_transpose(img)
+
+ if to_scale:
+ p.width = int(img.width * scale_by)
+ p.height = int(img.height * scale_by)
+
p.init_images = [img] * p.batch_size
+ image_path = Path(image)
if is_inpaint_batch:
# try to find corresponding mask for an image using simple filename matching
- mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
- # if not found use first one ("same mask for all images" use-case)
- if not mask_image_path in inpaint_masks:
+ if len(inpaint_masks) == 1:
mask_image_path = inpaint_masks[0]
+ else:
+ # try to find corresponding mask for an image using simple filename matching
+ mask_image_dir = Path(inpaint_mask_dir)
+ masks_found = list(mask_image_dir.glob(f"{image_path.stem}.*"))
+
+ if len(masks_found) == 0:
+ print(f"Warning: mask is not found for {image_path} in {mask_image_dir}. Skipping it.")
+ continue
+
+ # it should contain only 1 matching mask
+ # otherwise user has many masks with the same name but different extensions
+ mask_image_path = masks_found[0]
+
mask_image = Image.open(mask_image_path)
p.image_mask = mask_image
+ if use_png_info:
+ try:
+ info_img = img
+ if png_info_dir:
+ info_img_path = os.path.join(png_info_dir, os.path.basename(image))
+ info_img = Image.open(info_img_path)
+ geninfo, _ = imgutil.read_info_from_image(info_img)
+ parsed_parameters = parse_generation_parameters(geninfo)
+ parsed_parameters = {k: v for k, v in parsed_parameters.items() if k in (png_info_props or {})}
+ except Exception:
+ parsed_parameters = {}
+
+ p.prompt = prompt + (" " + parsed_parameters["Prompt"] if "Prompt" in parsed_parameters else "")
+ p.negative_prompt = negative_prompt + (" " + parsed_parameters["Negative prompt"] if "Negative prompt" in parsed_parameters else "")
+ p.seed = int(parsed_parameters.get("Seed", seed))
+ p.cfg_scale = float(parsed_parameters.get("CFG scale", cfg_scale))
+ p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
+ p.steps = int(parsed_parameters.get("Steps", steps))
+
proc = modules.scripts.scripts_img2img.run(p, *args)
if proc is None:
proc = process_images(p)
for n, processed_image in enumerate(proc.images):
- filename = os.path.basename(image)
+ filename = image_path.name
relpath = os.path.dirname(os.path.relpath(image, input_dir))
if n > 0:
@@ -89,7 +132,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): processed_image.save(os.path.join(output_dir, relpath, filename))
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
override_settings = create_override_settings_dict(override_settings_texts)
is_batch = mode == 5
@@ -103,7 +146,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s elif mode == 2: # inpaint
image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
- mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
+ mask = mask.convert('L').point(lambda x: 255 if x > 128 else 0, mode='1')
+ mask = ImageChops.lighter(alpha_mask, mask).convert('L')
image = image.convert("RGB")
elif mode == 3: # inpaint sketch
image = inpaint_color_sketch
@@ -125,7 +169,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s if image is not None:
image = ImageOps.exif_transpose(image)
- if selected_scale_tab == 1:
+ if selected_scale_tab == 1 and not is_batch:
assert image, "Can't scale by because no image is selected"
width = int(image.width * scale_by)
@@ -171,6 +215,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s p.scripts = modules.scripts.scripts_img2img
p.script_args = args
+ p.user = request.username
+
if shared.cmd_opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
@@ -180,7 +226,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s if is_batch:
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
- process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args)
+ process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
processed = Processed(p, [], p.seed, "")
else:
diff --git a/modules/interrogate.py b/modules/interrogate.py index 9f7d657f..a3ae1dd5 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -1,6 +1,5 @@ import os
import sys
-import traceback
from collections import namedtuple
from pathlib import Path
import re
@@ -11,7 +10,6 @@ import torch.hub from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
-import modules.shared as shared
from modules import devices, paths, shared, lowvram, modelloader, errors
blip_image_eval_size = 384
@@ -160,7 +158,7 @@ class InterrogateModels: text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
top_count = min(top_count, len(text_array))
- text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate)
+ text_tokens = clip.tokenize(list(text_array), truncate=True).to(devices.device_interrogate)
text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
text_features /= text_features.norm(dim=-1, keepdim=True)
@@ -186,8 +184,7 @@ class InterrogateModels: def interrogate(self, pil_image):
res = ""
- shared.state.begin()
- shared.state.job = 'interrogate'
+ shared.state.begin(job="interrogate")
try:
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()
@@ -208,8 +205,8 @@ class InterrogateModels: image_features /= image_features.norm(dim=-1, keepdim=True)
- for name, topn, items in self.categories():
- matches = self.rank(image_features, items, top_count=topn)
+ for cat in self.categories():
+ matches = self.rank(image_features, cat.items, top_count=cat.topn)
for match, score in matches:
if shared.opts.interrogate_return_ranks:
res += f", ({match}:{score/100:.3f})"
@@ -217,8 +214,7 @@ class InterrogateModels: res += f", {match}"
except Exception:
- print("Error interrogating", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report("Error interrogating", exc_info=True)
res += "<error>"
self.unload()
diff --git a/modules/launch_utils.py b/modules/launch_utils.py new file mode 100644 index 00000000..0e0dbca4 --- /dev/null +++ b/modules/launch_utils.py @@ -0,0 +1,344 @@ +# this scripts installs necessary requirements and launches main program in webui.py
+import subprocess
+import os
+import sys
+import importlib.util
+import platform
+import json
+from functools import lru_cache
+
+from modules import cmd_args, errors
+from modules.paths_internal import script_path, extensions_dir
+
+args, _ = cmd_args.parser.parse_known_args()
+
+python = sys.executable
+git = os.environ.get('GIT', "git")
+index_url = os.environ.get('INDEX_URL', "")
+dir_repos = "repositories"
+
+# Whether to default to printing command output
+default_command_live = (os.environ.get('WEBUI_LAUNCH_LIVE_OUTPUT') == "1")
+
+if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
+ os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
+
+
+def check_python_version():
+ is_windows = platform.system() == "Windows"
+ major = sys.version_info.major
+ minor = sys.version_info.minor
+ micro = sys.version_info.micro
+
+ if is_windows:
+ supported_minors = [10]
+ else:
+ supported_minors = [7, 8, 9, 10, 11]
+
+ if not (major == 3 and minor in supported_minors):
+ import modules.errors
+
+ modules.errors.print_error_explanation(f"""
+INCOMPATIBLE PYTHON VERSION
+
+This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
+If you encounter an error with "RuntimeError: Couldn't install torch." message,
+or any other error regarding unsuccessful package (library) installation,
+please downgrade (or upgrade) to the latest version of 3.10 Python
+and delete current Python and "venv" folder in WebUI's directory.
+
+You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3106/
+
+{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
+
+Use --skip-python-version-check to suppress this warning.
+""")
+
+
+@lru_cache()
+def commit_hash():
+ try:
+ return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
+ except Exception:
+ return "<none>"
+
+
+@lru_cache()
+def git_tag():
+ try:
+ return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
+ except Exception:
+ try:
+ from pathlib import Path
+ changelog_md = Path(__file__).parent.parent / "CHANGELOG.md"
+ with changelog_md.open(encoding="utf-8") as file:
+ return next((line.strip() for line in file if line.strip()), "<none>")
+ except Exception:
+ return "<none>"
+
+
+def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str:
+ if desc is not None:
+ print(desc)
+
+ run_kwargs = {
+ "args": command,
+ "shell": True,
+ "env": os.environ if custom_env is None else custom_env,
+ "encoding": 'utf8',
+ "errors": 'ignore',
+ }
+
+ if not live:
+ run_kwargs["stdout"] = run_kwargs["stderr"] = subprocess.PIPE
+
+ result = subprocess.run(**run_kwargs)
+
+ if result.returncode != 0:
+ error_bits = [
+ f"{errdesc or 'Error running command'}.",
+ f"Command: {command}",
+ f"Error code: {result.returncode}",
+ ]
+ if result.stdout:
+ error_bits.append(f"stdout: {result.stdout}")
+ if result.stderr:
+ error_bits.append(f"stderr: {result.stderr}")
+ raise RuntimeError("\n".join(error_bits))
+
+ return (result.stdout or "")
+
+
+def is_installed(package):
+ try:
+ spec = importlib.util.find_spec(package)
+ except ModuleNotFoundError:
+ return False
+
+ return spec is not None
+
+
+def repo_dir(name):
+ return os.path.join(script_path, dir_repos, name)
+
+
+def run_pip(command, desc=None, live=default_command_live):
+ if args.skip_install:
+ return
+
+ index_url_line = f' --index-url {index_url}' if index_url != '' else ''
+ return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
+
+
+def check_run_python(code: str) -> bool:
+ result = subprocess.run([python, "-c", code], capture_output=True, shell=False)
+ return result.returncode == 0
+
+
+def git_clone(url, dir, name, commithash=None):
+ # TODO clone into temporary dir and move if successful
+
+ if os.path.exists(dir):
+ if commithash is None:
+ return
+
+ current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}", live=False).strip()
+ if current_hash == commithash:
+ return
+
+ run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
+ run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
+ return
+
+ run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
+
+ if commithash is not None:
+ run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
+
+
+def git_pull_recursive(dir):
+ for subdir, _, _ in os.walk(dir):
+ if os.path.exists(os.path.join(subdir, '.git')):
+ try:
+ output = subprocess.check_output([git, '-C', subdir, 'pull', '--autostash'])
+ print(f"Pulled changes for repository in '{subdir}':\n{output.decode('utf-8').strip()}\n")
+ except subprocess.CalledProcessError as e:
+ print(f"Couldn't perform 'git pull' on repository in '{subdir}':\n{e.output.decode('utf-8').strip()}\n")
+
+
+def version_check(commit):
+ try:
+ import requests
+ commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
+ if commit != "<none>" and commits['commit']['sha'] != commit:
+ print("--------------------------------------------------------")
+ print("| You are not up to date with the most recent release. |")
+ print("| Consider running `git pull` to update. |")
+ print("--------------------------------------------------------")
+ elif commits['commit']['sha'] == commit:
+ print("You are up to date with the most recent release.")
+ else:
+ print("Not a git clone, can't perform version check.")
+ except Exception as e:
+ print("version check failed", e)
+
+
+def run_extension_installer(extension_dir):
+ path_installer = os.path.join(extension_dir, "install.py")
+ if not os.path.isfile(path_installer):
+ return
+
+ try:
+ env = os.environ.copy()
+ env['PYTHONPATH'] = os.path.abspath(".")
+
+ print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
+ except Exception as e:
+ errors.report(str(e))
+
+
+def list_extensions(settings_file):
+ settings = {}
+
+ try:
+ if os.path.isfile(settings_file):
+ with open(settings_file, "r", encoding="utf8") as file:
+ settings = json.load(file)
+ except Exception:
+ errors.report("Could not load settings", exc_info=True)
+
+ disabled_extensions = set(settings.get('disabled_extensions', []))
+ disable_all_extensions = settings.get('disable_all_extensions', 'none')
+
+ if disable_all_extensions != 'none':
+ return []
+
+ return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
+
+
+def run_extensions_installers(settings_file):
+ if not os.path.isdir(extensions_dir):
+ return
+
+ for dirname_extension in list_extensions(settings_file):
+ run_extension_installer(os.path.join(extensions_dir, dirname_extension))
+
+
+def prepare_environment():
+ torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
+ torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
+ requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
+
+ xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
+ gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "https://github.com/TencentARC/GFPGAN/archive/8d2447a2d918f8eba5a4a01463fd48e45126a379.zip")
+ clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
+ openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
+
+ stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
+ k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
+ codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
+ blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
+
+ stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
+ k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
+ codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
+ blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
+
+ try:
+ # the existance of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
+ os.remove(os.path.join(script_path, "tmp", "restart"))
+ os.environ.setdefault('SD_WEBUI_RESTARTING ', '1')
+ except OSError:
+ pass
+
+ if not args.skip_python_version_check:
+ check_python_version()
+
+ commit = commit_hash()
+ tag = git_tag()
+
+ print(f"Python {sys.version}")
+ print(f"Version: {tag}")
+ print(f"Commit hash: {commit}")
+
+ if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
+ run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
+
+ if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
+ raise RuntimeError(
+ 'Torch is not able to use GPU; '
+ 'add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'
+ )
+
+ if not is_installed("gfpgan"):
+ run_pip(f"install {gfpgan_package}", "gfpgan")
+
+ if not is_installed("clip"):
+ run_pip(f"install {clip_package}", "clip")
+
+ if not is_installed("open_clip"):
+ run_pip(f"install {openclip_package}", "open_clip")
+
+ if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
+ if platform.system() == "Windows":
+ if platform.python_version().startswith("3.10"):
+ run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
+ else:
+ print("Installation of xformers is not supported in this version of Python.")
+ print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
+ if not is_installed("xformers"):
+ exit(0)
+ elif platform.system() == "Linux":
+ run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+
+ if not is_installed("ngrok") and args.ngrok:
+ run_pip("install ngrok", "ngrok")
+
+ os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
+
+ git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
+ git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
+ git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
+ git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
+
+ if not is_installed("lpips"):
+ run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
+
+ if not os.path.isfile(requirements_file):
+ requirements_file = os.path.join(script_path, requirements_file)
+ run_pip(f"install -r \"{requirements_file}\"", "requirements")
+
+ run_extensions_installers(settings_file=args.ui_settings_file)
+
+ if args.update_check:
+ version_check(commit)
+
+ if args.update_all_extensions:
+ git_pull_recursive(extensions_dir)
+
+ if "--exit" in sys.argv:
+ print("Exiting because of --exit argument")
+ exit(0)
+
+
+def configure_for_tests():
+ if "--api" not in sys.argv:
+ sys.argv.append("--api")
+ if "--ckpt" not in sys.argv:
+ sys.argv.append("--ckpt")
+ sys.argv.append(os.path.join(script_path, "test/test_files/empty.pt"))
+ if "--skip-torch-cuda-test" not in sys.argv:
+ sys.argv.append("--skip-torch-cuda-test")
+ if "--disable-nan-check" not in sys.argv:
+ sys.argv.append("--disable-nan-check")
+
+ os.environ['COMMANDLINE_ARGS'] = ""
+
+
+def start():
+ print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
+ import webui
+ if '--nowebui' in sys.argv:
+ webui.api_only()
+ else:
+ webui.webui()
diff --git a/modules/localization.py b/modules/localization.py index ee9c65e7..e8f585da 100644 --- a/modules/localization.py +++ b/modules/localization.py @@ -1,8 +1,7 @@ import json
import os
-import sys
-import traceback
+from modules import errors
localizations = {}
@@ -31,7 +30,6 @@ def localization_js(current_localization_name: str) -> str: with open(fn, "r", encoding="utf8") as file:
data = json.load(file)
except Exception:
- print(f"Error loading localization from {fn}:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error loading localization from {fn}", exc_info=True)
return f"window.localization = {json.dumps(data)}"
diff --git a/modules/lowvram.py b/modules/lowvram.py index e254cc13..d95bcfbf 100644 --- a/modules/lowvram.py +++ b/modules/lowvram.py @@ -15,6 +15,8 @@ def send_everything_to_cpu(): def setup_for_low_vram(sd_model, use_medvram):
+ sd_model.lowvram = True
+
parents = {}
def send_me_to_gpu(module, _):
@@ -96,3 +98,7 @@ def setup_for_low_vram(sd_model, use_medvram): diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
for block in diff_model.output_blocks:
block.register_forward_pre_hook(send_me_to_gpu)
+
+
+def is_enabled(sd_model):
+ return getattr(sd_model, 'lowvram', False)
diff --git a/modules/mac_specific.py b/modules/mac_specific.py index 40ce2101..735847f5 100644 --- a/modules/mac_specific.py +++ b/modules/mac_specific.py @@ -1,20 +1,24 @@ import torch import platform -from modules import paths from modules.sd_hijack_utils import CondFunc from packaging import version -# has_mps is only available in nightly pytorch (for now) and macOS 12.3+. -# check `getattr` and try it for compatibility +# before torch version 1.13, has_mps is only available in nightly pytorch and macOS 12.3+, +# use check `getattr` and try it for compatibility. +# in torch version 1.13, backends.mps.is_available() and backends.mps.is_built() are introduced in to check mps availabilty, +# since torch 2.0.1+ nightly build, getattr(torch, 'has_mps', False) was deprecated, see https://github.com/pytorch/pytorch/pull/103279 def check_for_mps() -> bool: - if not getattr(torch, 'has_mps', False): - return False - try: - torch.zeros(1).to(torch.device("mps")) - return True - except Exception: - return False + if version.parse(torch.__version__) <= version.parse("2.0.1"): + if not getattr(torch, 'has_mps', False): + return False + try: + torch.zeros(1).to(torch.device("mps")) + return True + except Exception: + return False + else: + return torch.backends.mps.is_available() and torch.backends.mps.is_built() has_mps = check_for_mps() @@ -43,7 +47,7 @@ if has_mps: # MPS workaround for https://github.com/pytorch/pytorch/issues/79383 CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs), lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')) - # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 + # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs), lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps') # MPS workaround for https://github.com/pytorch/pytorch/issues/90532 @@ -61,4 +65,4 @@ if has_mps: # MPS workaround for https://github.com/pytorch/pytorch/issues/92311 if platform.processor() == 'i386': for funcName in ['torch.argmax', 'torch.Tensor.argmax']: - CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps')
\ No newline at end of file + CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps') diff --git a/modules/masking.py b/modules/masking.py index a5c4d2da..be9f84c7 100644 --- a/modules/masking.py +++ b/modules/masking.py @@ -4,7 +4,7 @@ from PIL import Image, ImageFilter, ImageOps def get_crop_region(mask, pad=0):
"""finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)"""
-
+
h, w = mask.shape
crop_left = 0
diff --git a/modules/modelloader.py b/modules/modelloader.py index a70aa0e3..098bcb79 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -1,4 +1,5 @@ -import glob +from __future__ import annotations + import os import shutil import importlib @@ -9,6 +10,29 @@ from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, Upscale from modules.paths import script_path, models_path +def load_file_from_url( + url: str, + *, + model_dir: str, + progress: bool = True, + file_name: str | None = None, +) -> str: + """Download a file from `url` into `model_dir`, using the file present if possible. + + Returns the path to the downloaded file. + """ + os.makedirs(model_dir, exist_ok=True) + if not file_name: + parts = urlparse(url) + file_name = os.path.basename(parts.path) + cached_file = os.path.abspath(os.path.join(model_dir, file_name)) + if not os.path.exists(cached_file): + print(f'Downloading: "{url}" to {cached_file}\n') + from torch.hub import download_url_to_file + download_url_to_file(url, cached_file, progress=progress) + return cached_file + + def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list: """ A one-and done loader to try finding the desired models in specified directories. @@ -40,16 +64,14 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None if os.path.islink(full_path) and not os.path.exists(full_path): print(f"Skipping broken symlink: {full_path}") continue - if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]): + if ext_blacklist is not None and any(full_path.endswith(x) for x in ext_blacklist): continue if full_path not in output: output.append(full_path) if model_url is not None and len(output) == 0: if download_name is not None: - from basicsr.utils.download_util import load_file_from_url - dl = load_file_from_url(model_url, model_path, True, download_name) - output.append(dl) + output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name)) else: output.append(model_url) @@ -60,7 +82,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None def friendly_name(file: str): - if "http" in file: + if file.startswith("http"): file = urlparse(file).path file = os.path.basename(file) @@ -96,8 +118,7 @@ def cleanup_models(): def move_files(src_path: str, dest_path: str, ext_filter: str = None): try: - if not os.path.exists(dest_path): - os.makedirs(dest_path) + os.makedirs(dest_path, exist_ok=True) if os.path.exists(src_path): for file in os.listdir(src_path): fullpath = os.path.join(src_path, file) @@ -108,12 +129,12 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None): print(f"Moving {file} from {src_path} to {dest_path}.") try: shutil.move(fullpath, dest_path) - except: + except Exception: pass if len(os.listdir(src_path)) == 0: print(f"Removing empty folder: {src_path}") shutil.rmtree(src_path, True) - except: + except Exception: pass @@ -127,7 +148,7 @@ def load_upscalers(): full_model = f"modules.{model_name}_model" try: importlib.import_module(full_model) - except: + except Exception: pass datas = [] @@ -145,7 +166,10 @@ def load_upscalers(): for cls in reversed(used_classes.values()): name = cls.__name__ cmd_name = f"{name.lower().replace('upscaler', '')}_models_path" - scaler = cls(commandline_options.get(cmd_name, None)) + commandline_model_path = commandline_options.get(cmd_name, None) + scaler = cls(commandline_model_path) + scaler.user_path = commandline_model_path + scaler.model_download_path = commandline_model_path or scaler.model_path datas += scaler.scalers shared.sd_upscalers = sorted( diff --git a/modules/models/diffusion/ddpm_edit.py b/modules/models/diffusion/ddpm_edit.py index f880bc3c..b892d5fc 100644 --- a/modules/models/diffusion/ddpm_edit.py +++ b/modules/models/diffusion/ddpm_edit.py @@ -52,7 +52,7 @@ class DDPM(pl.LightningModule): beta_schedule="linear", loss_type="l2", ckpt_path=None, - ignore_keys=[], + ignore_keys=None, load_only_unet=False, monitor="val/loss", use_ema=True, @@ -107,7 +107,7 @@ class DDPM(pl.LightningModule): print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet) # If initialing from EMA-only checkpoint, create EMA model after loading. if self.use_ema and not load_ema: @@ -194,7 +194,9 @@ class DDPM(pl.LightningModule): if context is not None: print(f"{context}: Restored training weights") - def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + def init_from_ckpt(self, path, ignore_keys=None, only_model=False): + ignore_keys = ignore_keys or [] + sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] @@ -228,9 +230,9 @@ class DDPM(pl.LightningModule): missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") - if len(missing) > 0: + if missing: print(f"Missing Keys: {missing}") - if len(unexpected) > 0: + if unexpected: print(f"Unexpected Keys: {unexpected}") def q_mean_variance(self, x_start, t): @@ -403,7 +405,7 @@ class DDPM(pl.LightningModule): @torch.no_grad() def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): - log = dict() + log = {} x = self.get_input(batch, self.first_stage_key) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) @@ -411,7 +413,7 @@ class DDPM(pl.LightningModule): log["inputs"] = x # get diffusion row - diffusion_row = list() + diffusion_row = [] x_start = x[:n_row] for t in range(self.num_timesteps): @@ -473,13 +475,13 @@ class LatentDiffusion(DDPM): conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) - super().__init__(conditioning_key=conditioning_key, *args, load_ema=load_ema, **kwargs) + super().__init__(*args, conditioning_key=conditioning_key, load_ema=load_ema, **kwargs) self.concat_mode = concat_mode self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key try: self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 - except: + except Exception: self.num_downs = 0 if not scale_by_std: self.scale_factor = scale_factor @@ -891,16 +893,6 @@ class LatentDiffusion(DDPM): c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) return self.p_losses(x, c, t, *args, **kwargs) - def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset - def rescale_bbox(bbox): - x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) - y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) - w = min(bbox[2] / crop_coordinates[2], 1 - x0) - h = min(bbox[3] / crop_coordinates[3], 1 - y0) - return x0, y0, w, h - - return [rescale_bbox(b) for b in bboxes] - def apply_model(self, x_noisy, t, cond, return_ids=False): if isinstance(cond, dict): @@ -1140,7 +1132,7 @@ class LatentDiffusion(DDPM): if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + [x[:batch_size] for x in cond[key]] for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] @@ -1171,8 +1163,10 @@ class LatentDiffusion(DDPM): if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(x0_partial) - if callback: callback(i) - if img_callback: img_callback(img, i) + if callback: + callback(i) + if img_callback: + img_callback(img, i) return img, intermediates @torch.no_grad() @@ -1219,8 +1213,10 @@ class LatentDiffusion(DDPM): if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(img) - if callback: callback(i) - if img_callback: img_callback(img, i) + if callback: + callback(i) + if img_callback: + img_callback(img, i) if return_intermediates: return img, intermediates @@ -1235,7 +1231,7 @@ class LatentDiffusion(DDPM): if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else - list(map(lambda x: x[:batch_size], cond[key])) for key in cond} + [x[:batch_size] for x in cond[key]] for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] return self.p_sample_loop(cond, @@ -1267,7 +1263,7 @@ class LatentDiffusion(DDPM): use_ddim = False - log = dict() + log = {} z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, @@ -1295,7 +1291,7 @@ class LatentDiffusion(DDPM): if plot_diffusion_rows: # get diffusion row - diffusion_row = list() + diffusion_row = [] z_start = z[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: @@ -1337,7 +1333,7 @@ class LatentDiffusion(DDPM): if inpaint: # make a simple center square - b, h, w = z.shape[0], z.shape[2], z.shape[3] + h, w = z.shape[2], z.shape[3] mask = torch.ones(N, h, w).to(self.device) # zeros will be filled in mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. @@ -1439,10 +1435,10 @@ class Layout2ImgDiffusion(LatentDiffusion): # TODO: move all layout-specific hacks to this class def __init__(self, cond_stage_key, *args, **kwargs): assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' - super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs) + super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs) def log_images(self, batch, N=8, *args, **kwargs): - logs = super().log_images(batch=batch, N=N, *args, **kwargs) + logs = super().log_images(*args, batch=batch, N=N, **kwargs) key = 'train' if self.training else 'validation' dset = self.trainer.datamodule.datasets[key] diff --git a/modules/models/diffusion/uni_pc/__init__.py b/modules/models/diffusion/uni_pc/__init__.py index e1265e3f..dbb35964 100644 --- a/modules/models/diffusion/uni_pc/__init__.py +++ b/modules/models/diffusion/uni_pc/__init__.py @@ -1 +1 @@ -from .sampler import UniPCSampler +from .sampler import UniPCSampler # noqa: F401 diff --git a/modules/models/diffusion/uni_pc/sampler.py b/modules/models/diffusion/uni_pc/sampler.py index a241c8a7..0a9defa1 100644 --- a/modules/models/diffusion/uni_pc/sampler.py +++ b/modules/models/diffusion/uni_pc/sampler.py @@ -54,7 +54,8 @@ class UniPCSampler(object): if conditioning is not None: if isinstance(conditioning, dict): ctmp = conditioning[list(conditioning.keys())[0]] - while isinstance(ctmp, list): ctmp = ctmp[0] + while isinstance(ctmp, list): + ctmp = ctmp[0] cbs = ctmp.shape[0] if cbs != batch_size: print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") diff --git a/modules/models/diffusion/uni_pc/uni_pc.py b/modules/models/diffusion/uni_pc/uni_pc.py index 11b330bc..d257a728 100644 --- a/modules/models/diffusion/uni_pc/uni_pc.py +++ b/modules/models/diffusion/uni_pc/uni_pc.py @@ -1,7 +1,6 @@ import torch -import torch.nn.functional as F import math -from tqdm.auto import trange +import tqdm class NoiseScheduleVP: @@ -179,13 +178,13 @@ def model_wrapper( model, noise_schedule, model_type="noise", - model_kwargs={}, + model_kwargs=None, guidance_type="uncond", #condition=None, #unconditional_condition=None, guidance_scale=1., classifier_fn=None, - classifier_kwargs={}, + classifier_kwargs=None, ): """Create a wrapper function for the noise prediction model. @@ -276,6 +275,9 @@ def model_wrapper( A noise prediction model that accepts the noised data and the continuous time as the inputs. """ + model_kwargs = model_kwargs or {} + classifier_kwargs = classifier_kwargs or {} + def get_model_input_time(t_continuous): """ Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. @@ -342,7 +344,7 @@ def model_wrapper( t_in = torch.cat([t_continuous] * 2) if isinstance(condition, dict): assert isinstance(unconditional_condition, dict) - c_in = dict() + c_in = {} for k in condition: if isinstance(condition[k], list): c_in[k] = [torch.cat([ @@ -353,7 +355,7 @@ def model_wrapper( unconditional_condition[k], condition[k]]) elif isinstance(condition, list): - c_in = list() + c_in = [] assert isinstance(unconditional_condition, list) for i in range(len(condition)): c_in.append(torch.cat([unconditional_condition[i], condition[i]])) @@ -757,40 +759,44 @@ class UniPC: vec_t = timesteps[0].expand((x.shape[0])) model_prev_list = [self.model_fn(x, vec_t)] t_prev_list = [vec_t] - # Init the first `order` values by lower order multistep DPM-Solver. - for init_order in range(1, order): - vec_t = timesteps[init_order].expand(x.shape[0]) - x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) - if model_x is None: - model_x = self.model_fn(x, vec_t) - if self.after_update is not None: - self.after_update(x, model_x) - model_prev_list.append(model_x) - t_prev_list.append(vec_t) - for step in trange(order, steps + 1): - vec_t = timesteps[step].expand(x.shape[0]) - if lower_order_final: - step_order = min(order, steps + 1 - step) - else: - step_order = order - #print('this step order:', step_order) - if step == steps: - #print('do not run corrector at the last step') - use_corrector = False - else: - use_corrector = True - x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) - if self.after_update is not None: - self.after_update(x, model_x) - for i in range(order - 1): - t_prev_list[i] = t_prev_list[i + 1] - model_prev_list[i] = model_prev_list[i + 1] - t_prev_list[-1] = vec_t - # We do not need to evaluate the final model value. - if step < steps: + with tqdm.tqdm(total=steps) as pbar: + # Init the first `order` values by lower order multistep DPM-Solver. + for init_order in range(1, order): + vec_t = timesteps[init_order].expand(x.shape[0]) + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) if model_x is None: model_x = self.model_fn(x, vec_t) - model_prev_list[-1] = model_x + if self.after_update is not None: + self.after_update(x, model_x) + model_prev_list.append(model_x) + t_prev_list.append(vec_t) + pbar.update() + + for step in range(order, steps + 1): + vec_t = timesteps[step].expand(x.shape[0]) + if lower_order_final: + step_order = min(order, steps + 1 - step) + else: + step_order = order + #print('this step order:', step_order) + if step == steps: + #print('do not run corrector at the last step') + use_corrector = False + else: + use_corrector = True + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) + if self.after_update is not None: + self.after_update(x, model_x) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list[-1] = model_x + pbar.update() else: raise NotImplementedError() if denoise_to_zero: diff --git a/modules/ngrok.py b/modules/ngrok.py index 7a7b4b26..0c713e27 100644 --- a/modules/ngrok.py +++ b/modules/ngrok.py @@ -1,6 +1,7 @@ -from pyngrok import ngrok, conf, exception +import ngrok -def connect(token, port, region): +# Connect to ngrok for ingress +def connect(token, port, options): account = None if token is None: token = 'None' @@ -10,28 +11,19 @@ def connect(token, port, region): token, username, password = token.split(':', 2) account = f"{username}:{password}" - config = conf.PyngrokConfig( - auth_token=token, region=region - ) - - # Guard for existing tunnels - existing = ngrok.get_tunnels(pyngrok_config=config) - if existing: - for established in existing: - # Extra configuration in the case that the user is also using ngrok for other tunnels - if established.config['addr'][-4:] == str(port): - public_url = existing[0].public_url - print(f'ngrok has already been connected to localhost:{port}! URL: {public_url}\n' - 'You can use this link after the launch is complete.') - return - + # For all options see: https://github.com/ngrok/ngrok-py/blob/main/examples/ngrok-connect-full.py + if not options.get('authtoken_from_env'): + options['authtoken'] = token + if account: + options['basic_auth'] = account + if not options.get('session_metadata'): + options['session_metadata'] = 'stable-diffusion-webui' + + try: - if account is None: - public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url - else: - public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True, auth=account).public_url - except exception.PyngrokNgrokError: - print(f'Invalid ngrok authtoken, ngrok connection aborted.\n' + public_url = ngrok.connect(f"127.0.0.1:{port}", **options).url() + except Exception as e: + print(f'Invalid ngrok authtoken? ngrok connection aborted due to: {e}\n' f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken') else: print(f'ngrok connected to localhost:{port}! URL: {public_url}\n' diff --git a/modules/paths.py b/modules/paths.py index acf1894b..bada804e 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -1,8 +1,8 @@ import os
import sys
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir # noqa: F401
-import modules.safe
+import modules.safe # noqa: F401
# data_path = cmd_opts_pre.data
@@ -20,7 +20,6 @@ assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possibl path_dirs = [
(sd_path, 'ldm', 'Stable Diffusion', []),
- (os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []),
(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
@@ -39,17 +38,3 @@ for d, must_exist, what, options in path_dirs: else:
sys.path.append(d)
paths[what] = d
-
-
-class Prioritize:
- def __init__(self, name):
- self.name = name
- self.path = None
-
- def __enter__(self):
- self.path = sys.path.copy()
- sys.path = [paths[self.name]] + sys.path
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- sys.path = self.path
- self.path = None
diff --git a/modules/paths_internal.py b/modules/paths_internal.py index 6765bafe..005a9b0a 100644 --- a/modules/paths_internal.py +++ b/modules/paths_internal.py @@ -2,8 +2,14 @@ import argparse
import os
+import sys
+import shlex
-script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
+sys.argv += shlex.split(commandline_args)
+
+modules_path = os.path.dirname(os.path.realpath(__file__))
+script_path = os.path.dirname(modules_path)
sd_configs_path = os.path.join(script_path, "configs")
sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
@@ -12,7 +18,7 @@ default_sd_model_file = sd_model_file # Parse the --data-dir flag first so we can use it as a base for our other argument default values
parser_pre = argparse.ArgumentParser(add_help=False)
-parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
+parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(modules_path), help="base path where all user data is stored", )
cmd_opts_pre = parser_pre.parse_known_args()[0]
data_path = cmd_opts_pre.data_dir
@@ -21,3 +27,5 @@ models_path = os.path.join(data_path, "models") extensions_dir = os.path.join(data_path, "extensions")
extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
config_states_dir = os.path.join(script_path, "config_states")
+
+roboto_ttf_file = os.path.join(modules_path, 'Roboto-Regular.ttf')
diff --git a/modules/postprocessing.py b/modules/postprocessing.py index 736315e2..136e9c88 100644 --- a/modules/postprocessing.py +++ b/modules/postprocessing.py @@ -9,8 +9,7 @@ from modules.shared import opts def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
devices.torch_gc()
- shared.state.begin()
- shared.state.job = 'extras'
+ shared.state.begin(job="extras")
image_data = []
image_names = []
@@ -54,7 +53,9 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, for image, name in zip(image_data, image_names):
shared.state.textinfo = name
- existing_pnginfo = image.info or {}
+ parameters, existing_pnginfo = images.read_info_from_image(image)
+ if parameters:
+ existing_pnginfo["parameters"] = parameters
pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))
diff --git a/modules/processing.py b/modules/processing.py index 1a76e552..21d1492c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -1,20 +1,20 @@ import json
+import logging
import math
import os
import sys
-import warnings
import hashlib
import torch
import numpy as np
-from PIL import Image, ImageFilter, ImageOps
+from PIL import Image, ImageOps
import random
import cv2
from skimage import exposure
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet
from modules.sd_hijack import model_hijack
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@@ -24,13 +24,13 @@ import modules.images as images import modules.styles
import modules.sd_models as sd_models
import modules.sd_vae as sd_vae
-import logging
from ldm.data.util import AddMiDaS
from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
from einops import repeat, rearrange
from blendmodes.blend import blendLayers, BlendType
+
# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
opt_f = 8
@@ -106,6 +106,9 @@ class StableDiffusionProcessing: """
The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
"""
+ cached_uc = [None, None]
+ cached_c = [None, None]
+
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
if sampler_index is not None:
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
@@ -150,6 +153,8 @@ class StableDiffusionProcessing: self.override_settings_restore_afterwards = override_settings_restore_afterwards
self.is_using_inpainting_conditioning = False
self.disable_extra_networks = False
+ self.token_merging_ratio = 0
+ self.token_merging_ratio_hr = 0
if not seed_enable_extras:
self.subseed = -1
@@ -165,7 +170,21 @@ class StableDiffusionProcessing: self.all_subseeds = None
self.iteration = 0
self.is_hr_pass = False
-
+ self.sampler = None
+
+ self.prompts = None
+ self.negative_prompts = None
+ self.extra_network_data = None
+ self.seeds = None
+ self.subseeds = None
+
+ self.step_multiplier = 1
+ self.cached_uc = StableDiffusionProcessing.cached_uc
+ self.cached_c = StableDiffusionProcessing.cached_c
+ self.uc = None
+ self.c = None
+
+ self.user = None
@property
def sd_model(self):
@@ -273,6 +292,64 @@ class StableDiffusionProcessing: def close(self):
self.sampler = None
+ self.c = None
+ self.uc = None
+ if not opts.experimental_persistent_cond_cache:
+ StableDiffusionProcessing.cached_c = [None, None]
+ StableDiffusionProcessing.cached_uc = [None, None]
+
+ def get_token_merging_ratio(self, for_hr=False):
+ if for_hr:
+ return self.token_merging_ratio_hr or opts.token_merging_ratio_hr or self.token_merging_ratio or opts.token_merging_ratio
+
+ return self.token_merging_ratio or opts.token_merging_ratio
+
+ def setup_prompts(self):
+ if type(self.prompt) == list:
+ self.all_prompts = self.prompt
+ else:
+ self.all_prompts = self.batch_size * self.n_iter * [self.prompt]
+
+ if type(self.negative_prompt) == list:
+ self.all_negative_prompts = self.negative_prompt
+ else:
+ self.all_negative_prompts = self.batch_size * self.n_iter * [self.negative_prompt]
+
+ self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
+ self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
+
+ def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):
+ """
+ Returns the result of calling function(shared.sd_model, required_prompts, steps)
+ using a cache to store the result if the same arguments have been used before.
