add all

main.py

@@ -5,6 +5,14 @@ from fastapi import FastAPI,Request
 import uvicorn
 import json
 
+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
+
 app = FastAPI()
 
 
@@ -16,8 +24,42 @@ def root():
     
 @app.post("/img2img")
 async def predict(url:str,prompt:str):
-  
-  return f"您好,{url+prompt}"    
+    MAX_QUEUE_SIZE = 4
+    start = time.time()
+    pipeline = get_pipeline()
+    
+    url = "https://img2.baidu.com/it/u=1845675188,2679793929&fm=253&fmt=auto&app=138&f=JPEG?w=667&h=500"
+    prompt = "a nice Comfortable and clean. According to Baidu Education Information, the adjectives for a room include: comfortable, clean, beautiful, spacious, warm, quiet, luxurious, pleasant, exquisite, and warm ,colorful, light room width sofa,8k"
+    
+    init_image = load_image(url).convert("RGB")
+    # image1 = replace_background(init_image.resize((256, 256)))
+    w, h = init_image.size
+    newW = 512
+    newH = int(h * newW / w)
+    img = init_image.resize((newW, newH))  
+    end1 = time.time()
+    print("加载管道:", end1 - start)
+    result = pipeline(
+        prompt=prompt,
+        image=img,
+        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)
+    
+    output_image.save("./imageclm5.png")
+    with open(output_image, 'rb') as image_file:
+        encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
+        print(encoded_string)
+        return encoded_string
+        
     
 @app.post("/predict")
 async def predict(request:Request):