# One-Step Diffusion-Based Image Compression with Semantic Distillation

arXiv: https://arxiv.org/abs/2505.16687 · HTML: https://mlsanity.com/paper/2505.16687/

## Key facts

- Cited by 26 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 57 of its 73 references that are in the map.
- First posted on arXiv 22 May 2025.

## Placed in

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

## Builds on (most cited first, 30 of 57)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [MaskGIT: Masked Generative Image Transformer](https://mlsanity.com/paper/2202.04200/) (2022)
- [One-step Diffusion with Distribution Matching Distillation](https://mlsanity.com/paper/2311.18828/) (2023)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Improved Distribution Matching Distillation for Fast Image Synthesis](https://mlsanity.com/paper/2405.14867/) (2024)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Finite Scalar Quantization: VQ-VAE Made Simple](https://mlsanity.com/paper/2309.15505/) (2023)
- [High-Fidelity Generative Image Compression](https://mlsanity.com/paper/2006.09965/) (2020)
- [One-Step Effective Diffusion Network for Real-World Image Super-Resolution](https://mlsanity.com/paper/2406.08177/) (2024)
- [SinSR: Diffusion-Based Image Super-Resolution in a Single Step](https://mlsanity.com/paper/2311.14760/) (2023)
- [Towards Robust Blind Face Restoration with Codebook Lookup Transformer](https://mlsanity.com/paper/2206.11253/) (2022)
- [Generative Adversarial Networks for Extreme Learned Image Compression](https://mlsanity.com/paper/1804.02958/) (2018)
- [Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff](https://mlsanity.com/paper/1901.07821/) (2019)
- [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023)
- [Neural Video Compression with Diverse Contexts](https://mlsanity.com/paper/2302.14402/) (2023)

## Built on by (most cited first, 26 of 26)

- [Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance](https://mlsanity.com/paper/2512.07480/) (2025)
- [CoD: A Diffusion Foundation Model for Image Compression](https://mlsanity.com/paper/2511.18706/) (2025)
- [Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression](https://mlsanity.com/paper/2511.06424/) (2025)
- [CoD-Lite: Real-Time Diffusion-Based Generative Image Compression](https://mlsanity.com/paper/2604.12525/) (2026)
- [DiT-IC: Aligned Diffusion Transformer for Efficient Image Compression](https://mlsanity.com/paper/2603.13162/) (2026)
- [Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression](https://mlsanity.com/paper/2604.10546/) (2026)
- [Generative Video Compression with One-Dimensional Latent Representation](https://mlsanity.com/paper/2603.15302/) (2026)
- [ProGIC: Progressive and Lightweight Generative Image Compression with Residual Vector Quantization](https://mlsanity.com/paper/2603.02897/) (2026)
- [Next-Frame Decoding for Ultra-Low-Bitrate Image Compression with Video Diffusion Priors](https://mlsanity.com/paper/2603.15129/) (2026)
- [ZeroGVC: Zero-Shot Generative Video Compression with Autoregressive Diffusion Priors](https://mlsanity.com/paper/2606.22371/) (2026)
- [CADC: Content Adaptive Diffusion-Based Generative Image Compression](https://mlsanity.com/paper/2602.21591/) (2026)
- [A Noise Constrained Diffusion (NC-Diffusion) Framework for High Fidelity Image Compression](https://mlsanity.com/paper/2604.06568/) (2026)
- [Generative Video Compression with Adaptive Score Distillation](https://mlsanity.com/paper/2607.22772/) (2026)
- [SDGIC: A Semantic Disambiguation-Guided Generative Image Compression Method for Ultra-Low Bitrates](https://mlsanity.com/paper/2512.06344/) (2025)
- [Training-Free Rate-Distortion-Perception Traversal With Diffusion](https://mlsanity.com/paper/2603.04005/) (2026)
- [Efficient Semantic Image Communication for Traffic Monitoring at the Edge](https://mlsanity.com/paper/2604.12622/) (2026)
- [Efficient Learned Image Compression without Entropy Coding](https://mlsanity.com/paper/2605.23323/) (2026)
- [FlowCodec: One-Step Flow Prior for Generative Image Compression](https://mlsanity.com/paper/2606.21030/) (2026)
- [Group-of-Latents: Perceptual Video Compression at Extreme Bitrates via Masked Latent Generative Modeling](https://mlsanity.com/paper/2607.19437/) (2026)
- [Tree-VQ: Progressive Image Compression from Pretrained Vector Quantizers](https://mlsanity.com/paper/2609.03641/) (2026)
- [Rate-Adaptive One-Step Diffusion Compression for AIGC Images](https://mlsanity.com/paper/2609.31795/) (2026)
- [Rethinking Generative Image Compression at Extremely Low Bitrates](https://mlsanity.com/paper/2609.39315/) (2026)
- [ResARC: Residual-Aware AutoRegressive Coding for Ultra-Low Bitrate Image Compression](https://mlsanity.com/paper/2609.39451/) (2026)
- [Unified Generative Video Compression with Autoregressive and Bidirectional Video Diffusion](https://mlsanity.com/paper/2610.13023/) (2026)
- [H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression](https://mlsanity.com/paper/2610.18125/) (2026)
- [Recasting DiT for Extreme Image Compression with Hierarchical Visual Priors](https://mlsanity.com/paper/2610.18607/) (2026)

## Abstract (arXiv)

While recent diffusion-based generative image codecs have shown impressive performance, their iterative sampling process introduces unpleasing latency. In this work, we revisit the design of a diffusion-based codec and argue that multi-step sampling is not necessary for generative compression. Based on this insight, we propose OneDC, a One-step Diffusion-based generative image Codec -- that integrates a latent compression module with a one-step diffusion generator. Recognizing the critical role of semantic guidance in one-step diffusion, we propose using the hyperprior as a semantic signal, overcoming the limitations of text prompts in representing complex visual content. To further enhance the semantic capability of the hyperprior, we introduce a semantic distillation mechanism that transfers knowledge from a pretrained generative tokenizer to the hyperprior codec. Additionally, we adopt a hybrid pixel- and latent-domain optimization to jointly enhance both reconstruction fidelity and perceptual realism. Extensive experiments demonstrate that OneDC achieves SOTA perceptual quality even with one-step generation, offering over 39% bitrate reduction and 20x faster decoding compared to prior multi-step diffusion-based codecs. Project: https://onedc-codec.github.io/