df -f #产看硬盘空间
free -h #查看内存信息
nvidia-smi # 查看显卡信息

python --version

conda install python=3.12

pip install --pre torch torchvision torchaudio --index-url [https://download.pytorch.org/whl/nightly/cu126](https://download.pytorch.org/whl/nightly/cu126)

git clone https://github.com/comfyanonymous/ComfyUI.git

pip install -r requirements.txt

python main.py

# concurrent-download.py
import os
from concurrent.futures import ThreadPoolExecutor, as_completed
from huggingface_hub import hf_hub_download

os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
# 定义要下载的模型和文件列表
models_and_files = [
    {"model_name": "Kijai/flux-fp8", "file_name": "flux1-dev-fp8.safetensors"},
    {"model_name": "comfyanonymous/flux_text_encoders", "file_name": "t5xxl_fp8_e4m3fn.safetensors"},
    {"model_name": "comfyanonymous/flux_text_encoders", "file_name": "clip_l.safetensors"},
    {"model_name": "black-forest-labs/FLUX.1-schnell", "file_name": "ae.safetensors"},
]

# 定义下载函数
def download_file(model_name, file_name):
    try:
        # 下载文件
        downloaded_file_path = hf_hub_download(repo_id=model_name, filename=file_name)
        return f"成功下载: {model_name}/{file_name} -> {downloaded_file_path}"
    except Exception as e:
        return f"下载失败: {model_name}/{file_name}, 错误: {str(e)}"

# 并发下载多个文件
def download_models_concurrently(models_and_files, max_workers=5):
    results = []
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        # 提交下载任务
        future_to_task = {
            executor.submit(download_file, task["model_name"], task["file_name"]): task
            for task in models_and_files
        }
        # 等待任务完成并收集结果
        for future in as_completed(future_to_task):
            task = future_to_task[future]
            try:
                result = future.result()
                results.append(result)
            except Exception as e:
                results.append(f"任务出错: {task}, 错误: {str(e)}")
    return results

# 主函数
if __name__ == "__main__":
    # 后台运行（将输出保存到日志文件）
    log_file = "download_log.txt"
    with open(log_file, "w") as f:
        f.write("开始下载模型文件...\n")

    # 执行并发下载
    results = download_models_concurrently(models_and_files)

    # 将结果写入日志文件
    with open(log_file, "a") as f:
        for result in results:
            f.write(result + "\n")
        f.write("所有任务已完成。\n")

    print(f"下载完成！详细信息请查看日志文件: {os.path.abspath(log_file)}")

nohup python concurrent-download.py > output.log 2>&1 &

df -f #产看硬盘空间
free -h #查看内存信息
nvidia-smi # 查看显卡信息

python --version

conda install python=3.12

pip install --pre torch torchvision torchaudio --index-url [https://download.pytorch.org/whl/nightly/cu126](https://download.pytorch.org/whl/nightly/cu126)

git clone https://github.com/comfyanonymous/ComfyUI.git

pip install -r requirements.txt

python main.py

# concurrent-download.py
import os
from concurrent.futures import ThreadPoolExecutor, as_completed
from huggingface_hub import hf_hub_download

os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
# 定义要下载的模型和文件列表
models_and_files = [
    {"model_name": "Kijai/flux-fp8", "file_name": "flux1-dev-fp8.safetensors"},
    {"model_name": "comfyanonymous/flux_text_encoders", "file_name": "t5xxl_fp8_e4m3fn.safetensors"},
    {"model_name": "comfyanonymous/flux_text_encoders", "file_name": "clip_l.safetensors"},
    {"model_name": "black-forest-labs/FLUX.1-schnell", "file_name": "ae.safetensors"},
]

# 定义下载函数
def download_file(model_name, file_name):
    try:
        # 下载文件
        downloaded_file_path = hf_hub_download(repo_id=model_name, filename=file_name)
        return f"成功下载: {model_name}/{file_name} -> {downloaded_file_path}"
    except Exception as e:
        return f"下载失败: {model_name}/{file_name}, 错误: {str(e)}"

# 并发下载多个文件
def download_models_concurrently(models_and_files, max_workers=5):
    results = []
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        # 提交下载任务
        future_to_task = {
            executor.submit(download_file, task["model_name"], task["file_name"]): task
            for task in models_and_files
        }
        # 等待任务完成并收集结果
        for future in as_completed(future_to_task):
            task = future_to_task[future]
            try:
                result = future.result()
                results.append(result)
            except Exception as e:
                results.append(f"任务出错: {task}, 错误: {str(e)}")
    return results

# 主函数
if __name__ == "__main__":
    # 后台运行（将输出保存到日志文件）
    log_file = "download_log.txt"
    with open(log_file, "w") as f:
        f.write("开始下载模型文件...\n")

    # 执行并发下载
    results = download_models_concurrently(models_and_files)

    # 将结果写入日志文件
    with open(log_file, "a") as f:
        for result in results:
            f.write(result + "\n")
        f.write("所有任务已完成。\n")

    print(f"下载完成！详细信息请查看日志文件: {os.path.abspath(log_file)}")

nohup python concurrent-download.py > output.log 2>&1 &