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README.md
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> [Wan2.1](https://github.com/Wan-Video/Wan2.1) release.
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A 1.3 B student distilled from a Wan2.1-14B teacher that generates video
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**continuously** rather than as a fixed clip: 750 ms of
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generated in 428 ms on one A100
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let it run. The text conditioning can be changed mid-stream without clearing the
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K/V cache, so the scene continues instead of cutting.
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**Code, setup and documentation: [JonathanColetti/LiveWan](https://github.com/JonathanColetti/LiveWan).** This repository holds
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weights and data only
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<p align="center"><img src="samples/world_p60_waterfall_16fps.mp4" width="480"></p>
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| `checkpoints/t14b_b64/opt_rank00{0..7}.pt` | 8 × 2.2 GB | FSDP-sharded AdamW state at step 3000, one shard per rank |
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Nothing here duplicates a public download. The Wan2.1 base model, its VAE and the
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umt5-xxl encoder are
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[Wan-AI/Wan2.1-T2V-1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) (17 GB)
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from its own repo, and the 14B teacher is only needed if you are distilling.
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- **The batch-64 premise is unsettled** — confounded with having simply trained
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longer.
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## Do not re-encode the prompts
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`data/prompts.pt` is the conditioning every clip in this project was generated and
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trained under. umt5-xxl embeddings are **hardware-dependent**: the same strings
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encoded on an A100 versus an H200 differ by 2.2% relative, while two runs on the
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same box agree to 4.9e-09. Re-encoding silently decouples the conditioning from the
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results and nothing will look wrong.
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## Licence
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> [Wan2.1](https://github.com/Wan-Video/Wan2.1) release.
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A 1.3 B student distilled from a Wan2.1-14B teacher that generates video
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**continuously** rather than as a fixed clip: 750 ms of 640x368 video per block,
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generated in 428 ms on one A100 80GB, extended block by block for as long as you
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let it run. The text conditioning can be changed mid-stream without clearing the
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K/V cache, so the scene continues instead of cutting.
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**Code, setup and documentation: [JonathanColetti/LiveWan](https://github.com/JonathanColetti/LiveWan).** This repository holds
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weights and data only. It is not usable on its own.
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<p align="center"><img src="samples/world_p60_waterfall_16fps.mp4" width="480"></p>
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| `checkpoints/t14b_b64/opt_rank00{0..7}.pt` | 8 × 2.2 GB | FSDP-sharded AdamW state at step 3000, one shard per rank |
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Nothing here duplicates a public download. The Wan2.1 base model, its VAE and the
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umt5-xxl encoder are not mirrored in this repository. `setup.sh` pulls
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[Wan-AI/Wan2.1-T2V-1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) (17 GB)
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from its own repo, and the 14B teacher is only needed if you are distilling.
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- **The batch-64 premise is unsettled** — confounded with having simply trained
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longer.
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## Licence
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