diff options
Diffstat (limited to 'modules/api')
| -rw-r--r-- | modules/api/api.py | 482 | ||||
| -rw-r--r-- | modules/api/models.py | 261 | ||||
| -rw-r--r-- | modules/api/processing.py | 99 |
3 files changed, 699 insertions, 143 deletions
diff --git a/modules/api/api.py b/modules/api/api.py index 5b0c934e..48a70a44 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -1,67 +1,461 @@ -from modules.api.processing import StableDiffusionProcessingAPI -from modules.processing import StableDiffusionProcessingTxt2Img, process_images -from modules.sd_samplers import all_samplers -from modules.extras import run_pnginfo -import modules.shared as shared -import uvicorn -from fastapi import Body, APIRouter, HTTPException -from fastapi.responses import JSONResponse -from pydantic import BaseModel, Field, Json -import json -import io import base64 +import io +import time +import datetime +import uvicorn +from threading import Lock +from io import BytesIO +from gradio.processing_utils import decode_base64_to_file +from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response +from fastapi.security import HTTPBasic, HTTPBasicCredentials +from secrets import compare_digest + +import modules.shared as shared +from modules import sd_samplers, deepbooru, sd_hijack +from modules.api.models import * +from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images +from modules.extras import run_extras, run_pnginfo +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, find_checkpoint_config +from modules.realesrgan_model import get_realesrgan_models +from modules import devices +from typing import List -sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None) +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 on of these: {' , '.join([x.name for x in sd_upscalers])}") -class TextToImageResponse(BaseModel): - images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") - parameters: Json - info: Json + +def validate_sampler_name(name): + config = sd_samplers.all_samplers_map.get(name, None) + if config is None: + raise HTTPException(status_code=404, detail="Sampler not found") + + return name + +def setUpscalers(req: dict): + reqDict = vars(req) + reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1) + reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2) + reqDict.pop('upscaler_1') + reqDict.pop('upscaler_2') + return reqDict + +def decode_base64_to_image(encoding): + if encoding.startswith("data:image/"): + encoding = encoding.split(";")[1].split(",")[1] + return Image.open(BytesIO(base64.b64decode(encoding))) + +def encode_pil_to_base64(image): + with io.BytesIO() as output_bytes: + + # Copy any text-only metadata + use_metadata = False + metadata = PngImagePlugin.PngInfo() + for key, value in image.info.items(): + if isinstance(key, str) and isinstance(value, str): + metadata.add_text(key, value) + use_metadata = True + + image.save( + output_bytes, "PNG", pnginfo=(metadata if use_metadata else None) + ) + bytes_data = output_bytes.getvalue() + return base64.b64encode(bytes_data) + +def api_middleware(app: FastAPI): + @app.middleware("http") + async def log_and_time(req: Request, call_next): + ts = time.time() + res: Response = await call_next(req) + duration = str(round(time.time() - ts, 4)) + res.headers["X-Process-Time"] = duration + endpoint = req.scope.get('path', 'err') + if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'): + print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format( + t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"), + code = res.status_code, + ver = req.scope.get('http_version', '0.0'), + cli = req.scope.get('client', ('0:0.0.0', 0))[0], + prot = req.scope.get('scheme', 'err'), + method = req.scope.get('method', 'err'), + endpoint = endpoint, + duration = duration, + )) + return res class Api: - def __init__(self, app, queue_lock): + def __init__(self, app: FastAPI, queue_lock: Lock): + if shared.cmd_opts.api_auth: + self.credentials = dict() + for auth in shared.cmd_opts.api_auth.split(","): + user, password = auth.split(":") + self.credentials[user] = password + self.router = APIRouter() self.app = app self.queue_lock = queue_lock - self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"]) - - def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ): - sampler_index = sampler_to_index(txt2imgreq.sampler_index) - - if sampler_index is None: - raise HTTPException(status_code=404, detail="Sampler not found") - + 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/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.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/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str]) + self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem]) + self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=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) + + def add_api_route(self, path: str, endpoint, **kwargs): + if shared.cmd_opts.api_auth: + return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs) + return self.app.add_api_route(path, endpoint, **kwargs) + + def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())): + if credentials.username in self.credentials: + if compare_digest(credentials.password, self.credentials[credentials.username]): + return True + + raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"}) + + def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): populate = txt2imgreq.copy(update={ # Override __init__ params - "sd_model": shared.sd_model, - "sampler_index": sampler_index[0], + "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index), "do_not_save_samples": True, "do_not_save_grid": True } ) - p = StableDiffusionProcessingTxt2Img(**vars(populate)) - # Override object param + if populate.sampler_name: + populate.sampler_index = None # prevent a warning later on + with self.queue_lock: + p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **vars(populate)) + + shared.state.begin() processed = process_images(p) - - b64images = [] - for i in processed.images: - buffer = io.BytesIO() - i.save(buffer, format="png") - b64images.append(base64.b64encode(buffer.getvalue())) + shared.state.end() + + + b64images = list(map(encode_pil_to_base64, processed.images)) + + return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js()) + + def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI): + init_images = img2imgreq.init_images + if init_images is None: + raise HTTPException(status_code=404, detail="Init image not found") + + mask = img2imgreq.mask + if mask: + mask = decode_base64_to_image(mask) + + populate = img2imgreq.copy(update={ # Override __init__ params + "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index), + "do_not_save_samples": True, + "do_not_save_grid": True, + "mask": mask + } + ) + if populate.sampler_name: + populate.sampler_index = None # prevent a warning later on + + args = vars(populate) + args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine. + + 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] + + shared.state.begin() + processed = process_images(p) + shared.state.end() + + b64images = list(map(encode_pil_to_base64, processed.images)) + + if not img2imgreq.include_init_images: + img2imgreq.init_images = None + img2imgreq.mask = None + + return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js()) + + def extras_single_image_api(self, req: ExtrasSingleImageRequest): + reqDict = setUpscalers(req) + + reqDict['image'] = decode_base64_to_image(reqDict['image']) + + with self.queue_lock: + result = 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]) + + def extras_batch_images_api(self, req: ExtrasBatchImagesRequest): + reqDict = setUpscalers(req) + + def prepareFiles(file): + file = decode_base64_to_file(file.data, file_path=file.name) + file.orig_name = file.name + return file + + reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList'])) + reqDict.pop('imageList') + + with self.queue_lock: + result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict) + + return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) + + def pnginfoapi(self, req: PNGInfoRequest): + if(not req.image.strip()): + return PNGInfoResponse(info="") + + result = run_pnginfo(decode_base64_to_image(req.image.strip())) + + return PNGInfoResponse(info=result[1]) + + def progressapi(self, req: 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()) + + # avoid dividing zero + progress = 0.01 + + if shared.state.job_count > 0: + progress += shared.state.job_no / shared.state.job_count + if shared.state.sampling_steps > 0: + progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps + + time_since_start = time.time() - shared.state.time_start + eta = (time_since_start/progress) + eta_relative = eta-time_since_start + + progress = min(progress, 1) + + shared.state.set_current_image() + + current_image = None + if shared.state.current_image and not req.skip_current_image: + current_image = encode_pil_to_base64(shared.state.current_image) + + return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image) + + def interrogateapi(self, interrogatereq: InterrogateRequest): + image_b64 = interrogatereq.image + if image_b64 is None: + raise HTTPException(status_code=404, detail="Image not found") + + img = decode_base64_to_image(image_b64) + img = img.convert('RGB') + + # Override object param + with self.queue_lock: + if interrogatereq.model == "clip": + processed = shared.interrogator.interrogate(img) + elif interrogatereq.model == "deepdanbooru": + processed = deepbooru.model.tag(img) + else: + raise HTTPException(status_code=404, detail="Model not found") + + return InterrogateResponse(caption=processed) + + def interruptapi(self): + shared.state.interrupt() + + return {} + + def skip(self): + shared.state.skip() + + def get_config(self): + options = {} + for key in shared.opts.data.keys(): + metadata = shared.opts.data_labels.get(key) + if(metadata is not None): + options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)}) + else: + options.update({key: shared.opts.data.get(key, None)}) + + return options + + def set_config(self, req: Dict[str, Any]): + for k, v in req.items(): + shared.opts.set(k, v) + + shared.opts.save(shared.config_filename) + return + + def get_cmd_flags(self): + return vars(shared.cmd_opts) + + def get_samplers(self): + return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers] + + def get_upscalers(self): + upscalers = [] + + for upscaler in shared.sd_upscalers: + u = upscaler.scaler + upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url}) + + return upscalers + + def get_sd_models(self): + return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()] + + def get_hypernetworks(self): + return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks] + + def get_face_restorers(self): + return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers] + + def