---
license: other
pipeline_tag: text-to-video
tags:
- video-generation
- text-to-video
- diffusion
- distribution-matching
- distillation
- wan
- arxiv:2604.03118
---
# 🧂 Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation
[](https://arxiv.org/abs/2604.03118)
[](https://xingtongge.github.io/Salt/)
[](https://github.com/XingtongGe/Salt)
[](https://huggingface.co/domiso/Salt)
[Xingtong Ge](https://xingtongge.github.io/)1,2, [Yi Zhang](https://zhangyi-3.github.io/)2, Yushi Huang1, Dailan He2, Xiahong Wang2, Bingqi Ma2, Guanglu Song2, Yu Liu2, Jun Zhang1
1The Hong Kong University of Science and Technology, 2Vivix Group Limited
European Conference on Computer Vision (**ECCV**), 2026
## Abstract
Distilling video generation models to extremely low inference budgets (e.g., 2-4 NFEs) is crucial for real-time deployment, yet remains challenging. Trajectory-style consistency distillation often becomes conservative under complex video dynamics, yielding over-smoothed appearance and weak motion. Distribution matching distillation (DMD) can recover sharp, mode-seeking samples, but its local training signals do not explicitly regularize how denoising updates compose across timesteps, making composed rollouts prone to drift. To overcome this challenge, we propose Self-Consistent Distribution Matching Distillation (SC-DMD), which explicitly regularizes the endpoint-consistent composition of consecutive denoising updates. For real-time autoregressive video generation, we further treat the KV cache as a quality-parameterized condition and propose cache-distribution-aware training. This training scheme applies SC-DMD over multi-step rollouts and introduces a cache-conditioned feature alignment objective that steers low-quality outputs toward high-quality references. Across extensive experiments on both non-autoregressive backbones (e.g., Wan 2.1) and autoregressive real-time paradigms (e.g., Self Forcing, Causal Forcing, and LongLive), Salt consistently improves low-NFE video generation quality while remaining compatible with diverse KV-cache memory mechanisms.
## Released Models
### Salt + Causal Forcing
- `checkpoints/salt_cf.pt`
- Supports 2-step and 4-step autoregressive generation.
- Use the EMA generator for inference.
### Salt + LongLive
- `checkpoints/salt_ll.pt`
- Supports 4-step autoregressive generation with LongLive KV-cache memory.
- Use the regular generator for inference.
The training prompt collection used by the released recipes is also included
at `prompts/vidprom_filtered_extended.txt`.
## Selected Results
### Text-to-video generation on VBench
| Model | NFE | Total | Quality | Semantic |
| --- | ---: | ---: | ---: | ---: |
| Self Forcing | 4 | 84.20 | 84.74 | **82.05** |
| Salt + Self Forcing | 4 | **84.47** | **85.27** | 81.28 |
| LongLive | 4 | 84.40 | 85.12 | 81.53 |
| Salt + LongLive | 4 | **84.93** | **85.41** | **83.00** |
| Causal Forcing | 4 | 84.62 | 85.41 | 81.47 |
| Salt + Causal Forcing | 4 | **85.08** | **85.96** | **81.59** |
| Salt + Causal Forcing | 2 | **84.80** | **85.63** | **81.49** |
## Usage
### 1. Prepare the code and artifacts
```bash
git clone https://github.com/XingtongGe/Salt.git
cd Salt
# Downloads checkpoints/ and prompts/ into the paths expected by the configs.
hf download domiso/Salt --local-dir .
```
Follow the installation and Wan2.1 preparation instructions in the
[GitHub repository](https://github.com/XingtongGe/Salt).
### 2. Salt + Causal Forcing
```bash
python inference.py \
--config_path configs/inference/salt_causal_forcing.yaml \
--checkpoint_path checkpoints/salt_cf.pt \
--data_path prompts/example_prompts.txt \
--output_folder outputs/salt_cf \
--use_ema
```
### 3. Salt + LongLive
```bash
python inference.py \
--config_path configs/inference/salt_longlive.yaml \
--checkpoint_path checkpoints/salt_ll.pt \
--data_path prompts/example_prompts.txt \
--output_folder outputs/salt_ll
```
## Training
The released prompt collection contains one prompt per line and matches the
default public recipes. Salt training does not require a video dataset.
The code repository provides recipes for:
- Self Forcing, Causal Forcing, and LongLive baselines;
- mixed-step SC-DMD; and
- mixed-step SC-DMD with TRD alignment.
All canonical mixed-step recipes sample 8-, 4-, and 2-step trajectories with
probabilities **0.4 / 0.4 / 0.2**. See the
[configuration guide](https://github.com/XingtongGe/Salt/tree/main/configs) for
the complete recipe matrix.
## License
Please review the code repository's license and third-party notices before
redistribution or commercial use. In particular, the LongLive backbone carries
a file-level CC-BY-NC-SA-4.0 notice. Model weights may also be subject to their
upstream backbone licenses.
## Citation
If you find this work useful, please cite:
```bibtex
@article{ge2026salt,
title={Salt: Self-consistent distribution matching with cache-aware training for fast video generation},
author={Ge, Xingtong and Zhang, Yi and Huang, Yushi and He, Dailan and Wang, Xiahong and Ma, Bingqi and Song, Guanglu and Liu, Yu and Zhang, Jun},
journal={arXiv preprint arXiv:2604.03118},
year={2026}
}
```