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@@ -20,13 +20,13 @@ tags:
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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 640×368 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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@@ -58,7 +58,7 @@ download these unless you intend to train.
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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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@@ -156,13 +156,7 @@ shows up as a shifted mean or changed variance; neither appeared.
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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
25
  let it run. The text conditioning can be changed mid-stream without clearing the
26
  K/V cache, so the scene continues instead of cutting.
27
 
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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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