+
+ cache is an array containing two elements. The first element is a tuple
+ representing the previously used arguments, or None if no arguments
+ have been used before. The second element is where the previously
+ computed result is stored.
+
+ caches is a list with items described above.
+ """
+ for cache in caches:
+ if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data) == cache[0]:
+ return cache[1]
+
+ cache = caches[0]
+
+ with devices.autocast():
+ cache[1] = function(shared.sd_model, required_prompts, steps)
+
+ cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data)
+ return cache[1]
+
+ def setup_conds(self):
+ sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
+ self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
+ self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
+ self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
+
+ def parse_extra_network_prompts(self):
+ self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
class Processed:
@@ -303,6 +380,8 @@ class Processed: self.styles = p.styles
self.job_timestamp = state.job_timestamp
self.clip_skip = opts.CLIP_stop_at_last_layers
+ self.token_merging_ratio = p.token_merging_ratio
+ self.token_merging_ratio_hr = p.token_merging_ratio_hr
self.eta = p.eta
self.ddim_discretize = p.ddim_discretize
@@ -310,6 +389,7 @@ class Processed: self.s_tmin = p.s_tmin
self.s_tmax = p.s_tmax
self.s_noise = p.s_noise
+ self.s_min_uncond = p.s_min_uncond
self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
@@ -360,6 +440,9 @@ class Processed: def infotext(self, p: StableDiffusionProcessing, index):
return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
+ def get_token_merging_ratio(self, for_hr=False):
+ return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio
+
# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
def slerp(val, low, high):
@@ -468,10 +551,17 @@ def program_version(): return res
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False):
index = position_in_batch + iteration * p.batch_size
clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
+ enable_hr = getattr(p, 'enable_hr', False)
+ token_merging_ratio = p.get_token_merging_ratio()
+ token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True)
+
+ uses_ensd = opts.eta_noise_seed_delta != 0
+ if uses_ensd:
+ uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)
generation_params = {
"Steps": p.steps,
@@ -485,27 +575,33 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
- "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
+ "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"Denoising strength": getattr(p, 'denoising_strength', None),
"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
"Clip skip": None if clip_skip <= 1 else clip_skip,
- "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
+ "ENSD": opts.eta_noise_seed_delta if uses_ensd else None,
+ "Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
+ "Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
"Init image hash": getattr(p, 'init_img_hash', None),
"RNG": opts.randn_source if opts.randn_source != "GPU" else None,
"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
+ **p.extra_generation_params,
"Version": program_version() if opts.add_version_to_infotext else None,
+ "User": p.user if opts.add_user_name_to_info else None,
}
- generation_params.update(p.extra_generation_params)
-
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
+ prompt_text = p.prompt if use_main_prompt else all_prompts[index]
negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
- return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
+ return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
def process_images(p: StableDiffusionProcessing) -> Processed:
+ if p.scripts is not None:
+ p.scripts.before_process(p)
+
stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
try:
@@ -523,9 +619,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if k == 'sd_vae':
sd_vae.reload_vae_weights()
+ sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
+
res = process_images_inner(p)
finally:
+ sd_models.apply_token_merging(p.sd_model, 0)
+
# restore opts to original state
if p.override_settings_restore_afterwards:
for k, v in stored_opts.items():
@@ -555,15 +655,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: comments = {}
- if type(p.prompt) == list:
- p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
- else:
- p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
-
- if type(p.negative_prompt) == list:
- p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
- else:
- p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
+ p.setup_prompts()
if type(seed) == list:
p.all_seeds = seed
@@ -575,8 +667,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: else:
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
- def infotext(iteration=0, position_in_batch=0):
- return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
+ def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
+ return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
@@ -587,29 +679,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: infotexts = []
output_images = []
- cached_uc = [None, None]
- cached_c = [None, None]
-
- def get_conds_with_caching(function, required_prompts, steps, cache):
- """
- Returns the result of calling function(shared.sd_model, required_prompts, steps)
- using a cache to store the result if the same arguments have been used before.
-
- cache is an array containing two elements. The first element is a tuple
- representing the previously used arguments, or None if no arguments
- have been used before. The second element is where the previously
- computed result is stored.
- """
-
- if cache[0] is not None and (required_prompts, steps) == cache[0]:
- return cache[1]
-
- with devices.autocast():
- cache[1] = function(shared.sd_model, required_prompts, steps)
-
- cache[0] = (required_prompts, steps)
- return cache[1]
-
with torch.no_grad(), p.sd_model.ema_scope():
with devices.autocast():
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
@@ -618,10 +687,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
sd_vae_approx.model()
+ sd_unet.apply_unet()
+
if state.job_count == -1:
state.job_count = p.n_iter
- extra_network_data = None
for n in range(p.n_iter):
p.iteration = n
@@ -631,25 +701,25 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if state.interrupted:
break
- prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
- negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
- seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
- subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+ p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
+ p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
if p.scripts is not None:
- p.scripts.before_process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
+ p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
- if len(prompts) == 0:
+ if len(p.prompts) == 0:
break
- prompts, extra_network_data = extra_networks.parse_prompts(prompts)
+ p.parse_extra_network_prompts()
if not p.disable_extra_networks:
with devices.autocast():
- extra_networks.activate(p, extra_network_data)
+ extra_networks.activate(p, p.extra_network_data)
if p.scripts is not None:
- p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
+ p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
# params.txt should be saved after scripts.process_batch, since the
# infotext could be modified by that callback
@@ -660,14 +730,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: processed = Processed(p, [], p.seed, "")
file.write(processed.infotext(p, 0))
- step_multiplier = 1
- if not shared.opts.dont_fix_second_order_samplers_schedule:
- try:
- step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
- except:
- pass
- uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
- c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
+ p.setup_conds()
if len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
@@ -677,7 +740,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: shared.state.job = f"Batch {n+1} out of {p.n_iter}"
with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
- samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
+ samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
for x in x_samples_ddim:
@@ -688,7 +751,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: del samples_ddim
- if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+ if lowvram.is_enabled(shared.sd_model):
lowvram.send_everything_to_cpu()
devices.torch_gc()
@@ -704,7 +767,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if p.restore_faces:
if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
- images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
+ images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
devices.torch_gc()
@@ -721,13 +784,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if p.color_corrections is not None and i < len(p.color_corrections):
if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
- images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+ images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
image = apply_color_correction(p.color_corrections[i], image)
image = apply_overlay(image, p.paste_to, i, p.overlay_images)
if opts.samples_save and not p.do_not_save_samples:
- images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
+ images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p)
text = infotext(n, i)
infotexts.append(text)
@@ -740,10 +803,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if opts.save_mask:
- images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+ images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
if opts.save_mask_composite:
- images.save_image(image_mask_composite, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+ images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
if opts.return_mask:
output_images.append(image_mask)
@@ -765,7 +828,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: grid = images.image_grid(output_images, p.batch_size)
if opts.return_grid:
- text = infotext()
+ text = infotext(use_main_prompt=True)
infotexts.insert(0, text)
if opts.enable_pnginfo:
grid.info["parameters"] = text
@@ -773,10 +836,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: index_of_first_image = 1
if opts.grid_save:
- images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
+ images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(use_main_prompt=True), short_filename=not opts.grid_extended_filename, p=p, grid=True)
- if not p.disable_extra_networks and extra_network_data:
- extra_networks.deactivate(p, extra_network_data)
+ if not p.disable_extra_networks and p.extra_network_data:
+ extra_networks.deactivate(p, p.extra_network_data)
devices.torch_gc()
@@ -785,7 +848,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: images_list=output_images,
seed=p.all_seeds[0],
info=infotext(),
- comments="".join(f"\n\n{comment}" for comment in comments),
+ comments="".join(f"{comment}\n" for comment in comments),
subseed=p.all_subseeds[0],
index_of_first_image=index_of_first_image,
infotexts=infotexts,
@@ -811,8 +874,10 @@ def old_hires_fix_first_pass_dimensions(width, height): class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None
+ cached_hr_uc = [None, None]
+ cached_hr_c = [None, None]
- def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, **kwargs):
+ def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
super().__init__(**kwargs)
self.enable_hr = enable_hr
self.denoising_strength = denoising_strength
@@ -823,6 +888,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.hr_resize_y = hr_resize_y
self.hr_upscale_to_x = hr_resize_x
self.hr_upscale_to_y = hr_resize_y
+ self.hr_sampler_name = hr_sampler_name
+ self.hr_prompt = hr_prompt
+ self.hr_negative_prompt = hr_negative_prompt
+ self.all_hr_prompts = None
+ self.all_hr_negative_prompts = None
if firstphase_width != 0 or firstphase_height != 0:
self.hr_upscale_to_x = self.width
@@ -834,8 +904,26 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_y = 0
self.applied_old_hires_behavior_to = None
+ self.hr_prompts = None
+ self.hr_negative_prompts = None
+ self.hr_extra_network_data = None
+
+ self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc
+ self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c
+ self.hr_c = None
+ self.hr_uc = None
+
def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
+ if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
+ self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
+
+ if tuple(self.hr_prompt) != tuple(self.prompt):
+ self.extra_generation_params["Hires prompt"] = self.hr_prompt
+
+ if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt):
+ self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt
+
if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
self.hr_resize_x = self.width
self.hr_resize_y = self.height
@@ -901,7 +989,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
if self.enable_hr and latent_scale_mode is None:
- assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}"
+ if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
+ raise Exception(f"could not find upscaler named {self.hr_upscaler}")
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
@@ -965,9 +1054,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): shared.state.nextjob()
- img2img_sampler_name = self.sampler_name
+ img2img_sampler_name = self.hr_sampler_name or self.sampler_name
+
if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM
img2img_sampler_name = 'DDIM'
+
self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
@@ -978,17 +1069,101 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = None
devices.torch_gc()
- samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
+ if not self.disable_extra_networks:
+ with devices.autocast():
+ extra_networks.activate(self, self.hr_extra_network_data)
+
+ with devices.autocast():
+ self.calculate_hr_conds()
+
+ sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
+
+ if self.scripts is not None:
+ self.scripts.before_hr(self)
+
+ samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
+
+ sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
self.is_hr_pass = False
return samples
+ def close(self):
+ super().close()
+ self.hr_c = None
+ self.hr_uc = None
+ if not opts.experimental_persistent_cond_cache:
+ StableDiffusionProcessingTxt2Img.cached_hr_uc = [None, None]
+ StableDiffusionProcessingTxt2Img.cached_hr_c = [None, None]
+
+ def setup_prompts(self):
+ super().setup_prompts()
+
+ if not self.enable_hr:
+ return
+
+ if self.hr_prompt == '':
+ self.hr_prompt = self.prompt
+
+ if self.hr_negative_prompt == '':
+ self.hr_negative_prompt = self.negative_prompt
+
+ if type(self.hr_prompt) == list:
+ self.all_hr_prompts = self.hr_prompt
+ else:
+ self.all_hr_prompts = self.batch_size * self.n_iter * [self.hr_prompt]
+
+ if type(self.hr_negative_prompt) == list:
+ self.all_hr_negative_prompts = self.hr_negative_prompt
+ else:
+ self.all_hr_negative_prompts = self.batch_size * self.n_iter * [self.hr_negative_prompt]
+
+ self.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_hr_prompts]
+ self.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_hr_negative_prompts]
+
+ def calculate_hr_conds(self):
+ if self.hr_c is not None:
+ return
+
+ self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
+ self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
+
+ def setup_conds(self):
+ super().setup_conds()
+
+ self.hr_uc = None
+ self.hr_c = None
+
+ if self.enable_hr:
+ if shared.opts.hires_fix_use_firstpass_conds:
+ self.calculate_hr_conds()
+
+ elif lowvram.is_enabled(shared.sd_model): # if in lowvram mode, we need to calculate conds right away, before the cond NN is unloaded
+ with devices.autocast():
+ extra_networks.activate(self, self.hr_extra_network_data)
+
+ self.calculate_hr_conds()
+
+ with devices.autocast():
+ extra_networks.activate(self, self.extra_network_data)
+
+ def parse_extra_network_prompts(self):
+ res = super().parse_extra_network_prompts()
+
+ if self.enable_hr:
+ self.hr_prompts = self.all_hr_prompts[self.iteration * self.batch_size:(self.iteration + 1) * self.batch_size]
+ self.hr_negative_prompts = self.all_hr_negative_prompts[self.iteration * self.batch_size:(self.iteration + 1) * self.batch_size]
+
+ self.hr_prompts, self.hr_extra_network_data = extra_networks.parse_prompts(self.hr_prompts)
+
+ return res
+
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sampler = None
- def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
+ def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = None, mask_blur_x: int = 4, mask_blur_y: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
super().__init__(**kwargs)
self.init_images = init_images
@@ -999,7 +1174,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.image_mask = mask
self.latent_mask = None
self.mask_for_overlay = None
- self.mask_blur = mask_blur
+ if mask_blur is not None:
+ mask_blur_x = mask_blur
+ mask_blur_y = mask_blur
+ self.mask_blur_x = mask_blur_x
+ self.mask_blur_y = mask_blur_y
self.inpainting_fill = inpainting_fill
self.inpaint_full_res = inpaint_full_res
self.inpaint_full_res_padding = inpaint_full_res_padding
@@ -1021,8 +1200,17 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if self.inpainting_mask_invert:
image_mask = ImageOps.invert(image_mask)
- if self.mask_blur > 0:
- image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
+ if self.mask_blur_x > 0:
+ np_mask = np.array(image_mask)
+ kernel_size = 2 * int(4 * self.mask_blur_x + 0.5) + 1
+ np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
+ image_mask = Image.fromarray(np_mask)
+
+ if self.mask_blur_y > 0:
+ np_mask = np.array(image_mask)
+ kernel_size = 2 * int(4 * self.mask_blur_y + 0.5) + 1
+ np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
+ image_mask = Image.fromarray(np_mask)
if self.inpaint_full_res:
self.mask_for_overlay = image_mask
@@ -1141,3 +1329,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): devices.torch_gc()
return samples
+
+ def get_token_merging_ratio(self, for_hr=False):
+ return self.token_merging_ratio or ("token_merging_ratio" in self.override_settings and opts.token_merging_ratio) or opts.token_merging_ratio_img2img or opts.token_merging_ratio
diff --git a/modules/progress.py b/modules/progress.py index 948e6f00..f405f07f 100644 --- a/modules/progress.py +++ b/modules/progress.py @@ -95,9 +95,20 @@ def progressapi(req: ProgressRequest): image = shared.state.current_image
if image is not None:
buffered = io.BytesIO()
- image.save(buffered, format="png")
+
+ if opts.live_previews_image_format == "png":
+ # using optimize for large images takes an enormous amount of time
+ if max(*image.size) <= 256:
+ save_kwargs = {"optimize": True}
+ else:
+ save_kwargs = {"optimize": False, "compress_level": 1}
+
+ else:
+ save_kwargs = {}
+
+ image.save(buffered, format=opts.live_previews_image_format, **save_kwargs)
base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
- live_preview = f"data:image/png;base64,{base64_image}"
+ live_preview = f"data:image/{opts.live_previews_image_format};base64,{base64_image}"
id_live_preview = shared.state.id_live_preview
else:
live_preview = None
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 69665372..0069d8b0 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -54,18 +54,21 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): """
def collect_steps(steps, tree):
- l = [steps]
+ res = [steps]
+
class CollectSteps(lark.Visitor):
def scheduled(self, tree):
tree.children[-1] = float(tree.children[-1])
if tree.children[-1] < 1:
tree.children[-1] *= steps
tree.children[-1] = min(steps, int(tree.children[-1]))
- l.append(tree.children[-1])
+ res.append(tree.children[-1])
+
def alternate(self, tree):
- l.extend(range(1, steps+1))
+ res.extend(range(1, steps+1))
+
CollectSteps().visit(tree)
- return sorted(set(l))
+ return sorted(set(res))
def at_step(step, tree):
class AtStep(lark.Transformer):
@@ -92,7 +95,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): def get_schedule(prompt):
try:
tree = schedule_parser.parse(prompt)
- except lark.exceptions.LarkError as e:
+ except lark.exceptions.LarkError:
if 0:
import traceback
traceback.print_exc()
@@ -140,7 +143,7 @@ def get_learned_conditioning(model, prompts, steps): conds = model.get_learned_conditioning(texts)
cond_schedule = []
- for i, (end_at_step, text) in enumerate(prompt_schedule):
+ for i, (end_at_step, _) in enumerate(prompt_schedule):
cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
cache[prompt] = cond_schedule
@@ -216,8 +219,8 @@ def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_s res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
for i, cond_schedule in enumerate(c):
target_index = 0
- for current, (end_at, cond) in enumerate(cond_schedule):
- if current_step <= end_at:
+ for current, entry in enumerate(cond_schedule):
+ if current_step <= entry.end_at_step:
target_index = current
break
res[i] = cond_schedule[target_index].cond
@@ -231,13 +234,13 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step): tensors = []
conds_list = []
- for batch_no, composable_prompts in enumerate(c.batch):
+ for composable_prompts in c.batch:
conds_for_batch = []
- for cond_index, composable_prompt in enumerate(composable_prompts):
+ for composable_prompt in composable_prompts:
target_index = 0
- for current, (end_at, cond) in enumerate(composable_prompt.schedules):
- if current_step <= end_at:
+ for current, entry in enumerate(composable_prompt.schedules):
+ if current_step <= entry.end_at_step:
target_index = current
break
@@ -333,11 +336,11 @@ def parse_prompt_attention(text): round_brackets.append(len(res))
elif text == '[':
square_brackets.append(len(res))
- elif weight is not None and len(round_brackets) > 0:
+ elif weight is not None and round_brackets:
multiply_range(round_brackets.pop(), float(weight))
- elif text == ')' and len(round_brackets) > 0:
+ elif text == ')' and round_brackets:
multiply_range(round_brackets.pop(), round_bracket_multiplier)
- elif text == ']' and len(square_brackets) > 0:
+ elif text == ']' and square_brackets:
multiply_range(square_brackets.pop(), square_bracket_multiplier)
else:
parts = re.split(re_break, text)
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py index efd7fca5..0700b853 100644 --- a/modules/realesrgan_model.py +++ b/modules/realesrgan_model.py @@ -1,15 +1,13 @@ import os
-import sys
-import traceback
import numpy as np
from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
from realesrgan import RealESRGANer
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import cmd_opts, opts
-from modules import modelloader
+from modules import modelloader, errors
+
class UpscalerRealESRGAN(Upscaler):
def __init__(self, path):
@@ -17,9 +15,9 @@ class UpscalerRealESRGAN(Upscaler): self.user_path = path
super().__init__()
try:
- from basicsr.archs.rrdbnet_arch import RRDBNet
- from realesrgan import RealESRGANer
- from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+ from basicsr.archs.rrdbnet_arch import RRDBNet # noqa: F401
+ from realesrgan import RealESRGANer # noqa: F401
+ from realesrgan.archs.srvgg_arch import SRVGGNetCompact # noqa: F401
self.enable = True
self.scalers = []
scalers = self.load_models(path)
@@ -36,8 +34,7 @@ class UpscalerRealESRGAN(Upscaler): self.scalers.append(scaler)
except Exception:
- print("Error importing Real-ESRGAN:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report("Error importing Real-ESRGAN", exc_info=True)
self.enable = False
self.scalers = []
@@ -45,9 +42,10 @@ class UpscalerRealESRGAN(Upscaler): if not self.enable:
return img
- info = self.load_model(path)
- if not os.path.exists(info.local_data_path):
- print(f"Unable to load RealESRGAN model: {info.name}")
+ try:
+ info = self.load_model(path)
+ except Exception:
+ errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
return img
upsampler = RealESRGANer(
@@ -65,21 +63,17 @@ class UpscalerRealESRGAN(Upscaler): return image
def load_model(self, path):
- try:
- info = next(iter([scaler for scaler in self.scalers if scaler.data_path == path]), None)
-
- if info is None:
- print(f"Unable to find model info: {path}")
- return None
-
- if info.local_data_path.startswith("http"):
- info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
-
- return info
- except Exception as e:
- print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
- return None
+ for scaler in self.scalers:
+ if scaler.data_path == path:
+ if scaler.local_data_path.startswith("http"):
+ scaler.local_data_path = modelloader.load_file_from_url(
+ scaler.data_path,
+ model_dir=self.model_download_path,
+ )
+ if not os.path.exists(scaler.local_data_path):
+ raise FileNotFoundError(f"RealESRGAN data missing: {scaler.local_data_path}")
+ return scaler
+ raise ValueError(f"Unable to find model info: {path}")
def load_models(self, _):
return get_realesrgan_models(self)
@@ -134,6 +128,5 @@ def get_realesrgan_models(scaler): ),
]
return models
- except Exception as e:
- print("Error making Real-ESRGAN models list:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ except Exception:
+ errors.report("Error making Real-ESRGAN models list", exc_info=True)
diff --git a/modules/restart.py b/modules/restart.py new file mode 100644 index 00000000..18eacaf3 --- /dev/null +++ b/modules/restart.py @@ -0,0 +1,23 @@ +import os +from pathlib import Path + +from modules.paths_internal import script_path + + +def is_restartable() -> bool: + """ + Return True if the webui is restartable (i.e. there is something watching to restart it with) + """ + return bool(os.environ.get('SD_WEBUI_RESTART')) + + +def restart_program() -> None: + """creates file tmp/restart and immediately stops the process, which webui.bat/webui.sh interpret as a command to start webui again""" + + (Path(script_path) / "tmp" / "restart").touch() + + stop_program() + + +def stop_program() -> None: + os._exit(0) diff --git a/modules/safe.py b/modules/safe.py index e1a67f73..b1d08a79 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -2,8 +2,6 @@ import pickle
import collections
-import sys
-import traceback
import torch
import numpy
@@ -11,7 +9,10 @@ import _codecs import zipfile
import re
+
# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
+from modules import errors
+
TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
def encode(*args):
@@ -95,16 +96,16 @@ def check_pt(filename, extra_handler): except zipfile.BadZipfile:
- # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
+ # if it's not a zip file, it's an old pytorch format, with five objects written to pickle
with open(filename, "rb") as file:
unpickler = RestrictedUnpickler(file)
unpickler.extra_handler = extra_handler
- for i in range(5):
+ for _ in range(5):
unpickler.load()
def load(filename, *args, **kwargs):
- return load_with_extra(filename, extra_handler=global_extra_handler, *args, **kwargs)
+ return load_with_extra(filename, *args, extra_handler=global_extra_handler, **kwargs)
def load_with_extra(filename, extra_handler=None, *args, **kwargs):
@@ -136,17 +137,20 @@ def load_with_extra(filename, extra_handler=None, *args, **kwargs): check_pt(filename, extra_handler)
except pickle.UnpicklingError:
- print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
- print("-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
- print("You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
+ errors.report(
+ f"Error verifying pickled file from {filename}\n"
+ "-----> !!!! The file is most likely corrupted !!!! <-----\n"
+ "You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n",
+ exc_info=True,
+ )
return None
-
except Exception:
- print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
- print("\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
- print("You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
+ errors.report(
+ f"Error verifying pickled file from {filename}\n"
+ f"The file may be malicious, so the program is not going to read it.\n"
+ f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n",
+ exc_info=True,
+ )
return None
return unsafe_torch_load(filename, *args, **kwargs)
@@ -190,4 +194,3 @@ with safe.Extra(handler): unsafe_torch_load = torch.load
torch.load = load
global_extra_handler = None
-
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 17109732..77ee55ee 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -1,16 +1,16 @@ -import sys
-import traceback
-from collections import namedtuple
import inspect
+import os
+from collections import namedtuple
from typing import Optional, Dict, Any
from fastapi import FastAPI
from gradio import Blocks
+from modules import errors, timer
+
def report_exception(c, job):
- print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error executing callback {job} for {c.script}", exc_info=True)
class ImageSaveParams:
@@ -32,27 +32,42 @@ class CFGDenoiserParams: def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
self.x = x
"""Latent image representation in the process of being denoised"""
-
+
self.image_cond = image_cond
"""Conditioning image"""
-
+
self.sigma = sigma
"""Current sigma noise step value"""
-
+
self.sampling_step = sampling_step
"""Current Sampling step number"""
-
+
self.total_sampling_steps = total_sampling_steps
"""Total number of sampling steps planned"""
-
+
self.text_cond = text_cond
""" Encoder hidden states of text conditioning from prompt"""
-
+
self.text_uncond = text_uncond
""" Encoder hidden states of text conditioning from negative prompt"""
class CFGDenoisedParams:
+ def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
+ self.x = x
+ """Latent image representation in the process of being denoised"""
+
+ self.sampling_step = sampling_step
+ """Current Sampling step number"""
+
+ self.total_sampling_steps = total_sampling_steps
+ """Total number of sampling steps planned"""
+
+ self.inner_model = inner_model
+ """Inner model reference used for denoising"""
+
+
+class AfterCFGCallbackParams:
def __init__(self, x, sampling_step, total_sampling_steps):
self.x = x
"""Latent image representation in the process of being denoised"""
@@ -87,6 +102,7 @@ callback_map = dict( callbacks_image_saved=[],
callbacks_cfg_denoiser=[],
callbacks_cfg_denoised=[],
+ callbacks_cfg_after_cfg=[],
callbacks_before_component=[],
callbacks_after_component=[],
callbacks_image_grid=[],
@@ -94,6 +110,8 @@ callback_map = dict( callbacks_script_unloaded=[],
callbacks_before_ui=[],
callbacks_on_reload=[],
+ callbacks_list_optimizers=[],
+ callbacks_list_unets=[],
)
@@ -106,6 +124,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI): for c in callback_map['callbacks_app_started']:
try:
c.callback(demo, app)
+ timer.startup_timer.record(os.path.basename(c.script))
except Exception:
report_exception(c, 'app_started_callback')
@@ -186,6 +205,14 @@ def cfg_denoised_callback(params: CFGDenoisedParams): report_exception(c, 'cfg_denoised_callback')
+def cfg_after_cfg_callback(params: AfterCFGCallbackParams):
+ for c in callback_map['callbacks_cfg_after_cfg']:
+ try:
+ c.callback(params)
+ except Exception:
+ report_exception(c, 'cfg_after_cfg_callback')
+
+
def before_component_callback(component, **kwargs):
for c in callback_map['callbacks_before_component']:
try:
@@ -234,16 +261,40 @@ def before_ui_callback(): report_exception(c, 'before_ui')
+def list_optimizers_callback():
+ res = []
+
+ for c in callback_map['callbacks_list_optimizers']:
+ try:
+ c.callback(res)
+ except Exception:
+ report_exception(c, 'list_optimizers')
+
+ return res
+
+
+def list_unets_callback():
+ res = []
+
+ for c in callback_map['callbacks_list_unets']:
+ try:
+ c.callback(res)
+ except Exception:
+ report_exception(c, 'list_unets')
+
+ return res
+
+
def add_callback(callbacks, fun):
stack = [x for x in inspect.stack() if x.filename != __file__]
- filename = stack[0].filename if len(stack) > 0 else 'unknown file'
+ filename = stack[0].filename if stack else 'unknown file'
callbacks.append(ScriptCallback(filename, fun))
-
+
def remove_current_script_callbacks():
stack = [x for x in inspect.stack() if x.filename != __file__]
- filename = stack[0].filename if len(stack) > 0 else 'unknown file'
+ filename = stack[0].filename if stack else 'unknown file'
if filename == 'unknown file':
return
for callback_list in callback_map.values():
@@ -332,6 +383,14 @@ def on_cfg_denoised(callback): add_callback(callback_map['callbacks_cfg_denoised'], callback)
+def on_cfg_after_cfg(callback):
+ """register a function to be called in the kdiffussion cfg_denoiser method after cfg calculations are completed.
