support ascend npu

docs/en/Pipeline_Usage/GPU_support.md

@@ -0,0 +1,58 @@
+# GPU/NPU Support
+
+`DiffSynth-Studio` supports various GPUs and NPUs. This document explains how to run model inference and training on these devices.
+
+Before you begin, please follow the [Installation Guide](/docs/en/Pipeline_Usage/Setup.md) to install the required GPU/NPU dependencies.
+
+## NVIDIA GPU
+
+All sample code provided by this project supports NVIDIA GPUs by default, requiring no additional modifications.
+
+## AMD GPU
+
+AMD provides PyTorch packages based on ROCm, so most models can run without code changes. A small number of models may not be compatible due to their reliance on CUDA-specific instructions.
+
+## Ascend NPU
+
+When using Ascend NPU, you need to replace `"cuda"` with `"npu"` in your code.
+
+For example, here is the inference code for **Wan2.1-T2V-1.3B**, modified for Ascend NPU:
+
+```diff
+import torch
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+-   "preparing_device": "cuda",
++   "preparing_device": "npu",
+    "computation_dtype": torch.bfloat16,
+-   "computation_device": "cuda",
++   "computation_device": "npu",
+}
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+-   device="cuda",
++   device="npu",
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
+-   vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
++   vram_limit=torch.npu.mem_get_info("npu")[1] / (1024 ** 3) - 2,
+)
+
+video = pipe(
+    prompt="Documentary-style photography: a lively puppy running swiftly across lush green grass. The puppy has brownish-yellow fur, upright ears, and an alert, joyful expression. Sunlight bathes its body, making the fur appear exceptionally soft and shiny. The background is an open field with occasional wildflowers, and faint blue sky with scattered white clouds in the distance. Strong perspective captures the motion of the running puppy and the vitality of the surrounding grass. Mid-shot, side-moving viewpoint.",
+    negative_prompt="Overly vibrant colors, overexposed, static, blurry details, subtitles, artistic style, painting, still image, overall grayish tone, worst quality, low quality, JPEG artifacts, ugly, distorted, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, fused fingers, motionless scene, cluttered background, three legs, many people in background, walking backward",
+    seed=0, tiled=True,
+)
+save_video(video, "video.mp4", fps=15, quality=5)
+```

docs/en/Pipeline_Usage/Setup.md

@@ -36,6 +36,8 @@ Ascend NPU support is provided via the `torch-npu` package. Taking version `2.1.
 pip install torch-npu==2.1.0.post17
 ```
 
+When using Ascend NPU, please replace `"cuda"` with `"npu"` in your Python code. For details, see [NPU Support](/docs/en/Pipeline_Usage/GPU_support.md#ascend-npu).
+
 ## Other Installation Issues
 
 If you encounter issues during installation, they may be caused by upstream dependencies. Please refer to the documentation for these packages: