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cs.CV2026
A Tilted Seesaw: Revisiting Autoencoder Trade-off for Controllable Diffusion
Pu Cao, Yiyang Ma, Feng Zhou +3
In latent diffusion models, the autoencoder (AE) is typically expected to balance two capabilities: faithful reconstruction and a generation-friendly latent space (e.g., low gFID).…
cs.CV2025
ResDiT: Evoking the Intrinsic Resolution Scalability in Diffusion Transformers
Yiyang Ma, Feng Zhou, Xuedan Yin +3
Leveraging pre-trained Diffusion Transformers (DiTs) for high-resolution (HR) image synthesis often leads to spatial layout collapse and degraded texture fidelity. Prior work mitig…
cs.CV2025
Exploring Position Encoding in Diffusion U-Net for Training-free High-resolution Image Generation
Feng Zhou, Pu Cao, Yiyang Ma +2
Denoising higher-resolution latents via a pre-trained U-Net leads to repetitive and disordered image patterns. Although recent studies make efforts to improve generative quality by…