131 lines
3.5 KiB
Python
131 lines
3.5 KiB
Python
import os
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import json
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from enum import Enum
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import socket
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import logging
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from contextlib import closing
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from invoke import task
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from rich import print
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from rich.prompt import IntPrompt, Prompt, Confirm
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from rich.logging import RichHandler
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FORMAT = "%(message)s"
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logging.basicConfig(
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level="INFO", format=FORMAT, datefmt="[%X]", handlers=[RichHandler()]
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)
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log = logging.getLogger("lama-cleaner")
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def find_free_port() -> int:
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with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
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s.bind(("", 0))
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s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
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return s.getsockname()[1]
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CONFIG_PATH = "config.json"
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class MODEL(str, Enum):
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SD15 = "sd1.5"
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LAMA = "lama"
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class DEVICE(str, Enum):
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CUDA = "cuda"
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CPU = "cpu"
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@task
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def info(c):
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print("Environment information".center(60, "-"))
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try:
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c.run("git --version")
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c.run("conda --version")
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c.run("which python")
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c.run("python --version")
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c.run("which pip")
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c.run("pip --version")
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c.run('pip list | grep "torch\|lama\|diffusers\|opencv\|cuda"')
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except:
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pass
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print("-" * 60)
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@task(pre=[info])
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def config(c, disable_device_choice=False):
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# TODO: 提示选择模型，选择设备，端口，host
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# 如果是 sd 模型，提示接受条款和输入 huggingface token
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model = Prompt.ask(
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"Choice model", choices=[MODEL.SD15, MODEL.LAMA], default=MODEL.SD15
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)
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hf_access_token = ""
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if model == MODEL.SD15:
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while True:
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hf_access_token = Prompt.ask(
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"Huggingface access token (https://huggingface.co/docs/hub/security-tokens)"
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)
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if hf_access_token == "":
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log.warning("Access token is required to download model")
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else:
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break
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if disable_device_choice:
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device = DEVICE.CPU
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else:
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device = Prompt.ask(
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"Choice device", choices=[DEVICE.CUDA, DEVICE.CPU], default=DEVICE.CUDA
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)
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if device == DEVICE.CUDA:
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import torch
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if not torch.cuda.is_available():
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log.warning(
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"Did not find CUDA device on your computer, fallback to cpu"
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)
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device = DEVICE.CPU
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desktop = Confirm.ask("Start as desktop app?", default=True)
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configs = {
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"model": model,
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"device": device,
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"hf_access_token": hf_access_token,
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"desktop": desktop,
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}
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log.info(f"Save config to {CONFIG_PATH}")
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with open(CONFIG_PATH, "w", encoding="utf-8") as f:
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json.dump(configs, f, indent=2, ensure_ascii=False)
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log.info(f"Config finish, you can close this window.")
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@task(pre=[info])
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def start(c):
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if not os.path.exists(CONFIG_PATH):
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log.info("Config file not exists, please run config.sh first")
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exit()
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log.info(f"Load config from {CONFIG_PATH}")
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with open(CONFIG_PATH, "r", encoding="utf-8") as f:
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configs = json.load(f)
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model = configs["model"]
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device = configs["device"]
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hf_access_token = configs["hf_access_token"]
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desktop = configs["desktop"]
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port = find_free_port()
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log.info(f"Using random port: {port}")
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if desktop:
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c.run(
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f"lama-cleaner --model {model} --device {device} --hf_access_token={hf_access_token} --port {port} --gui --gui-size 1400 900"
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)
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else:
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c.run(
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f"lama-cleaner --model {model} --device {device} --hf_access_token={hf_access_token} --port {port}"
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)
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