Boosting Latent Diffusion with Perceptual Objectives
Résumé
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.
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