Title: Pyramid Patchification Flow for Visual Generation
Primary area: generative models
Keywords: visual generation，flow matching，pyramidal patchification

Abstract: Diffusion Transformers (DiTs) typically use the same patch size for
$\operatorname{Patchify}$
across timesteps, 
enforcing a constant token budget across timesteps.
In this paper, we introduce Pyramidal Patchification Flow (PPFlow),
which reduces the number of tokens for high-noise timesteps
to improve the sampling efficiency.
The idea is simple:
use larger patches at higher-noise timesteps and smaller patches at lower-noise timesteps.
The implementation is easy:
share the DiT's transformer blocks across timesteps, 
and learn separate linear projections 
for different patch sizes in
$\operatorname{Patchify}$ 
and 
$\operatorname{Unpatchify}$. 
Unlike Pyramidal Flow
that operates on pyramid representations,,
our approach operates over
full latent representations,
eliminating trajectory ``jump points'', 
and thus avoiding re-noising tricks for sampling. 
Training from pretrained SiT-XL/2 requires only $+8.9\%$ additional training FLOPs and delivers 
$2.02\times$ 
denoising speedups with image generation quality kept; 
training from scratch achieves comparable
sampling speedup, 
e.g., 
$2.04\times$ speedup in SiT-B.
Training from text-to-image model FLUX.1, PPFlow can achieve $1.61 - 1.86 \times$ speedup from 512 to 2048 resolution with comparable quality.

TL;DR: A new method to accelerate diffusion model with pyramidal patchification.