get_realesrgan_models(self): + return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)] + + def get_prompt_styles(self): + styleList = [] + for k in shared.prompt_styles.styles: + style = shared.prompt_styles.styles[k] + styleList.append({"name":style[0], "prompt": style[1], "negative_prompt": style[2]}) + + return styleList + + def get_artists_categories(self): + return shared.artist_db.cats + + def get_artists(self): + return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists] + + def get_embeddings(self): + db = sd_hijack.model_hijack.embedding_db + + def convert_embedding(embedding): + return { + "step": embedding.step, + "sd_checkpoint": embedding.sd_checkpoint, + "sd_checkpoint_name": embedding.sd_checkpoint_name, + "shape": embedding.shape, + "vectors": embedding.vectors, + } + + def convert_embeddings(embeddings): + return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()} + + return { + "loaded": convert_embeddings(db.word_embeddings), + "skipped": convert_embeddings(db.skipped_embeddings), + } + + def refresh_checkpoints(self): + shared.refresh_checkpoints() + + def create_embedding(self, args: dict): + try: + shared.state.begin() + 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 = "create embedding filename: {filename}".format(filename = filename)) + except AssertionError as e: + shared.state.end() + return TrainResponse(info = "create embedding error: {error}".format(error = e)) - return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info)) - - + def create_hypernetwork(self, args: dict): + try: + shared.state.begin() + filename = create_hypernetwork(**args) # create empty embedding + shared.state.end() + return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename)) + except AssertionError as e: + shared.state.end() + return TrainResponse(info = "create hypernetwork error: {error}".format(error = e)) - def img2imgapi(self): - raise NotImplementedError + def preprocess(self, args: dict): + try: + shared.state.begin() + preprocess(**args) # quick operation unless blip/booru interrogation is enabled + shared.state.end() + return PreprocessResponse(info = 'preprocess complete') + except KeyError as e: + shared.state.end() + return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e)) + except AssertionError as e: + shared.state.end() + return PreprocessResponse(info = "preprocess error: {error}".format(error = e)) + except FileNotFoundError as e: + shared.state.end() + return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e)) - def extrasapi(self): - raise NotImplementedError + def train_embedding(self, args: dict): + try: + shared.state.begin() + apply_optimizations = shared.opts.training_xattention_optimizations + error = None + filename = '' + if not apply_optimizations: + sd_hijack.undo_optimizations() + try: + embedding, filename = train_embedding(**args) # can take a long time to complete + except Exception as e: + error = e + finally: + if not apply_optimizations: + sd_hijack.apply_optimizations() + shared.state.end() + return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error)) + except AssertionError as msg: + shared.state.end() + return TrainResponse(info = "train embedding error: {msg}".format(msg = msg)) - def pnginfoapi(self): - raise NotImplementedError + def train_hypernetwork(self, args: dict): + try: + shared.state.begin() + initial_hypernetwork = shared.loaded_hypernetwork + apply_optimizations = shared.opts.training_xattention_optimizations + error = None + filename = '' + if not apply_optimizations: + sd_hijack.undo_optimizations() + try: + hypernetwork, filename = train_hypernetwork(*args) + except Exception as e: + error = e + finally: + shared.loaded_hypernetwork = initial_hypernetwork + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) + if not apply_optimizations: + sd_hijack.apply_optimizations() + shared.state.end() + return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error)) + except AssertionError as msg: + shared.state.end() + return TrainResponse(info = "train embedding error: {error}".format(error = error)) def launch(self, server_name, port): self.app.include_router(self.router) diff --git a/modules/api/models.py b/modules/api/models.py new file mode 100644 index 00000000..4a632c68 --- /dev/null +++ b/modules/api/models.py @@ -0,0 +1,261 @@ +import inspect +from pydantic import BaseModel, Field, create_model +from typing import Any, Optional +from typing_extensions import Literal +from inflection import underscore +from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img +from modules.shared import sd_upscalers, opts, parser +from typing import Dict, List + +API_NOT_ALLOWED = [ + "self", + "kwargs", + "sd_model", + "outpath_samples", + "outpath_grids", + "sampler_index", + "do_not_save_samples", + "do_not_save_grid", + "extra_generation_params", + "overlay_images", + "do_not_reload_embeddings", + "seed_enable_extras", + "prompt_for_display", + "sampler_noise_scheduler_override", + "ddim_discretize" +] + +class ModelDef(BaseModel): + """Assistance Class for Pydantic Dynamic Model Generation""" + + field: str + field_alias: str + field_type: Any + field_value: Any + field_exclude: bool = False + + +class PydanticModelGenerator: + """ + Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about: + source_data is a snapshot of the default values produced by the class + params are the names of the actual keys required by __init__ + """ + + def __init__( + self, + model_name: str = None, + class_instance = None, + additional_fields = None, + ): + def field_type_generator(k, v): + # field_type = str if not overrides.get(k) else overrides[k]["type"] + # print(k, v.annotation, v.default) + field_type = v.annotation + + return Optional[field_type] + + def merge_class_params(class_): + all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_))) + parameters = {} + for classes in all_classes: + parameters = {**parameters, **inspect.signature(classes.