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 from modules.realesrgan_model import get_realesrgan_models from modules import devices from typing import List 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])}") 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 init_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 if shared.cmd_opts.api_log: print('API {t} {code} {prot}/{ver} {method} {p} {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'), p = req.scope.get('path', 'err'), duration = duration, )) return res class Api: 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 init_api_middleware(self.app) self.queue_lock = queue_lock 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 "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index), "do_not_save_samples": True, "do_not_save_grid": True } ) 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) 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": x.config} 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)) 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 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 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 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) uvicorn.run(self.app, host=server_name, port=port)