+ The callback is called with one argument:
+ - params: AfterCFGCallbackParams - parameters to be passed to the script for post-processing after cfg calculation.
+ """
+ add_callback(callback_map['callbacks_cfg_after_cfg'], callback)
+
+
def on_before_component(callback):
"""register a function to be called before a component is created.
The callback is called with arguments:
@@ -377,3 +436,18 @@ def on_before_ui(callback): """register a function to be called before the UI is created."""
add_callback(callback_map['callbacks_before_ui'], callback)
+
+
+def on_list_optimizers(callback):
+ """register a function to be called when UI is making a list of cross attention optimization options.
+ The function will be called with one argument, a list, and shall add objects of type modules.sd_hijack_optimizations.SdOptimization
+ to it."""
+
+ add_callback(callback_map['callbacks_list_optimizers'], callback)
+
+
+def on_list_unets(callback):
+ """register a function to be called when UI is making a list of alternative options for unet.
+ The function will be called with one argument, a list, and shall add objects of type modules.sd_unet.SdUnetOption to it."""
+
+ add_callback(callback_map['callbacks_list_unets'], callback)
diff --git a/modules/script_loading.py b/modules/script_loading.py index a7d2203f..306a1f35 100644 --- a/modules/script_loading.py +++ b/modules/script_loading.py @@ -1,8 +1,7 @@ import os
-import sys
-import traceback
import importlib.util
-from types import ModuleType
+
+from modules import errors
def load_module(path):
@@ -28,5 +27,4 @@ def preload_extensions(extensions_dir, parser): module.preload(parser)
except Exception:
- print(f"Error running preload() for {preload_script}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error running preload() for {preload_script}", exc_info=True)
diff --git a/modules/scripts.py b/modules/scripts.py index d945b89f..7d9dd59f 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -1,12 +1,12 @@ import os
import re
import sys
-import traceback
+import inspect
from collections import namedtuple
import gradio as gr
-from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing
+from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing, errors, timer
AlwaysVisible = object()
@@ -17,6 +17,12 @@ class PostprocessImageArgs: class Script:
+ name = None
+ """script's internal name derived from title"""
+
+ section = None
+ """name of UI section that the script's controls will be placed into"""
+
filename = None
args_from = None
args_to = None
@@ -25,8 +31,8 @@ class Script: is_txt2img = False
is_img2img = False
- """A gr.Group component that has all script's UI inside it"""
group = None
+ """A gr.Group component that has all script's UI inside it"""
infotext_fields = None
"""if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
@@ -38,6 +44,9 @@ class Script: various "Send to <X>" buttons when clicked
"""
+ api_info = None
+ """Generated value of type modules.api.models.ScriptInfo with information about the script for API"""
+
def title(self):
"""this function should return the title of the script. This is what will be displayed in the dropdown menu."""
@@ -76,6 +85,15 @@ class Script: pass
+ def before_process(self, p, *args):
+ """
+ This function is called very early before processing begins for AlwaysVisible scripts.
+ You can modify the processing object (p) here, inject hooks, etc.
+ args contains all values returned by components from ui()
+ """
+
+ pass
+
def process(self, p, *args):
"""
This function is called before processing begins for AlwaysVisible scripts.
@@ -99,6 +117,21 @@ class Script: pass
+ def after_extra_networks_activate(self, p, *args, **kwargs):
+ """
+ Calledafter extra networks activation, before conds calculation
+ allow modification of the network after extra networks activation been applied
+ won't be call if p.disable_extra_networks
+
+ **kwargs will have those items:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+ - seeds - list of seeds for current batch
+ - subseeds - list of subseeds for current batch
+ - extra_network_data - list of ExtraNetworkParams for current stage
+ """
+ pass
+
def process_batch(self, p, *args, **kwargs):
"""
Same as process(), but called for every batch.
@@ -169,6 +202,11 @@ class Script: return f'script_{tabname}{title}_{item_id}'
+ def before_hr(self, p, *args):
+ """
+ This function is called before hires fix start.
+ """
+ pass
current_basedir = paths.script_path
@@ -231,8 +269,8 @@ def load_scripts(): syspath = sys.path
def register_scripts_from_module(module):
- for key, script_class in module.__dict__.items():
- if type(script_class) != type:
+ for script_class in module.__dict__.values():
+ if not inspect.isclass(script_class):
continue
if issubclass(script_class, Script):
@@ -258,21 +296,25 @@ def load_scripts(): register_scripts_from_module(script_module)
except Exception:
- print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error loading script: {scriptfile.filename}", exc_info=True)
finally:
sys.path = syspath
current_basedir = paths.script_path
+ timer.startup_timer.record(scriptfile.filename)
+
+ global scripts_txt2img, scripts_img2img, scripts_postproc
+
+ scripts_txt2img = ScriptRunner()
+ scripts_img2img = ScriptRunner()
+ scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
try:
- res = func(*args, **kwargs)
- return res
+ return func(*args, **kwargs)
except Exception:
- print(f"Error calling: {filename}/{funcname}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error calling: {filename}/{funcname}", exc_info=True)
return default
@@ -285,6 +327,7 @@ class ScriptRunner: self.titles = []
self.infotext_fields = []
self.paste_field_names = []
+ self.inputs = [None]
def initialize_scripts(self, is_img2img):
from modules import scripts_auto_postprocessing
@@ -295,9 +338,9 @@ class ScriptRunner: auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
- for script_class, path, basedir, script_module in auto_processing_scripts + scripts_data:
- script = script_class()
- script.filename = path
+ for script_data in auto_processing_scripts + scripts_data:
+ script = script_data.script_class()
+ script.filename = script_data.path
script.is_txt2img = not is_img2img
script.is_img2img = is_img2img
@@ -312,49 +355,74 @@ class ScriptRunner: self.scripts.append(script)
self.selectable_scripts.append(script)
- def setup_ui(self):
- self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
+ def create_script_ui(self, script):
+ import modules.api.models as api_models
- inputs = [None]
- inputs_alwayson = [True]
+ script.args_from = len(self.inputs)
+ script.args_to = len(self.inputs)
- def create_script_ui(script, inputs, inputs_alwayson):
- script.args_from = len(inputs)
- script.args_to = len(inputs)
+ controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
- controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
+ if controls is None:
+ return
- if controls is None:
- return
+ script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
+ api_args = []
- for control in controls:
- control.custom_script_source = os.path.basename(script.filename)
+ for control in controls:
+ control.custom_script_source = os.path.basename(script.filename)
- if script.infotext_fields is not None:
- self.infotext_fields += script.infotext_fields
+ arg_info = api_models.ScriptArg(label=control.label or "")
- if script.paste_field_names is not None:
- self.paste_field_names += script.paste_field_names
+ for field in ("value", "minimum", "maximum", "step", "choices"):
+ v = getattr(control, field, None)
+ if v is not None:
+ setattr(arg_info, field, v)
- inputs += controls
- inputs_alwayson += [script.alwayson for _ in controls]
- script.args_to = len(inputs)
+ api_args.append(arg_info)
- for script in self.alwayson_scripts:
- with gr.Group() as group:
- create_script_ui(script, inputs, inputs_alwayson)
+ script.api_info = api_models.ScriptInfo(
+ name=script.name,
+ is_img2img=script.is_img2img,
+ is_alwayson=script.alwayson,
+ args=api_args,
+ )
- script.group = group
+ if script.infotext_fields is not None:
+ self.infotext_fields += script.infotext_fields
- dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
- inputs[0] = dropdown
+ if script.paste_field_names is not None:
+ self.paste_field_names += script.paste_field_names
+
+ self.inputs += controls
+ script.args_to = len(self.inputs)
+
+ def setup_ui_for_section(self, section, scriptlist=None):
+ if scriptlist is None:
+ scriptlist = self.alwayson_scripts
+
+ for script in scriptlist:
+ if script.alwayson and script.section != section:
+ continue
- for script in self.selectable_scripts:
- with gr.Group(visible=False) as group:
- create_script_ui(script, inputs, inputs_alwayson)
+ with gr.Group(visible=script.alwayson) as group:
+ self.create_script_ui(script)
script.group = group
+ def prepare_ui(self):
+ self.inputs = [None]
+
+ def setup_ui(self):
+ self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
+
+ self.setup_ui_for_section(None)
+
+ dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
+ self.inputs[0] = dropdown
+
+ self.setup_ui_for_section(None, self.selectable_scripts)
+
def select_script(script_index):
selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
@@ -378,6 +446,7 @@ class ScriptRunner: )
self.script_load_ctr = 0
+
def onload_script_visibility(params):
title = params.get('Script', None)
if title:
@@ -388,10 +457,10 @@ class ScriptRunner: else:
return gr.update(visible=False)
- self.infotext_fields.append( (dropdown, lambda x: gr.update(value=x.get('Script', 'None'))) )
- self.infotext_fields.extend( [(script.group, onload_script_visibility) for script in self.selectable_scripts] )
+ self.infotext_fields.append((dropdown, lambda x: gr.update(value=x.get('Script', 'None'))))
+ self.infotext_fields.extend([(script.group, onload_script_visibility) for script in self.selectable_scripts])
- return inputs
+ return self.inputs
def run(self, p, *args):
script_index = args[0]
@@ -411,14 +480,21 @@ class ScriptRunner: return processed
+ def before_process(self, p):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.before_process(p, *script_args)
+ except Exception:
+ errors.report(f"Error running before_process: {script.filename}", exc_info=True)
+
def process(self, p):
for script in self.alwayson_scripts:
try:
script_args = p.script_args[script.args_from:script.args_to]
script.process(p, *script_args)
except Exception:
- print(f"Error running process: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error running process: {script.filename}", exc_info=True)
def before_process_batch(self, p, **kwargs):
for script in self.alwayson_scripts:
@@ -426,8 +502,15 @@ class ScriptRunner: script_args = p.script_args[script.args_from:script.args_to]
script.before_process_batch(p, *script_args, **kwargs)
except Exception:
- print(f"Error running before_process_batch: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error running before_process_batch: {script.filename}", exc_info=True)
+
+ def after_extra_networks_activate(self, p, **kwargs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.after_extra_networks_activate(p, *script_args, **kwargs)
+ except Exception:
+ errors.report(f"Error running after_extra_networks_activate: {script.filename}", exc_info=True)
def process_batch(self, p, **kwargs):
for script in self.alwayson_scripts:
@@ -435,8 +518,7 @@ class ScriptRunner: script_args = p.script_args[script.args_from:script.args_to]
script.process_batch(p, *script_args, **kwargs)
except Exception:
- print(f"Error running process_batch: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error running process_batch: {script.filename}", exc_info=True)
def postprocess(self, p, processed):
for script in self.alwayson_scripts:
@@ -444,8 +526,7 @@ class ScriptRunner: script_args = p.script_args[script.args_from:script.args_to]
script.postprocess(p, processed, *script_args)
except Exception:
- print(f"Error running postprocess: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error running postprocess: {script.filename}", exc_info=True)
def postprocess_batch(self, p, images, **kwargs):
for script in self.alwayson_scripts:
@@ -453,8 +534,7 @@ class ScriptRunner: script_args = p.script_args[script.args_from:script.args_to]
script.postprocess_batch(p, *script_args, images=images, **kwargs)
except Exception:
- print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error running postprocess_batch: {script.filename}", exc_info=True)
def postprocess_image(self, p, pp: PostprocessImageArgs):
for script in self.alwayson_scripts:
@@ -462,24 +542,21 @@ class ScriptRunner: script_args = p.script_args[script.args_from:script.args_to]
script.postprocess_image(p, pp, *script_args)
except Exception:
- print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
def before_component(self, component, **kwargs):
for script in self.scripts:
try:
script.before_component(component, **kwargs)
except Exception:
- print(f"Error running before_component: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error running before_component: {script.filename}", exc_info=True)
def after_component(self, component, **kwargs):
for script in self.scripts:
try:
script.after_component(component, **kwargs)
except Exception:
- print(f"Error running after_component: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error running after_component: {script.filename}", exc_info=True)
def reload_sources(self, cache):
for si, script in list(enumerate(self.scripts)):
@@ -492,7 +569,7 @@ class ScriptRunner: module = script_loading.load_module(script.filename)
cache[filename] = module
- for key, script_class in module.__dict__.items():
+ for script_class in module.__dict__.values():
if type(script_class) == type and issubclass(script_class, Script):
self.scripts[si] = script_class()
self.scripts[si].filename = filename
@@ -500,9 +577,18 @@ class ScriptRunner: self.scripts[si].args_to = args_to
-scripts_txt2img = ScriptRunner()
-scripts_img2img = ScriptRunner()
-scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
+ def before_hr(self, p):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.before_hr(p, *script_args)
+ except Exception:
+ errors.report(f"Error running before_hr: {script.filename}", exc_info=True)
+
+
+scripts_txt2img: ScriptRunner = None
+scripts_img2img: ScriptRunner = None
+scripts_postproc: scripts_postprocessing.ScriptPostprocessingRunner = None
scripts_current: ScriptRunner = None
@@ -512,14 +598,7 @@ def reload_script_body_only(): scripts_img2img.reload_sources(cache)
-def reload_scripts():
- global scripts_txt2img, scripts_img2img, scripts_postproc
-
- load_scripts()
-
- scripts_txt2img = ScriptRunner()
- scripts_img2img = ScriptRunner()
- scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
+reload_scripts = load_scripts # compatibility alias
def add_classes_to_gradio_component(comp):
diff --git a/modules/scripts_auto_postprocessing.py b/modules/scripts_auto_postprocessing.py index 30d6d658..d63078de 100644 --- a/modules/scripts_auto_postprocessing.py +++ b/modules/scripts_auto_postprocessing.py @@ -17,7 +17,7 @@ class ScriptPostprocessingForMainUI(scripts.Script): return self.postprocessing_controls.values()
def postprocess_image(self, p, script_pp, *args):
- args_dict = {k: v for k, v in zip(self.postprocessing_controls, args)}
+ args_dict = dict(zip(self.postprocessing_controls, args))
pp = scripts_postprocessing.PostprocessedImage(script_pp.image)
pp.info = {}
diff --git a/modules/scripts_postprocessing.py b/modules/scripts_postprocessing.py index b11568c0..bac1335d 100644 --- a/modules/scripts_postprocessing.py +++ b/modules/scripts_postprocessing.py @@ -66,9 +66,9 @@ class ScriptPostprocessingRunner: def initialize_scripts(self, scripts_data):
self.scripts = []
- for script_class, path, basedir, script_module in scripts_data:
- script: ScriptPostprocessing = script_class()
- script.filename = path
+ for script_data in scripts_data:
+ script: ScriptPostprocessing = script_data.script_class()
+ script.filename = script_data.path
if script.name == "Simple Upscale":
continue
@@ -124,7 +124,7 @@ class ScriptPostprocessingRunner: script_args = args[script.args_from:script.args_to]
process_args = {}
- for (name, component), value in zip(script.controls.items(), script_args):
+ for (name, _component), value in zip(script.controls.items(), script_args):
process_args[name] = value
script.process(pp, **process_args)
diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py index c4a09d15..9fc89dc6 100644 --- a/modules/sd_disable_initialization.py +++ b/modules/sd_disable_initialization.py @@ -61,7 +61,7 @@ class DisableInitialization: if res is None:
res = original(url, *args, local_files_only=False, **kwargs)
return res
- except Exception as e:
+ except Exception:
return original(url, *args, local_files_only=False, **kwargs)
def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index f4bb0266..3b6f95ce 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -3,7 +3,7 @@ from torch.nn.functional import silu from types import MethodType
import modules.textual_inversion.textual_inversion
-from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
from modules.hypernetworks import hypernetwork
from modules.shared import cmd_opts
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
@@ -28,57 +28,65 @@ ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] ldm.modules.attention.print = lambda *args: None
ldm.modules.diffusionmodules.model.print = lambda *args: None
+optimizers = []
+current_optimizer: sd_hijack_optimizations.SdOptimization = None
+
+
+def list_optimizers():
+ new_optimizers = script_callbacks.list_optimizers_callback()
+
+ new_optimizers = [x for x in new_optimizers if x.is_available()]
+
+ new_optimizers = sorted(new_optimizers, key=lambda x: x.priority, reverse=True)
+
+ optimizers.clear()
+ optimizers.extend(new_optimizers)
+
+
+def apply_optimizations(option=None):
+ global current_optimizer
-def apply_optimizations():
undo_optimizations()
+ if len(optimizers) == 0:
+ # a script can access the model very early, and optimizations would not be filled by then
+ current_optimizer = None
+ return ''
+
ldm.modules.diffusionmodules.model.nonlinearity = silu
ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
-
- optimization_method = None
- can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention")) # not everyone has torch 2.x to use sdp
-
- if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
- print("Applying xformers cross attention optimization.")
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
- ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
- optimization_method = 'xformers'
- elif cmd_opts.opt_sdp_no_mem_attention and can_use_sdp:
- print("Applying scaled dot product cross attention optimization (without memory efficient attention).")
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward
- ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward
- optimization_method = 'sdp-no-mem'
- elif cmd_opts.opt_sdp_attention and can_use_sdp:
- print("Applying scaled dot product cross attention optimization.")
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
- ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward
- optimization_method = 'sdp'
- elif cmd_opts.opt_sub_quad_attention:
- print("Applying sub-quadratic cross attention optimization.")
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
- ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward
- optimization_method = 'sub-quadratic'
- elif cmd_opts.opt_split_attention_v1:
- print("Applying v1 cross attention optimization.")
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
- optimization_method = 'V1'
- elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not cmd_opts.opt_split_attention and not torch.cuda.is_available()):
- print("Applying cross attention optimization (InvokeAI).")
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
- optimization_method = 'InvokeAI'
- elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
- print("Applying cross attention optimization (Doggettx).")
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
- ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
- optimization_method = 'Doggettx'
-
- return optimization_method
+ if current_optimizer is not None:
+ current_optimizer.undo()
+ current_optimizer = None
+
+ selection = option or shared.opts.cross_attention_optimization
+ if selection == "Automatic" and len(optimizers) > 0:
+ matching_optimizer = next(iter([x for x in optimizers if x.cmd_opt and getattr(shared.cmd_opts, x.cmd_opt, False)]), optimizers[0])
+ else:
+ matching_optimizer = next(iter([x for x in optimizers if x.title() == selection]), None)
+
+ if selection == "None":
+ matching_optimizer = None
+ elif selection == "Automatic" and shared.cmd_opts.disable_opt_split_attention:
+ matching_optimizer = None
+ elif matching_optimizer is None:
+ matching_optimizer = optimizers[0]
+
+ if matching_optimizer is not None:
+ print(f"Applying attention optimization: {matching_optimizer.name}... ", end='')
+ matching_optimizer.apply()
+ print("done.")
