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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
Preliminary Explorations with GPT-4o(mni) Native Image Generation
Pu Cao, Feng Zhou, Junyi Ji +8
Recently, the visual generation ability by GPT-4o(mni) has been unlocked by OpenAI. It demonstrates a very remarkable generation capability with excellent multimodal condition unde…
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…