__init__).parameters} + return parameters + + + self._model_name = model_name + self._class_data = merge_class_params(class_instance) + + self._model_def = [ + ModelDef( + field=underscore(k), + field_alias=k, + field_type=field_type_generator(k, v), + field_value=v.default + ) + for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED + ] + + for fields in additional_fields: + self._model_def.append(ModelDef( + field=underscore(fields["key"]), + field_alias=fields["key"], + field_type=fields["type"], + field_value=fields["default"], + field_exclude=fields["exclude"] if "exclude" in fields else False)) + + def generate_model(self): + """ + Creates a pydantic BaseModel + from the json and overrides provided at initialization + """ + fields = { + d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def + } + DynamicModel = create_model(self._model_name, **fields) + DynamicModel.__config__.allow_population_by_field_name = True + DynamicModel.__config__.allow_mutation = True + return DynamicModel + +StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( + "StableDiffusionProcessingTxt2Img", + StableDiffusionProcessingTxt2Img, + [{"key": "sampler_index", "type": str, "default": "Euler"}] +).generate_model() + +StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( + "StableDiffusionProcessingImg2Img", + StableDiffusionProcessingImg2Img, + [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}] +).generate_model() + +class TextToImageResponse(BaseModel): + images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + parameters: dict + info: str + +class ImageToImageResponse(BaseModel): + images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + parameters: dict + info: str + +class ExtrasBaseRequest(BaseModel): + resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.") + show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?") + gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.") + codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.") + codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.") + upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.") + upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.") + upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.") + upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?") + upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") + upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") + extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.") + upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?") + +class ExtraBaseResponse(BaseModel): + html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.") + +class ExtrasSingleImageRequest(ExtrasBaseRequest): + image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.") + +class ExtrasSingleImageResponse(ExtraBaseResponse): + image: str = Field(default=None, title="Image", description="The generated image in base64 format.") + +class FileData(BaseModel): + data: str = Field(title="File data", description="Base64 representation of the file") + name: str = Field(title="File name") + +class ExtrasBatchImagesRequest(ExtrasBaseRequest): + imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings") + +class ExtrasBatchImagesResponse(ExtraBaseResponse): + images: List[str] = Field(title="Images", description="The generated images in base64 format.") + +class PNGInfoRequest(BaseModel): + image: str = Field(title="Image", description="The base64 encoded PNG image") + +class PNGInfoResponse(BaseModel): + info: str = Field(title="Image info", description="A string with all the info the image had") + +class ProgressRequest(BaseModel): + skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization") + +class ProgressResponse(BaseModel): + progress: float = Field(title="Progress", description="The progress with a range of 0 to 1") + eta_relative: float = Field(title="ETA in secs") + state: dict = Field(title="State", description="The current state snapshot") + current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.") + +class InterrogateRequest(BaseModel): + image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.") + model: str = Field(default="clip", title="Model", description="The interrogate model used.") + +class InterrogateResponse(BaseModel): + caption: str = Field(default=None, title="Caption", description="The generated caption for the image.") + +class TrainResponse(BaseModel): + info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.") + +class CreateResponse(BaseModel): + info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.") + +class PreprocessResponse(BaseModel): + info: str = Field(title="Preprocess info", description="Response string from preprocessing task.") + +fields = {} +for key, metadata in opts.data_labels.items(): + value = opts.data.get(key) + optType = opts.typemap.get(type(metadata.default), type(value)) + + if (metadata