+ current_optimizer = matching_optimizer
+ return current_optimizer.name
+ else:
+ print("Disabling attention optimization")
+ return ''
def undo_optimizations():
- ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
+ ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
@@ -92,12 +100,12 @@ def fix_checkpoint(): def weighted_loss(sd_model, pred, target, mean=True):
#Calculate the weight normally, but ignore the mean
loss = sd_model._old_get_loss(pred, target, mean=False)
-
+
#Check if we have weights available
weight = getattr(sd_model, '_custom_loss_weight', None)
if weight is not None:
loss *= weight
-
+
#Return the loss, as mean if specified
return loss.mean() if mean else loss
@@ -105,7 +113,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs): try:
#Temporarily append weights to a place accessible during loss calc
sd_model._custom_loss_weight = w
-
+
#Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
#Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
if not hasattr(sd_model, '_old_get_loss'):
@@ -118,9 +126,9 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs): try:
#Delete temporary weights if appended
del sd_model._custom_loss_weight
- except AttributeError as e:
+ except AttributeError:
pass
-
+
#If we have an old loss function, reset the loss function to the original one
if hasattr(sd_model, '_old_get_loss'):
sd_model.get_loss = sd_model._old_get_loss
@@ -133,7 +141,7 @@ def apply_weighted_forward(sd_model): def undo_weighted_forward(sd_model):
try:
del sd_model.weighted_forward
- except AttributeError as e:
+ except AttributeError:
pass
@@ -150,6 +158,13 @@ class StableDiffusionModelHijack: def __init__(self):
self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
+ def apply_optimizations(self, option=None):
+ try:
+ self.optimization_method = apply_optimizations(option)
+ except Exception as e:
+ errors.display(e, "applying cross attention optimization")
+ undo_optimizations()
+
def hijack(self, m):
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
model_embeddings = m.cond_stage_model.roberta.embeddings
@@ -169,7 +184,7 @@ class StableDiffusionModelHijack: if m.cond_stage_key == "edit":
sd_hijack_unet.hijack_ddpm_edit()
- self.optimization_method = apply_optimizations()
+ self.apply_optimizations()
self.clip = m.cond_stage_model
@@ -182,9 +197,14 @@ class StableDiffusionModelHijack: self.layers = flatten(m)
+ if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
+ ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
+
+ ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
+
def undo_hijack(self, m):
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
- m.cond_stage_model = m.cond_stage_model.wrapped
+ m.cond_stage_model = m.cond_stage_model.wrapped
elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
m.cond_stage_model = m.cond_stage_model.wrapped
@@ -203,6 +223,8 @@ class StableDiffusionModelHijack: self.layers = None
self.clip = None
+ ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
+
def apply_circular(self, enable):
if self.circular_enabled == enable:
return
@@ -216,10 +238,17 @@ class StableDiffusionModelHijack: self.comments = []
def get_prompt_lengths(self, text):
+ if self.clip is None:
+ return "-", "-"
+
_, token_count = self.clip.process_texts([text])
return token_count, self.clip.get_target_prompt_token_count(token_count)
+ def redo_hijack(self, m):
+ self.undo_hijack(m)
+ self.hijack(m)
+
class EmbeddingsWithFixes(torch.nn.Module):
def __init__(self, wrapped, embeddings):
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py index 9fa5c5c5..3b5a7666 100644 --- a/modules/sd_hijack_clip.py +++ b/modules/sd_hijack_clip.py @@ -167,7 +167,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): chunk.multipliers += [weight] * emb_len
position += embedding_length_in_tokens
- if len(chunk.tokens) > 0 or len(chunks) == 0:
+ if chunk.tokens or not chunks:
next_chunk(is_last=True)
return chunks, token_count
@@ -223,7 +223,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): self.hijack.fixes = [x.fixes for x in batch_chunk]
for fixes in self.hijack.fixes:
- for position, embedding in fixes:
+ for _position, embedding in fixes:
used_embeddings[embedding.name] = embedding
z = self.process_tokens(tokens, multipliers)
diff --git a/modules/sd_hijack_clip_old.py b/modules/sd_hijack_clip_old.py index a3476e95..c5c6270b 100644 --- a/modules/sd_hijack_clip_old.py +++ b/modules/sd_hijack_clip_old.py @@ -74,7 +74,7 @@ def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, text self.hijack.comments += hijack_comments
- if len(used_custom_terms) > 0:
+ if used_custom_terms:
embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
self.hijack.comments.append(f"Used embeddings: {embedding_names}")
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index 55a2ce4d..c1977b19 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -1,16 +1,10 @@ -import os import torch -from einops import repeat -from omegaconf import ListConfig - import ldm.models.diffusion.ddpm import ldm.models.diffusion.ddim import ldm.models.diffusion.plms -from ldm.models.diffusion.ddpm import LatentDiffusion -from ldm.models.diffusion.plms import PLMSSampler -from ldm.models.diffusion.ddim import DDIMSampler, noise_like +from ldm.models.diffusion.ddim import noise_like from ldm.models.diffusion.sampling_util import norm_thresholding @@ -29,7 +23,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F if isinstance(c, dict): assert isinstance(unconditional_conditioning, dict) - c_in = dict() + c_in = {} for k in c: if isinstance(c[k], list): c_in[k] = [ diff --git a/modules/sd_hijack_ip2p.py b/modules/sd_hijack_ip2p.py index 3c727d3b..6fe6b6ff 100644 --- a/modules/sd_hijack_ip2p.py +++ b/modules/sd_hijack_ip2p.py @@ -1,8 +1,5 @@ -import collections import os.path -import sys -import gc -import time + def should_hijack_ip2p(checkpoint_info): from modules import sd_models_config @@ -10,4 +7,4 @@ def should_hijack_ip2p(checkpoint_info): ckpt_basename = os.path.basename(checkpoint_info.filename).lower() cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower() - return "pix2pix" in ckpt_basename and not "pix2pix" in cfg_basename + return "pix2pix" in ckpt_basename and "pix2pix" not in cfg_basename diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index f10865cd..53e27ade 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -1,6 +1,5 @@ +from __future__ import annotations
import math
-import sys
-import traceback
import psutil
import torch
@@ -9,10 +8,129 @@ from torch import einsum from ldm.util import default
from einops import rearrange
-from modules import shared, errors, devices
+from modules import shared, errors, devices, sub_quadratic_attention
from modules.hypernetworks import hypernetwork
-from .sub_quadratic_attention import efficient_dot_product_attention
+import ldm.modules.attention
+import ldm.modules.diffusionmodules.model
+
+diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
+
+
+class SdOptimization:
+ name: str = None
+ label: str | None = None
+ cmd_opt: str | None = None
+ priority: int = 0
+
+ def title(self):
+ if self.label is None:
+ return self.name
+
+ return f"{self.name} - {self.label}"
+
+ def is_available(self):
+ return True
+
+ def apply(self):
+ pass
+
+ def undo(self):
+ ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+
+
+class SdOptimizationXformers(SdOptimization):
+ name = "xformers"
+ cmd_opt = "xformers"
+ priority = 100
+
+ def is_available(self):
+ return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.cuda.is_available() and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0))
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = xformers_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward
+
+
+class SdOptimizationSdpNoMem(SdOptimization):
+ name = "sdp-no-mem"
+ label = "scaled dot product without memory efficient attention"
+ cmd_opt = "opt_sdp_no_mem_attention"
+ priority = 80
+
+ def is_available(self):
+ return hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward
+
+
+class SdOptimizationSdp(SdOptimizationSdpNoMem):
+ name = "sdp"
+ label = "scaled dot product"
+ cmd_opt = "opt_sdp_attention"
+ priority = 70
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward
+
+
+class SdOptimizationSubQuad(SdOptimization):
+ name = "sub-quadratic"
+ cmd_opt = "opt_sub_quad_attention"
+ priority = 10
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward
+
+
+class SdOptimizationV1(SdOptimization):
+ name = "V1"
+ label = "original v1"
+ cmd_opt = "opt_split_attention_v1"
+ priority = 10
+
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
+
+
+class SdOptimizationInvokeAI(SdOptimization):
+ name = "InvokeAI"
+ cmd_opt = "opt_split_attention_invokeai"
+
+ @property
+ def priority(self):
+ return 1000 if not torch.cuda.is_available() else 10
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
+
+
+class SdOptimizationDoggettx(SdOptimization):
+ name = "Doggettx"
+ cmd_opt = "opt_split_attention"
+ priority = 90
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
+
+
+def list_optimizers(res):
+ res.extend([
+ SdOptimizationXformers(),
+ SdOptimizationSdpNoMem(),
+ SdOptimizationSdp(),
+ SdOptimizationSubQuad(),
+ SdOptimizationV1(),
+ SdOptimizationInvokeAI(),
+ SdOptimizationDoggettx(),
+ ])
if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
@@ -20,8 +138,7 @@ if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers: import xformers.ops
shared.xformers_available = True
except Exception:
- print("Cannot import xformers", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report("Cannot import xformers", exc_info=True)
def get_available_vram():
@@ -49,7 +166,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): v_in = self.to_v(context_v)
del context, context_k, context_v, x
- q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+ q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
del q_in, k_in, v_in
dtype = q.dtype
@@ -62,10 +179,10 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): end = i + 2
s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
s1 *= self.scale
-
+
s2 = s1.softmax(dim=-1)
del s1
-
+
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
del s2
del q, k, v
@@ -95,43 +212,43 @@ def split_cross_attention_forward(self, x, context=None, mask=None): with devices.without_autocast(disable=not shared.opts.upcast_attn):
k_in = k_in * self.scale
-
+
del context, x
-
- q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+
+ q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
del q_in, k_in, v_in
-
+
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-
+
mem_free_total = get_available_vram()
-
+
gb = 1024 ** 3
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
modifier = 3 if q.element_size() == 2 else 2.5
mem_required = tensor_size * modifier
steps = 1
-
+
if mem_required > mem_free_total:
steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
-
+
if steps > 64:
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
-
+
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
-
+
s2 = s1.softmax(dim=-1, dtype=q.dtype)
del s1
-
+
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
del s2
-
+
del q, k, v
r1 = r1.to(dtype)
@@ -228,8 +345,8 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): with devices.without_autocast(disable=not shared.opts.upcast_attn):
k = k * self.scale
-
- q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+ q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
r = einsum_op(q, k, v)
r = r.to(dtype)
return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
@@ -296,11 +413,10 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_ if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
# the big matmul fits into our memory limit; do everything in 1 chunk,
# i.e. send it down the unchunked fast-path
- query_chunk_size = q_tokens
kv_chunk_size = k_tokens
with devices.without_autocast(disable=q.dtype == v.dtype):
- return efficient_dot_product_attention(
+ return sub_quadratic_attention.efficient_dot_product_attention(
q,
k,
v,
@@ -335,7 +451,7 @@ def xformers_attention_forward(self, x, context=None, mask=None): k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
- q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
+ q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
del q_in, k_in, v_in
dtype = q.dtype
@@ -370,7 +486,7 @@ def scaled_dot_product_attention_forward(self, x, context=None, mask=None): q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
-
+
del q_in, k_in, v_in
dtype = q.dtype
@@ -452,7 +568,7 @@ def cross_attention_attnblock_forward(self, x): h3 += x
return h3
-
+
def xformers_attnblock_forward(self, x):
try:
h_ = x
@@ -461,7 +577,7 @@ def xformers_attnblock_forward(self, x): k = self.k(h_)
v = self.v(h_)
b, c, h, w = q.shape
- q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+ q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
dtype = q.dtype
if shared.opts.upcast_attn:
q, k = q.float(), k.float()
@@ -483,10 +599,10 @@ def sdp_attnblock_forward(self, x): k = self.k(h_)
v = self.v(h_)
b, c, h, w = q.shape
- q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+ q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
dtype = q.dtype
if shared.opts.upcast_attn:
- q, k = q.float(), k.float()
+ q, k, v = q.float(), k.float(), v.float()
q = q.contiguous()
k = k.contiguous()
v = v.contiguous()
@@ -507,7 +623,7 @@ def sub_quad_attnblock_forward(self, x): k = self.k(h_)
v = self.v(h_)
b, c, h, w = q.shape
- q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+ q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
q = q.contiguous()
k = k.contiguous()
v = v.contiguous()
diff --git a/modules/sd_hijack_xlmr.py b/modules/sd_hijack_xlmr.py index 4ac51c38..28528329 100644 --- a/modules/sd_hijack_xlmr.py +++ b/modules/sd_hijack_xlmr.py @@ -1,8 +1,6 @@ -import open_clip.tokenizer
import torch
from modules import sd_hijack_clip, devices
-from modules.shared import opts
class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):
diff --git a/modules/sd_models.py b/modules/sd_models.py index 36f643e1..f65f4e36 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -14,10 +14,10 @@ import ldm.modules.midas as midas from ldm.util import instantiate_from_config
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
-from modules.paths import models_path
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet
from modules.sd_hijack_inpainting import do_inpainting_hijack
from modules.timer import Timer
+import tomesd
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@@ -87,8 +87,7 @@ class CheckpointInfo: try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
-
- from transformers import logging, CLIPModel
+ from transformers import logging, CLIPModel # noqa: F401
logging.set_verbosity_error()
except Exception:
@@ -96,10 +95,8 @@ except Exception: def setup_model():
- if not os.path.exists(model_path):
- os.makedirs(model_path)
+ os.makedirs(model_path, exist_ok=True)
- list_models()
enable_midas_autodownload()
@@ -166,21 +163,22 @@ def model_hash(filename): def select_checkpoint():
+ """Raises `FileNotFoundError` if no checkpoints are found."""
model_checkpoint = shared.opts.sd_model_checkpoint
-
+
checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
if checkpoint_info is not None:
return checkpoint_info
if len(checkpoints_list) == 0:
- print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
+ error_message = "No checkpoints found. When searching for checkpoints, looked at:"
if shared.cmd_opts.ckpt is not None:
- print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
- print(f" - directory {model_path}", file=sys.stderr)
+ error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}"
+ error_message += f"\n - directory {model_path}"
if shared.cmd_opts.ckpt_dir is not None:
- print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
- print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr)
- exit(1)
+ error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}"
+ error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations."
+ raise FileNotFoundError(error_message)
checkpoint_info = next(iter(checkpoints_list.values()))
if model_checkpoint is not None:
@@ -239,7 +237,7 @@ def read_metadata_from_safetensors(filename): if isinstance(v, str) and v[0:1] == '{':
try:
res[k] = json.loads(v)
- except Exception as e:
+ except Exception:
pass
return res
@@ -249,7 +247,12 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None _, extension = os.path.splitext(checkpoint_file)
if extension.lower() == ".safetensors":
device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
- pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
+
+ if not shared.opts.disable_mmap_load_safetensors:
+ pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
+ else:
+ pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
+ pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
else:
pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
@@ -315,8 +318,6 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer timer.record("apply half()")
- devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
- devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
devices.dtype_unet = model.model.diffusion_model.dtype
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
@@ -374,7 +375,7 @@ def enable_midas_autodownload(): if not os.path.exists(path):
if not os.path.exists(midas_path):
mkdir(midas_path)
-
+
print(f"Downloading midas model weights for {model_type} to {path}")
request.urlretrieve(midas_urls[model_type], path)
print(f"{model_type} downloaded")
@@ -410,15 +411,22 @@ sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_w class SdModelData:
def __init__(self):
self.sd_model = None
+ self.was_loaded_at_least_once = False
self.lock = threading.Lock()
def get_sd_model(self):
+ if self.was_loaded_at_least_once:
+ return self.sd_model
+
if self.sd_model is None:
with self.lock:
+ if self.sd_model is not None or self.was_loaded_at_least_once:
+ return self.sd_model
+
try:
load_model()
except Exception as e:
- errors.display(e, "loading stable diffusion model")
+ errors.display(e, "loading stable diffusion model", full_traceback=True)
print("", file=sys.stderr)
print("Stable diffusion model failed to load", file=sys.stderr)
self.sd_model = None
@@ -467,7 +475,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None): try:
with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
sd_model = instantiate_from_config(sd_config.model)
- except Exception as e:
+ except Exception:
pass
if sd_model is None:
@@ -493,6 +501,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None): sd_model.eval()
model_data.sd_model = sd_model
+ model_data.was_loaded_at_least_once = True
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
@@ -502,6 +511,11 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None): timer.record("scripts callbacks")
+ with devices.autocast(), torch.no_grad():
+ sd_model.cond_stage_model_empty_prompt = sd_model.cond_stage_model([""])
+
+ timer.record("calculate empty prompt")
+
print(f"Model loaded in {timer.summary()}.")
return sd_model
@@ -521,6 +535,8 @@ def reload_model_weights(sd_model=None, info=None): if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return
+ sd_unet.apply_unet("None")
+
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()
else:
@@ -538,13 +554,12 @@ def reload_model_weights(sd_model=None, info=None): if sd_model is None or checkpoint_config != sd_model.used_config:
del sd_model
- checkpoints_loaded.clear()
load_model(checkpoint_info, already_loaded_state_dict=state_dict)
return model_data.sd_model
try:
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
- except Exception as e:
+ except Exception:
print("Failed to load checkpoint, restoring previous")
load_model_weights(sd_model, current_checkpoint_info, None, timer)
raise
@@ -565,7 +580,7 @@ def reload_model_weights(sd_model=None, info=None): def unload_model_weights(sd_model=None, info=None):
- from modules import lowvram, devices, sd_hijack
+ from modules import devices, sd_hijack
timer = Timer()
if model_data.sd_model:
@@ -580,3 +595,29 @@ def unload_model_weights(sd_model=None, info=None): print(f"Unloaded weights {timer.summary()}.")
return sd_model
+
+
+def apply_token_merging(sd_model, token_merging_ratio):
+ """
+ Applies speed and memory optimizations from tomesd.
+ """
+
+ current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0)
+
+ if current_token_merging_ratio == token_merging_ratio:
+ return
+
+ if current_token_merging_ratio > 0:
+ tomesd.remove_patch(sd_model)
+
+ if token_merging_ratio > 0:
+ tomesd.apply_patch(
+ sd_model,
+ ratio=token_merging_ratio,
+ use_rand=False, # can cause issues with some samplers
+ merge_attn=True,
+ merge_crossattn=False,
+ merge_mlp=False
+ )
+
+ sd_model.applied_token_merged_ratio = token_merging_ratio
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py index 7a79925a..9bfe1237 100644 --- a/modules/sd_models_config.py +++ b/modules/sd_models_config.py @@ -1,4 +1,3 @@ -import re
import os
import torch
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index ff361f22..f22aad8f 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -1,7 +1,7 @@ from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared
# imports for functions that previously were here and are used by other modules
-from modules.sd_samplers_common import samples_to_image_grid, sample_to_image
+from modules.sd_samplers_common import samples_to_image_grid, sample_to_image # noqa: F401
all_samplers = [
*sd_samplers_kdiffusion.samplers_data_k_diffusion,
@@ -14,12 +14,18 @@ samplers_for_img2img = [] samplers_map = {}
-def create_sampler(name, model):
+def find_sampler_config(name):
if name is not None:
config = all_samplers_map.get(name, None)
else:
config = all_samplers[0]
+ return config
+
+
+def create_sampler(name, model):
+ config = find_sampler_config(name)
+
assert config is not None, f'bad sampler name: {name}'
sampler = config.constructor(model)
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index bc074238..763829f1 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -2,7 +2,7 @@ from collections import namedtuple import numpy as np
import torch
from PIL import Image
-from modules import devices, processing, images, sd_vae_approx
+from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd
from modules.shared import opts, state
import modules.shared as shared
@@ -22,7 +22,7 @@ def setup_img2img_steps(p, steps=None): return steps, t_enc
-approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
+approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3}
def single_sample_to_image(sample, approximation=None):
@@ -30,15 +30,19 @@ def single_sample_to_image(sample, approximation=None): approximation = approximation_indexes.get(opts.show_progress_type, 0)
if approximation == 2:
- x_sample = sd_vae_approx.cheap_approximation(sample)
+ x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5
elif approximation == 1:
- x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
+ x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach() * 0.5 + 0.5
+ elif approximation == 3:
+ x_sample = sample * 1.5
+ x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
else:
- x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
+ x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
- x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+ x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
+
return Image.fromarray(x_sample)
@@ -58,6 +62,25 @@ def store_latent(decoded): shared.state.assign_current_image(sample_to_image(decoded))
+def is_sampler_using_eta_noise_seed_delta(p):
+ """returns whether sampler from config will use eta noise seed delta for image creation"""
+
+ sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
+
+ eta = p.eta
+
+ if eta is None and p.sampler is not None:
+ eta = p.sampler.eta
+
+ if eta is None and sampler_config is not None:
+ eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0
+
+ if eta == 0:
+ return False
+
+ return sampler_config.options.get("uses_ensd", False)
+
+
class InterruptedException(BaseException):
pass
diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py index bfcc5574..bdae8b40 100644 --- a/modules/sd_samplers_compvis.py +++ b/modules/sd_samplers_compvis.py @@ -11,7 +11,7 @@ import modules.models.diffusion.uni_pc samplers_data_compvis = [
- sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
+ sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True, "uses_ensd": True}),
sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}),
]
@@ -55,7 +55,7 @@ class VanillaStableDiffusionSampler: def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning)
- res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
+ res = self.orig_p_sample_ddim(x_dec, cond, ts, *args, unconditional_conditioning=unconditional_conditioning, **kwargs)
x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res)
@@ -83,7 +83,7 @@ class VanillaStableDiffusionSampler: conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
- assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
+ assert all(len(conds) == 1 for conds in conds_list), 'composition via AND is not supported for DDIM/PLMS samplers'
cond = tensor
# for DDIM, shapes must match, we can't just process cond and uncond independently;
@@ -134,7 +134,11 @@ class VanillaStableDiffusionSampler: self.update_step(x)
def initialize(self, p):
- self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
+ if self.is_ddim:
+ self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
+ else:
+ self.eta = 0.0
+
if self.eta != 0.0:
p.extra_generation_params["Eta DDIM"] = self.eta
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 0fc9f456..71581b76 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -1,7 +1,6 @@ from collections import deque
import torch
import inspect
-import einops
import k_diffusion.sampling
from modules import prompt_parser, devices, sd_samplers_common
@@ -9,25 +8,28 @@ from modules.shared import opts, state import modules.shared as shared
from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
+from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
samplers_k_diffusion = [
- ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
+ ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
('Euler', 'sample_euler', ['k_euler'], {}),
('LMS', 'sample_lms', ['k_lms'], {}),
- ('Heun', 'sample_heun', ['k_heun'], {}),
+ ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
- ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
- ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+ ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
+ ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
- ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
- ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
- ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
+ ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
+ ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
+ ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
+ ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
- ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
- ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
- ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+ ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+ ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+ ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
- ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
+ ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
+ ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
]
samplers_data_k_diffusion = [
@@ -42,6 +44,14 @@ sampler_extra_params = { 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
}
+k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
+k_diffusion_scheduler = {
+ 'Automatic': None,
+ 'karras': k_diffusion.sampling.get_sigmas_karras,
+ 'exponential': k_diffusion.sampling.get_sigmas_exponential,
+ 'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
+}
+
class CFGDenoiser(torch.nn.Module):
"""
@@ -59,6 +69,7 @@ class CFGDenoiser(torch.nn.Module): self.init_latent = None
self.step = 0
self.image_cfg_scale = None
+ self.padded_cond_uncond = False
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
@@ -87,17 +98,17 @@ class CFGDenoiser(torch.nn.Module): conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
- assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+ assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
if shared.sd_model.model.conditioning_key == "crossattn-adm":
image_uncond = torch.zeros_like(image_cond)
- make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
+ make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
else:
image_uncond = image_cond
- make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
+ make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
if not is_edit_model:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
@@ -123,6 +134,18 @@ class CFGDenoiser(torch.nn.Module): x_in = x_in[:-batch_size]
sigma_in = sigma_in[:-batch_size]
+ self.padded_cond_uncond = False
+ if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
+ empty = shared.sd_model.cond_stage_model_empty_prompt
+ num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
+
+ if num_repeats < 0:
+ tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
+ self.padded_cond_uncond = True
+ elif num_repeats > 0:
+ uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
+ self.padded_cond_uncond = True
+
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
cond_in = torch.cat([tensor, uncond, uncond])
@@ -161,7 +184,7 @@ class CFGDenoiser(torch.nn.Module): fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
- denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
+ denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
cfg_denoised_callback(denoised_params)
devices.test_for_nans(x_out, "unet")
@@ -181,6 +204,10 @@ class CFGDenoiser(torch.nn.Module): if self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
+ after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
+ cfg_after_cfg_callback(after_cfg_callback_params)
+ denoised = after_cfg_callback_params.x
+
self.step += 1
return denoised
@@ -224,7 +251,7 @@ class KDiffusionSampler: self.sampler_noises = None
self.stop_at = None
self.eta = None
- self.config = None
+ self.config = None # set by the function calling the constructor
self.last_latent = None
self.s_min_uncond = None
@@ -249,6 +276,13 @@ class KDiffusionSampler: try:
return func()
+ except RecursionError:
+ print(
+ 'Encountered RecursionError during sampling, returning last latent. '
+ 'rho >5 with a polyexponential scheduler may cause this error. '
+ 'You should try to use a smaller rho value instead.'
+ )
+ return self.last_latent
except sd_samplers_common.InterruptedException:
return self.last_latent
@@ -288,6 +322,31 @@ class KDiffusionSampler: if p.sampler_noise_scheduler_override:
sigmas = p.sampler_noise_scheduler_override(steps)
+ elif opts.k_sched_type != "Automatic":
+ m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+ sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
+ sigmas_kwargs = {
+ 'sigma_min': sigma_min,
+ 'sigma_max': sigma_max,
+ }
+
+ sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+ p.extra_generation_params["Schedule type"] = opts.k_sched_type
+
+ if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
+ sigmas_kwargs['sigma_min'] = opts.sigma_min
+ p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
+ if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
+ sigmas_kwargs['sigma_max'] = opts.sigma_max
+ p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
+
+ default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
+
+ if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
+ sigmas_kwargs['rho'] = opts.rho
+ p.extra_generation_params["Schedule rho"] = opts.rho
+
+ sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
@@ -317,7 +376,7 @@ class KDiffusionSampler: sigma_sched = sigmas[steps - t_enc - 1:]
xi = x + noise * sigma_sched[0]
-
+
extra_params_kwargs = self.initialize(p)
parameters = inspect.signature(self.func).parameters
@@ -333,22 +392,25 @@ class KDiffusionSampler: if 'sigmas' in parameters:
extra_params_kwargs['sigmas'] = sigma_sched
- if self.funcname == 'sample_dpmpp_sde':
+ if self.config.options.get('brownian_noise', False):
noise_sampler = self.create_noise_sampler(x, sigmas, p)
extra_params_kwargs['noise_sampler'] = noise_sampler
self.model_wrap_cfg.init_latent = x
self.last_latent = x
- extra_args={
- 'cond': conditioning,
- 'image_cond': image_conditioning,
- 'uncond': unconditional_conditioning,
+ extra_args = {
+ 'cond': conditioning,
+ 'image_cond': image_conditioning,
+ 'uncond': unconditional_conditioning,
'cond_scale': p.cfg_scale,
's_min_uncond': self.s_min_uncond
}
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+ if self.model_wrap_cfg.padded_cond_uncond:
+ p.extra_generation_params["Pad conds"] = True
+
return samples
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
@@ -369,18 +431,21 @@ class KDiffusionSampler: else:
extra_params_kwargs['sigmas'] = sigmas
- if self.funcname == 'sample_dpmpp_sde':
+ if self.config.options.get('brownian_noise', False):
noise_sampler = self.create_noise_sampler(x, sigmas, p)
extra_params_kwargs['noise_sampler'] = noise_sampler
self.last_latent = x
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
- 'cond': conditioning,
- 'image_cond': image_conditioning,
- 'uncond': unconditional_conditioning,
+ 'cond': conditioning,
+ 'image_cond': image_conditioning,
+ 'uncond': unconditional_conditioning,
'cond_scale': p.cfg_scale,
's_min_uncond': self.s_min_uncond
}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+ if self.model_wrap_cfg.padded_cond_uncond:
+ p.extra_generation_params["Pad conds"] = True
+
return samples
diff --git a/modules/sd_unet.py b/modules/sd_unet.py new file mode 100644 index 00000000..6d708ad2 --- /dev/null +++ b/modules/sd_unet.py @@ -0,0 +1,92 @@ +import torch.nn
+import ldm.modules.diffusionmodules.openaimodel
+
+from modules import script_callbacks, shared, devices
+
+unet_options = []
+current_unet_option = None
+current_unet = None
+
+
+def list_unets():
+ new_unets = script_callbacks.list_unets_callback()
+
+ unet_options.clear()
+ unet_options.extend(new_unets)
+
+
+def get_unet_option(option=None):
+ option = option or shared.opts.sd_unet
+
+ if option == "None":
+ return None
+
+ if option == "Automatic":
+ name = shared.sd_model.sd_checkpoint_info.model_name
+
+ options = [x for x in unet_options if x.model_name == name]
+
+ option = options[0].label if options else "None"
+
+ return next(iter([x for x in unet_options if x.label == option]), None)
+
+
+def apply_unet(option=None):
+ global current_unet_option
+ global current_unet
+
+ new_option = get_unet_option(option)
+ if new_option == current_unet_option:
+ return
+
+ if current_unet is not None:
+ print(f"Dectivating unet: {current_unet.option.label}")
+ current_unet.deactivate()
+
+ current_unet_option = new_option
+ if current_unet_option is None:
+ current_unet = None
+
+ if not (shared.cmd_opts.lowvram or shared.cmd_opts.medvram):
+ shared.sd_model.model.diffusion_model.to(devices.device)
+
+ return
+
+ shared.sd_model.model.diffusion_model.to(devices.cpu)
+ devices.torch_gc()
+
+ current_unet = current_unet_option.create_unet()
+ current_unet.option = current_unet_option
+ print(f"Activating unet: {current_unet.option.label}")
+ current_unet.activate()
+
+
+class SdUnetOption:
+ model_name = None
+ """name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
+
+ label = None
+ """name of the unet in UI"""
+
+ def create_unet(self):
+ """returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
+ raise NotImplementedError()
+
+
+class SdUnet(torch.nn.Module):
+ def forward(self, x, timesteps, context, *args, **kwargs):
+ raise NotImplementedError()
+
+ def activate(self):
+ pass
+
+ def deactivate(self):
+ pass
+
+
+def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
+ if current_unet is not None:
+ return current_unet.forward(x, timesteps, context, *args, **kwargs)
+
+ return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
+
diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 521e485a..e4ff2994 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -1,8 +1,5 @@ -import torch -import safetensors.torch import os import collections -from collections import namedtuple from modules import paths, shared, devices, script_callbacks, sd_models import glob from copy import deepcopy @@ -88,10 +85,10 @@ def refresh_vae_list(): def find_vae_near_checkpoint(checkpoint_file): - checkpoint_path = os.path.splitext(checkpoint_file)[0] - for vae_location in [f"{checkpoint_path}.vae.pt", f"{checkpoint_path}.vae.ckpt", f"{checkpoint_path}.vae.safetensors"]: - if os.path.isfile(vae_location): - return vae_location + checkpoint_path = os.path.basename(checkpoint_file).rsplit('.', 1)[0] + for vae_file in vae_dict.values(): + if os.path.basename(vae_file).startswith(checkpoint_path): + return vae_file return None diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py new file mode 100644 index 00000000..5e8496e8 --- /dev/null +++ b/modules/sd_vae_taesd.py @@ -0,0 +1,88 @@ +""" +Tiny AutoEncoder for Stable Diffusion +(DNN for encoding / decoding SD's latent space) + +https://github.com/madebyollin/taesd +""" +import os +import torch +import torch.nn as nn + +from modules import devices, paths_internal + +sd_vae_taesd = None + + +def conv(n_in, n_out, **kwargs): + return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs) + + +class Clamp(nn.Module): + @staticmethod + def forward(x): + return torch.tanh(x / 3) * 3 + + +class Block(nn.Module): + def __init__(self, n_in, n_out): + super().__init__() + self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) + self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() + self.fuse = nn.ReLU() + + def forward(self, x): + return self.fuse(self.conv(x) + self.skip(x)) + + +def decoder(): + return nn.Sequential( + Clamp(), conv(4, 64), nn.ReLU(), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), conv(64, 3), + ) + + +class TAESD(nn.Module): + latent_magnitude = 3 + latent_shift = 0.5 + + def __init__(self, decoder_path="taesd_decoder.pth"): + """Initialize pretrained TAESD on the given device from the given checkpoints.""" + super().__init__() + self.decoder = decoder() + self.decoder.load_state_dict( + torch.load(decoder_path, map_location='cpu' if devices.device.type != 'cuda' else None)) + + @staticmethod + def unscale_latents(x): + """[0, 1] -> raw latents""" + return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) + + +def download_model(model_path): + model_url = 'https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth' + + if not os.path.exists(model_path): + os.makedirs(os.path.dirname(model_path), exist_ok=True) + + print(f'Downloading TAESD decoder to: {model_path}') + torch.hub.download_url_to_file(model_url, model_path) + + +def model(): + global sd_vae_taesd + + if sd_vae_taesd is None: + model_path = os.path.join(paths_internal.models_path, "VAE-taesd", "taesd_decoder.pth") + download_model(model_path) + + if os.path.exists(model_path): + sd_vae_taesd = TAESD(model_path) + sd_vae_taesd.eval() + sd_vae_taesd.to(devices.device, devices.dtype) + else: + raise FileNotFoundError('TAESD model not found') + + return sd_vae_taesd.decoder diff --git a/modules/shared.py b/modules/shared.py index b3508883..48478a68 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -1,13 +1,14 @@ -import argparse
import datetime
import json
import os
+import re
import sys
+import threading
import time
-import requests
+import logging
-from PIL import Image
import gradio as gr
+import torch
import tqdm
import modules.interrogate
@@ -15,8 +16,11 @@ import modules.memmon import modules.styles
import modules.devices as devices
from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
-from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir
+from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
from ldm.models.diffusion.ddpm import LatentDiffusion
+from typing import Optional
+
+log = logging.getLogger(__name__)
demo = None
@@ -44,19 +48,6 @@ restricted_opts = { "outdir_init_images"
}
-ui_reorder_categories = [
- "inpaint",
- "sampler",
- "checkboxes",
- "hires_fix",
- "dimensions",
- "cfg",
- "seed",
- "batch",
- "override_settings",
- "scripts",
-]
-
# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
gradio_hf_hub_themes = [
"gradio/glass",
@@ -77,6 +68,9 @@ cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_op devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
+devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
+devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
+
device = devices.device
weight_load_location = None if cmd_opts.lowram else "cpu"
@@ -113,14 +107,56 @@ class State: id_live_preview = 0
textinfo = None
time_start = None
- need_restart = False
server_start = None
+ _server_command_signal = threading.Event()
+ _server_command: Optional[str] = None
+
+ @property
+ def need_restart(self) -> bool:
+ # Compatibility getter for need_restart.
