Instructions to use FastVideo/FastWan2.1-T2V-14B-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FastVideo/FastWan2.1-T2V-14B-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/FastWan2.1-T2V-14B-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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@@ -42,7 +42,7 @@ This model is jointly finetuned with [DMD](https://arxiv.org/pdf/2405.14867) and
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Training was conducted on **8 nodes with 64 H200 GPUs** in total, using a `global batch size = 64`.
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We enable `gradient checkpointing`, set `HSDP_shard_dim = 8`, `sequence_parallel_size = 4`, and use `learning rate = 1e-5`.
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We set **VSA attention sparsity** to 0.9, and training runs for **3000 steps (~52 hours)**
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The detailed training example script is available [here](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/distill/Wan-Syn-480P/distill_dmd_VSA_t2v_14B_480P.slurm).
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Training was conducted on **8 nodes with 64 H200 GPUs** in total, using a `global batch size = 64`.
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We enable `gradient checkpointing`, set `HSDP_shard_dim = 8`, `sequence_parallel_size = 4`, and use `learning rate = 1e-5`.
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We set **VSA attention sparsity** to 0.9, and training runs for **3000 steps (~52 hours)**
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The detailed **training example script** is available [here](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/distill/Wan-Syn-480P/distill_dmd_VSA_t2v_14B_480P.slurm).
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