Instructions to use Efficient-Large-Model/SANA-WM_bidirectional with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Efficient-Large-Model/SANA-WM_bidirectional with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Efficient-Large-Model/SANA-WM_bidirectional", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -14,6 +14,17 @@ A 2.6 B parameter image-to-video diffusion model conditioned on a per-frame
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camera trajectory, paired with the LTX-2 sink-bidirectional Euler refiner
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for high-fidelity decoding.
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| Component | Path in repo | Size |
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| Sana DiT (Stage 1) | `dit/sana_wm_1600m_720p.safetensors` | 10 GB |
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camera trajectory, paired with the LTX-2 sink-bidirectional Euler refiner
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for high-fidelity decoding.
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Paper: <https://arxiv.org/abs/2605.15178>
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```bibtex
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@article{sanawm,
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title = {SANA-WM: Camera-Controlled Image-to-Video Diffusion},
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author = {NVIDIA},
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journal = {arXiv preprint arXiv:2605.15178},
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year = {2026}
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}
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```
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| Component | Path in repo | Size |
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|----------------------------|-------------------------------------------|-------|
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| Sana DiT (Stage 1) | `dit/sana_wm_1600m_720p.safetensors` | 10 GB |
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