---
license: apache-2.0
tags:
- source-code
- video-generation
- pytorch
---
# Elastic Forcing

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.

[Model checkpoints](https://huggingface.co/Elastic-forcing/Elastic-forcing-checkpoint) · [1.3B guide](1.3B/README.md) · [14B guide](14B/README.md)

## Models

| 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 |

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.

## Getting started

```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
```

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/
```

The model guides describe dependencies, required assets, training settings, and inference commands.

## Evaluation

| Model | VBench Total ↑ | Quality ↑ | Semantic ↑ |
|---|---:|---:|---:|
| Elastic Forcing 1.3B | 84.25 | 85.06 | 80.99 |

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.

## Acknowledgments and license

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).

Source-code terms are provided in [LICENSE](LICENSE). Upstream source notices are retained; model assets and datasets follow their respective upstream terms.