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import sys |
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import os |
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import asyncio |
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import base64 |
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import logging |
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from io import BytesIO |
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from pathlib import Path |
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import uvicorn |
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from config import Config |
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from fastapi import FastAPI |
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from fastapi.middleware.cors import CORSMiddleware |
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from fastapi.staticfiles import StaticFiles |
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from PIL import Image |
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from pydantic import BaseModel |
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sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "..")) |
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from utils.wrapper import StreamDiffusionWrapper |
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logger = logging.getLogger("uvicorn") |
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PROJECT_DIR = Path(__file__).parent.parent |
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class PredictInputModel(BaseModel): |
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""" |
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The input model for the /predict endpoint. |
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""" |
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prompt: str |
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class PredictResponseModel(BaseModel): |
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""" |
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The response model for the /predict endpoint. |
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""" |
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base64_image: str |
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class UpdatePromptResponseModel(BaseModel): |
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""" |
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The response model for the /update_prompt endpoint. |
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""" |
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prompt: str |
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class Api: |
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def __init__(self, config: Config) -> None: |
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""" |
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Initialize the API. |
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Parameters |
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---------- |
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config : Config |
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The configuration. |
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""" |
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self.config = config |
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self.stream_diffusion = StreamDiffusionWrapper( |
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mode=config.mode, |
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model_id_or_path=config.model_id_or_path, |
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lcm_lora_id=config.lcm_lora_id, |
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vae_id=config.vae_id, |
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device=config.device, |
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dtype=config.dtype, |
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acceleration=config.acceleration, |
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t_index_list=config.t_index_list, |
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warmup=config.warmup, |
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use_safety_checker=config.use_safety_checker, |
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cfg_type="none", |
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) |
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self.app = FastAPI() |
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self.app.add_api_route( |
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"/api/predict", |
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self._predict, |
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methods=["POST"], |
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response_model=PredictResponseModel, |
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) |
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self.app.add_middleware( |
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CORSMiddleware, |
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allow_origins=["*"], |
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allow_credentials=True, |
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allow_methods=["*"], |
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allow_headers=["*"], |
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) |
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self.app.mount( |
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"/", StaticFiles(directory="../view/build", html=True), name="public" |
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) |
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self._predict_lock = asyncio.Lock() |
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self._update_prompt_lock = asyncio.Lock() |
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async def _predict(self, inp: PredictInputModel) -> PredictResponseModel: |
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""" |
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Predict an image and return. |
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Parameters |
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---------- |
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inp : PredictInputModel |
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The input. |
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Returns |
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------- |
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PredictResponseModel |
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The prediction result. |
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""" |
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async with self._predict_lock: |
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return PredictResponseModel( |
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base64_image=self._pil_to_base64( |
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self.stream_diffusion(prompt=inp.prompt) |
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) |
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) |
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def _pil_to_base64(self, image: Image.Image, format: str = "JPEG") -> bytes: |
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""" |
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Convert a PIL image to base64. |
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Parameters |
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---------- |
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image : Image.Image |
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The PIL image. |
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format : str |
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The image format, by default "JPEG". |
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Returns |
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------- |
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bytes |
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The base64 image. |
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""" |
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buffered = BytesIO() |
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image.convert("RGB").save(buffered, format=format) |
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return base64.b64encode(buffered.getvalue()).decode("ascii") |
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def _base64_to_pil(self, base64_image: str) -> Image.Image: |
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""" |
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Convert a base64 image to PIL. |
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Parameters |
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---------- |
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base64_image : str |
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The base64 image. |
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Returns |
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------- |
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Image.Image |
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The PIL image. |
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""" |
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if "base64," in base64_image: |
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base64_image = base64_image.split("base64,")[1] |
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return Image.open(BytesIO(base64.b64decode(base64_image))).convert("RGB") |
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if __name__ == "__main__": |
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from config import Config |
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config = Config() |
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uvicorn.run( |
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Api(config).app, |
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host=config.host, |
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port=config.port, |
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workers=config.workers, |
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) |
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