main.py

from fastapi import FastAPI,Body

import uvicorn
import json
import logging
from PIL import Image
import time
from constants import DESCRIPTION, LOGO
from model import get_pipeline
from utils import replace_background
from diffusers.utils import load_image
import base64
import io
from datetime import datetime

app = FastAPI(name="mutilParam")
pipeline = get_pipeline()

#Endpoints 
#Root endpoints
@app.get("/")
def root():
    return {"API": "Sum of 2 Squares"}
    
@app.post("/img2img")
async def predict(prompt=Body(...),imgbase64data=Body(...),userId=Body(None)):
    pipeline = get_pipeline()
    MAX_QUEUE_SIZE = 4
    start = time.time()
    print("参数",imgbase64data,prompt)
    image_data = base64.b64decode(imgbase64data)
    image1 = Image.open(io.BytesIO(image_data))
    w, h = image1.size
    newW = 512
    newH = int(h * newW / w)
    img = image1.resize((newW, newH))  
    end1 = time.time()
    now = datetime.now()
    print(now)
    print("图像:", img.size)
    print("加载管道:", end1 - start)
    result = pipeline(
        prompt=prompt,
        image=image1,
        strength=0.6,
        seed=10,
        width=256,
        height=256,
        guidance_scale=1,
        num_inference_steps=4,
    )
    output_image = result.images[0]
    end2 = time.time()
    print("测试",output_image)
    print("s生成完成:", end2 - end1)    
    # 将图片对象转换为bytes
    image_data = io.BytesIO()

    # 将图像保存到BytesIO对象中，格式为JPEG
    output_image.save(image_data, format='JPEG')
    
    # 将BytesIO对象的内容转换为字节串
    image_data_bytes = image_data.getvalue()
    output_image_base64 = base64.b64encode(image_data_bytes).decode('utf-8')
    print("完成的图片:", output_image_base64)
    logger = logging.getLogger('')
    logger.info(output_image_base64)
    return output_image_base64
        
    
@app.post("/predict")
async def predict(prompt=Body(...)):
  return f"您好,{prompt}"