# Adversarial Diffusion Compression for Real-World Image Super-Resolution

arXiv: https://arxiv.org/abs/2411.13383 · HTML: https://mlsanity.com/paper/2411.13383/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 94 of its 128 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 30 of 94)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [One-step Diffusion with Distribution Matching Distillation](https://mlsanity.com/paper/2311.18828/) (2023)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023)
- [On Distillation of Guided Diffusion Models](https://mlsanity.com/paper/2210.03142/) (2022)
- [Improved Distribution Matching Distillation for Fast Image Synthesis](https://mlsanity.com/paper/2405.14867/) (2024)

## Built on by (most cited first, 30 of 52)

- [Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach](https://mlsanity.com/paper/2412.03017/) (2024)
- [DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution](https://mlsanity.com/paper/2503.23580/) (2025)
- [Perceive, Understand and Restore: Real-World Image Super-Resolution with Autoregressive Multimodal Generative Models](https://mlsanity.com/paper/2503.11073/) (2025)
- [Multi-Agent Image Restoration](https://mlsanity.com/paper/2503.09403/) (2025)
- [Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Training](https://mlsanity.com/paper/2507.20291/) (2025)
- [Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision](https://mlsanity.com/paper/2504.04903/) (2025)
- [NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study](https://mlsanity.com/paper/2504.15003/) (2025)
- [PocketSR: The Super-Resolution Expert in Your Pocket Mobiles](https://mlsanity.com/paper/2510.03012/) (2025)
- [Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data](https://mlsanity.com/paper/2503.21694/) (2025)
- [Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution](https://mlsanity.com/paper/2508.16557/) (2025)
- [TinySR: Pruning Diffusion for Real-World Image Super-Resolution](https://mlsanity.com/paper/2508.17434/) (2025)
- [Ultra-Low Bitrate Perceptual Image Compression with Shallow Encoder](https://mlsanity.com/paper/2512.12229/) (2025)
- [Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion](https://mlsanity.com/paper/2512.23709/) (2025)
- [Fast Image Super-Resolution via Consistency Rectified Flow](https://mlsanity.com/paper/2605.12377/) (2026)
- [The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview](https://mlsanity.com/paper/2604.17306/) (2026)
- [RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought](https://mlsanity.com/paper/2506.16796/) (2025)
- [VOSR: A Vision-Only Generative Model for Image Super-Resolution](https://mlsanity.com/paper/2604.03225/) (2026)
- [OctoT2I: A Self-Evolving Agentic Text-to-Image Router](https://mlsanity.com/paper/2606.01803/) (2026)
- [One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation](https://mlsanity.com/paper/2503.13358/) (2025)
- [FinPercep-RM: A Fine-grained Reward Model and Co-evolutionary Curriculum for RL-based Real-world Super-Resolution](https://mlsanity.com/paper/2512.22647/) (2025)
- [DiffVC-RT: Towards Practical Real-Time Diffusion-based Perceptual Neural Video Compression](https://mlsanity.com/paper/2601.20564/) (2026)
- [Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution](https://mlsanity.com/paper/2509.23980/) (2025)
- [DP2O-SR: Direct Perceptual Preference Optimization for Real-World Image Super-Resolution](https://mlsanity.com/paper/2510.18851/) (2025)
- [F2IDiff: Real-world Image Super-resolution using Feature to Image Diffusion Foundation Model](https://mlsanity.com/paper/2512.24473/) (2025)
- [NanoSD: Edge Efficient Foundation Model for Real Time Image Restoration](https://mlsanity.com/paper/2601.09823/) (2026)
- [Eliminating VAE for Fast and High-Resolution Generative Detail Restoration](https://mlsanity.com/paper/2602.10630/) (2026)
- [HybridStitch: Pixel and Timestep Level Model Stitching for Diffusion Acceleration](https://mlsanity.com/paper/2603.07815/) (2026)
- [ColorFLUX: A Structure-Color Decoupling Framework for Old Photo Colorization](https://mlsanity.com/paper/2603.28162/) (2026)
- [OP4KSR: One-Step Patch-Free 4K Super-Resolution with Periodic Artifact Suppression](https://mlsanity.com/paper/2605.13457/) (2026)
- [GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction](https://mlsanity.com/paper/2512.16357/) (2025)

## Abstract (arXiv)

Real-world image super-resolution (Real-ISR) aims to reconstruct high-resolution images from low-resolution inputs degraded by complex, unknown processes. While many Stable Diffusion (SD)-based Real-ISR methods have achieved remarkable success, their slow, multi-step inference hinders practical deployment. Recent SD-based one-step networks like OSEDiff and S3Diff alleviate this issue but still incur high computational costs due to their reliance on large pretrained SD models. This paper proposes a novel Real-ISR method, AdcSR, by distilling the one-step diffusion network OSEDiff into a streamlined diffusion-GAN model under our Adversarial Diffusion Compression (ADC) framework. We meticulously examine the modules of OSEDiff, categorizing them into two types: (1) Removable (VAE encoder, prompt extractor, text encoder, etc.) and (2) Prunable (denoising UNet and VAE decoder). Since direct removal and pruning can degrade the model's generation capability, we pretrain our pruned VAE decoder to restore its ability to decode images and employ adversarial distillation to compensate for performance loss. This ADC-based diffusion-GAN hybrid design effectively reduces complexity by 73% in inference time, 78% in computation, and 74% in parameters, while preserving the model's generation capability. Experiments manifest that our proposed AdcSR achieves competitive recovery quality on both synthetic and real-world datasets, offering up to 9.3× speedup over previous one-step diffusion-based methods. Code and models are available at https://github.com/Guaishou74851/AdcSR.