| --- |
| license: apache-2.0 |
| tags: |
| - source-code |
| - video-generation |
| - pytorch |
| --- |
| # Elastic Forcing |
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| Elastic Forcing post-trains autoregressive video generators by matching generated and reference videos in frozen representation spaces. It combines hybrid Nyström–Monte Carlo distribution estimation with gradient replay. |
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| [Model checkpoints](https://huggingface.co/Elastic-forcing/Elastic-forcing-checkpoint) · [1.3B guide](1.3B/README.md) · [14B guide](14B/README.md) |
|
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| ## Models |
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| | Backbone | Frozen feature encoders | Training GPUs | Training updates | Inference | |
| |---|---|---:|---:|---| |
| | Wan2.1-T2V-1.3B | DINOv3 + V-JEPA 2 + VideoMAEv2 | 4 | 150 | Four causal denoising steps per chunk | |
| | Wan2.1-T2V-14B | DINOv3 + V-JEPA 2 + VideoMAEv2 | 8 | 80 | Four causal denoising steps and terminal cleanup | |
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| Both models generate 81-frame videos at 832 × 480 resolution and 16 FPS. Training uses a distribution batch of 256 generated videos, a differentiation batch of 128 generated videos, and a reference minibatch of 128 videos. |
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| ## Getting started |
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| ```bash |
| git clone https://huggingface.co/Elastic-forcing/Elastic-forcing-code |
| cd Elastic-forcing-code |
| |
| # Select the model size and follow its guide. |
| cd 1.3B |
| # Or: cd 14B |
| ``` |
|
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| Each model directory includes its dependencies, configuration, asset guide, training launcher, and inference launcher. Download the matching generator checkpoint from the checkpoint repository and prepare the Wan2.1 base-model assets locally. Training additionally requires an ODE initialization, frozen feature encoders, and reference features. |
|
|
| ```text |
| Elastic-forcing-code/ |
| ├── 1.3B/ |
| │ ├── code/ |
| │ ├── configs/ |
| │ ├── scripts/ |
| │ └── README.md |
| ├── 14B/ |
| │ ├── code/ |
| │ ├── configs/ |
| │ ├── scripts/ |
| │ └── README.md |
| └── docs/ |
| ``` |
|
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| The model guides describe dependencies, required assets, training settings, and inference commands. |
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| ## Evaluation |
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| | Model | VBench Total ↑ | Quality ↑ | Semantic ↑ | |
| |---|---:|---:|---:| |
| | Elastic Forcing 1.3B | 84.25 | 85.06 | 80.99 | |
|
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| Per-dimension scores are provided in the [reference results](1.3B/results/reference/three-encoder-summary.json). Evaluate generated videos using the official [VBench](https://github.com/Vchitect/VBench) implementation. |
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| ## Acknowledgments and license |
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| This implementation builds on [Self-Forcing](https://github.com/guandeh17/Self-Forcing), [Wan2.1](https://github.com/Wan-Video/Wan2.1), [DINOv3](https://github.com/facebookresearch/dinov3), [V-JEPA 2](https://github.com/facebookresearch/vjepa2), and [VideoMAEv2](https://github.com/OpenGVLab/VideoMAEv2). |
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| Source-code terms are provided in [LICENSE](LICENSE). Upstream source notices are retained; model assets and datasets follow their respective upstream terms. |
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