# One Step Diffusion-based Super-Resolution with Time-Aware Distillation arXiv: https://arxiv.org/abs/2408.07476 ยท HTML: https://mlsanity.com/paper/2408.07476/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. - Builds on 24 of its 73 references that are in the map. - First posted on arXiv 14 Aug 2024. ## Placed in - Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/) ## Builds on (most cited first, 24 of 24) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022) - [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017) - [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022) - [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022) - [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021) - [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023) - [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023) - [One-step Diffusion with Distribution Matching Distillation](https://mlsanity.com/paper/2311.18828/) (2023) - [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023) - [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023) - [ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2108.02938/) (2021) - [Exploiting Diffusion Prior for Real-World Image Super-Resolution](https://mlsanity.com/paper/2305.07015/) (2023) - [ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting](https://mlsanity.com/paper/2307.12348/) (2023) - [Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation](https://mlsanity.com/paper/2403.12015/) (2024) - [Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization](https://mlsanity.com/paper/2308.14469/) (2023) - [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) - [DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics](https://mlsanity.com/paper/2310.13268/) (2023) - [DifFace: Blind Face Restoration with Diffused Error Contraction](https://mlsanity.com/paper/2212.06512/) (2022) - [Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling](https://mlsanity.com/paper/1812.01608/) (2018) - [Progressive Distillation for Fast Sampling of Diffusion Models](https://mlsanity.com/paper/2202.00512/) (2022) - [AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation](https://mlsanity.com/paper/2404.01717/) (2024) ## Built on by (most cited first, 17 of 17) - [Adversarial Diffusion Compression for Real-World Image Super-Resolution](https://mlsanity.com/paper/2411.13383/) (2024) - [SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training](https://mlsanity.com/paper/2506.05301/) (2025) - [One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation](https://mlsanity.com/paper/2502.01993/) (2025) - [Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminator](https://mlsanity.com/paper/2410.04224/) (2024) - [Dual Prompting Image Restoration with Diffusion Transformers](https://mlsanity.com/paper/2504.17825/) (2025) - [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025) - [Fast Image Super-Resolution via Consistency Rectified Flow](https://mlsanity.com/paper/2605.12377/) (2026) - [One-Step Diffusion-based Real-World Image Super-Resolution with Visual Perception Distillation](https://mlsanity.com/paper/2506.02605/) (2025) - [RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought](https://mlsanity.com/paper/2506.16796/) (2025) - [AuthFace: Towards Authentic Blind Face Restoration with Face-oriented Generative Diffusion Prior](https://mlsanity.com/paper/2410.09864/) (2024) - [DUO-VSR: Dual-Stream Distillation for One-Step Video Super-Resolution](https://mlsanity.com/paper/2603.22271/) (2026) - [Fast, faithful and photorealistic diffusion-based image super-resolution with enhanced Flow Map models](https://mlsanity.com/paper/2601.16660/) (2026) - [Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling](https://mlsanity.com/paper/2505.12048/) (2025) - [Mixture of Ranks with Degradation-Aware Routing for One-Step Real-World Image Super-Resolution](https://mlsanity.com/paper/2511.16024/) (2025) - [ChopGrad: Pixel-Wise Losses for Latent Video Diffusion via Truncated Backpropagation](https://mlsanity.com/paper/2603.17812/) (2026) - [Stabilizing, Scaling & Enhancing MeanFlow for Large-scale Diffusion Distillation](https://mlsanity.com/paper/2605.17834/) (2026) - [SPARK: Input-Conditioned Sparse Activation Modulation for Frozen DiT-based Super-Resolution](https://mlsanity.com/paper/2609.03813/) (2026) ## Abstract (arXiv) Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. However, these approaches typically require tens or even hundreds of iterative samplings, resulting in significant latency. Recently, techniques have been devised to enhance the sampling efficiency of diffusion-based SR models via knowledge distillation. Nonetheless, when aligning the knowledge of student and teacher models, these solutions either solely rely on pixel-level loss constraints or neglect the fact that diffusion models prioritize varying levels of information at different time steps. To accomplish effective and efficient image super-resolution, we propose a time-aware diffusion distillation method, named TAD-SR. Specifically, we introduce a novel score distillation strategy to align the data distribution between the outputs of the student and teacher models after minor noise perturbation. This distillation strategy enables the student network to concentrate more on the high-frequency details. Furthermore, to mitigate performance limitations stemming from distillation, we integrate a latent adversarial loss and devise a time-aware discriminator that leverages diffusion priors to effectively distinguish between real images and generated images. Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method achieves comparable or even superior performance compared to both previous state-of-the-art (SOTA) methods and the teacher model in just one sampling step. Codes are available at https://github.com/LearningHx/TAD-SR.