main.py

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}"