change flask

app.py

@@ -8,6 +8,38 @@ from models.interface_types import InterfaceType
 from constants import DEVICE
 from state import get_settings
 
+
+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")
+
+from typing import Any
+from backend.models.lcmdiffusion_setting import DiffusionTask
+
+from frontend.utils import is_reshape_required
+from concurrent.futures import ThreadPoolExecutor
+
+app_settings = get_settings()
+
+context = Context(InterfaceType.WEBUI)
+previous_width = 0
+previous_height = 0
+previous_model_id = ""
+previous_num_of_images = 0
+
 parser = ArgumentParser(description=f"FAST SD CPU {constants.APP_VERSION}")
 parser.add_argument(
     "-s",
@@ -161,3 +193,92 @@ print("Starting web UI mode")
 start_webui(
     args.share,
 )
+
+app.get("/")
+def root():
+    return {"API": "hello"}
+    
+@app.post("/img2img")
+async def predict(prompt=Body(...),imgbase64data=Body(...),negative_prompt=Body(None),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)
+    global previous_height, previous_width, previous_model_id, previous_num_of_images, app_settings
+    
+    app_settings.settings.lcm_diffusion_setting.prompt = prompt
+    app_settings.settings.lcm_diffusion_setting.negative_prompt = negative_prompt
+    app_settings.settings.lcm_diffusion_setting.init_image = img
+    app_settings.settings.lcm_diffusion_setting.strength = 0.6
+   
+    app_settings.settings.lcm_diffusion_setting.diffusion_task = (
+        DiffusionTask.image_to_image.value
+    )
+    model_id = app_settings.settings.lcm_diffusion_setting.openvino_lcm_model_id
+    reshape = False
+    app_settings.settings.lcm_diffusion_setting.image_height=newH
+    image_width = app_settings.settings.lcm_diffusion_setting.image_width
+    image_height = app_settings.settings.lcm_diffusion_setting.image_height
+    num_images = app_settings.settings.lcm_diffusion_setting.number_of_images
+    reshape = is_reshape_required(
+        previous_width,
+        image_width,
+        previous_height,
+        image_height,
+        previous_model_id,
+        model_id,
+        previous_num_of_images,
+        num_images,
+    )
+
+
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        future = executor.submit(
+            context.generate_text_to_image,
+            app_settings.settings,
+            reshape,
+            DEVICE,
+        )
+        images = future.result()
+    # images = context.generate_text_to_image(
+    #     app_settings.settings,
+    #     reshape,
+    #     DEVICE,
+    # )
+    previous_width = image_width
+    previous_height = image_height
+    previous_model_id = model_id
+    previous_num_of_images = num_images
+    output_image = 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}"