Advancing End-to-End Pixel Space Generative Modeling via Self-supervised
  Pre-training
Authors: name: Jiachen Lei, name: Keli Liu, name: Julius Berner, name: Haiming Yu, name: Hongkai Zheng, name: Jiahong Wu, name: Xiangxiang Chu
Organization: AMAP-ML

Pixel-space generative models are often more difficult to train and generally
underperform compared to their latent-space counterparts, leaving a persistent
performance and efficiency gap. In this paper, we introduce a novel two-stage
training framework that closes this gap for pixel-space diffusion and
consistency models. In the first stage, we pre-train encoders to capture
meaningful semantics from clean images while aligning them with points along
the same deterministic sampling trajectory, which evolves points from the prior
to the data distribution. In the second stage, we integrate the encoder with a
randomly initialized decoder and fine-tune the complete model end-to-end for
both diffusion and consistency models. Our training framework demonstrates
strong empirical performance on ImageNet dataset. Specifically, our diffusion
model reaches an FID of 2.04 on ImageNet-256 and 2.35 on ImageNet-512 with 75
number of function evaluations (NFE), surpassing prior pixel-space methods by a
large margin in both generation quality and efficiency while rivaling leading
VAE-based models at comparable training cost. Furthermore, on ImageNet-256, our
consistency model achieves an impressive FID of 8.82 in a single sampling step,
significantly surpassing its latent-space counterpart. To the best of our
knowledge, this marks the first successful training of a consistency model
directly on high-resolution images without relying on pre-trained VAEs or
diffusion models.