File size: 1,448 Bytes
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license: apache-2.0
base_model:
- Wan-AI/Wan2.1-T2V-1.3B
- Wan-AI/Wan2.1-T2V-14B
tags:
- video-generation
- autoregressive
- text-to-video
---
# Clean Forcing — adapted base checkpoints
Checkpoints for **Clean Forcing: Drift-Resistant Autoregressive Video Diffusion with a Frozen Base**:
the causally adapted Wan2.1 bases (repo root) and the 14 drift-corrector LoRAs (`weights/`, rank 16 on
self-attention q/k/v/o; `weights/README.md` maps each LoRA to its base and paper row).
```bash
# from the code repo root: LoRAs land in weights/, bases in the repo root
huggingface-cli download illustro1/clean-forcing --local-dir .
mv adapted_base_4000.pt self_forcing/wan_cache/
```
| File | Base | Size | Used for |
|---|---|---|---|
| `adapted_base_4000.pt` | Wan2.1-T2V-1.3B + rank-64 causal adapter (merged, step 4K) | 0.57 GB | all 1.3B results (Table 1 and ablations) |
| `wan14b_adapted_base_4000.pt` | Wan2.1-T2V-14B + rank-64 causal adapter (merged, step 4K) | 8.4 GB | the 14B scale-transfer appendix |
Load the merged weights with `torch.load(path)["merged"]` into the block-causal pipeline (see the code repo's `RUN.md`).
Verify downloads with `sha256sum -c SHA256SUMS` (bases) and `cd weights && sha256sum -c SHA256SUMS` (LoRAs).
- Code: https://github.com/wnqw/clean_forcing
- Project page and paper: https://clean-forcing.github.io/
Both checkpoints are derived from Wan2.1 (Apache-2.0) and are released under the same license.
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