TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution

• URL: http://arxiv.org/abs/2606.09608v1
• Date: Mon, 08 Jun 2026 15:18:04 GMT
• Title: TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution
• Abstract summary: TUDSR is a Twice Upsampling-Diffusion framework for higher image super-resolution.<n>It achieves state-of-the-art performance across multiple benchmarks.<n>Experiments further demonstrate that TUDSR-S generates high-quality images at the resolutions of $10242$ and even $20482$, significantly outperforming existing approaches.
• Score: 15.21755753128945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion-based generative models have achieved remarkable success in real-world image super-resolution (SR). With tiled diffusion techniques, these models can produce high-resolution images that exceed their native-supported resolution. However, the quality of such high-resolution (e.g $2048^2$) outputs often remains extremely poor, primarily due to two factors we consider: the image upsampling ratio (e.g $\times8$) exceeding the model's native-supported upsampling ratio (e.g $\times4$), and the model's native-supported resolution. In practice, training a native high-resolution model requires larger architectures, which incur significant computational overhead and GPU memory costs, making it hard on limited-resource equipment. Thus, we present TUDSR, a Twice Upsampling-Diffusion framework for higher SR. The TUDSR framework mainly consists of two stages: the first involves training at $R$-resolution, and the second introduces a looped chunk-based training strategy at $NR$-resolution. Each stage adapts a one-step GAN architecture comprising a generator and a discriminator. Based on SD2.1-base, we develop TUDSR-S, which achieves state-of-the-art performance across multiple benchmarks. Extensive experiments further demonstrate that TUDSR-S generates high-quality images at the resolutions of $1024^2$ and even $2048^2$, significantly outperforming existing approaches. Code is available at https://github.com/wuer5/TUDSR.

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