Diffusion models trained with mean squared error loss tend to generate
unrealistic samples. Current state-of-the-art models rely on classifier-free
guidance to improve sample quality, yet its surprising effectiveness is not
fully understood. In this paper, We show that the effectiveness of
classifier-free guidance partly originates from it being a form of implicit
perceptual guidance. As a result, we can directly incorporate perceptual loss
in diffusion training to improve sample quality. Since the score matching
objective used in diffusion training strongly resembles the denoising
autoencoder objective used in unsupervised training of perceptual networks, the
diffusion model itself is a perceptual network and can be used to generate
meaningful perceptual loss. We propose a novel self-perceptual objective that
results in diffusion models capable of generating more realistic samples. For
conditional generation, our method only improves sample quality without
entanglement with the conditional input and therefore does not sacrifice sample
diversity. Our method can also improve sample quality for unconditional
generation, which was not possible with classifier-free guidance before.

通过引入自我知觉目标，将扩散模型训练中使用的有噪自动编码器目标与无监督感知网络训练中使用的去噪自动编码器目标关联，我们提出了一种新的扩散模型，可以生成更真实的样本，而不会牺牲样本多样性。