+ return self.server_command == "restart"
+
+ @need_restart.setter
+ def need_restart(self, value: bool) -> None:
+ # Compatibility setter for need_restart.
+ if value:
+ self.server_command = "restart"
+
+ @property
+ def server_command(self):
+ return self._server_command
+
+ @server_command.setter
+ def server_command(self, value: Optional[str]) -> None:
+ """
+ Set the server command to `value` and signal that it's been set.
+ """
+ self._server_command = value
+ self._server_command_signal.set()
+
+ def wait_for_server_command(self, timeout: Optional[float] = None) -> Optional[str]:
+ """
+ Wait for server command to get set; return and clear the value and signal.
+ """
+ if self._server_command_signal.wait(timeout):
+ self._server_command_signal.clear()
+ req = self._server_command
+ self._server_command = None
+ return req
+ return None
+
+ def request_restart(self) -> None:
+ self.interrupt()
+ self.server_command = "restart"
+ log.info("Received restart request")
def skip(self):
self.skipped = True
+ log.info("Received skip request")
def interrupt(self):
self.interrupted = True
+ log.info("Received interrupt request")
def nextjob(self):
if opts.live_previews_enable and opts.show_progress_every_n_steps == -1:
@@ -144,7 +180,7 @@ class State: return obj
- def begin(self):
+ def begin(self, job: str = "(unknown)"):
self.sampling_step = 0
self.job_count = -1
self.processing_has_refined_job_count = False
@@ -158,10 +194,13 @@ class State: self.interrupted = False
self.textinfo = None
self.time_start = time.time()
-
+ self.job = job
devices.torch_gc()
+ log.info("Starting job %s", job)
def end(self):
+ duration = time.time() - self.time_start
+ log.info("Ending job %s (%.2f seconds)", self.job, duration)
self.job = ""
self.job_count = 0
@@ -202,8 +241,9 @@ interrogator = modules.interrogate.InterrogateModels("interrogate") face_restorers = []
+
class OptionInfo:
- def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
+ def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after=''):
self.default = default
self.label = label
self.component = component
@@ -212,9 +252,37 @@ class OptionInfo: self.section = section
self.refresh = refresh
+ self.comment_before = comment_before
+ """HTML text that will be added after label in UI"""
+
+ self.comment_after = comment_after
+ """HTML text that will be added before label in UI"""
+
+ def link(self, label, url):
+ self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
+ return self
+
+ def js(self, label, js_func):
+ self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
+ return self
+
+ def info(self, info):
+ self.comment_after += f"<span class='info'>({info})</span>"
+ return self
+
+ def html(self, html):
+ self.comment_after += html
+ return self
+
+ def needs_restart(self):
+ self.comment_after += " <span class='info'>(requires restart)</span>"
+ return self
+
+
+
def options_section(section_identifier, options_dict):
- for k, v in options_dict.items():
+ for v in options_dict.values():
v.section = section_identifier
return options_dict
@@ -243,7 +311,7 @@ options_templates = {} options_templates.update(options_section(('saving-images', "Saving images/grids"), {
"samples_save": OptionInfo(True, "Always save all generated images"),
"samples_format": OptionInfo('png', 'File format for images'),
- "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
+ "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
"grid_save": OptionInfo(True, "Always save all generated image grids"),
@@ -251,7 +319,12 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
"grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
+ "grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
+ "font": OptionInfo("", "Font for image grids that have text"),
+ "grid_text_active_color": OptionInfo("#000000", "Text color for image grids", ui_components.FormColorPicker, {}),
+ "grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
+ "grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
@@ -262,10 +335,10 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
"webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
- "export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
+ "export_for_4chan": OptionInfo(True, "Save copy of large images as JPG").info("if the file size is above the limit, or either width or height are above the limit"),
"img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
"target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
- "img_max_size_mp": OptionInfo(200, "Maximum image size, in megapixels", gr.Number),
+ "img_max_size_mp": OptionInfo(200, "Maximum image size", gr.Number).info("in megapixels"),
"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
@@ -293,31 +366,31 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
"grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
- "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
+ "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
}))
options_templates.update(options_section(('upscaling', "Upscaling"), {
- "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
- "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
- "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
+ "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
+ "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
+ "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
- "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
- "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
}))
options_templates.update(options_section(('face-restoration', "Face restoration"), {
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
- "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+ "code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
}))
options_templates.update(options_section(('system', "System"), {
"show_warnings": OptionInfo(False, "Show warnings in console."),
- "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
+ "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
+ "list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
+ "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
}))
options_templates.update(options_section(('training', "Training"), {
@@ -339,20 +412,31 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
- "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
+ "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
+ "sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
- "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
+ "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
"img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", ui_components.FormColorPicker, {}),
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
- "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+ "enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
- "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
- "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
+ "comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
+ "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
- "randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
+ "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
+}))
+
+options_templates.update(options_section(('optimizations', "Optimizations"), {
+ "cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
+ "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+ "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
+ "token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
+ "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
+ "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length").info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
+ "experimental_persistent_cond_cache": OptionInfo(False, "persistent cond cache").info("Experimental, keep cond caches across jobs, reduce overhead."),
}))
options_templates.update(options_section(('compatibility', "Compatibility"), {
@@ -361,89 +445,109 @@ options_templates.update(options_section(('compatibility', "Compatibility"), { "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
"dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
+ "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
}))
options_templates.update(options_section(('interrogate', "Interrogate Options"), {
- "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
- "interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
- "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
- "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
- "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
- "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
+ "interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
+ "interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
+ "interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
+ "interrogate_clip_min_length": OptionInfo(24, "BLIP: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
+ "interrogate_clip_max_length": OptionInfo(48, "BLIP: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
+ "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file").info("0 = No limit"),
"interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
- "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
- "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
- "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
- "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
- "deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
+ "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "deepbooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+ "deepbooru_sort_alpha": OptionInfo(True, "deepbooru: sort tags alphabetically").info("if not: sort by score"),
+ "deepbooru_use_spaces": OptionInfo(True, "deepbooru: use spaces in tags").info("if not: use underscores"),
+ "deepbooru_escape": OptionInfo(True, "deepbooru: escape (\\) brackets").info("so they are used as literal brackets and not for emphasis"),
+ "deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
}))
options_templates.update(options_section(('extra_networks', "Extra Networks"), {
+ "extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
+ "extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
"extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
- "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
- "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
- "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+ "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
+ "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
+ "extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
+ "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_restart(),
+ "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *hypernetworks]}, refresh=reload_hypernetworks),
}))
options_templates.update(options_section(('ui', "User interface"), {
+ "localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_restart(),
+ "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes}).needs_restart(),
+ "img2img_editor_height": OptionInfo(720, "img2img: height of image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_restart(),
"return_grid": OptionInfo(True, "Show grid in results for web"),
"return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
"return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
- "font": OptionInfo("", "Font for image grids that have text"),
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
- "js_modal_lightbox_gamepad": OptionInfo(True, "Navigate image viewer with gamepad"),
+ "js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
- "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
- "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
+ "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_restart(),
+ "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_restart(),
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
- "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
- "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
- "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
- "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
- "localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
- "gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
+ "keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
+ "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_restart(),
+ "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
+ "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
+ "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_restart(),
+ "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires sampler selection").needs_restart(),
+ "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(),
+ "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(),
}))
options_templates.update(options_section(('infotext', "Infotext"), {
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
+ "add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
"add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
- "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
+ "disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
+ "infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
+<li>Ignore: keep prompt and styles dropdown as it is.</li>
+<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
+<li>Discard: remove style text from prompt, keep styles dropdown as it is.</li>
+<li>Apply if any: remove style text from prompt; if any styles are found in prompt, put them into styles dropdown, otherwise keep it as it is.</li>
+</ul>"""),
+
}))
options_templates.update(options_section(('ui', "Live previews"), {
"show_progressbar": OptionInfo(True, "Show progressbar"),
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
+ "live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
- "show_progress_every_n_steps": OptionInfo(10, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
- "show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
+ "show_progress_every_n_steps": OptionInfo(10, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}).info("in sampling steps - show new live preview image every N sampling steps; -1 = only show after completion of batch"),
+ "show_progress_type": OptionInfo("Approx NN", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap", "TAESD"]}).info("Full = slow but pretty; Approx NN and TAESD = fast but low quality; Approx cheap = super fast but terrible otherwise"),
"live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
- "live_preview_refresh_period": OptionInfo(1000, "Progressbar/preview update period, in milliseconds")
+ "live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
}))
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
- "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
- "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ "hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}).needs_restart(),
+ "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; higher = more unperdictable results"),
+ "eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; applies to Euler a and other samplers that have a in them"),
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- 's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
- 'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
+ 'k_sched_type': OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
+ 'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
+ 'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise schedule"),
+ 'rho': OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a more steep noise schedule (decreases faster)"),
+ 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
+ 'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
- 'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}),
+ 'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}).info("must be < sampling steps"),
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
}))
@@ -460,6 +564,7 @@ options_templates.update(options_section((None, "Hidden options"), { "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
}))
+
options_templates.update()
@@ -553,6 +658,10 @@ class Options: if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
+ # 1.4.0 ui_reorder
+ if isinstance(self.data.get('ui_reorder'), str) and self.data.get('ui_reorder') and "ui_reorder_list" not in self.data:
+ self.data['ui_reorder_list'] = [i.strip() for i in self.data.get('ui_reorder').split(',')]
+
bad_settings = 0
for k, v in self.data.items():
info = self.data_labels.get(k, None)
@@ -571,7 +680,9 @@ class Options: func()
def dumpjson(self):
- d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
+ d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
+ d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
+ d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
return json.dumps(d)
def add_option(self, key, info):
@@ -582,11 +693,11 @@ class Options: section_ids = {}
settings_items = self.data_labels.items()
- for k, item in settings_items:
+ for _, item in settings_items:
if item.section not in section_ids:
section_ids[item.section] = len(section_ids)
- self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
+ self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
def cast_value(self, key, value):
"""casts an arbitrary to the same type as this setting's value with key
@@ -722,8 +833,12 @@ mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts) mem_mon.start()
+def natural_sort_key(s, regex=re.compile('([0-9]+)')):
+ return [int(text) if text.isdigit() else text.lower() for text in regex.split(s)]
+
+
def listfiles(dirname):
- filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")]
+ filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=natural_sort_key) if not x.startswith(".")]
return [file for file in filenames if os.path.isfile(file)]
@@ -748,11 +863,17 @@ def walk_files(path, allowed_extensions=None): if allowed_extensions is not None:
allowed_extensions = set(allowed_extensions)
- for root, dirs, files in os.walk(path):
- for filename in files:
+ items = list(os.walk(path, followlinks=True))
+ items = sorted(items, key=lambda x: natural_sort_key(x[0]))
+
+ for root, _, files in items:
+ for filename in sorted(files, key=natural_sort_key):
if allowed_extensions is not None:
_, ext = os.path.splitext(filename)
if ext not in allowed_extensions:
continue
+ if not opts.list_hidden_files and ("/." in root or "\\." in root):
+ continue
+
yield os.path.join(root, filename)
diff --git a/modules/shared_items.py b/modules/shared_items.py index e792a134..89792e88 100644 --- a/modules/shared_items.py +++ b/modules/shared_items.py @@ -21,3 +21,49 @@ def refresh_vae_list(): import modules.sd_vae
modules.sd_vae.refresh_vae_list()
+
+
+def cross_attention_optimizations():
+ import modules.sd_hijack
+
+ return ["Automatic"] + [x.title() for x in modules.sd_hijack.optimizers] + ["None"]
+
+
+def sd_unet_items():
+ import modules.sd_unet
+
+ return ["Automatic"] + [x.label for x in modules.sd_unet.unet_options] + ["None"]
+
+
+def refresh_unet_list():
+ import modules.sd_unet
+
+ modules.sd_unet.list_unets()
+
+
+ui_reorder_categories_builtin_items = [
+ "inpaint",
+ "sampler",
+ "checkboxes",
+ "hires_fix",
+ "dimensions",
+ "cfg",
+ "seed",
+ "batch",
+ "override_settings",
+]
+
+
+def ui_reorder_categories():
+ from modules import scripts
+
+ yield from ui_reorder_categories_builtin_items
+
+ sections = {}
+ for script in scripts.scripts_txt2img.scripts + scripts.scripts_img2img.scripts:
+ if isinstance(script.section, str):
+ sections[script.section] = 1
+
+ yield from sections
+
+ yield "scripts"
diff --git a/modules/styles.py b/modules/styles.py index 11642075..ec0e1bc5 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -1,18 +1,10 @@ -# We need this so Python doesn't complain about the unknown StableDiffusionProcessing-typehint at runtime
-from __future__ import annotations
-
import csv
import os
import os.path
+import re
import typing
-import collections.abc as abc
-import tempfile
import shutil
-if typing.TYPE_CHECKING:
- # Only import this when code is being type-checked, it doesn't have any effect at runtime
- from .processing import StableDiffusionProcessing
-
class PromptStyle(typing.NamedTuple):
name: str
@@ -37,6 +29,44 @@ def apply_styles_to_prompt(prompt, styles): return prompt
+re_spaces = re.compile(" +")
+
+
+def extract_style_text_from_prompt(style_text, prompt):
+ stripped_prompt = re.sub(re_spaces, " ", prompt.strip())
+ stripped_style_text = re.sub(re_spaces, " ", style_text.strip())
+ if "{prompt}" in stripped_style_text:
+ left, right = stripped_style_text.split("{prompt}", 2)
+ if stripped_prompt.startswith(left) and stripped_prompt.endswith(right):
+ prompt = stripped_prompt[len(left):len(stripped_prompt)-len(right)]
+ return True, prompt
+ else:
+ if stripped_prompt.endswith(stripped_style_text):
+ prompt = stripped_prompt[:len(stripped_prompt)-len(stripped_style_text)]
+
+ if prompt.endswith(', '):
+ prompt = prompt[:-2]
+
+ return True, prompt
+
+ return False, prompt
+
+
+def extract_style_from_prompts(style: PromptStyle, prompt, negative_prompt):
+ if not style.prompt and not style.negative_prompt:
+ return False, prompt, negative_prompt
+
+ match_positive, extracted_positive = extract_style_text_from_prompt(style.prompt, prompt)
+ if not match_positive:
+ return False, prompt, negative_prompt
+
+ match_negative, extracted_negative = extract_style_text_from_prompt(style.negative_prompt, negative_prompt)
+ if not match_negative:
+ return False, prompt, negative_prompt
+
+ return True, extracted_positive, extracted_negative
+
+
class StyleDatabase:
def __init__(self, path: str):
self.no_style = PromptStyle("None", "", "")
@@ -52,7 +82,7 @@ class StyleDatabase: return
with open(self.path, "r", encoding="utf-8-sig", newline='') as file:
- reader = csv.DictReader(file)
+ reader = csv.DictReader(file, skipinitialspace=True)
for row in reader:
# Support loading old CSV format with "name, text"-columns
prompt = row["prompt"] if "prompt" in row else row["text"]
@@ -76,10 +106,34 @@ class StyleDatabase: if os.path.exists(path):
shutil.copy(path, f"{path}.bak")
- fd = os.open(path, os.O_RDWR|os.O_CREAT)
+ fd = os.open(path, os.O_RDWR | os.O_CREAT)
with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
# _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
# and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
writer.writeheader()
- writer.writerows(style._asdict() for k, style in self.styles.items())
+ writer.writerows(style._asdict() for k, style in self.styles.items())
+
+ def extract_styles_from_prompt(self, prompt, negative_prompt):
+ extracted = []
+
+ applicable_styles = list(self.styles.values())
+
+ while True:
+ found_style = None
+
+ for style in applicable_styles:
+ is_match, new_prompt, new_neg_prompt = extract_style_from_prompts(style, prompt, negative_prompt)
+ if is_match:
+ found_style = style
+ prompt = new_prompt
+ negative_prompt = new_neg_prompt
+ break
+
+ if not found_style:
+ break
+
+ applicable_styles.remove(found_style)
+ extracted.append(found_style.name)
+
+ return list(reversed(extracted)), prompt, negative_prompt
diff --git a/modules/sub_quadratic_attention.py b/modules/sub_quadratic_attention.py index 05595323..497568eb 100644 --- a/modules/sub_quadratic_attention.py +++ b/modules/sub_quadratic_attention.py @@ -179,7 +179,7 @@ def efficient_dot_product_attention( chunk_idx, min(query_chunk_size, q_tokens) ) - + summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale) summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk compute_query_chunk_attn: ComputeQueryChunkAttn = partial( @@ -201,14 +201,15 @@ def efficient_dot_product_attention( key=key, value=value, ) - - # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, - # and pass slices to be mutated, instead of torch.cat()ing the returned slices - res = torch.cat([ - compute_query_chunk_attn( + + res = torch.zeros_like(query) + for i in range(math.ceil(q_tokens / query_chunk_size)): + attn_scores = compute_query_chunk_attn( query=get_query_chunk(i * query_chunk_size), key=key, value=value, - ) for i in range(math.ceil(q_tokens / query_chunk_size)) - ], dim=1) + ) + + res[:, i * query_chunk_size:i * query_chunk_size + attn_scores.shape[1], :] = attn_scores + return res diff --git a/modules/sysinfo.py b/modules/sysinfo.py new file mode 100644 index 00000000..5f15ac4f --- /dev/null +++ b/modules/sysinfo.py @@ -0,0 +1,162 @@ +import json
+import os
+import sys
+import traceback
+
+import platform
+import hashlib
+import pkg_resources
+import psutil
+import re
+
+import launch
+from modules import paths_internal, timer
+
+checksum_token = "DontStealMyGamePlz__WINNERS_DONT_USE_DRUGS__DONT_COPY_THAT_FLOPPY"
+environment_whitelist = {
+ "GIT",
+ "INDEX_URL",
+ "WEBUI_LAUNCH_LIVE_OUTPUT",
+ "GRADIO_ANALYTICS_ENABLED",
+ "PYTHONPATH",
+ "TORCH_INDEX_URL",
+ "TORCH_COMMAND",
+ "REQS_FILE",
+ "XFORMERS_PACKAGE",
+ "GFPGAN_PACKAGE",
+ "CLIP_PACKAGE",
+ "OPENCLIP_PACKAGE",
+ "STABLE_DIFFUSION_REPO",
+ "K_DIFFUSION_REPO",
+ "CODEFORMER_REPO",
+ "BLIP_REPO",
+ "STABLE_DIFFUSION_COMMIT_HASH",
+ "K_DIFFUSION_COMMIT_HASH",
+ "CODEFORMER_COMMIT_HASH",
+ "BLIP_COMMIT_HASH",
+ "COMMANDLINE_ARGS",
+ "IGNORE_CMD_ARGS_ERRORS",
+}
+
+
+def pretty_bytes(num, suffix="B"):
+ for unit in ["", "K", "M", "G", "T", "P", "E", "Z", "Y"]:
+ if abs(num) < 1024 or unit == 'Y':
+ return f"{num:.0f}{unit}{suffix}"
+ num /= 1024
+
+
+def get():
+ res = get_dict()
+
+ text = json.dumps(res, ensure_ascii=False, indent=4)
+
+ h = hashlib.sha256(text.encode("utf8"))
+ text = text.replace(checksum_token, h.hexdigest())
+
+ return text
+
+
+re_checksum = re.compile(r'"Checksum": "([0-9a-fA-F]{64})"')
+
+
+def check(x):
+ m = re.search(re_checksum, x)
+ if not m:
+ return False
+
+ replaced = re.sub(re_checksum, f'"Checksum": "{checksum_token}"', x)
+
+ h = hashlib.sha256(replaced.encode("utf8"))
+ return h.hexdigest() == m.group(1)
+
+
+def get_dict():
+ ram = psutil.virtual_memory()
+
+ res = {
+ "Platform": platform.platform(),
+ "Python": platform.python_version(),
+ "Version": launch.git_tag(),
+ "Commit": launch.commit_hash(),
+ "Script path": paths_internal.script_path,
+ "Data path": paths_internal.data_path,
+ "Extensions dir": paths_internal.extensions_dir,
+ "Checksum": checksum_token,
+ "Commandline": sys.argv,
+ "Torch env info": get_torch_sysinfo(),
+ "Exceptions": get_exceptions(),
+ "CPU": {
+ "model": platform.processor(),
+ "count logical": psutil.cpu_count(logical=True),
+ "count physical": psutil.cpu_count(logical=False),
+ },
+ "RAM": {
+ x: pretty_bytes(getattr(ram, x, 0)) for x in ["total", "used", "free", "active", "inactive", "buffers", "cached", "shared"] if getattr(ram, x, 0) != 0
+ },
+ "Extensions": get_extensions(enabled=True),
+ "Inactive extensions": get_extensions(enabled=False),
+ "Environment": get_environment(),
+ "Config": get_config(),
+ "Startup": timer.startup_record,
+ "Packages": sorted([f"{pkg.key}=={pkg.version}" for pkg in pkg_resources.working_set]),
+ }
+
+ return res
+
+
+def format_traceback(tb):
+ return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
+
+
+def get_exceptions():
+ try:
+ from modules import errors
+
+ return [{"exception": str(e), "traceback": format_traceback(tb)} for e, tb in reversed(errors.exception_records)]
+ except Exception as e:
+ return str(e)
+
+
+def get_environment():
+ return {k: os.environ[k] for k in sorted(os.environ) if k in environment_whitelist}
+
+
+re_newline = re.compile(r"\r*\n")
+
+
+def get_torch_sysinfo():
+ try:
+ import torch.utils.collect_env
+ info = torch.utils.collect_env.get_env_info()._asdict()
+
+ return {k: re.split(re_newline, str(v)) if "\n" in str(v) else v for k, v in info.items()}
+ except Exception as e:
+ return str(e)
+
+
+def get_extensions(*, enabled):
+
+ try:
+ from modules import extensions
+
+ def to_json(x: extensions.Extension):
+ return {
+ "name": x.name,
+ "path": x.path,
+ "version": x.version,
+ "branch": x.branch,
+ "remote": x.remote,
+ }
+
+ return [to_json(x) for x in extensions.extensions if not x.is_builtin and x.enabled == enabled]
+ except Exception as e:
+ return str(e)
+
+
+def get_config():
+ try:
+ from modules import shared
+ return shared.opts.data
+ except Exception as e:
+ return str(e)
diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py index ba1bdcd4..1675e39a 100644 --- a/modules/textual_inversion/autocrop.py +++ b/modules/textual_inversion/autocrop.py @@ -1,10 +1,8 @@ import cv2
import requests
import os
-from collections import defaultdict
-from math import log, sqrt
import numpy as np
-from PIL import Image, ImageDraw
+from PIL import ImageDraw
GREEN = "#0F0"
BLUE = "#00F"
@@ -12,63 +10,64 @@ RED = "#F00" def crop_image(im, settings):
- """ Intelligently crop an image to the subject matter """
-
- scale_by = 1
- if is_landscape(im.width, im.height):
- scale_by = settings.crop_height / im.height
- elif is_portrait(im.width, im.height):
- scale_by = settings.crop_width / im.width
- elif is_square(im.width, im.height):
- if is_square(settings.crop_width, settings.crop_height):
- scale_by = settings.crop_width / im.width
- elif is_landscape(settings.crop_width, settings.crop_height):
- scale_by = settings.crop_width / im.width
- elif is_portrait(settings.crop_width, settings.crop_height):
- scale_by = settings.crop_height / im.height
-
- im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
- im_debug = im.copy()
-
- focus = focal_point(im_debug, settings)
-
- # take the focal point and turn it into crop coordinates that try to center over the focal
- # point but then get adjusted back into the frame
- y_half = int(settings.crop_height / 2)
- x_half = int(settings.crop_width / 2)
-
- x1 = focus.x - x_half
- if x1 < 0:
- x1 = 0
- elif x1 + settings.crop_width > im.width:
- x1 = im.width - settings.crop_width
-
- y1 = focus.y - y_half
- if y1 < 0:
- y1 = 0
- elif y1 + settings.crop_height > im.height:
- y1 = im.height - settings.crop_height
-
- x2 = x1 + settings.crop_width
- y2 = y1 + settings.crop_height
-
- crop = [x1, y1, x2, y2]
-
- results = []
-
- results.append(im.crop(tuple(crop)))
-
- if settings.annotate_image:
- d = ImageDraw.Draw(im_debug)
- rect = list(crop)
- rect[2] -= 1
- rect[3] -= 1
- d.rectangle(rect, outline=GREEN)
- results.append(im_debug)
- if settings.destop_view_image:
- im_debug.show()
-
- return results
+ """ Intelligently crop an image to the subject matter """
+
+ scale_by = 1
+ if is_landscape(im.width, im.height):
+ scale_by = settings.crop_height / im.height
+ elif is_portrait(im.width, im.height):
+ scale_by = settings.crop_width / im.width
+ elif is_square(im.width, im.height):
+ if is_square(settings.crop_width, settings.crop_height):
+ scale_by = settings.crop_width / im.width
+ elif is_landscape(settings.crop_width, settings.crop_height):
+ scale_by = settings.crop_width / im.width
+ elif is_portrait(settings.crop_width, settings.crop_height):
+ scale_by = settings.crop_height / im.height
+
+
+ im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
+ im_debug = im.copy()
+
+ focus = focal_point(im_debug, settings)
+
+ # take the focal point and turn it into crop coordinates that try to center over the focal
+ # point but then get adjusted back into the frame
+ y_half = int(settings.crop_height / 2)
+ x_half = int(settings.crop_width / 2)
+
+ x1 = focus.x - x_half
+ if x1 < 0:
+ x1 = 0
+ elif x1 + settings.crop_width > im.width:
+ x1 = im.width - settings.crop_width
+
+ y1 = focus.y - y_half
+ if y1 < 0:
+ y1 = 0
+ elif y1 + settings.crop_height > im.height:
+ y1 = im.height - settings.crop_height
+
+ x2 = x1 + settings.crop_width
+ y2 = y1 + settings.crop_height
+
+ crop = [x1, y1, x2, y2]
+
+ results = []
+
+ results.append(im.crop(tuple(crop)))
+
+ if settings.annotate_image:
+ d = ImageDraw.Draw(im_debug)
+ rect = list(crop)
+ rect[2] -= 1
+ rect[3] -= 1
+ d.rectangle(rect, outline=GREEN)
+ results.append(im_debug)
+ if settings.destop_view_image:
+ im_debug.show()
+
+ return results
def focal_point(im, settings):
corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
@@ -78,29 +77,29 @@ def focal_point(im, settings): pois = []
weight_pref_total = 0
- if len(corner_points) > 0:
+ if corner_points:
weight_pref_total += settings.corner_points_weight
- if len(entropy_points) > 0:
+ if entropy_points:
weight_pref_total += settings.entropy_points_weight
- if len(face_points) > 0:
+ if face_points:
weight_pref_total += settings.face_points_weight
corner_centroid = None
- if len(corner_points) > 0:
+ if corner_points:
corner_centroid = centroid(corner_points)
- corner_centroid.weight = settings.corner_points_weight / weight_pref_total
+ corner_centroid.weight = settings.corner_points_weight / weight_pref_total
pois.append(corner_centroid)
entropy_centroid = None
- if len(entropy_points) > 0:
+ if entropy_points:
entropy_centroid = centroid(entropy_points)
entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
pois.append(entropy_centroid)
face_centroid = None
- if len(face_points) > 0:
+ if face_points:
face_centroid = centroid(face_points)
- face_centroid.weight = settings.face_points_weight / weight_pref_total
+ face_centroid.weight = settings.face_points_weight / weight_pref_total
pois.append(face_centroid)
average_point = poi_average(pois, settings)
@@ -134,7 +133,7 @@ def focal_point(im, settings): d.rectangle(f.bounding(4), outline=color)
d.ellipse(average_point.bounding(max_size), outline=GREEN)
-
+
return average_point
@@ -185,10 +184,10 @@ def image_face_points(im, settings): try:
faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
- except:
+ except Exception:
continue
- if len(faces) > 0:
+ if faces:
rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
return []
@@ -262,10 +261,11 @@ def image_entropy(im): hist = hist[hist > 0]
return -np.log2(hist / hist.sum()).sum()
+
def centroid(pois):
- x = [poi.x for poi in pois]
- y = [poi.y for poi in pois]
- return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois))
+ x = [poi.x for poi in pois]
+ y = [poi.y for poi in pois]
+ return PointOfInterest(sum(x) / len(pois), sum(y) / len(pois))
def poi_average(pois, settings):
@@ -283,59 +283,58 @@ def poi_average(pois, settings): def is_landscape(w, h):
- return w > h
+ return w > h
def is_portrait(w, h):
- return h > w
+ return h > w
def is_square(w, h):
- return w == h
+ return w == h
def download_and_cache_models(dirname):
- download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
- model_file_name = 'face_detection_yunet.onnx'
+ download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
+ model_file_name = 'face_detection_yunet.onnx'
- if not os.path.exists(dirname):
- os.makedirs(dirname)
+ os.makedirs(dirname, exist_ok=True)
- cache_file = os.path.join(dirname, model_file_name)
- if not os.path.exists(cache_file):
- print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
- response = requests.get(download_url)
- with open(cache_file, "wb") as f:
- f.write(response.content)
+ cache_file = os.path.join(dirname, model_file_name)
+ if not os.path.exists(cache_file):
+ print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
+ response = requests.get(download_url)
+ with open(cache_file, "wb") as f:
+ f.write(response.content)
- if os.path.exists(cache_file):
- return cache_file
- return None
+ if os.path.exists(cache_file):
+ return cache_file
+ return None
class PointOfInterest:
- def __init__(self, x, y, weight=1.0, size=10):
- self.x = x
- self.y = y
- self.weight = weight
- self.size = size
+ def __init__(self, x, y, weight=1.0, size=10):
+ self.x = x
+ self.y = y
+ self.weight = weight
+ self.size = size
- def bounding(self, size):
- return [
- self.x - size//2,
- self.y - size//2,
- self.x + size//2,
- self.y + size//2
- ]
+ def bounding(self, size):
+ return [
+ self.x - size // 2,
+ self.y - size // 2,
+ self.x + size // 2,
+ self.y + size // 2
+ ]
class Settings:
- def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
- self.crop_width = crop_width
- self.crop_height = crop_height
- self.corner_points_weight = corner_points_weight
- self.entropy_points_weight = entropy_points_weight
- self.face_points_weight = face_points_weight
- self.annotate_image = annotate_image
- self.destop_view_image = False
- self.dnn_model_path = dnn_model_path
+ def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
+ self.crop_width = crop_width
+ self.crop_height = crop_height
+ self.corner_points_weight = corner_points_weight
+ self.entropy_points_weight = entropy_points_weight
+ self.face_points_weight = face_points_weight
+ self.annotate_image = annotate_image
+ self.destop_view_image = False
+ self.dnn_model_path = dnn_model_path
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 41610e03..7ee05061 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -32,7 +32,7 @@ class DatasetEntry: 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', varsize=False, use_weight=False):
- 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 shared.opts.dataset_filename_word_regex else None
self.placeholder_token = placeholder_token
@@ -118,7 +118,7 @@ class PersonalizedBase(Dataset): weight = torch.ones(latent_sample.shape)
else:
weight = None
-
+
if latent_sampling_method == "random":
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
else:
@@ -243,4 +243,4 @@ class BatchLoaderRandom(BatchLoader): return self
def collate_wrapper_random(batch):
- return BatchLoaderRandom(batch)
\ No newline at end of file + return BatchLoaderRandom(batch)
diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index 5593f88c..81cff7bf 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -1,11 +1,11 @@ import base64
import json
+import warnings
+
import numpy as np
import zlib
-from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
-from fonts.ttf import Roboto
+from PIL import Image, ImageDraw
import torch
-from modules.shared import opts
class EmbeddingEncoder(json.JSONEncoder):
@@ -17,7 +17,7 @@ class EmbeddingEncoder(json.JSONEncoder): class EmbeddingDecoder(json.JSONDecoder):
def __init__(self, *args, **kwargs):
- json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
+ json.JSONDecoder.__init__(self, *args, object_hook=self.object_hook, **kwargs)
def object_hook(self, d):
if 'TORCHTENSOR' in d:
@@ -131,17 +131,17 @@ def extract_image_data_embed(image): def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
+ from modules.images import get_font
+ if textfont:
+ warnings.warn(
+ 'passing in a textfont to caption_image_overlay is deprecated and does nothing',
+ DeprecationWarning,
+ stacklevel=2,
+ )
from math import cos
image = srcimage.copy()
fontsize = 32
- if textfont is None:
- try:
- textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
- textfont = opts.font or Roboto
- except Exception:
- textfont = Roboto
-
factor = 1.5
gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
for y in range(image.size[1]):
@@ -152,12 +152,12 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t draw = ImageDraw.Draw(image)
- font = ImageFont.truetype(textfont, fontsize)
+ font = get_font(fontsize)
padding = 10
_, _, w, h = draw.textbbox((0, 0), title, font=font)
fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
- font = ImageFont.truetype(textfont, fontsize)
+ font = get_font(fontsize)
_, _, w, h = draw.textbbox((0, 0), title, font=font)
draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
@@ -168,7 +168,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t _, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
- font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))
+ font = get_font(min(fontsize_left, fontsize_mid, fontsize_right))
draw.text((padding, image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
draw.text((image.size[0]/2, image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py index f63fc72f..c56bea45 100644 --- a/modules/textual_inversion/learn_schedule.py +++ b/modules/textual_inversion/learn_schedule.py @@ -12,7 +12,7 @@ class LearnScheduleIterator: self.it = 0
self.maxit = 0
try:
- for i, pair in enumerate(pairs):
+ for pair in pairs:
if not pair.strip():
continue
tmp = pair.split(':')
@@ -32,8 +32,8 @@ class LearnScheduleIterator: self.maxit += 1
return
assert self.rates
- except (ValueError, AssertionError):
- raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.')
