Instructions to use FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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README.md
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## Model Overview
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- 3-step inference is supported and achieves up to **
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- Our model is trained on **
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- Finetuning and inference scripts are available in the [FastVideo](https://github.com/hao-ai-lab/FastVideo) repository:
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- [1 Node/GPU debugging finetuning script](https://github.com/hao-ai-lab/FastVideo/blob/main/scripts/distill/v1_distill_dmd_wan_VSA.sh)
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- [Slurm training example script](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P/distill_dmd_VSA_t2v_1.3B.slurm)
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## Model Overview
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- 3-step inference is supported and achieves up to **50x** speed up for denoising loop on a single **H100** GPU.
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- Our model is trained on **121×704×1280** resolution, but it supports generating videos with **any resolution**.(quality may degrade)
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- Finetuning and inference scripts are available in the [FastVideo](https://github.com/hao-ai-lab/FastVideo) repository:
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- [1 Node/GPU debugging finetuning script](https://github.com/hao-ai-lab/FastVideo/blob/main/scripts/distill/v1_distill_dmd_wan_VSA.sh)
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- [Slurm training example script](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P/distill_dmd_VSA_t2v_1.3B.slurm)
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