Latent diffusion models (LDMs) power state-of-the-art high-resolution generative image models. LDMs learn the data distribution in the latent space of an autoencoder (AE) and produce images by mapping the generated latents into RGB image space using the AE decoder. While this approach allows for efficient model training and sampling, it induces a disconnect between the training of the diffusion model and the decoder, resulting in a loss of detail in the generated images. To remediate this disconnect, we propose to leverage the internal features of the decoder to define a latent perceptual loss (LPL). This loss encourages the models to create sharper and more realistic images. Our loss can be seamlessly integrated with common autoencoders used in latent diffusion models, and can be applied to different generative modeling paradigms such as DDPM with epsilon and velocity prediction, as well as flow matching. Extensive experiments with models trained on three datasets at 256 and 512 resolution show improved quantitative -- with boosts between 6% and 20% in FID -- and qualitative results when using our perceptual loss.

本研究针对潜在扩散模型在生成图像过程中因模型训练与解码器之间的脱节导致细节损失的问题，提出了一种新的潜在感知损失方法，旨在提高生成图像的细节和真实感。通过将内部特征与常见自编码器结合，实验结果表明，使用此感知损失后，模型在多个数据集上展示了显著的量化和质量提升。 

通过感知目标提升潜在扩散