SD-DiT: Unleashing the Power of Self-supervised Discrimination in Diffusion Transformer

TL;DR

This paper introduces SD-DiT, a novel approach that leverages self-supervised discrimination in diffusion transformers to improve training efficiency and generative quality, addressing limitations of previous mask strategies.

Contribution

The work proposes a teacher-student framework for DiT that decouples discriminative and generative objectives, enhancing training effectiveness and addressing mask strategy limitations.

Findings

Achieves a better balance between training cost and generative quality.

Outperforms previous methods on ImageNet in efficiency and quality.

Introduces a novel discriminative loss for inter-image alignment.

Abstract

Diffusion Transformer (DiT) has emerged as the new trend of generative diffusion models on image generation. In view of extremely slow convergence in typical DiT, recent breakthroughs have been driven by mask strategy that significantly improves the training efficiency of DiT with additional intra-image contextual learning. Despite this progress, mask strategy still suffers from two inherent limitations: (a) training-inference discrepancy and (b) fuzzy relations between mask reconstruction & generative diffusion process, resulting in sub-optimal training of DiT. In this work, we address these limitations by novelly unleashing the self-supervised discrimination knowledge to boost DiT training. Technically, we frame our DiT in a teacher-student manner. The teacher-student discriminative pairs are built on the diffusion noises along the same Probability Flow Ordinary Differential Equation (PF-ODE). Instead of applying mask reconstruction loss over both DiT encoder and decoder, we decouple DiT encoder and decoder to separately tackle discriminative and generative objectives. In particular, by encoding discriminative pairs with student and teacher DiT encoders, a new discriminative loss is designed to encourage the inter-image alignment in the self-supervised embedding space. After that, student samples are fed into student DiT decoder to perform the typical generative diffusion task. Extensive experiments are conducted on ImageNet dataset, and our method achieves a competitive balance between training cost and generative capacity.