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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 ma
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