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README.md
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@@ -320,6 +320,8 @@ We test the computational efficiency of different **Wan2.1** models on different
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> (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
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> (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
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## Community Contributions
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- [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides more support for Wan, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to [their examples](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo).
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> (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
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> (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
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> 💡Note: T2V-14B is slower than I2V-14B because the former samples 50 steps while the latter uses 40 steps.
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## Community Contributions
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- [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides more support for Wan, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to [their examples](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo).
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