+ except (ValueError, AssertionError) as e:
+ raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.') from e
def __iter__(self):
diff --git a/modules/textual_inversion/logging.py b/modules/textual_inversion/logging.py index 734a4b6f..45823eb1 100644 --- a/modules/textual_inversion/logging.py +++ b/modules/textual_inversion/logging.py @@ -2,11 +2,51 @@ import datetime import json
import os
-saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "gradient_step", "latent_sampling_method"}
-saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
-saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
+saved_params_shared = {
+ "batch_size",
+ "clip_grad_mode",
+ "clip_grad_value",
+ "create_image_every",
+ "data_root",
+ "gradient_step",
+ "initial_step",
+ "latent_sampling_method",
+ "learn_rate",
+ "log_directory",
+ "model_hash",
+ "model_name",
+ "num_of_dataset_images",
+ "steps",
+ "template_file",
+ "training_height",
+ "training_width",
+}
+saved_params_ti = {
+ "embedding_name",
+ "num_vectors_per_token",
+ "save_embedding_every",
+ "save_image_with_stored_embedding",
+}
+saved_params_hypernet = {
+ "activation_func",
+ "add_layer_norm",
+ "hypernetwork_name",
+ "layer_structure",
+ "save_hypernetwork_every",
+ "use_dropout",
+ "weight_init",
+}
saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
-saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"}
+saved_params_previews = {
+ "preview_cfg_scale",
+ "preview_height",
+ "preview_negative_prompt",
+ "preview_prompt",
+ "preview_sampler_index",
+ "preview_seed",
+ "preview_steps",
+ "preview_width",
+}
def save_settings_to_file(log_directory, all_params):
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index da0bcb26..dbd856bd 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -1,17 +1,13 @@ import os
from PIL import Image, ImageOps
import math
-import platform
-import sys
import tqdm
-import time
from modules import paths, shared, images, deepbooru
-from modules.shared import opts, cmd_opts
from modules.textual_inversion import autocrop
-def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
+def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.15, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
try:
if process_caption:
shared.interrogator.load()
@@ -51,7 +47,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti caption += shared.interrogator.generate_caption(image)
if params.process_caption_deepbooru:
- if len(caption) > 0:
+ if caption:
caption += ", "
caption += deepbooru.model.tag_multi(image)
@@ -71,7 +67,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti caption = caption.strip()
- if len(caption) > 0:
+ if caption:
with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
file.write(caption)
@@ -129,7 +125,7 @@ def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, thr default=None
)
return wh and center_crop(image, *wh)
-
+
def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
width = process_width
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 4368eb63..bb6f211c 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -1,7 +1,4 @@ import os
-import sys
-import traceback
-import inspect
from collections import namedtuple
import torch
@@ -15,7 +12,7 @@ import numpy as np from PIL import Image, PngImagePlugin
from torch.utils.tensorboard import SummaryWriter
-from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint
+from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors
import modules.textual_inversion.dataset
from modules.textual_inversion.learn_schedule import LearnRateScheduler
@@ -30,7 +27,7 @@ textual_inversion_templates = {} def list_textual_inversion_templates():
textual_inversion_templates.clear()
- for root, dirs, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):
+ for root, _, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):
for fn in fns:
path = os.path.join(root, fn)
@@ -121,16 +118,29 @@ class EmbeddingDatabase: self.embedding_dirs.clear()
def register_embedding(self, embedding, model):
- self.word_embeddings[embedding.name] = embedding
-
- ids = model.cond_stage_model.tokenize([embedding.name])[0]
+ return self.register_embedding_by_name(embedding, model, embedding.name)
+ def register_embedding_by_name(self, embedding, model, name):
+ ids = model.cond_stage_model.tokenize([name])[0]
first_id = ids[0]
if first_id not in self.ids_lookup:
self.ids_lookup[first_id] = []
-
- self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)
-
+ if name in self.word_embeddings:
+ # remove old one from the lookup list
+ lookup = [x for x in self.ids_lookup[first_id] if x[1].name!=name]
+ else:
+ lookup = self.ids_lookup[first_id]
+ if embedding is not None:
+ lookup += [(ids, embedding)]
+ self.ids_lookup[first_id] = sorted(lookup, key=lambda x: len(x[0]), reverse=True)
+ if embedding is None:
+ # unregister embedding with specified name
+ if name in self.word_embeddings:
+ del self.word_embeddings[name]
+ if len(self.ids_lookup[first_id])==0:
+ del self.ids_lookup[first_id]
+ return None
+ self.word_embeddings[name] = embedding
return embedding
def get_expected_shape(self):
@@ -167,8 +177,7 @@ class EmbeddingDatabase: # textual inversion embeddings
if 'string_to_param' in data:
param_dict = data['string_to_param']
- if hasattr(param_dict, '_parameters'):
- param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
+ param_dict = getattr(param_dict, '_parameters', param_dict) # fix for torch 1.12.1 loading saved file from torch 1.11
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
emb = next(iter(param_dict.items()))[1]
# diffuser concepts
@@ -199,7 +208,7 @@ class EmbeddingDatabase: if not os.path.isdir(embdir.path):
return
- for root, dirs, fns in os.walk(embdir.path, followlinks=True):
+ for root, _, fns in os.walk(embdir.path, followlinks=True):
for fn in fns:
try:
fullfn = os.path.join(root, fn)
@@ -209,14 +218,13 @@ class EmbeddingDatabase: self.load_from_file(fullfn, fn)
except Exception:
- print(f"Error loading embedding {fn}:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error loading embedding {fn}", exc_info=True)
continue
def load_textual_inversion_embeddings(self, force_reload=False):
if not force_reload:
need_reload = False
- for path, embdir in self.embedding_dirs.items():
+ for embdir in self.embedding_dirs.values():
if embdir.has_changed():
need_reload = True
break
@@ -229,7 +237,7 @@ class EmbeddingDatabase: self.skipped_embeddings.clear()
self.expected_shape = self.get_expected_shape()
- for path, embdir in self.embedding_dirs.items():
+ for embdir in self.embedding_dirs.values():
self.load_from_dir(embdir)
embdir.update()
@@ -243,7 +251,7 @@ class EmbeddingDatabase: if self.previously_displayed_embeddings != displayed_embeddings:
self.previously_displayed_embeddings = displayed_embeddings
print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
- if len(self.skipped_embeddings) > 0:
+ if self.skipped_embeddings:
print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
def find_embedding_at_position(self, tokens, offset):
@@ -325,16 +333,16 @@ def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epo tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step)
def tensorboard_add_scaler(tensorboard_writer, tag, value, step):
- tensorboard_writer.add_scalar(tag=tag,
+ tensorboard_writer.add_scalar(tag=tag,
scalar_value=value, global_step=step)
def tensorboard_add_image(tensorboard_writer, tag, pil_image, step):
# Convert a pil image to a torch tensor
img_tensor = torch.as_tensor(np.array(pil_image, copy=True))
- img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0],
+ img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0],
len(pil_image.getbands()))
img_tensor = img_tensor.permute((2, 0, 1))
-
+
tensorboard_writer.add_image(tag, img_tensor, global_step=step)
def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_model_every, create_image_every, log_directory, name="embedding"):
@@ -404,7 +412,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st if initial_step >= steps:
shared.state.textinfo = "Model has already been trained beyond specified max steps"
return embedding, filename
-
+
scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \
torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \
@@ -414,7 +422,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st # 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
-
+
if shared.opts.training_enable_tensorboard:
tensorboard_writer = tensorboard_setup(log_directory)
@@ -441,7 +449,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st optimizer_saved_dict = torch.load(f"{filename}.optim", map_location='cpu')
if embedding.checksum() == optimizer_saved_dict.get('hash', None):
optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
-
+
if optimizer_state_dict is not None:
optimizer.load_state_dict(optimizer_state_dict)
print("Loaded existing optimizer from checkpoint")
@@ -470,7 +478,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st try:
sd_hijack_checkpoint.add()
- for i in range((steps-initial_step) * gradient_step):
+ for _ in range((steps-initial_step) * gradient_step):
if scheduler.finished:
break
if shared.state.interrupted:
@@ -487,7 +495,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st if clip_grad:
clip_grad_sched.step(embedding.step)
-
+
with devices.autocast():
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
if use_weight:
@@ -515,7 +523,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st # go back until we reach gradient accumulation steps
if (j + 1) % gradient_step != 0:
continue
-
+
if clip_grad:
clip_grad(embedding.vec, clip_grad_sched.learn_rate)
@@ -603,7 +611,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st try:
vectorSize = list(data['string_to_param'].values())[0].shape[0]
- except Exception as e:
+ except Exception:
vectorSize = '?'
checkpoint = sd_models.select_checkpoint()
@@ -634,8 +642,7 @@ Last saved image: {html.escape(last_saved_image)}<br/> filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
except Exception:
- print(traceback.format_exc(), file=sys.stderr)
- pass
+ errors.report("Error training embedding", exc_info=True)
finally:
pbar.leave = False
pbar.close()
diff --git a/modules/timer.py b/modules/timer.py index ba92be33..da99e49f 100644 --- a/modules/timer.py +++ b/modules/timer.py @@ -1,11 +1,30 @@ import time
+class TimerSubcategory:
+ def __init__(self, timer, category):
+ self.timer = timer
+ self.category = category
+ self.start = None
+ self.original_base_category = timer.base_category
+
+ def __enter__(self):
+ self.start = time.time()
+ self.timer.base_category = self.original_base_category + self.category + "/"
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ elapsed_for_subcategroy = time.time() - self.start
+ self.timer.base_category = self.original_base_category
+ self.timer.add_time_to_record(self.original_base_category + self.category, elapsed_for_subcategroy)
+ self.timer.record(self.category)
+
+
class Timer:
def __init__(self):
self.start = time.time()
self.records = {}
self.total = 0
+ self.base_category = ''
def elapsed(self):
end = time.time()
@@ -13,18 +32,29 @@ class Timer: self.start = end
return res
- def record(self, category, extra_time=0):
- e = self.elapsed()
+ def add_time_to_record(self, category, amount):
if category not in self.records:
self.records[category] = 0
- self.records[category] += e + extra_time
+ self.records[category] += amount
+
+ def record(self, category, extra_time=0):
+ e = self.elapsed()
+
+ self.add_time_to_record(self.base_category + category, e + extra_time)
+
self.total += e + extra_time
+ def subcategory(self, name):
+ self.elapsed()
+
+ subcat = TimerSubcategory(self, name)
+ return subcat
+
def summary(self):
res = f"{self.total:.1f}s"
- additions = [x for x in self.records.items() if x[1] >= 0.1]
+ additions = [(category, time_taken) for category, time_taken in self.records.items() if time_taken >= 0.1 and '/' not in category]
if not additions:
return res
@@ -34,5 +64,13 @@ class Timer: return res
+ def dump(self):
+ return {'total': self.total, 'records': self.records}
+
def reset(self):
self.__init__()
+
+
+startup_timer = Timer()
+
+startup_record = None
diff --git a/modules/txt2img.py b/modules/txt2img.py index 16841d0f..6aa79f23 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -1,18 +1,16 @@ import modules.scripts
-from modules import sd_samplers
+from modules import sd_samplers, processing
from modules.generation_parameters_copypaste import create_override_settings_dict
-from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
- StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, cmd_opts
import modules.shared as shared
-import modules.processing as processing
from modules.ui import plaintext_to_html
+import gradio as gr
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, override_settings_texts, *args):
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
override_settings = create_override_settings_dict(override_settings_texts)
- p = StableDiffusionProcessingTxt2Img(
+ p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
@@ -41,19 +39,24 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step hr_second_pass_steps=hr_second_pass_steps,
hr_resize_x=hr_resize_x,
hr_resize_y=hr_resize_y,
+ hr_sampler_name=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else None,
+ hr_prompt=hr_prompt,
+ hr_negative_prompt=hr_negative_prompt,
override_settings=override_settings,
)
p.scripts = modules.scripts.scripts_txt2img
p.script_args = args
+ p.user = request.username
+
if cmd_opts.enable_console_prompts:
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
processed = modules.scripts.scripts_txt2img.run(p, *args)
if processed is None:
- processed = process_images(p)
+ processed = processing.process_images(p)
p.close()
diff --git a/modules/ui.py b/modules/ui.py index f07bcc41..39d226ad 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1,29 +1,25 @@ -import html
+import datetime
import json
-import math
import mimetypes
import os
-import platform
-import random
import sys
-import tempfile
-import time
-import traceback
-from functools import partial, reduce
+from functools import reduce
import warnings
import gradio as gr
-import gradio.routes
import gradio.utils
import numpy as np
-from PIL import Image, PngImagePlugin
+from PIL import Image, PngImagePlugin # noqa: F401
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing, progress
-from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML
-from modules.paths import script_path, data_path
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
+from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
+from modules.paths import script_path
+from modules.ui_common import create_refresh_button
+from modules.ui_gradio_extensions import reload_javascript
-from modules.shared import opts, cmd_opts, restricted_opts
+
+from modules.shared import opts, cmd_opts
import modules.codeformer_model
import modules.generation_parameters_copypaste as parameters_copypaste
@@ -34,7 +30,6 @@ import modules.shared as shared import modules.styles
import modules.textual_inversion.ui
from modules import prompt_parser
-from modules.images import save_image
from modules.sd_hijack import model_hijack
from modules.sd_samplers import samplers, samplers_for_img2img
from modules.textual_inversion import textual_inversion
@@ -42,6 +37,8 @@ import modules.hypernetworks.ui from modules.generation_parameters_copypaste import image_from_url_text
import modules.extras
+create_setting_component = ui_settings.create_setting_component
+
warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
@@ -59,7 +56,7 @@ if cmd_opts.ngrok is not None: ngrok.connect(
cmd_opts.ngrok,
cmd_opts.port if cmd_opts.port is not None else 7860,
- cmd_opts.ngrok_region
+ cmd_opts.ngrok_options
)
@@ -82,6 +79,8 @@ clear_prompt_symbol = '\U0001f5d1\ufe0f' # 🗑️ extra_networks_symbol = '\U0001F3B4' # 🎴
switch_values_symbol = '\U000021C5' # ⇅
restore_progress_symbol = '\U0001F300' # 🌀
+detect_image_size_symbol = '\U0001F4D0' # 📐
+up_down_symbol = '\u2195\ufe0f' # ↕️
def plaintext_to_html(text):
@@ -93,16 +92,6 @@ def send_gradio_gallery_to_image(x): return None
return image_from_url_text(x[0])
-def visit(x, func, path=""):
- if hasattr(x, 'children'):
- if isinstance(x, gr.Tabs) and x.elem_id is not None:
- # Tabs element can't have a label, have to use elem_id instead
- func(f"{path}/Tabs@{x.elem_id}", x)
- for c in x.children:
- visit(c, func, path)
- elif x.label is not None:
- func(f"{path}/{x.label}", x)
-
def add_style(name: str, prompt: str, negative_prompt: str):
if name is None:
@@ -166,7 +155,7 @@ def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_di img = Image.open(image)
filename = os.path.basename(image)
left, _ = os.path.splitext(filename)
- print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a'))
+ print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
return [gr.update(), None]
@@ -206,8 +195,8 @@ def create_seed_inputs(target_interface): seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")
- random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
- random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
+ random_seed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_seed')}", show_progress=False, inputs=[], outputs=[])
+ random_subseed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_subseed')}", show_progress=False, inputs=[], outputs=[])
def change_visibility(show):
return {comp: gr_show(show) for comp in seed_extras}
@@ -246,10 +235,9 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: all_seeds = gen_info.get('all_seeds', [-1])
res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
- except json.decoder.JSONDecodeError as e:
- if gen_info_string != '':
- print("Error parsing JSON generation info:", file=sys.stderr)
- print(gen_info_string, file=sys.stderr)
+ except json.decoder.JSONDecodeError:
+ if gen_info_string:
+ errors.report(f"Error parsing JSON generation info: {gen_info_string}")
return [res, gr_show(False)]
@@ -288,12 +276,12 @@ def create_toprow(is_img2img): with gr.Row():
with gr.Column(scale=80):
with gr.Row():
- prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)")
+ prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
with gr.Row():
with gr.Column(scale=80):
with gr.Row():
- negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")
+ negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
button_interrogate = None
button_deepbooru = None
@@ -384,25 +372,6 @@ def apply_setting(key, value): return getattr(opts, key)
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
- def refresh():
- refresh_method()
- args = refreshed_args() if callable(refreshed_args) else refreshed_args
-
- for k, v in args.items():
- setattr(refresh_component, k, v)
-
- return gr.update(**(args or {}))
-
- refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
- refresh_button.click(
- fn=refresh,
- inputs=[],
- outputs=[refresh_component]
- )
- return refresh_button
-
-
def create_output_panel(tabname, outdir):
return ui_common.create_output_panel(tabname, outdir)
@@ -421,27 +390,17 @@ def create_sampler_and_steps_selection(choices, tabname): def ordered_ui_categories():
- user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder.split(","))}
+ user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
- for i, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
+ for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
yield category
-def get_value_for_setting(key):
- value = getattr(opts, key)
-
- info = opts.data_labels[key]
- args = info.component_args() if callable(info.component_args) else info.component_args or {}
- args = {k: v for k, v in args.items() if k not in {'precision'}}
-
- return gr.update(value=value, **args)
-
-
def create_override_settings_dropdown(tabname, row):
dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
dropdown.change(
- fn=lambda x: gr.Dropdown.update(visible=len(x) > 0),
+ fn=lambda x: gr.Dropdown.update(visible=bool(x)),
inputs=[dropdown],
outputs=[dropdown],
)
@@ -472,6 +431,8 @@ def create_ui(): with gr.Row().style(equal_height=False):
with gr.Column(variant='compact', elem_id="txt2img_settings"):
+ modules.scripts.scripts_txt2img.prepare_ui()
+
for category in ordered_ui_categories():
if category == "sampler":
steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
@@ -515,6 +476,17 @@ def create_ui(): hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
+ with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
+ hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")
+
+ with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
+ with gr.Column(scale=80):
+ with gr.Row():
+ hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
+ with gr.Column(scale=80):
+ with gr.Row():
+ hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+
elif category == "batch":
if not opts.dimensions_and_batch_together:
with FormRow(elem_id="txt2img_column_batch"):
@@ -529,15 +501,21 @@ def create_ui(): with FormGroup(elem_id="txt2img_script_container"):
custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+ else:
+ modules.scripts.scripts_txt2img.setup_ui_for_section(category)
+
hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
- for input in hr_resolution_preview_inputs:
- input.change(
+
+ for component in hr_resolution_preview_inputs:
+ event = component.release if isinstance(component, gr.Slider) else component.change
+
+ event(
fn=calc_resolution_hires,
inputs=hr_resolution_preview_inputs,
outputs=[hr_final_resolution],
show_progress=False,
)
- input.change(
+ event(
None,
_js="onCalcResolutionHires",
inputs=hr_resolution_preview_inputs,
@@ -576,7 +554,11 @@ def create_ui(): hr_second_pass_steps,
hr_resize_x,
hr_resize_y,
+ hr_sampler_index,
+ hr_prompt,
+ hr_negative_prompt,
override_settings,
+
] + custom_inputs,
outputs=[
@@ -591,7 +573,7 @@ def create_ui(): txt2img_prompt.submit(**txt2img_args)
submit.click(**txt2img_args)
- res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
+ res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
restore_progress_button.click(
fn=progress.restore_progress,
@@ -614,7 +596,8 @@ def create_ui(): outputs=[
txt2img_prompt,
txt_prompt_img
- ]
+ ],
+ show_progress=False,
)
enable_hr.change(
@@ -639,6 +622,7 @@ def create_ui(): (subseed_strength, "Variation seed strength"),
(seed_resize_from_w, "Seed resize from-1"),
(seed_resize_from_h, "Seed resize from-2"),
+ (txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
(denoising_strength, "Denoising strength"),
(enable_hr, lambda d: "Denoising strength" in d),
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
@@ -647,6 +631,11 @@ def create_ui(): (hr_second_pass_steps, "Hires steps"),
(hr_resize_x, "Hires resize-1"),
(hr_resize_y, "Hires resize-2"),
+ (hr_sampler_index, "Hires sampler"),
+ (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
+ (hr_prompt, "Hires prompt"),
+ (hr_negative_prompt, "Hires negative prompt"),
+ (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
*modules.scripts.scripts_txt2img.infotext_fields
]
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
@@ -704,19 +693,19 @@ def create_ui(): img2img_selected_tab = gr.State(0)
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
- init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=480)
+ init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=opts.img2img_editor_height)
add_copy_image_controls('img2img', init_img)
with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
- sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480)
+ sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
add_copy_image_controls('sketch', sketch)
with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
- init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
+ init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
add_copy_image_controls('inpaint', init_img_with_mask)
with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
- inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480)
+ inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
inpaint_color_sketch_orig = gr.State(None)
add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
@@ -736,17 +725,20 @@ def create_ui(): with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
gr.HTML(
- f"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
- f"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
+ "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
+ "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
f"{hidden}</p>"
)
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+ with gr.Accordion("PNG info", open=False):
+ img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
+ img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
+ img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
- img2img_image_inputs = [init_img, sketch, init_img_with_mask, inpaint_color_sketch]
for i, tab in enumerate(img2img_tabs):
tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
@@ -773,6 +765,8 @@ def create_ui(): with FormRow():
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
+ modules.scripts.scripts_img2img.prepare_ui()
+
for category in ordered_ui_categories():
if category == "sampler":
steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
@@ -783,15 +777,16 @@ def create_ui(): selected_scale_tab = gr.State(value=0)
with gr.Tabs():
- with gr.Tab(label="Resize to") as tab_scale_to:
+ with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
with FormRow():
with gr.Column(elem_id="img2img_column_size", scale=4):
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+ detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
- with gr.Tab(label="Resize by") as tab_scale_by:
+ with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
with FormRow():
@@ -881,6 +876,8 @@ def create_ui(): inputs=[],
outputs=[inpaint_controls, mask_alpha],
)
+ else:
+ modules.scripts.scripts_img2img.setup_ui_for_section(category)
img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
@@ -895,7 +892,8 @@ def create_ui(): outputs=[
img2img_prompt,
img2img_prompt_img
- ]
+ ],
+ show_progress=False,
)
img2img_args = dict(
@@ -940,6 +938,9 @@ def create_ui(): img2img_batch_output_dir,
img2img_batch_inpaint_mask_dir,
override_settings,
+ img2img_batch_use_png_info,
+ img2img_batch_png_info_props,
+ img2img_batch_png_info_dir,
] + custom_inputs,
outputs=[
img2img_gallery,
@@ -967,7 +968,16 @@ def create_ui(): img2img_prompt.submit(**img2img_args)
submit.click(**img2img_args)
- res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
+
+ res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
+
+ detect_image_size_btn.click(
+ fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
+ _js="currentImg2imgSourceResolution",
+ inputs=[dummy_component, dummy_component, dummy_component],
+ outputs=[width, height],
+ show_progress=False,
+ )
restore_progress_button.click(
fn=progress.restore_progress,
@@ -1035,6 +1045,7 @@ def create_ui(): (subseed_strength, "Variation seed strength"),
(seed_resize_from_w, "Seed resize from-1"),
(seed_resize_from_h, "Seed resize from-2"),
+ (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
(denoising_strength, "Denoising strength"),
(mask_blur, "Mask blur"),
*modules.scripts.scripts_img2img.infotext_fields
@@ -1189,7 +1200,7 @@ def create_ui(): process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
-
+
with gr.Column(visible=False) as process_multicrop_col:
gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
with gr.Row():
@@ -1201,7 +1212,7 @@ def create_ui(): with gr.Row():
process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
-
+
with gr.Row():
with gr.Column(scale=3):
gr.HTML(value="")
@@ -1230,7 +1241,7 @@ def create_ui(): )
def get_textual_inversion_template_names():
- return sorted([x for x in textual_inversion.textual_inversion_templates])
+ return sorted(textual_inversion.textual_inversion_templates)
with gr.Tab(label="Train", id="train"):
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
@@ -1238,13 +1249,13 @@ def create_ui(): train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
- train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
- create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
+ train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
+ create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
with FormRow():
embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
-
+
with FormRow():
clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
@@ -1290,8 +1301,8 @@ def create_ui(): with gr.Column(elem_id='ti_gallery_container'):
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
- ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
- ti_progress = gr.HTML(elem_id="ti_progress", value="")
+ gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
+ gr.HTML(elem_id="ti_progress", value="")
ti_outcome = gr.HTML(elem_id="ti_error", value="")
create_embedding.click(
@@ -1444,194 +1455,10 @@ def create_ui(): outputs=[],
)
- def create_setting_component(key, is_quicksettings=False):
- def fun():
- return opts.data[key] if key in opts.data else opts.data_labels[key].default
-
- info = opts.data_labels[key]
- t = type(info.default)
-
- args = info.component_args() if callable(info.component_args) else info.component_args
-
- if info.component is not None:
- comp = info.component
- elif t == str:
- comp = gr.Textbox
- elif t == int:
- comp = gr.Number
- elif t == bool:
- comp = gr.Checkbox
- else:
- raise Exception(f'bad options item type: {t} for key {key}')
-
- elem_id = f"setting_{key}"
-
- if info.refresh is not None:
- if is_quicksettings:
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
- create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
- else:
- with FormRow():
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
- create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
- else:
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-
- return res
-
- components = []
- component_dict = {}
- shared.settings_components = component_dict
-
- script_callbacks.ui_settings_callback()
- opts.reorder()
-
- def run_settings(*args):
- changed = []
-
- for key, value, comp in zip(opts.data_labels.keys(), args, components):
- assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
-
- for key, value, comp in zip(opts.data_labels.keys(), args, components):
- if comp == dummy_component:
- continue
-
- if opts.set(key, value):
- changed.append(key)
-
- try:
- opts.save(shared.config_filename)
- except RuntimeError:
- return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
- return opts.dumpjson(), f'{len(changed)} settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}.'
