# OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates arXiv: https://arxiv.org/abs/2505.16091 ยท HTML: https://mlsanity.com/paper/2505.16091/ ## 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 16 of its 60 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, 16 of 16) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023) - [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022) - [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017) - [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018) - [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016) - [Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise](https://mlsanity.com/paper/2208.09392/) (2022) - [EVC: Towards Real-Time Neural Image Compression with Mask Decay](https://mlsanity.com/paper/2302.05071/) (2023) - [Idempotence and Perceptual Image Compression](https://mlsanity.com/paper/2401.08920/) (2024) - [Compressed Image Generation with Denoising Diffusion Codebook Models](https://mlsanity.com/paper/2502.01189/) (2025) - [Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity](https://mlsanity.com/paper/2403.02944/) (2024) - [Causal Context Adjustment Loss for Learned Image Compression](https://mlsanity.com/paper/2410.04847/) (2024) - [PerCo (SD): Open Perceptual Compression](https://mlsanity.com/paper/2409.20255/) (2024) - [Compression-Aware One-Step Diffusion Model for JPEG Artifact Removal](https://mlsanity.com/paper/2502.09873/) (2025) ## Built on by (most cited first, 26 of 26) - [CoD: A Diffusion Foundation Model for Image Compression](https://mlsanity.com/paper/2511.18706/) (2025) - [YODA: Yet Another One-step Diffusion-based Video Compressor](https://mlsanity.com/paper/2601.01141/) (2026) - [Ultra-Low Bitrate Perceptual Image Compression with Shallow Encoder](https://mlsanity.com/paper/2512.12229/) (2025) - [CoD-Lite: Real-Time Diffusion-Based Generative Image Compression](https://mlsanity.com/paper/2604.12525/) (2026) - [Dual-Representation Image Compression at Ultra-Low Bitrates via Explicit Semantics and Implicit Textures](https://mlsanity.com/paper/2602.05213/) (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) - [Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast Image Compression](https://mlsanity.com/paper/2508.04979/) (2025) - [SLIM: Semantic-based Low-bitrate Image compression for Machines by leveraging diffusion](https://mlsanity.com/paper/2512.18200/) (2025) - [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) - [Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing](https://mlsanity.com/paper/2607.19064/) (2026) - [Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution](https://mlsanity.com/paper/2509.23980/) (2025) - [CADC: Content Adaptive Diffusion-Based Generative Image Compression](https://mlsanity.com/paper/2602.21591/) (2026) - [Progressive Learned Image Compression for Machine Perception](https://mlsanity.com/paper/2512.20070/) (2025) - [Advances in Diffusion-Based Generative Compression](https://mlsanity.com/paper/2601.18932/) (2026) - [Exploiting Semantic and Pixel Representations for Ultra-Low Bitrate Image Compression](https://mlsanity.com/paper/2606.01608/) (2026) - [Faithful Extreme Image Rescaling with Learnable Reversible Transformation and Semantic Priors](https://mlsanity.com/paper/2605.00605/) (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) - [FLM: Frequency-Aware Language Models for Generative Image Compression](https://mlsanity.com/paper/2608.28687/) (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) - [Recasting DiT for Extreme Image Compression with Hierarchical Visual Priors](https://mlsanity.com/paper/2610.18607/) (2026) ## Abstract (arXiv) Pretrained latent diffusion models have shown strong potential for lossy image compression, owing to their powerful generative priors. Most existing diffusion-based methods reconstruct images by iteratively denoising from random noise, guided by compressed latent representations. While these approaches have achieved high reconstruction quality, their multi-step sampling process incurs substantial computational overhead. Moreover, they typically require training separate models for different compression bit-rates, leading to significant training and storage costs. To address these challenges, we propose a one-step diffusion codec across multiple bit-rates. termed OSCAR. Specifically, our method views compressed latents as noisy variants of the original latents, where the level of distortion depends on the bit-rate. This perspective allows them to be modeled as intermediate states along a diffusion trajectory. By establishing a mapping from the compression bit-rate to a pseudo diffusion timestep, we condition a single generative model to support reconstructions at multiple bit-rates. Meanwhile, we argue that the compressed latents retain rich structural information, thereby making one-step denoising feasible. Thus, OSCAR replaces iterative sampling with a single denoising pass, significantly improving inference efficiency. Extensive experiments demonstrate that OSCAR achieves superior performance in both quantitative and visual quality metrics. The code and models are available at https://github.com/jp-guo/OSCAR.