Pixel-Space Post-Training of Latent Diffusion Models
Authors: name: Christina Zhang, name: Simran Motwani, name: Matthew Yu, name: Ji Hou, name: Felix Juefei-Xu, name: Sam Tsai, name: Peter Vajda, name: Zijian He, name: Jialiang Wang

Latent diffusion models (LDMs) have made significant advancements in the
field of image generation in recent years. One major advantage of LDMs is their
ability to operate in a compressed latent space, allowing for more efficient
training and deployment. However, despite these advantages, challenges with
LDMs still remain. For example, it has been observed that LDMs often generate
high-frequency details and complex compositions imperfectly. We hypothesize
that one reason for these flaws is due to the fact that all pre- and
post-training of LDMs are done in latent space, which is typically 8 times 8
lower spatial-resolution than the output images. To address this issue, we
propose adding pixel-space supervision in the post-training process to better
preserve high-frequency details. Experimentally, we show that adding a
pixel-space objective significantly improves both supervised quality
fine-tuning and preference-based post-training by a large margin on a
state-of-the-art DiT transformer and U-Net diffusion models in both visual
quality and visual flaw metrics, while maintaining the same text alignment
quality.