-
- def run_settings_single(value, key):
- if not opts.same_type(value, opts.data_labels[key].default):
- return gr.update(visible=True), opts.dumpjson()
-
- if not opts.set(key, value):
- return gr.update(value=getattr(opts, key)), opts.dumpjson()
-
- opts.save(shared.config_filename)
-
- return get_value_for_setting(key), opts.dumpjson()
-
- with gr.Blocks(analytics_enabled=False) as settings_interface:
- with gr.Row():
- with gr.Column(scale=6):
- settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
- with gr.Column():
- restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
-
- result = gr.HTML(elem_id="settings_result")
-
- quicksettings_names = opts.quicksettings_list
- quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'}
-
- quicksettings_list = []
-
- previous_section = None
- current_tab = None
- current_row = None
- with gr.Tabs(elem_id="settings"):
- for i, (k, item) in enumerate(opts.data_labels.items()):
- section_must_be_skipped = item.section[0] is None
-
- if previous_section != item.section and not section_must_be_skipped:
- elem_id, text = item.section
-
- if current_tab is not None:
- current_row.__exit__()
- current_tab.__exit__()
-
- gr.Group()
- current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
- current_tab.__enter__()
- current_row = gr.Column(variant='compact')
- current_row.__enter__()
-
- previous_section = item.section
-
- if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
- quicksettings_list.append((i, k, item))
- components.append(dummy_component)
- elif section_must_be_skipped:
- components.append(dummy_component)
- else:
- component = create_setting_component(k)
- component_dict[k] = component
- components.append(component)
-
- if current_tab is not None:
- current_row.__exit__()
- current_tab.__exit__()
-
- with gr.TabItem("Actions", id="actions", elem_id="settings_tab_actions"):
- request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
- download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
- reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
- with gr.Row():
- unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
- reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
-
- with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
- gr.HTML(shared.html("licenses.html"), elem_id="licenses")
-
- gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
-
-
- def unload_sd_weights():
- modules.sd_models.unload_model_weights()
-
- def reload_sd_weights():
- modules.sd_models.reload_model_weights()
-
- unload_sd_model.click(
- fn=unload_sd_weights,
- inputs=[],
- outputs=[]
- )
-
- reload_sd_model.click(
- fn=reload_sd_weights,
- inputs=[],
- outputs=[]
- )
-
- request_notifications.click(
- fn=lambda: None,
- inputs=[],
- outputs=[],
- _js='function(){}'
- )
-
- download_localization.click(
- fn=lambda: None,
- inputs=[],
- outputs=[],
- _js='download_localization'
- )
+ loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
- def reload_scripts():
- modules.scripts.reload_script_body_only()
- reload_javascript() # need to refresh the html page
-
- reload_script_bodies.click(
- fn=reload_scripts,
- inputs=[],
- outputs=[]
- )
-
- def request_restart():
- shared.state.interrupt()
- shared.state.need_restart = True
-
- restart_gradio.click(
- fn=request_restart,
- _js='restart_reload',
- inputs=[],
- outputs=[],
- )
+ settings = ui_settings.UiSettings()
+ settings.create_ui(loadsave, dummy_component)
interfaces = [
(txt2img_interface, "txt2img", "txt2img"),
@@ -1639,11 +1466,11 @@ def create_ui(): (extras_interface, "Extras", "extras"),
(pnginfo_interface, "PNG Info", "pnginfo"),
(modelmerger_interface, "Checkpoint Merger", "modelmerger"),
- (train_interface, "Train", "ti"),
+ (train_interface, "Train", "train"),
]
interfaces += script_callbacks.ui_tabs_callback()
- interfaces += [(settings_interface, "Settings", "settings")]
+ interfaces += [(settings.interface, "Settings", "settings")]
extensions_interface = ui_extensions.create_ui()
interfaces += [(extensions_interface, "Extensions", "extensions")]
@@ -1653,76 +1480,48 @@ def create_ui(): shared.tab_names.append(label)
with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
- with gr.Row(elem_id="quicksettings", variant="compact"):
- for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
- component = create_setting_component(k, is_quicksettings=True)
- component_dict[k] = component
+ settings.add_quicksettings()
parameters_copypaste.connect_paste_params_buttons()
with gr.Tabs(elem_id="tabs") as tabs:
- for interface, label, ifid in interfaces:
+ tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
+ sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999))
+
+ for interface, label, ifid in sorted_interfaces:
if label in shared.opts.hidden_tabs:
continue
with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
interface.render()
+ for interface, _label, ifid in interfaces:
+ if ifid in ["extensions", "settings"]:
+ continue
+
+ loadsave.add_block(interface, ifid)
+
+ loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
+
+ loadsave.setup_ui()
+
if os.path.exists(os.path.join(script_path, "notification.mp3")):
- audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+ gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
footer = shared.html("footer.html")
- footer = footer.format(versions=versions_html())
+ footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
gr.HTML(footer, elem_id="footer")
- text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
- settings_submit.click(
- fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
- inputs=components,
- outputs=[text_settings, result],
- )
-
- for i, k, item in quicksettings_list:
- component = component_dict[k]
- info = opts.data_labels[k]
-
- change_handler = component.release if hasattr(component, 'release') else component.change
- change_handler(
- fn=lambda value, k=k: run_settings_single(value, key=k),
- inputs=[component],
- outputs=[component, text_settings],
- show_progress=info.refresh is not None,
- )
+ settings.add_functionality(demo)
update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
- text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+ settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
- button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
- button_set_checkpoint.click(
- fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
- _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
- inputs=[component_dict['sd_model_checkpoint'], dummy_component],
- outputs=[component_dict['sd_model_checkpoint'], text_settings],
- )
-
- component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
-
- def get_settings_values():
- return [get_value_for_setting(key) for key in component_keys]
-
- demo.load(
- fn=get_settings_values,
- inputs=[],
- outputs=[component_dict[k] for k in component_keys],
- queue=False,
- )
-
def modelmerger(*args):
try:
results = modules.extras.run_modelmerger(*args)
except Exception as e:
- print("Error loading/saving model file:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report("Error loading/saving model file", exc_info=True)
modules.sd_models.list_models() # to remove the potentially missing models from the list
return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
return results
@@ -1750,102 +1549,13 @@ def create_ui(): primary_model_name,
secondary_model_name,
tertiary_model_name,
- component_dict['sd_model_checkpoint'],
+ settings.component_dict['sd_model_checkpoint'],
modelmerger_result,
]
)
- ui_config_file = cmd_opts.ui_config_file
- ui_settings = {}
- settings_count = len(ui_settings)
- error_loading = False
-
- try:
- if os.path.exists(ui_config_file):
- with open(ui_config_file, "r", encoding="utf8") as file:
- ui_settings = json.load(file)
- except Exception:
- error_loading = True
- print("Error loading settings:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- def loadsave(path, x):
- def apply_field(obj, field, condition=None, init_field=None):
- key = f"{path}/{field}"
-
- if getattr(obj, 'custom_script_source', None) is not None:
- key = f"customscript/{obj.custom_script_source}/{key}"
-
- if getattr(obj, 'do_not_save_to_config', False):
- return
-
- saved_value = ui_settings.get(key, None)
- if saved_value is None:
- ui_settings[key] = getattr(obj, field)
- elif condition and not condition(saved_value):
- pass
-
- # this warning is generally not useful;
- # print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
- else:
- setattr(obj, field, saved_value)
- if init_field is not None:
- init_field(saved_value)
-
- if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:
- apply_field(x, 'visible')
-
- if type(x) == gr.Slider:
- apply_field(x, 'value')
- apply_field(x, 'minimum')
- apply_field(x, 'maximum')
- apply_field(x, 'step')
-
- if type(x) == gr.Radio:
- apply_field(x, 'value', lambda val: val in x.choices)
-
- if type(x) == gr.Checkbox:
- apply_field(x, 'value')
-
- if type(x) == gr.Textbox:
- apply_field(x, 'value')
-
- if type(x) == gr.Number:
- apply_field(x, 'value')
-
- if type(x) == gr.Dropdown:
- def check_dropdown(val):
- if getattr(x, 'multiselect', False):
- return all([value in x.choices for value in val])
- else:
- return val in x.choices
-
- apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
-
- def check_tab_id(tab_id):
- tab_items = list(filter(lambda e: isinstance(e, gr.TabItem), x.children))
- if type(tab_id) == str:
- tab_ids = [t.id for t in tab_items]
- return tab_id in tab_ids
- elif type(tab_id) == int:
- return tab_id >= 0 and tab_id < len(tab_items)
- else:
- return False
-
- if type(x) == gr.Tabs:
- apply_field(x, 'selected', check_tab_id)
-
- visit(txt2img_interface, loadsave, "txt2img")
- visit(img2img_interface, loadsave, "img2img")
- visit(extras_interface, loadsave, "extras")
- visit(modelmerger_interface, loadsave, "modelmerger")
- visit(train_interface, loadsave, "train")
-
- loadsave(f"webui/Tabs@{tabs.elem_id}", tabs)
-
- if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
- with open(ui_config_file, "w", encoding="utf8") as file:
- json.dump(ui_settings, file, indent=4)
+ loadsave.dump_defaults()
+ demo.ui_loadsave = loadsave
# Required as a workaround for change() event not triggering when loading values from ui-config.json
interp_description.value = update_interp_description(interp_method.value)
@@ -1853,70 +1563,6 @@ def create_ui(): return demo
-def webpath(fn):
- if fn.startswith(script_path):
- web_path = os.path.relpath(fn, script_path).replace('\\', '/')
- else:
- web_path = os.path.abspath(fn)
-
- return f'file={web_path}?{os.path.getmtime(fn)}'
-
-
-def javascript_html():
- # Ensure localization is in `window` before scripts
- head = f'<script type="text/javascript">{localization.localization_js(shared.opts.localization)}</script>\n'
-
- script_js = os.path.join(script_path, "script.js")
- head += f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
-
- for script in modules.scripts.list_scripts("javascript", ".js"):
- head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
-
- for script in modules.scripts.list_scripts("javascript", ".mjs"):
- head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
-
- if cmd_opts.theme:
- head += f'<script type="text/javascript">set_theme(\"{cmd_opts.theme}\");</script>\n'
-
- return head
-
-
-def css_html():
- head = ""
-
- def stylesheet(fn):
- return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
-
- for cssfile in modules.scripts.list_files_with_name("style.css"):
- if not os.path.isfile(cssfile):
- continue
-
- head += stylesheet(cssfile)
-
- if os.path.exists(os.path.join(data_path, "user.css")):
- head += stylesheet(os.path.join(data_path, "user.css"))
-
- return head
-
-
-def reload_javascript():
- js = javascript_html()
- css = css_html()
-
- def template_response(*args, **kwargs):
- res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
- res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
- res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
- res.init_headers()
- return res
-
- gradio.routes.templates.TemplateResponse = template_response
-
-
-if not hasattr(shared, 'GradioTemplateResponseOriginal'):
- shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
-
-
def versions_html():
import torch
import launch
@@ -1933,15 +1579,15 @@ def versions_html(): return f"""
version: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{tag}</a>
- •
+ • 
python: <span title="{sys.version}">{python_version}</span>
- •
+ • 
torch: {getattr(torch, '__long_version__',torch.__version__)}
- •
+ • 
xformers: {xformers_version}
- •
+ • 
gradio: {gr.__version__}
- •
+ • 
checkpoint: <a id="sd_checkpoint_hash">N/A</a>
"""
@@ -1960,3 +1606,17 @@ def setup_ui_api(app): app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
+
+ app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
+
+ def download_sysinfo(attachment=False):
+ from fastapi.responses import PlainTextResponse
+
+ text = sysinfo.get()
+ filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
+
+ return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
+
+ app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
+ app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
+
diff --git a/modules/ui_common.py b/modules/ui_common.py index 27ab3ebb..57c2d0ad 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -10,8 +10,11 @@ import subprocess as sp from modules import call_queue, shared
from modules.generation_parameters_copypaste import image_from_url_text
import modules.images
+from modules.ui_components import ToolButton
+
folder_symbol = '\U0001f4c2' # 📂
+refresh_symbol = '\U0001f504' # 🔄
def update_generation_info(generation_info, html_info, img_index):
@@ -50,9 +53,10 @@ def save_files(js_data, images, do_make_zip, index): save_to_dirs = shared.opts.use_save_to_dirs_for_ui
extension: str = shared.opts.samples_format
start_index = 0
+ only_one = False
if index > -1 and shared.opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-
+ only_one = True
images = [images[index]]
start_index = index
@@ -70,6 +74,7 @@ def save_files(js_data, images, do_make_zip, index): is_grid = image_index < p.index_of_first_image
i = 0 if is_grid else (image_index - p.index_of_first_image)
+ p.batch_index = image_index-1
fullfn, txt_fullfn = modules.images.save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
filename = os.path.relpath(fullfn, path)
@@ -83,7 +88,10 @@ def save_files(js_data, images, do_make_zip, index): # Make Zip
if do_make_zip:
- zip_filepath = os.path.join(path, "images.zip")
+ zip_fileseed = p.all_seeds[index-1] if only_one else p.all_seeds[0]
+ namegen = modules.images.FilenameGenerator(p, zip_fileseed, p.all_prompts[0], image, True)
+ zip_filename = namegen.apply(shared.opts.grid_zip_filename_pattern or "[datetime]_[[model_name]]_[seed]-[seed_last]")
+ zip_filepath = os.path.join(path, f"{zip_filename}.zip")
from zipfile import ZipFile
with ZipFile(zip_filepath, "w") as zip_file:
@@ -211,3 +219,23 @@ Requested path was: {f} ))
return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
+
+
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+ def refresh():
+ refresh_method()
+ args = refreshed_args() if callable(refreshed_args) else refreshed_args
+
+ for k, v in args.items():
+ setattr(refresh_component, k, v)
+
+ return gr.update(**(args or {}))
+
+ refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
+ refresh_button.click(
+ fn=refresh,
+ inputs=[],
+ outputs=[refresh_component]
+ )
+ return refresh_button
+
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index d9faf85a..dff522ef 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -1,9 +1,8 @@ import json
import os.path
-import sys
+import threading
import time
from datetime import datetime
-import traceback
import git
@@ -12,7 +11,7 @@ import html import shutil
import errno
-from modules import extensions, shared, paths, config_states
+from modules import extensions, shared, paths, config_states, errors, restart
from modules.paths_internal import config_states_dir
from modules.call_queue import wrap_gradio_gpu_call
@@ -45,15 +44,16 @@ def apply_and_restart(disable_list, update_list, disable_all): try:
ext.fetch_and_reset_hard()
except Exception:
- print(f"Error getting updates for {ext.name}:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error getting updates for {ext.name}", exc_info=True)
shared.opts.disabled_extensions = disabled
shared.opts.disable_all_extensions = disable_all
shared.opts.save(shared.config_filename)
- shared.state.interrupt()
- shared.state.need_restart = True
+ if restart.is_restartable():
+ restart.restart_program()
+ else:
+ restart.stop_program()
def save_config_state(name):
@@ -91,8 +91,7 @@ def restore_config_state(confirmed, config_state_name, restore_type): if restore_type == "webui" or restore_type == "both":
config_states.restore_webui_config(config_state)
- shared.state.interrupt()
- shared.state.need_restart = True
+ shared.state.request_restart()
return ""
@@ -115,8 +114,7 @@ def check_updates(id_task, disable_list): if 'FETCH_HEAD' not in str(e):
raise
except Exception:
- print(f"Error checking updates for {ext.name}:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
+ errors.report(f"Error checking updates for {ext.name}", exc_info=True)
shared.state.nextjob()
@@ -127,7 +125,9 @@ def make_commit_link(commit_hash, remote, text=None): if text is None:
text = commit_hash[:8]
if remote.startswith("https://github.com/"):
- href = os.path.join(remote, "commit", commit_hash)
+ if remote.endswith(".git"):
+ remote = remote[:-4]
+ href = remote + "/commit/" + commit_hash
return f'<a href="{href}" target="_blank">{text}</a>'
else:
return text
@@ -138,9 +138,14 @@ def extension_table(): <table id="extensions">
<thead>
<tr>
- <th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
+ <th>
+ <input class="gr-check-radio gr-checkbox all_extensions_toggle" type="checkbox" {'checked="checked"' if all(ext.enabled for ext in extensions.extensions) else ''} onchange="toggle_all_extensions(event)" />
+ <abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr>
+ </th>
<th>URL</th>
- <th><abbr title="Extension version">Version</abbr></th>
+ <th>Branch</th>
+ <th>Version</th>
+ <th>Date</th>
<th><abbr title="Use checkbox to mark the extension for update; it will be updated when you click apply button">Update</abbr></th>
</tr>
</thead>
@@ -148,6 +153,7 @@ def extension_table(): """
for ext in extensions.extensions:
+ ext: extensions.Extension
ext.read_info_from_repo()
remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
@@ -167,9 +173,11 @@ def extension_table(): code += f"""
<tr>
- <td><label{style}><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
+ <td><label{style}><input class="gr-check-radio gr-checkbox extension_toggle" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''} onchange="toggle_extension(event)" />{html.escape(ext.name)}</label></td>
<td>{remote}</td>
+ <td>{ext.branch}</td>
<td>{version_link}</td>
+ <td>{time.asctime(time.gmtime(ext.commit_date))}</td>
<td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
</tr>
"""
@@ -320,6 +328,11 @@ def normalize_git_url(url): def install_extension_from_url(dirname, url, branch_name=None):
check_access()
+ if isinstance(dirname, str):
+ dirname = dirname.strip()
+ if isinstance(url, str):
+ url = url.strip()
+
assert url, 'No URL specified'
if dirname is None or dirname == "":
@@ -332,7 +345,8 @@ def install_extension_from_url(dirname, url, branch_name=None): assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
normalized_url = normalize_git_url(url)
- assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
+ if any(x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url):
+ raise Exception(f'Extension with this URL is already installed: {url}')
tmpdir = os.path.join(paths.data_path, "tmp", dirname)
@@ -340,12 +354,12 @@ def install_extension_from_url(dirname, url, branch_name=None): shutil.rmtree(tmpdir, True)
if not branch_name:
# if no branch is specified, use the default branch
- with git.Repo.clone_from(url, tmpdir) as repo:
+ with git.Repo.clone_from(url, tmpdir, filter=['blob:none']) as repo:
repo.remote().fetch()
for submodule in repo.submodules:
submodule.update()
else:
- with git.Repo.clone_from(url, tmpdir, branch=branch_name) as repo:
+ with git.Repo.clone_from(url, tmpdir, filter=['blob:none'], branch=branch_name) as repo:
repo.remote().fetch()
for submodule in repo.submodules:
submodule.update()
@@ -410,9 +424,19 @@ sort_ordering = [ (False, lambda x: x.get('name', 'z')),
(True, lambda x: x.get('name', 'z')),
(False, lambda x: 'z'),
+ (True, lambda x: x.get('commit_time', '')),
+ (True, lambda x: x.get('created_at', '')),
+ (True, lambda x: x.get('stars', 0)),
]
+def get_date(info: dict, key):
+ try:
+ return datetime.strptime(info.get(key), "%Y-%m-%dT%H:%M:%SZ").strftime("%Y-%m-%d")
+ except (ValueError, TypeError):
+ return ''
+
+
def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=""):
extlist = available_extensions["extensions"]
installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
@@ -437,7 +461,10 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=" for ext in sorted(extlist, key=sort_function, reverse=sort_reverse):
name = ext.get("name", "noname")
+ stars = int(ext.get("stars", 0))
added = ext.get('added', 'unknown')
+ update_time = get_date(ext, 'commit_time')
+ create_time = get_date(ext, 'created_at')
url = ext.get("url", None)
description = ext.get("description", "")
extension_tags = ext.get("tags", [])
@@ -448,7 +475,7 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=" existing = installed_extension_urls.get(normalize_git_url(url), None)
extension_tags = extension_tags + ["installed"] if existing else extension_tags
- if len([x for x in extension_tags if x in tags_to_hide]) > 0:
+ if any(x for x in extension_tags if x in tags_to_hide):
hidden += 1
continue
@@ -464,10 +491,11 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=" code += f"""
<tr>
<td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a><br />{tags_text}</td>
- <td>{html.escape(description)}<p class="info"><span class="date_added">Added: {html.escape(added)}</span></p></td>
+ <td>{html.escape(description)}<p class="info">
+ <span class="date_added">Update: {html.escape(update_time)} Added: {html.escape(added)} Created: {html.escape(create_time)}</span><span class="star_count">stars: <b>{stars}</b></a></p></td>
<td>{install_code}</td>
</tr>
-
+
"""
for tag in [x for x in extension_tags if x not in tags]:
@@ -484,17 +512,31 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=" return code, list(tags)
+def preload_extensions_git_metadata():
+ t0 = time.time()
+ for extension in extensions.extensions:
+ extension.read_info_from_repo()
+ print(
+ f"preload_extensions_git_metadata for "
+ f"{len(extensions.extensions)} extensions took "
+ f"{time.time() - t0:.2f}s"
+ )
+
+
def create_ui():
import modules.ui
config_states.list_config_states()
+ threading.Thread(target=preload_extensions_git_metadata).start()
+
with gr.Blocks(analytics_enabled=False) as ui:
- with gr.Tabs(elem_id="tabs_extensions") as tabs:
+ with gr.Tabs(elem_id="tabs_extensions"):
with gr.TabItem("Installed", id="installed"):
with gr.Row(elem_id="extensions_installed_top"):
- apply = gr.Button(value="Apply and restart UI", variant="primary")
+ apply_label = ("Apply and restart UI" if restart.is_restartable() else "Apply and quit")
+ apply = gr.Button(value=apply_label, variant="primary")
check = gr.Button(value="Check for updates")
extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
@@ -508,7 +550,8 @@ def create_ui(): </span>
"""
info = gr.HTML(html)
- extensions_table = gr.HTML(lambda: extension_table())
+ extensions_table = gr.HTML('Loading...')
+ ui.load(fn=extension_table, inputs=[], outputs=[extensions_table])
apply.click(
fn=apply_and_restart,
@@ -533,18 +576,18 @@ def create_ui(): with gr.Row():
hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
- sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index")
+ sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order",'update time', 'create time', "stars"], type="index")
- with gr.Row():
+ with gr.Row():
search_extensions_text = gr.Text(label="Search").style(container=False)
-
+
install_result = gr.HTML()
available_extensions_table = gr.HTML()
refresh_available_extensions_button.click(
- fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
+ fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update(), gr.update()]),
inputs=[available_extensions_index, hide_tags, sort_column],
- outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result, search_extensions_text],
+ outputs=[available_extensions_index, available_extensions_table, hide_tags, search_extensions_text, install_result],
)
install_extension_button.click(
@@ -579,9 +622,9 @@ def create_ui(): install_result = gr.HTML(elem_id="extension_install_result")
install_button.click(
- fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
+ fn=modules.ui.wrap_gradio_call(lambda *args: [gr.update(), *install_extension_from_url(*args)], extra_outputs=[gr.update(), gr.update()]),
inputs=[install_dirname, install_url, install_branch],
- outputs=[extensions_table, install_result],
+ outputs=[install_url, extensions_table, install_result],
)
with gr.TabItem("Backup/Restore"):
@@ -595,7 +638,8 @@ def create_ui(): config_save_button = gr.Button(value="Save Current Config")
config_states_info = gr.HTML("")
- config_states_table = gr.HTML(lambda: update_config_states_table("Current"))
+ config_states_table = gr.HTML("Loading...")
+ ui.load(fn=update_config_states_table, inputs=[config_states_list], outputs=[config_states_table])
config_save_button.click(fn=save_config_state, inputs=[config_save_name], outputs=[config_states_list, config_states_info])
@@ -608,4 +652,5 @@ def create_ui(): outputs=[config_states_table],
)
+
return ui
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index 8c3dea56..693cafb6 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -1,11 +1,10 @@ -import glob
import os.path
import urllib.parse
from pathlib import Path
-from PIL import PngImagePlugin
from modules import shared
-from modules.images import read_info_from_image
+from modules.images import read_info_from_image, save_image_with_geninfo
+from modules.ui import up_down_symbol
import gradio as gr
import json
import html
@@ -27,12 +26,12 @@ def register_page(page): def fetch_file(filename: str = ""):
from starlette.responses import FileResponse
- if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
+ if not any(Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs):
raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
ext = os.path.splitext(filename)[1].lower()
- if ext not in (".png", ".jpg", ".webp"):
- raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.")
+ if ext not in (".png", ".jpg", ".jpeg", ".webp", ".gif"):
+ raise ValueError(f"File cannot be fetched: {filename}. Only png, jpg, webp, and gif.")
# would profit from returning 304
return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
@@ -91,8 +90,8 @@ class ExtraNetworksPage: subdirs = {}
for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
- for root, dirs, files in os.walk(parentdir):
- for dirname in dirs:
+ for root, dirs, _ in sorted(os.walk(parentdir, followlinks=True), key=lambda x: shared.natural_sort_key(x[0])):
+ for dirname in sorted(dirs, key=shared.natural_sort_key):
x = os.path.join(root, dirname)
if not os.path.isdir(x):
@@ -106,6 +105,9 @@ class ExtraNetworksPage: if not is_empty and not subdir.endswith("/"):
subdir = subdir + "/"
+ if ("/." in subdir or subdir.startswith(".")) and not shared.opts.extra_networks_show_hidden_directories:
+ continue
+
subdirs[subdir] = 1
if subdirs:
@@ -148,6 +150,10 @@ class ExtraNetworksPage: return []
def create_html_for_item(self, item, tabname):
+ """
+ Create HTML for card item in tab tabname; can return empty string if the item is not meant to be shown.
+ """
+
preview = item.get("preview", None)
onclick = item.get("onclick", None)
@@ -156,7 +162,7 @@ class ExtraNetworksPage: height = f"height: {shared.opts.extra_networks_card_height}px;" if shared.opts.extra_networks_card_height else ''
width = f"width: {shared.opts.extra_networks_card_width}px;" if shared.opts.extra_networks_card_width else ''
- background_image = f"background-image: url(\"{html.escape(preview)}\");" if preview else ''
+ background_image = f'<img src="{html.escape(preview)}" class="preview" loading="lazy">' if preview else ''
metadata_button = ""
metadata = item.get("metadata")
if metadata:
@@ -170,12 +176,21 @@ class ExtraNetworksPage: if filename.startswith(absdir):
local_path = filename[len(absdir):]
- # if this is true, the item must not be show in the default view, and must instead only be
+ # if this is true, the item must not be shown in the default view, and must instead only be
# shown when searching for it
- serach_only = "/." in local_path or "\\." in local_path
+ if shared.opts.extra_networks_hidden_models == "Always":
+ search_only = False
+ else:
+ search_only = "/." in local_path or "\\." in local_path
+
+ if search_only and shared.opts.extra_networks_hidden_models == "Never":
+ return ""
+
+ sort_keys = " ".join([html.escape(f'data-sort-{k}={v}') for k, v in item.get("sort_keys", {}).items()]).strip()
args = {
- "style": f"'display: none; {height}{width}{background_image}'",
+ "background_image": background_image,
+ "style": f"'display: none; {height}{width}'",
"prompt": item.get("prompt", None),
"tabname": json.dumps(tabname),
"local_preview": json.dumps(item["local_preview"]),
@@ -185,17 +200,30 @@ class ExtraNetworksPage: "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
"search_term": item.get("search_term", ""),
"metadata_button": metadata_button,
- "serach_only": " search_only" if serach_only else "",
+ "search_only": " search_only" if search_only else "",
+ "sort_keys": sort_keys,
}
return self.card_page.format(**args)
+ def get_sort_keys(self, path):
+ """
+ List of default keys used for sorting in the UI.
