# YODA: Yet Another One-step Diffusion-based Video Compressor

arXiv: https://arxiv.org/abs/2601.01141 · HTML: https://mlsanity.com/paper/2601.01141/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 2 of its 46 references that are in the map.
- First posted on arXiv 3 Jan 2026.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 2 of 2)

- [OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates](https://mlsanity.com/paper/2505.16091/) (2025)
- [DiffVC-OSD: One-Step Diffusion-based Perceptual Neural Video Compression Framework](https://mlsanity.com/paper/2508.07682/) (2025)

## Built on by (most cited first, 9 of 9)

- [DiffVC-RT: Towards Practical Real-Time Diffusion-based Perceptual Neural Video Compression](https://mlsanity.com/paper/2601.20564/) (2026)
- [ZeroGVC: Zero-Shot Generative Video Compression with Autoregressive Diffusion Priors](https://mlsanity.com/paper/2606.22371/) (2026)
- [Generative Video Compression with Adaptive Score Distillation](https://mlsanity.com/paper/2607.22772/) (2026)
- [GVC-RT: Towards Real-Time Generative Video Compression at Ultra-Low Bitrates](https://mlsanity.com/paper/2608.04891/) (2026)
- [Group-of-Latents: Perceptual Video Compression at Extreme Bitrates via Masked Latent Generative Modeling](https://mlsanity.com/paper/2607.19437/) (2026)
- [Hadamard-Domain Model Quantization for Learned Image Coding](https://mlsanity.com/paper/2608.01653/) (2026)
- [DiffVC-ONE: Diffusion-based Generative Video Compression with One-Step Video Diffusion Transformer](https://mlsanity.com/paper/2608.20515/) (2026)
- [VoRTeC: Taming Foundation Flow for One-step Real time Video Compression](https://mlsanity.com/paper/2609.02291/) (2026)
- [A Spatiotemporal Semantic Importance-Guided Unified Compression and Editing Framework for AI-Generated Videos](https://mlsanity.com/paper/2610.05779/) (2026)

## Abstract (arXiv)

While one-step diffusion models have recently excelled in perceptual image compression, their application to video remains limited. Prior efforts typically rely on pretrained 2D autoencoders that generate per-frame latent representations independently, thereby neglecting temporal dependencies. We present YODA--Yet Another One-step Diffusion-based Video Compressor--which embeds multiscale features from temporal references for both latent generation and latent coding to better exploit spatial-temporal correlations for more compact representation, and employs a linear Diffusion Transformer (DiT) for efficient one-step denoising. YODA achieves state-of-the-art perceptual performance, consistently outperforming traditional and deep-learning baselines on LPIPS, DISTS, FID, and KID. Source code will be publicly available at https://github.com/NJUVISION/YODA.