Author

Xing-Fu Yi

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Preprint Open access Sep 2026

Fill2SR: Repurposing Inpainting Diffusion Transformers for Real-World Super-Resolution

Recent real-world image super-resolution (SR) methods often adapt text-to-image (T2I) backbones with ControlNet-style branches or spatial conditioning tokens, which increases memory and computes with resolution and often constrains training to a fixed scale. We propose Fill2SR, which repurposes a masked-inpainting Diff...

Xing-Fu Yi, Xiao-Xue Yu · 1 citation

Preprint Sep 2026

In-Token Learning for High-Fidelity Image Restoration via Diffusion Transformers

We present In-Token Learning, an image restoration framework that adapts a pretrained diffusion transformer using conditional rectified flow matching. Clean targets paired with degraded inputs supervise transport from Gaussian noise to restored images. Spatially aligned degraded-image tokens are fused with evolving lat...

Xing-Fu Yi, Xiao-Xue Yu · 0 citations