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Diffusion Model with Perceptual Loss

Diffusion models without guidance tend to generate unrealistic samples, yet the cause of this problem is not fully studied. Our analysis suggests that the loss objective plays an important role in shaping the learned distribution and the common mean squared error loss is not optimal. We hypothesize that a better loss objective can be designed with inductive biases and propose a novel self-perceptual loss that utilizes the diffusion model itself as the perceptual loss. Our work demonstrates that perceptual loss can be used in diffusion training to improve sample quality effectively. Models trained using our objective can generate realistic samples without guidance. We hope our work paves the way for more future explorations of the diffusion loss objective.

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