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SPARK: Input-Conditioned Sparse Activation Modulation for Frozen DiT-based Super-Resolution

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Real-world image super-resolution (SR) increasingly relies on Diffusion Transformer (DiT) backbones, whose internal activations can be dominated by a small number of massive channels. Yet improving perceptual quality in these models still typically requires fine-tuning the network or attaching additional adapters, leaving this structured activation space largely unexplored for adaptation. We investigate whether dominant channels can instead serve as a compact adaptation interface for frozen DiT-based SR models. We first characterize their behavior in pretrained SR backbones and show through co

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