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app.py

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+from app_settings import AppSettings
+from utils import show_system_info
+import constants
+from argparse import ArgumentParser
+from context import Context
+from constants import APP_VERSION, LCM_DEFAULT_MODEL_OPENVINO
+from models.interface_types import InterfaceType
+from constants import DEVICE
+from state import get_settings
+import traceback
+
+
+from fastapi import FastAPI,Body
+
+import uvicorn
+import json
+import logging
+from PIL import Image
+import time
+
+from diffusers.utils import load_image
+import base64
+import io
+from datetime import datetime
+
+from typing import Any
+from backend.models.lcmdiffusion_setting import DiffusionTask
+
+from frontend.utils import is_reshape_required
+from concurrent.futures import ThreadPoolExecutor
+
+
+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",
+#     "--share",
+#     action="store_true",
+#     help="Create sharable link(Web UI)",
+#     required=False,
+# )
+# group = parser.add_mutually_exclusive_group(required=False)
+# group.add_argument(
+#     "-g",
+#     "--gui",
+#     action="store_true",
+#     help="Start desktop GUI",
+# )
+# group.add_argument(
+#     "-w",
+#     "--webui",
+#     action="store_true",
+#     help="Start Web UI",
+# )
+# group.add_argument(
+#     "-r",
+#     "--realtime",
+#     action="store_true",
+#     help="Start realtime inference UI(experimental)",
+# )
+# group.add_argument(
+#     "-v",
+#     "--version",
+#     action="store_true",
+#     help="Version",
+# )
+# parser.add_argument(
+#     "--lcm_model_id",
+#     type=str,
+#     help="Model ID or path,Default SimianLuo/LCM_Dreamshaper_v7",
+#     default="SimianLuo/LCM_Dreamshaper_v7",
+# )
+# parser.add_argument(
+#     "--prompt",
+#     type=str,
+#     help="Describe the image you want to generate",
+# )
+# parser.add_argument(
+#     "--image_height",
+#     type=int,
+#     help="Height of the image",
+#     default=512,
+# )
+# parser.add_argument(
+#     "--image_width",
+#     type=int,
+#     help="Width of the image",
+#     default=512,
+# )
+# parser.add_argument(
+#     "--inference_steps",
+#     type=int,
+#     help="Number of steps,default : 4",
+#     default=4,
+# )
+# parser.add_argument(
+#     "--guidance_scale",
+#     type=int,
+#     help="Guidance scale,default : 1.0",
+#     default=1.0,
+# )
+
+# parser.add_argument(
+#     "--number_of_images",
+#     type=int,
+#     help="Number of images to generate ,default : 1",
+#     default=1,
+# )
+# parser.add_argument(
+#     "--seed",
+#     type=int,
+#     help="Seed,default : -1 (disabled) ",
+#     default=-1,
+# )
+# parser.add_argument(
+#     "--use_openvino",
+#     action="store_true",
+#     help="Use OpenVINO model",
+# )
+
+# parser.add_argument(
+#     "--use_offline_model",
+#     action="store_true",
+#     help="Use offline model",
+# )
+# parser.add_argument(
+#     "--use_safety_checker",
+#     action="store_false",
+#     help="Use safety checker",
+# )
+# parser.add_argument(
+#     "--use_lcm_lora",
+#     action="store_true",
+#     help="Use LCM-LoRA",
+# )
+# parser.add_argument(
+#     "--base_model_id",
+#     type=str,
+#     help="LCM LoRA base model ID,Default Lykon/dreamshaper-8",
+#     default="Lykon/dreamshaper-8",
+# )
+# parser.add_argument(
+#     "--lcm_lora_id",
+#     type=str,
+#     help="LCM LoRA model ID,Default latent-consistency/lcm-lora-sdv1-5",
+#     default="latent-consistency/lcm-lora-sdv1-5",
+# )
+# parser.add_argument(
+#     "-i",
+#     "--interactive",
+#     action="store_true",
+#     help="Interactive CLI mode",
+# )
+# parser.add_argument(
+#     "--use_tiny_auto_encoder",
+#     action="store_true",
+#     help="Use tiny auto encoder for SD (TAESD)",
+# )
+# args = parser.parse_args()
+
+# if args.version:
+#     print(APP_VERSION)
+#     exit()
+
+# parser.print_help()
+show_system_info()
+print(f"Using device : {constants.DEVICE}")
+app_settings = get_settings()
+
+print(f"Found {len(app_settings.lcm_models)} LCM models in config/lcm-models.txt")
+print(
+    f"Found {len(app_settings.stable_diffsuion_models)} stable diffusion models in config/stable-diffusion-models.txt"
+)
+print(
+    f"Found {len(app_settings.lcm_lora_models)} LCM-LoRA models in config/lcm-lora-models.txt"
+)
+print(
+    f"Found {len(app_settings.openvino_lcm_models)} OpenVINO LCM models in config/openvino-lcm-models.txt"
+)
+app_settings.settings.lcm_diffusion_setting.use_openvino = True
+# from frontend.webui.ui import start_webui
+
+# print("Starting web UI mode")
+# start_webui(
+#     args.share,
+# )
+
+# app = FastAPI(name="mutilParam")
+print("我执行了")
+# @app.get("/")
+# def root():
+#     return {"API": "hello"}
+
+# @app.post("/img2img")
+# async def predict(prompt=Body(...),imgbase64data=Body(...),negative_prompt=Body(None),userId=Body(None)):
+#     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 = image1
+#     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()
+#     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)
+#     return output_image_base64
+        
+    
+# @app.post("/predict")
+# async def predict(prompt=Body(...)):
+#   return f"您好,{prompt}"