is not None): + fields.update({key: (Optional[optType], Field( + default=metadata.default ,description=metadata.label))}) + else: + fields.update({key: (Optional[optType], Field())}) + +OptionsModel = create_model("Options", **fields) + +flags = {} +_options = vars(parser)['_option_string_actions'] +for key in _options: + if(_options[key].dest != 'help'): + flag = _options[key] + _type = str + if _options[key].default is not None: _type = type(_options[key].default) + flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))}) + +FlagsModel = create_model("Flags", **flags) + +class SamplerItem(BaseModel): + name: str = Field(title="Name") + aliases: List[str] = Field(title="Aliases") + options: Dict[str, str] = Field(title="Options") + +class UpscalerItem(BaseModel): + name: str = Field(title="Name") + model_name: Optional[str] = Field(title="Model Name") + model_path: Optional[str] = Field(title="Path") + model_url: Optional[str] = Field(title="URL") + +class SDModelItem(BaseModel): + title: str = Field(title="Title") + model_name: str = Field(title="Model Name") + hash: str = Field(title="Hash") + filename: str = Field(title="Filename") + config: str = Field(title="Config file") + +class HypernetworkItem(BaseModel): + name: str = Field(title="Name") + path: Optional[str] = Field(title="Path") + +class FaceRestorerItem(BaseModel): + name: str = Field(title="Name") + cmd_dir: Optional[str] = Field(title="Path") + +class RealesrganItem(BaseModel): + name: str = Field(title="Name") + path: Optional[str] = Field(title="Path") + scale: Optional[int] = Field(title="Scale") + +class PromptStyleItem(BaseModel): + name: str = Field(title="Name") + prompt: Optional[str] = Field(title="Prompt") + negative_prompt: Optional[str] = Field(title="Negative Prompt") + +class ArtistItem(BaseModel): + name: str = Field(title="Name") + score: float = Field(title="Score") + category: str = Field(title="Category") + +class EmbeddingItem(BaseModel): + step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available") + sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available") + sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead") + shape: int = Field(title="Shape", description="The length of each individual vector in the embedding") + vectors: int = Field(title="Vectors", description="The number of vectors in the embedding") + +class EmbeddingsResponse(BaseModel): + loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model") + skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
\ No newline at end of file diff --git a/modules/api/processing.py b/modules/api/processing.py deleted file mode 100644 index 4c541241..00000000 --- a/modules/api/processing.py +++ /dev/null @@ -1,99 +0,0 @@ -from inflection import underscore -from typing import Any, Dict, Optional -from pydantic import BaseModel, Field, create_model -from modules.processing import StableDiffusionProcessingTxt2Img -import inspect - - -API_NOT_ALLOWED = [ - "self", - "kwargs", - "sd_model", - "outpath_samples", - "outpath_grids", - "sampler_index", - "do_not_save_samples", - "do_not_save_grid", - "extra_generation_params", - "overlay_images", - "do_not_reload_embeddings", - "seed_enable_extras", - "prompt_for_display", - "sampler_noise_scheduler_override", - "ddim_discretize" -] - -class ModelDef(BaseModel): - """Assistance Class for Pydantic Dynamic Model Generation""" - - field: str - field_alias: str - field_type: Any - field_value: Any - - -class PydanticModelGenerator: - """ - Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about: - source_data is a snapshot of the default values produced by the class - params are the names of the actual keys required by __init__ - """ - - def __init__( - self, - model_name: str = None, - class_instance = None, - additional_fields = None, - ): - def field_type_generator(k, v): - # field_type = str if not overrides.get(k) else overrides[k]["type"] - # print(k, v.annotation, v.default) - field_type = v.annotation - - return Optional[field_type] - - def merge_class_params(class_): - all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_))) - parameters = {} - for classes in all_classes: - parameters = {**parameters, **inspect.signature(classes.__init__).parameters} - return parameters - - - self._model_name = model_name - self._class_data = merge_class_params(class_instance) - self._model_def = [ - ModelDef( - field=underscore(k), - field_alias=k, - field_type=field_type_generator(k, v), - field_value=v.default - ) - for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED - ] - - for fields in additional_fields: - self._model_def.append(ModelDef( - field=underscore(fields["key"]), - field_alias=fields["key"], - field_type=fields["type"], - field_value=fields["default"])) - - def generate_model(self): - """ - Creates a pydantic BaseModel - from the json and overrides provided at initialization - """ - fields = { - d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def - } - DynamicModel = create_model(self._model_name, **fields) - DynamicModel.__config__.allow_population_by_field_name = True - DynamicModel.__config__.allow_mutation = True - return DynamicModel - -StableDiffusionProcessingAPI = PydanticModelGenerator( - "StableDiffusionProcessingTxt2Img", - StableDiffusionProcessingTxt2Img, - [{"key": "sampler_index", "type": str, "default": "Euler"}] -).generate_model()
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