本地化部署AI实施方法及测试#

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# gpt-oss-20b
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lms get openai/gpt-oss-20b
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# gpt-oss 120b
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lms get openai/gpt-oss-120b

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pip install --upgrade diffusers transformers scipy
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

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python -c "import torch; print(torch.cuda.is_available())"

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import torch
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from diffusers import StableDiffusionPipeline
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def main():
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    model_id = "CompVis/stable-diffusion-v1-4"
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    device = "cuda" if torch.cuda.is_available() else "cpu"
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    # 加载模型，float16 省显存（显卡显存低于4GB一定要用）
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    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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    pipe = pipe.to(device)
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    # 如果显存有限，启用切片减少占用
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    pipe.enable_attention_slicing()
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    prompt = "a photo of an astronaut riding a horse on mars"
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    print(f"Generating image for prompt: {prompt}")
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    image = pipe(prompt).images[0]
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    # 保存生成的图片
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    image.save("astronaut_rides_horse.png")
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    print("Image saved as astronaut_rides_horse.png")
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if __name__ == "__main__":
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    main()

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import os
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import torch
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from diffusers import StableDiffusionPipeline
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# 先设置环境变量（可选）
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os.environ["HF_HOME"] = "D:/models" # 可以更改到自己想到的地方
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os.environ["TRANSFORMERS_CACHE"] = "D:/models" # 路径同上
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def main():
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    device = "cuda" if torch.cuda.is_available() else "cpu"
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    pipe = StableDiffusionPipeline.from_pretrained(
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        "CompVis/stable-diffusion-v1-4",
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        cache_dir="D:/models",
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        torch_dtype=torch.float16
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    )
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    pipe = pipe.to(device)
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    pipe.enable_attention_slicing()
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    prompt = "a photo of an astronaut riding a horse on mars"
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    image = pipe(prompt).images[0]
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    image.save("astronaut_rides_horse.png")
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    print("Image saved.")
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if __name__ == "__main__":
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    main()

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image.save("astronaut_rides_horse.png")

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image.save("XXX/Desktop/astronaut_rides_horse.png")

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import os
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import torch
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from diffusers import StableDiffusionPipeline
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# -----------------------------
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# 1. 设置模型路径和缓存目录
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# -----------------------------
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MODEL_PATH = r"D:/models/stable-diffusion-v1-4"  # 本地模型目录
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OUTPUT_DIR = r"D:/models/output"                 # 输出图片保存目录
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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# -----------------------------
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# 2. 初始化模型 (只做一次)
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# -----------------------------
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = StableDiffusionPipeline.from_pretrained(
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    MODEL_PATH,
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    torch_dtype=torch.float16  # 节省显存
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).to(device)
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pipe.enable_attention_slicing()  # 显存优化，尤其显卡小于8GB
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# -----------------------------
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# 3. 定义生成图片函数
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# -----------------------------
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def generate_image(prompt, filename):
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    """生成图片并保存"""
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    image = pipe(prompt).images[0]
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    save_path = os.path.join(OUTPUT_DIR, filename)
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    image.save(save_path)
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    print(f"Image saved: {save_path}")
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# -----------------------------
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# 4. 批量生成示例
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# -----------------------------
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prompts = [
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    ("a fantasy landscape with dragons", "dragons.png"),
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    ("a futuristic city at sunset", "city.png"),
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    ("a cute cat wearing a spacesuit", "cat.png")
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]
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for prompt_text, filename in prompts:
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    generate_image(prompt_text, filename)
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print("All images generated!")

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D:.
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├─blobs
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├─refs
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└─snapshots
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    └─133a221b8aa7292a167afc5127cb63fb5005638b
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        ├─feature_extractor
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        ├─safety_checker
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        ├─scheduler
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        │  └─.ipynb_checkpoints
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        ├─text_encoder
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        ├─tokenizer
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        ├─model_index.json
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        ├─unet
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        └─vae