The paper applies the locality-free principle to a Diffusion Transformer (DiT) for class-conditional image generation on ImageNet. They compare their pixel-based variant (DiT/1) operating on latent tokens from a VQGAN to the standard patch-based DiT (DiT/2). The pixel-based DiT achieves better FID, sFID, and Inception Score, demonstrating that the removal of locality is beneficial even in a generative setting with a different input representation and architecture.
Evidence
correlational
Key metric
FID (↓): DiT-l/2 (4.16) vs. DiT-l/1 (4.05); sFID (↓): DiT-l/2 (4.97) vs. DiT-l/1 (4.66); IS (↑): DiT-l/2 (210.18) vs. DiT-l/1 (232.95).
Caveat
The extended training results (1400 epochs) show a larger gap, but the main comparison is at 400 epochs. The paper notes the high computational cost of the pixel-based approach.