+ """
+ pth = Path(path)
+ stat = pth.stat()
+ return {
+ "date_created": int(stat.st_ctime or 0),
+ "date_modified": int(stat.st_mtime or 0),
+ "name": pth.name.lower(),
+ }
+
def find_preview(self, path):
"""
Find a preview PNG for a given path (without extension) and call link_preview on it.
"""
- preview_extensions = ["png", "jpg", "webp"]
+ preview_extensions = ["png", "jpg", "jpeg", "webp"]
if shared.opts.samples_format not in preview_extensions:
preview_extensions.append(shared.opts.samples_format)
@@ -220,10 +248,19 @@ class ExtraNetworksPage: return None
-def intialize():
+def initialize():
extra_pages.clear()
+def register_default_pages():
+ from modules.ui_extra_networks_textual_inversion import ExtraNetworksPageTextualInversion
+ from modules.ui_extra_networks_hypernets import ExtraNetworksPageHypernetworks
+ from modules.ui_extra_networks_checkpoints import ExtraNetworksPageCheckpoints
+ register_page(ExtraNetworksPageTextualInversion())
+ register_page(ExtraNetworksPageHypernetworks())
+ register_page(ExtraNetworksPageCheckpoints())
+
+
class ExtraNetworksUi:
def __init__(self):
self.pages = None
@@ -263,18 +300,20 @@ def create_ui(container, button, tabname): ui.stored_extra_pages = pages_in_preferred_order(extra_pages.copy())
ui.tabname = tabname
- with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
+ with gr.Tabs(elem_id=tabname+"_extra_tabs"):
for page in ui.stored_extra_pages:
page_id = page.title.lower().replace(" ", "_")
with gr.Tab(page.title, id=page_id):
elem_id = f"{tabname}_{page_id}_cards_html"
- page_elem = gr.HTML('', elem_id=elem_id)
+ page_elem = gr.HTML('Loading...', elem_id=elem_id)
ui.pages.append(page_elem)
page_elem.change(fn=lambda: None, _js='function(){applyExtraNetworkFilter(' + json.dumps(tabname) + '); return []}', inputs=[], outputs=[])
gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
+ gr.Dropdown(choices=['Default Sort', 'Date Created', 'Date Modified', 'Name'], value='Default Sort', elem_id=tabname+"_extra_sort", multiselect=False, visible=False, show_label=False, interactive=True)
+ gr.Button(up_down_symbol, elem_id=tabname+"_extra_sortorder")
button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh")
ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
@@ -283,13 +322,24 @@ def create_ui(container, button, tabname): def toggle_visibility(is_visible):
is_visible = not is_visible
- if is_visible and not ui.pages_contents:
+ return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary"))
+
+ def fill_tabs(is_empty):
+ """Creates HTML for extra networks' tabs when the extra networks button is clicked for the first time."""
+
+ if not ui.pages_contents:
refresh()
- return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary")), *ui.pages_contents
+ if is_empty:
+ return True, *ui.pages_contents
+
+ return True, *[gr.update() for _ in ui.pages_contents]
state_visible = gr.State(value=False)
- button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button, *ui.pages])
+ button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button], show_progress=False)
+
+ state_empty = gr.State(value=True)
+ button.click(fn=fill_tabs, inputs=[state_empty], outputs=[state_empty, *ui.pages], show_progress=False)
def refresh():
for pg in ui.stored_extra_pages:
@@ -327,18 +377,13 @@ def setup_ui(ui, gallery): is_allowed = False
for extra_page in ui.stored_extra_pages:
- if any([path_is_parent(x, filename) for x in extra_page.allowed_directories_for_previews()]):
+ if any(path_is_parent(x, filename) for x in extra_page.allowed_directories_for_previews()):
is_allowed = True
break
assert is_allowed, f'writing to {filename} is not allowed'
- if geninfo:
- pnginfo_data = PngImagePlugin.PngInfo()
- pnginfo_data.add_text('parameters', geninfo)
- image.save(filename, pnginfo=pnginfo_data)
- else:
- image.save(filename)
+ save_image_with_geninfo(image, geninfo, filename)
return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py index a17aa9c9..8b9ab71b 100644 --- a/modules/ui_extra_networks_checkpoints.py +++ b/modules/ui_extra_networks_checkpoints.py @@ -14,7 +14,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage): def list_items(self):
checkpoint: sd_models.CheckpointInfo
- for name, checkpoint in sd_models.checkpoints_list.items():
+ for index, (name, checkpoint) in enumerate(sd_models.checkpoints_list.items()):
path, ext = os.path.splitext(checkpoint.filename)
yield {
"name": checkpoint.name_for_extra,
@@ -24,6 +24,8 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage): "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
"onclick": '"' + html.escape(f"""return selectCheckpoint({json.dumps(name)})""") + '"',
"local_preview": f"{path}.{shared.opts.samples_format}",
+ "sort_keys": {'default': index, **self.get_sort_keys(checkpoint.filename)},
+
}
def allowed_directories_for_previews(self):
diff --git a/modules/ui_extra_networks_hypernets.py b/modules/ui_extra_networks_hypernets.py index 6187e000..7c19b532 100644 --- a/modules/ui_extra_networks_hypernets.py +++ b/modules/ui_extra_networks_hypernets.py @@ -12,7 +12,7 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage): shared.reload_hypernetworks()
def list_items(self):
- for name, path in shared.hypernetworks.items():
+ for index, (name, path) in enumerate(shared.hypernetworks.items()):
path, ext = os.path.splitext(path)
yield {
@@ -23,6 +23,8 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage): "search_term": self.search_terms_from_path(path),
"prompt": json.dumps(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
"local_preview": f"{path}.preview.{shared.opts.samples_format}",
+ "sort_keys": {'default': index, **self.get_sort_keys(path + ext)},
+
}
def allowed_directories_for_previews(self):
diff --git a/modules/ui_extra_networks_textual_inversion.py b/modules/ui_extra_networks_textual_inversion.py index 6944d559..58a61c55 100644 --- a/modules/ui_extra_networks_textual_inversion.py +++ b/modules/ui_extra_networks_textual_inversion.py @@ -13,7 +13,7 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage): sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
def list_items(self):
- for embedding in sd_hijack.model_hijack.embedding_db.word_embeddings.values():
+ for index, embedding in enumerate(sd_hijack.model_hijack.embedding_db.word_embeddings.values()):
path, ext = os.path.splitext(embedding.filename)
yield {
"name": embedding.name,
@@ -23,6 +23,8 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage): "search_term": self.search_terms_from_path(embedding.filename),
"prompt": json.dumps(embedding.name),
"local_preview": f"{path}.preview.{shared.opts.samples_format}",
+ "sort_keys": {'default': index, **self.get_sort_keys(embedding.filename)},
+
}
def allowed_directories_for_previews(self):
diff --git a/modules/ui_gradio_extensions.py b/modules/ui_gradio_extensions.py new file mode 100644 index 00000000..b824b113 --- /dev/null +++ b/modules/ui_gradio_extensions.py @@ -0,0 +1,69 @@ +import os
+import gradio as gr
+
+from modules import localization, shared, scripts
+from modules.paths import script_path, data_path
+
+
+def webpath(fn):
+ if fn.startswith(script_path):
+ web_path = os.path.relpath(fn, script_path).replace('\\', '/')
+ else:
+ web_path = os.path.abspath(fn)
+
+ return f'file={web_path}?{os.path.getmtime(fn)}'
+
+
+def javascript_html():
+ # Ensure localization is in `window` before scripts
+ head = f'<script type="text/javascript">{localization.localization_js(shared.opts.localization)}</script>\n'
+
+ script_js = os.path.join(script_path, "script.js")
+ head += f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
+
+ for script in scripts.list_scripts("javascript", ".js"):
+ head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
+
+ for script in scripts.list_scripts("javascript", ".mjs"):
+ head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
+
+ if shared.cmd_opts.theme:
+ head += f'<script type="text/javascript">set_theme(\"{shared.cmd_opts.theme}\");</script>\n'
+
+ return head
+
+
+def css_html():
+ head = ""
+
+ def stylesheet(fn):
+ return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
+
+ for cssfile in scripts.list_files_with_name("style.css"):
+ if not os.path.isfile(cssfile):
+ continue
+
+ head += stylesheet(cssfile)
+
+ if os.path.exists(os.path.join(data_path, "user.css")):
+ head += stylesheet(os.path.join(data_path, "user.css"))
+
+ return head
+
+
+def reload_javascript():
+ js = javascript_html()
+ css = css_html()
+
+ def template_response(*args, **kwargs):
+ res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
+ res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
+ res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
+ res.init_headers()
+ return res
+
+ gr.routes.templates.TemplateResponse = template_response
+
+
+if not hasattr(shared, 'GradioTemplateResponseOriginal'):
+ shared.GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse
diff --git a/modules/ui_loadsave.py b/modules/ui_loadsave.py new file mode 100644 index 00000000..0052a5cc --- /dev/null +++ b/modules/ui_loadsave.py @@ -0,0 +1,210 @@ +import json
+import os
+
+import gradio as gr
+
+from modules import errors
+from modules.ui_components import ToolButton
+
+
+class UiLoadsave:
+ """allows saving and restorig default values for gradio components"""
+
+ def __init__(self, filename):
+ self.filename = filename
+ self.ui_settings = {}
+ self.component_mapping = {}
+ self.error_loading = False
+ self.finalized_ui = False
+
+ self.ui_defaults_view = None
+ self.ui_defaults_apply = None
+ self.ui_defaults_review = None
+
+ try:
+ if os.path.exists(self.filename):
+ self.ui_settings = self.read_from_file()
+ except Exception as e:
+ self.error_loading = True
+ errors.display(e, "loading settings")
+
+ def add_component(self, path, x):
+ """adds component to the registry of tracked components"""
+
+ assert not self.finalized_ui
+
+ def apply_field(obj, field, condition=None, init_field=None):
+ key = f"{path}/{field}"
+
+ if getattr(obj, 'custom_script_source', None) is not None:
+ key = f"customscript/{obj.custom_script_source}/{key}"
+
+ if getattr(obj, 'do_not_save_to_config', False):
+ return
+
+ saved_value = self.ui_settings.get(key, None)
+ if saved_value is None:
+ self.ui_settings[key] = getattr(obj, field)
+ elif condition and not condition(saved_value):
+ pass
+ else:
+ setattr(obj, field, saved_value)
+ if init_field is not None:
+ init_field(saved_value)
+
+ if field == 'value' and key not in self.component_mapping:
+ self.component_mapping[key] = x
+
+ if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton, gr.Button] and x.visible:
+ apply_field(x, 'visible')
+
+ if type(x) == gr.Slider:
+ apply_field(x, 'value')
+ apply_field(x, 'minimum')
+ apply_field(x, 'maximum')
+ apply_field(x, 'step')
+
+ if type(x) == gr.Radio:
+ apply_field(x, 'value', lambda val: val in x.choices)
+
+ if type(x) == gr.Checkbox:
+ apply_field(x, 'value')
+
+ if type(x) == gr.Textbox:
+ apply_field(x, 'value')
+
+ if type(x) == gr.Number:
+ apply_field(x, 'value')
+
+ if type(x) == gr.Dropdown:
+ def check_dropdown(val):
+ if getattr(x, 'multiselect', False):
+ return all(value in x.choices for value in val)
+ else:
+ return val in x.choices
+
+ apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
+
+ def check_tab_id(tab_id):
+ tab_items = list(filter(lambda e: isinstance(e, gr.TabItem), x.children))
+ if type(tab_id) == str:
+ tab_ids = [t.id for t in tab_items]
+ return tab_id in tab_ids
+ elif type(tab_id) == int:
+ return 0 <= tab_id < len(tab_items)
+ else:
+ return False
+
+ if type(x) == gr.Tabs:
+ apply_field(x, 'selected', check_tab_id)
+
+ def add_block(self, x, path=""):
+ """adds all components inside a gradio block x to the registry of tracked components"""
+
+ if hasattr(x, 'children'):
+ if isinstance(x, gr.Tabs) and x.elem_id is not None:
+ # Tabs element can't have a label, have to use elem_id instead
+ self.add_component(f"{path}/Tabs@{x.elem_id}", x)
+ for c in x.children:
+ self.add_block(c, path)
+ elif x.label is not None:
+ self.add_component(f"{path}/{x.label}", x)
+ elif isinstance(x, gr.Button) and x.value is not None:
+ self.add_component(f"{path}/{x.value}", x)
+
+ def read_from_file(self):
+ with open(self.filename, "r", encoding="utf8") as file:
+ return json.load(file)
+
+ def write_to_file(self, current_ui_settings):
+ with open(self.filename, "w", encoding="utf8") as file:
+ json.dump(current_ui_settings, file, indent=4)
+
+ def dump_defaults(self):
+ """saves default values to a file unless tjhe file is present and there was an error loading default values at start"""
+
+ if self.error_loading and os.path.exists(self.filename):
+ return
+
+ self.write_to_file(self.ui_settings)
+
+ def iter_changes(self, current_ui_settings, values):
+ """
+ given a dictionary with defaults from a file and current values from gradio elements, returns
+ an iterator over tuples of values that are not the same between the file and the current;
+ tuple contents are: path, old value, new value
+ """
+
+ for (path, component), new_value in zip(self.component_mapping.items(), values):
+ old_value = current_ui_settings.get(path)
+
+ choices = getattr(component, 'choices', None)
+ if isinstance(new_value, int) and choices:
+ if new_value >= len(choices):
+ continue
+
+ new_value = choices[new_value]
+
+ if new_value == old_value:
+ continue
+
+ if old_value is None and new_value == '' or new_value == []:
+ continue
+
+ yield path, old_value, new_value
+
+ def ui_view(self, *values):
+ text = ["<table><thead><tr><th>Path</th><th>Old value</th><th>New value</th></thead><tbody>"]
+
+ for path, old_value, new_value in self.iter_changes(self.read_from_file(), values):
+ if old_value is None:
+ old_value = "<span class='ui-defaults-none'>None</span>"
+
+ text.append(f"<tr><td>{path}</td><td>{old_value}</td><td>{new_value}</td></tr>")
+
+ if len(text) == 1:
+ text.append("<tr><td colspan=3>No changes</td></tr>")
+
+ text.append("</tbody>")
+ return "".join(text)
+
+ def ui_apply(self, *values):
+ num_changed = 0
+
+ current_ui_settings = self.read_from_file()
+
+ for path, _, new_value in self.iter_changes(current_ui_settings.copy(), values):
+ num_changed += 1
+ current_ui_settings[path] = new_value
+
+ if num_changed == 0:
+ return "No changes."
+
+ self.write_to_file(current_ui_settings)
+
+ return f"Wrote {num_changed} changes."
+
+ def create_ui(self):
+ """creates ui elements for editing defaults UI, without adding any logic to them"""
+
+ gr.HTML(
+ f"This page allows you to change default values in UI elements on other tabs.<br />"
+ f"Make your changes, press 'View changes' to review the changed default values,<br />"
+ f"then press 'Apply' to write them to {self.filename}.<br />"
+ f"New defaults will apply after you restart the UI.<br />"
+ )
+
+ with gr.Row():
+ self.ui_defaults_view = gr.Button(value='View changes', elem_id="ui_defaults_view", variant="secondary")
+ self.ui_defaults_apply = gr.Button(value='Apply', elem_id="ui_defaults_apply", variant="primary")
+
+ self.ui_defaults_review = gr.HTML("")
+
+ def setup_ui(self):
+ """adds logic to elements created with create_ui; all add_block class must be made before this"""
+
+ assert not self.finalized_ui
+ self.finalized_ui = True
+
+ self.ui_defaults_view.click(fn=self.ui_view, inputs=list(self.component_mapping.values()), outputs=[self.ui_defaults_review])
+ self.ui_defaults_apply.click(fn=self.ui_apply, inputs=list(self.component_mapping.values()), outputs=[self.ui_defaults_review])
diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py index f25639e5..c7dc1154 100644 --- a/modules/ui_postprocessing.py +++ b/modules/ui_postprocessing.py @@ -1,5 +1,5 @@ import gradio as gr
-from modules import scripts_postprocessing, scripts, shared, gfpgan_model, codeformer_model, ui_common, postprocessing, call_queue
+from modules import scripts, shared, ui_common, postprocessing, call_queue
import modules.generation_parameters_copypaste as parameters_copypaste
diff --git a/modules/ui_settings.py b/modules/ui_settings.py new file mode 100644 index 00000000..0c560b30 --- /dev/null +++ b/modules/ui_settings.py @@ -0,0 +1,289 @@ +import gradio as gr
+
+from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo
+from modules.call_queue import wrap_gradio_call
+from modules.shared import opts
+from modules.ui_components import FormRow
+from modules.ui_gradio_extensions import reload_javascript
+
+
+def get_value_for_setting(key):
+ value = getattr(opts, key)
+
+ info = opts.data_labels[key]
+ args = info.component_args() if callable(info.component_args) else info.component_args or {}
+ args = {k: v for k, v in args.items() if k not in {'precision'}}
+
+ return gr.update(value=value, **args)
+
+
+def create_setting_component(key, is_quicksettings=False):
+ def fun():
+ return opts.data[key] if key in opts.data else opts.data_labels[key].default
+
+ info = opts.data_labels[key]
+ t = type(info.default)
+
+ args = info.component_args() if callable(info.component_args) else info.component_args
+
+ if info.component is not None:
+ comp = info.component
+ elif t == str:
+ comp = gr.Textbox
+ elif t == int:
+ comp = gr.Number
+ elif t == bool:
+ comp = gr.Checkbox
+ else:
+ raise Exception(f'bad options item type: {t} for key {key}')
+
+ elem_id = f"setting_{key}"
+
+ if info.refresh is not None:
+ if is_quicksettings:
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+ ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
+ else:
+ with FormRow():
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+ ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
+ else:
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+
+ return res
+
+
+class UiSettings:
+ submit = None
+ result = None
+ interface = None
+ components = None
+ component_dict = None
+ dummy_component = None
+ quicksettings_list = None
+ quicksettings_names = None
+ text_settings = None
+
+ def run_settings(self, *args):
+ changed = []
+
+ for key, value, comp in zip(opts.data_labels.keys(), args, self.components):
+ assert comp == self.dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+
+ for key, value, comp in zip(opts.data_labels.keys(), args, self.components):
+ if comp == self.dummy_component:
+ continue
+
+ if opts.set(key, value):
+ changed.append(key)
+
+ try:
+ opts.save(shared.config_filename)
+ except RuntimeError:
+ return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+ return opts.dumpjson(), f'{len(changed)} settings changed{": " if changed else ""}{", ".join(changed)}.'
+
+ def run_settings_single(self, value, key):
+ if not opts.same_type(value, opts.data_labels[key].default):
+ return gr.update(visible=True), opts.dumpjson()
+
+ if not opts.set(key, value):
+ return gr.update(value=getattr(opts, key)), opts.dumpjson()
+
+ opts.save(shared.config_filename)
+
+ return get_value_for_setting(key), opts.dumpjson()
+
+ def create_ui(self, loadsave, dummy_component):
+ self.components = []
+ self.component_dict = {}
+ self.dummy_component = dummy_component
+
+ shared.settings_components = self.component_dict
+
+ script_callbacks.ui_settings_callback()
+ opts.reorder()
+
+ with gr.Blocks(analytics_enabled=False) as settings_interface:
+ with gr.Row():
+ with gr.Column(scale=6):
+ self.submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
+ with gr.Column():
+ restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
+
+ self.result = gr.HTML(elem_id="settings_result")
+
+ self.quicksettings_names = opts.quicksettings_list
+ self.quicksettings_names = {x: i for i, x in enumerate(self.quicksettings_names) if x != 'quicksettings'}
+
+ self.quicksettings_list = []
+
+ previous_section = None
+ current_tab = None
+ current_row = None
+ with gr.Tabs(elem_id="settings"):
+ for i, (k, item) in enumerate(opts.data_labels.items()):
+ section_must_be_skipped = item.section[0] is None
+
+ if previous_section != item.section and not section_must_be_skipped:
+ elem_id, text = item.section
+
+ if current_tab is not None:
+ current_row.__exit__()
+ current_tab.__exit__()
+
+ gr.Group()
+ current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
+ current_tab.__enter__()
+ current_row = gr.Column(variant='compact')
+ current_row.__enter__()
+
+ previous_section = item.section
+
+ if k in self.quicksettings_names and not shared.cmd_opts.freeze_settings:
+ self.quicksettings_list.append((i, k, item))
+ self.components.append(dummy_component)
+ elif section_must_be_skipped:
+ self.components.append(dummy_component)
+ else:
+ component = create_setting_component(k)
+ self.component_dict[k] = component
+ self.components.append(component)
+
+ if current_tab is not None:
+ current_row.__exit__()
+ current_tab.__exit__()
+
+ with gr.TabItem("Defaults", id="defaults", elem_id="settings_tab_defaults"):
+ loadsave.create_ui()
+
+ with gr.TabItem("Sysinfo", id="sysinfo", elem_id="settings_tab_sysinfo"):
+ gr.HTML('<a href="./internal/sysinfo-download" class="sysinfo_big_link" download>Download system info</a><br /><a href="./internal/sysinfo">(or open as text in a new page)</a>', elem_id="sysinfo_download")
+
+ with gr.Row():
+ with gr.Column(scale=1):
+ sysinfo_check_file = gr.File(label="Check system info for validity", type='binary')
+ with gr.Column(scale=1):
+ sysinfo_check_output = gr.HTML("", elem_id="sysinfo_validity")
+ with gr.Column(scale=100):
+ pass
+
+ with gr.TabItem("Actions", id="actions", elem_id="settings_tab_actions"):
+ request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+ download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+ reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
+ with gr.Row():
+ unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
+ reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
+
+ with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
+ gr.HTML(shared.html("licenses.html"), elem_id="licenses")
+
+ gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
+
+ self.text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
+
+ unload_sd_model.click(
+ fn=sd_models.unload_model_weights,
+ inputs=[],
+ outputs=[]
+ )
+
+ reload_sd_model.click(
+ fn=sd_models.reload_model_weights,
+ inputs=[],
+ outputs=[]
+ )
+
+ request_notifications.click(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[],
+ _js='function(){}'
+ )
+
+ download_localization.click(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[],
+ _js='download_localization'
+ )
+
+ def reload_scripts():
+ scripts.reload_script_body_only()
+ reload_javascript() # need to refresh the html page
+
+ reload_script_bodies.click(
+ fn=reload_scripts,
+ inputs=[],
+ outputs=[]
+ )
+
+ restart_gradio.click(
+ fn=shared.state.request_restart,
+ _js='restart_reload',
+ inputs=[],
+ outputs=[],
+ )
+
+ def check_file(x):
+ if x is None:
+ return ''
+
+ if sysinfo.check(x.decode('utf8', errors='ignore')):
+ return 'Valid'
+
+ return 'Invalid'
+
+ sysinfo_check_file.change(
+ fn=check_file,
+ inputs=[sysinfo_check_file],
+ outputs=[sysinfo_check_output],
+ )
+
+ self.interface = settings_interface
+
+ def add_quicksettings(self):
+ with gr.Row(elem_id="quicksettings", variant="compact"):
+ for _i, k, _item in sorted(self.quicksettings_list, key=lambda x: self.quicksettings_names.get(x[1], x[0])):
+ component = create_setting_component(k, is_quicksettings=True)
+ self.component_dict[k] = component
+
+ def add_functionality(self, demo):
+ self.submit.click(
+ fn=wrap_gradio_call(lambda *args: self.run_settings(*args), extra_outputs=[gr.update()]),
+ inputs=self.components,
+ outputs=[self.text_settings, self.result],
+ )
+
+ for _i, k, _item in self.quicksettings_list:
+ component = self.component_dict[k]
+ info = opts.data_labels[k]
+
+ change_handler = component.release if hasattr(component, 'release') else component.change
+ change_handler(
+ fn=lambda value, k=k: self.run_settings_single(value, key=k),
+ inputs=[component],
+ outputs=[component, self.text_settings],
+ show_progress=info.refresh is not None,
+ )
+
+ button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
+ button_set_checkpoint.click(
+ fn=lambda value, _: self.run_settings_single(value, key='sd_model_checkpoint'),
+ _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
+ inputs=[self.component_dict['sd_model_checkpoint'], self.dummy_component],
+ outputs=[self.component_dict['sd_model_checkpoint'], self.text_settings],
+ )
+
+ component_keys = [k for k in opts.data_labels.keys() if k in self.component_dict]
+
+ def get_settings_values():
+ return [get_value_for_setting(key) for key in component_keys]
+
+ demo.load(
+ fn=get_settings_values,
+ inputs=[],
+ outputs=[self.component_dict[k] for k in component_keys],
+ queue=False,
+ )
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py index 46fa9cb0..fb75137e 100644 --- a/modules/ui_tempdir.py +++ b/modules/ui_tempdir.py @@ -3,7 +3,7 @@ import tempfile from collections import namedtuple
from pathlib import Path
-import gradio as gr
+import gradio.components
from PIL import PngImagePlugin
@@ -23,7 +23,7 @@ def register_tmp_file(gradio, filename): def check_tmp_file(gradio, filename):
if hasattr(gradio, 'temp_file_sets'):
- return any([filename in fileset for fileset in gradio.temp_file_sets])
+ return any(filename in fileset for fileset in gradio.temp_file_sets)
if hasattr(gradio, 'temp_dirs'):
return any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in gradio.temp_dirs)
@@ -31,13 +31,16 @@ def check_tmp_file(gradio, filename): return False
-def save_pil_to_file(pil_image, dir=None):
+def save_pil_to_file(self, pil_image, dir=None, format="png"):
already_saved_as = getattr(pil_image, 'already_saved_as', None)
if already_saved_as and os.path.isfile(already_saved_as):
register_tmp_file(shared.demo, already_saved_as)
+ filename = already_saved_as
- file_obj = Savedfile(f'{already_saved_as}?{os.path.getmtime(already_saved_as)}')
- return file_obj
+ if not shared.opts.save_images_add_number:
+ filename += f'?{os.path.getmtime(already_saved_as)}'
+
+ return filename
if shared.opts.temp_dir != "":
dir = shared.opts.temp_dir
@@ -51,11 +54,11 @@ def save_pil_to_file(pil_image, dir=None): file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
- return file_obj
+ return file_obj.name
# override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
+gradio.components.IOComponent.pil_to_temp_file = save_pil_to_file
def on_tmpdir_changed():
@@ -72,7 +75,7 @@ def cleanup_tmpdr(): if temp_dir == "" or not os.path.isdir(temp_dir):
return
- for root, dirs, files in os.walk(temp_dir, topdown=False):
+ for root, _, files in os.walk(temp_dir, topdown=False):
for name in files:
_, extension = os.path.splitext(name)
if extension != ".png":
diff --git a/modules/upscaler.py b/modules/upscaler.py index e2eaa730..e682bbaa 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -2,8 +2,6 @@ import os from abc import abstractmethod import PIL -import numpy as np -import torch from PIL import Image import modules.shared @@ -36,6 +34,7 @@ class Upscaler: self.half = not modules.shared.cmd_opts.no_half self.pre_pad = 0 self.mod_scale = None + self.model_download_path = None if self.model_path is None and self.name: self.model_path = os.path.join(shared.models_path, self.name) @@ -43,9 +42,9 @@ class Upscaler: os.makedirs(self.model_path, exist_ok=True) try: - import cv2 + import cv2 # noqa: F401 self.can_tile = True - except: + except Exception: pass @abstractmethod @@ -54,10 +53,10 @@ class Upscaler: def upscale(self, img: PIL.Image, scale, selected_model: str = None): self.scale = scale - dest_w = int(img.width * scale) - dest_h = int(img.height * scale) + dest_w = int((img.width * scale) // 8 * 8) + dest_h = int((img.height * scale) // 8 * 8) - for i in range(3): + for _ in range(3): shape = (img.width, img.height) img = self.do_upscale(img, selected_model) @@ -78,7 +77,7 @@ class Upscaler: pass def find_models(self, ext_filter=None) -> list: - return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path) + return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path, ext_filter=ext_filter) def update_status(self, prompt): print(f"\nextras: {prompt}", file=shared.progress_print_out) diff --git a/modules/xlmr.py b/modules/xlmr.py index beab3fdf..a407a3ca 100644 --- a/modules/xlmr.py +++ b/modules/xlmr.py @@ -1,4 +1,4 @@ -from transformers import BertPreTrainedModel,BertModel,BertConfig +from transformers import BertPreTrainedModel, BertConfig import torch.nn as nn import torch from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig @@ -28,7 +28,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel): config_class = BertSeriesConfig def __init__(self, config=None, **kargs): - # modify initialization for autoloading + # modify initialization for autoloading if config is None: config = XLMRobertaConfig() config.attention_probs_dropout_prob= 0.1 @@ -74,7 +74,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel): text["attention_mask"] = torch.tensor( text['attention_mask']).to(device) features = self(**text) - return features['projection_state'] + return features['projection_state'] def forward( self, @@ -134,4 +134,4 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel): class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation): base_model_prefix = 'roberta' - config_class= RobertaSeriesConfig
\ No newline at end of file + config_class= RobertaSeriesConfig |