AI RESEARCH
HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion
arXiv CS.CV
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ArXi:2605.15741v1 Announce Type: new Pixel-space diffusion models bypass the reconstruction bottleneck of Variational Autoencoders (VAEs) but face a fundamental "granularity dilemma": capturing global semantics favors large patch scales, while generating high-fidelity details demands fine-grained inputs. To address this issue, we propose HyperDiT, a unified framework establishing Hyper-Connected Cross-Scale Interactions to bridge the semantic and pixel manifold.