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Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly enhances the DiffSynth-Studio documentation by adding comprehensive guides on accelerating diffusion model inference. The new content covers practical methods such as multi-GPU parallel processing and PyTorch's computation graph compilation, offering users clear instructions and examples to optimize model performance and reduce inference times. Highlights
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Code Review
This pull request introduces new documentation for inference acceleration techniques in diffusion models, covering multi-GPU parallel inference and computation graph compilation, in both English and Chinese. The navigation has also been updated to include these new pages. Feedback indicates that several links in the English documentation are broken due to incorrect Google search URL wrapping, and the Python code examples in both language versions contain unused imports. Additionally, the Chinese documentation file is missing a trailing newline character.
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| DiffSynth-Studio adopts a multi-GPU inference solution using Unified Sequence Parallel (USP). It splits the token sequence in the DiT across multiple GPUs for parallel processing. The underlying implementation is based on [xDiT](https://github.com/xdit-project/xDiT). Please note that unified sequence parallelism introduces additional communication overhead, so the actual speedup ratio is usually lower than the number of GPUs. | ||
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| Currently, DiffSynth-Studio supports unified sequence parallel acceleration for the [Wan](https://www.google.com/search?q=../Model_Details/Wan.md) and [MOVA](https://www.google.com/search?q=../Model_Details/Wan.md) models. |
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The links for the "Wan" and "MOVA" models are broken. They appear to be wrapped in a Google search URL. Please correct them to be direct relative links.
| Currently, DiffSynth-Studio supports unified sequence parallel acceleration for the [Wan](https://www.google.com/search?q=../Model_Details/Wan.md) and [MOVA](https://www.google.com/search?q=../Model_Details/Wan.md) models. | |
| Currently, DiffSynth-Studio supports unified sequence parallel acceleration for the [Wan](../Model_Details/Wan.md) and [MOVA](../Model_Details/Wan.md) models. |
| from PIL import Image | ||
| from diffsynth.utils.data import save_video, VideoData |
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The Image and VideoData imports are unused in this code snippet. It's good practice to remove unused imports to keep the example code clean and focused.
| from PIL import Image | |
| from diffsynth.utils.data import save_video, VideoData | |
| from diffsynth.utils.data import save_video |
| from PIL import Image | ||
| from diffsynth.utils.data import save_video, VideoData |
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The Image and VideoData imports are unused in this code snippet. It's good practice to remove unused imports to keep the example code clean and focused.
| from PIL import Image | |
| from diffsynth.utils.data import save_video, VideoData | |
| from diffsynth.utils.data import save_video |
| ```python | ||
| class QwenImageDiT(torch.nn.Module): | ||
| _repeated_blocks = ["QwenImageTransformerBlock"] | ||
| ``` No newline at end of file |
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It is a good practice to end files with a newline character for POSIX compatibility.
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