from modules.api.processing import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.sd_samplers import all_samplers import modules.shared as shared import uvicorn from fastapi import APIRouter, HTTPException import json import io import base64 from modules.api.models import * from PIL import Image from modules.extras import run_extras from gradio import processing_utils 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="Upscaler not found") sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None) def img_to_base64(img: str): buffer = io.BytesIO() img.save(buffer, format="png") return base64.b64encode(buffer.getvalue()) def base64_to_bytes(base64Img: str): if "," in base64Img: base64Img = base64Img.split(",")[1] return io.BytesIO(base64.b64decode(base64Img)) def base64_to_images(base64Imgs: list[str]): imgs = [] for img in base64Imgs: img = Image.open(base64_to_bytes(img)) imgs.append(img) return imgs class ImageToImageResponse(BaseModel): images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") parameters: Json info: Json class Api: def __init__(self, app, queue_lock): self.router = APIRouter() self.app = app self.queue_lock = queue_lock self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse) self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"]) self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse) self.app.add_api_route("/sdapi/v1/extra-batch-image", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse) def __base64_to_image(self, base64_string): # if has a comma, deal with prefix if "," in base64_string: base64_string = base64_string.split(",")[1] imgdata = base64.b64decode(base64_string) # convert base64 to PIL image return Image.open(io.BytesIO(imgdata)) def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): sampler_index = sampler_to_index(txt2imgreq.sampler_index) if sampler_index is None: raise HTTPException(status_code=404, detail="Sampler not found") populate = txt2imgreq.copy(update={ # Override __init__ params "sd_model": shared.sd_model, "sampler_index": sampler_index[0], "do_not_save_samples": True, "do_not_save_grid": True } ) p = StableDiffusionProcessingTxt2Img(**vars(populate)) # Override object param with self.queue_lock: processed = process_images(p) b64images = list(map(img_to_base64, processed.images)) return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info)) def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI): sampler_index = sampler_to_index(img2imgreq.sampler_index) if sampler_index is None: raise HTTPException(status_code=404, detail="Sampler not found") 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 = self.__base64_to_image(mask) populate = img2imgreq.copy(update={ # Override __init__ params "sd_model": shared.sd_model, "sampler_index": sampler_index[0], "do_not_save_samples": True, "do_not_save_grid": True, "mask": mask } ) p = StableDiffusionProcessingImg2Img(**vars(populate)) imgs = [] for img in init_images: img = self.__base64_to_image(img) imgs = [img] * p.batch_size p.init_images = imgs # Override object param with self.queue_lock: processed = process_images(p) b64images = [] for i in processed.images: buffer = io.BytesIO() i.save(buffer, format="png") b64images.append(base64.b64encode(buffer.getvalue())) return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info)) def extras_single_image_api(self, req: ExtrasSingleImageRequest): upscaler1Index = upscaler_to_index(req.upscaler_1) upscaler2Index = upscaler_to_index(req.upscaler_2) reqDict = vars(req) reqDict.pop('upscaler_1') reqDict.pop('upscaler_2') reqDict['image'] = processing_utils.decode_base64_to_file(reqDict['image']) with self.queue_lock: result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="") return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0]), html_info_x=result[1], html_info=result[2]) def extras_batch_images_api(self, req: ExtrasBatchImagesRequest): upscaler1Index = upscaler_to_index(req.upscaler_1) upscaler2Index = upscaler_to_index(req.upscaler_2) reqDict = vars(req) reqDict.pop('upscaler_1') reqDict.pop('upscaler_2') reqDict['image_folder'] = list(map(processing_utils.decode_base64_to_file, reqDict['imageList'])) reqDict.pop('imageList') with self.queue_lock: result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=1, image="", input_dir="", output_dir="") return ExtrasBatchImagesResponse(images=list(map(processing_utils.encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2]) def extras_folder_processing_api(self): raise NotImplementedError def pnginfoapi(self): raise NotImplementedError def launch(self, server_name, port): self.app.include_router(self.router) uvicorn.run(self.app, host=server_name, port=port)