delete

app.py

@@ -1,274 +0,0 @@
-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}"