JLT: Clean-Latent Prediction in Latent Diffusion Transformers 事件

JLT: Clean-Latent Prediction in Latent Diffusion Transformers arXiv:2605.27102v1 Announce Type: new Abstract: Flow matching with clean-data prediction has shown that regressing the clean point can exploit low-dimensional structure more effectively than predicting an ambient noised quantity. We ask whether this principle remains useful after images are mapped into a learned latent space, where compression has already removed much of the raw pixel variability. We introduce JLT, a 130M latent diff