Stable Diffusion 1.5 - 昇腾 NPU 推理部署

sd15-anime-npu/
├── model/                    # 模型文件（diffusers 格式）
│   ├── unet/                 # UNet 权重 (686 keys, ~2GB)
│   ├── vae/                  # VAE 权重 (248 keys, 335MB)
│   ├── text_encoder/         # Text Encoder (197 keys, 492MB)
│   ├── tokenizer/            # Tokenizer
│   └── scheduler/            # 调度器配置
├── inference.py              # NPU 推理脚本
├── verify_npu.py             # NPU 验证脚本
├── output/                   # 输出目录 (NPU 生成图像)
└── README.md                 # 本文档

python3 -c "import torch; import torch_npu; print(f'NPU: {torch.npu.device_count()}')"

cd /opt/atomgit/sd15-anime-npu
python3 inference.py

============================================================
SD1.5 Ascend NPU Inference
============================================================
NPU available: True
NPU device: Ascend910_9362
NPU count: 2
Loading model from ./model ...
Pipeline ready on npu:0 (dtype=torch.float16)
Generating: 'a cute anime girl with blue hair, masterpiece, best quality...' (20 steps, seed=42)
100%|██████████| 20/20 [00:00<00:00, 22.45it/s]
Done in 1.2s (0.8M pixels)
Saved to ./output/npu_demo_1.png
Generating: 'beautiful anime landscape, cherry blossoms, sunset...' (20 steps, seed=42)
100%|██████████| 20/20 [00:00<00:00, 26.56it/s]
Done in 0.8s (0.8M pixels)
Saved to ./output/npu_demo_2.png
Generating: 'anime cat girl, cute, detailed...' (20 steps, seed=42)
100%|██████████| 20/20 [00:00<00:00, 26.54it/s]
Done in 0.8s (0.8M pixels)
Saved to ./output/npu_demo_3.png

✅ All generations complete!

============================================================
SD1.5 Anime - NPU Verification
============================================================

[CHECK] NPU available: True
[CHECK] NPU count: 2
[CHECK] NPU name: Ascend910_9362
[CHECK] torch version: 2.9.0+cpu
[CHECK] torch_npu version: 2.9.0.post1+gitee7ba04

[INFO] Loading model from: ./model
Loading pipeline components...: 100%|██████████| 6/6 [00:01<00:00,  3.53it/s]
[INFO] Model loaded in 3.4s

[TEST] Running: anime_girl_cherry
[TEST] Prompt: 1girl, silver hair, blue eyes, school uniform, cherry blosso...
100%|██████████| 20/20 [00:00<00:00, 21.12it/s]
[TEST] Time: 1.3s | Hash: 4aad68960565 | Size: 529933 bytes

[TEST] Running: anime_boy_sunset
[TEST] Prompt: 1boy, black hair, red eyes, standing on a hill, sunset, dram...
100%|██████████| 20/20 [00:00<00:00, 24.97it/s]
[TEST] Time: 0.9s | Hash: d5b1a4904566 | Size: 342069 bytes

[TEST] Running: anime_landscape
[TEST] Prompt: no humans, scenery, mountain, lake, reflection, autumn, colo...
100%|██████████| 20/20 [00:00<00:00, 25.01it/s]
[TEST] Time: 0.9s | Hash: 3c35f6d65259 | Size: 503506 bytes

============================================================
VERIFICATION SUMMARY
============================================================
Test Case                 Time(s)    Hash            Size         Status
------------------------------------------------------------------------
anime_girl_cherry         1.3        4aad68960565    529933       PASS ✅
anime_boy_sunset          0.9        d5b1a4904566    342069       PASS ✅
anime_landscape           0.9        3c35f6d65259    503506       PASS ✅

NPU Memory: allocated=2.01GB, reserved=3.12GB

Overall: PASS ✅

$ file output/*.png
output/npu_demo_1.png:    PNG image data, 512 x 512, 8-bit/color RGB, non-interlaced
output/npu_demo_2.png:    PNG image data, 512 x 512, 8-bit/color RGB, non-interlaced
output/npu_demo_3.png:    PNG image data, 512 x 512, 8-bit/color RGB, non-interlaced
output/verify_anime_girl_cherry.png: PNG image data, 512 x 512, 8-bit/color RGB
output/verify_anime_boy_sunset.png:  PNG image data, 512 x 512, 8-bit/color RGB
output/verify_anime_landscape.png:   PNG image data, 512 x 512, 8-bit/color RGB

=== NPU 图像质量分析 ===
npu_demo_1.png: mean=130.8, std=75.8, unique colors=256
npu_demo_2.png: mean=108.0, std=54.7, unique colors=256
npu_demo_3.png: mean=113.2, std=75.9, unique colors=256

✅ 颜色分布正常，非全黑/全白
✅ 256 级灰度完整
✅ 图像内容多样（风景、人物、动物）

# 方案1: 使用纯 CPU PyTorch
pip install torch --index-url https://download.pytorch.org/whl/cpu

# 方案2: 使用 Docker 隔离
docker run -it python:3.11-slim pip install torch diffusers

# 方案3: 使用 ModelScope 官方示例
modelscope download --model AI-ModelScope/stable-diffusion-v1-5 --output_dir ./ref

from inference import load_pipeline, generate
import torch

# 加载 pipeline
pipe = load_pipeline(device="npu:0", dtype=torch.float16)

# 生成图像
image = generate(
    pipe,
    prompt="your text prompt",
    num_inference_steps=20,  # 推理步数
    guidance_scale=7.5,      # 引导系数
    height=512,
    width=512,
    seed=42,                 # 随机种子（可选）
)

image.save("output/result.png")