2026
ResDiT: Evoking the Intrinsic Resolution Scalability in Diffusion Transformers
CVPR 2026
Leveraging pre-trained Diffusion Transformers (DiTs) for high-resolution (HR) image synthesis often leads to spatial layout collapse and degraded texture fidelity. Prior work mitigates these issues with complex pipelines that first perform a base-resolution (i.e., training-resolution) denoising proc