teasr: training-efficient any-step diffusion transformer for real-world image super-resolution

• URL: http://arxiv.org/abs/2606.16188v1
• Date: Mon, 15 Jun 2026 04:02:37 GMT
• Title: teasr: training-efficient any-step diffusion transformer for real-world image super-resolution
• Abstract summary: TEASR is a training-efficient any-step diffusion framework for Real-ISR.<n>Our key idea is to perform self-adversarial distillation within a single diffusion model.<n>We propose a timestep-aware rectification strategy that stabilizes one-step generation across noise levels.
• Score: 10.733502031936958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models excel in Real-World Image Super-Resolution (Real-ISR) due to their powerful generative priors but suffer from slow iterative sampling. Although existing one-step distillation methods accelerate inference, they typically require auxiliary teacher models that inflate training memory and restrict scalability to large-scale architectures. Furthermore, these fixed-step models lack the flexibility to trade off speed for quality. In this paper, we propose TEASR, a training-efficient any-step diffusion framework for Real-ISR that enables both one-step and multi-step restoration within a unified model. Our key idea is to perform self-adversarial distillation within a single diffusion model, eliminating the need for auxiliary teachers or discriminators. Specifically, we propose a timestep-aware rectification strategy that stabilizes one-step generation across noise levels. These two designs further enables the distillation of 20B-parameter diffusion models on a single GPU, significantly improving training efficiency. Moreover, we introduce a dual-branch diffusion transformer with decoupled timestep condition to separate the current noise state and the denoising target to enhance sampling quality. Extensive experiments demonstrate that TEASR supports seamless any-step sampling and consistently outperforms state-of-the-art methods across multiple datasets.

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