DiffusionBench: On Holistic Evaluation of Diffusion Transformers

2026-06-23 · 17:59 UTC ·Xingjian Leng, Jaskirat Singh, Zhanhao Liang... ·1 min read

Brief

DiffusionBench argues that DiT evaluation has been overly concentrated on class-conditional ImageNet and introduces NanoGen, a unified training/evaluation framework that reproduces SOTA ImageNet DiT results and—with 12 lines of config—trains competitive text-to-image models. After training 21 latent diffusion models, the authors find low/negative Pearson correlations (−0.377 to −0.580) between ImageNet and T2I rankings, motivating DiffusionBench as a holistic benchmark to better capture broad generative progress.

Why it matters

NanoGen, a unified DiT training/evaluation framework, matches state-of-the-art DiT baselines on ImageNet and can train competitive text-to-image (T2I) models with just 12 lines of configuration change; it supports RAE, VAE, pixel-space, and MeanFlow diffusion methods under both ImageNet and T2I setups.

Key details

After training 21 latent diffusion models with NanoGen, method rankings between ImageNet and T2I show weak/negative agreement: Pearson correlations range from -0.377 to -0.580 across three evaluation metrics, prompting the authors to publish DiffusionBench (ImageNet+T2I) and recommend reporting it instead of ImageNet-only results.

Abstract

Diffusion transformer (DiT) research on image generation has converged to a single evaluation setup: class-conditional generation on ImageNet. While methods improve the FID and related metrics, it is increasingly unclear whether they reflect real progress in generative modeling. The natural alternative, i.e., text-to-image (T2I) generation, is perceived as too costly or inconvenient to train and evaluate and is often skipped. We argue that this perception no longer holds. We introduce NanoGen, a unified DiT training and evaluation framework. NanoGen matches state-of-the-art DiT baselines on ImageNet and, with 12 lines of configuration change, also trains competitive text-to-image models. It currently supports RAE, VAE, pixel-space, and MeanFlow diffusion methods under both ImageNet and T2I setups. Under NanoGen, training T2I requires comparable compute to ImageNet. After training 21 latent diffusion models with NanoGen, we observe that method ranking shows no strong correlation between ImageNet and T2I generation: Pearson correlation is between -0.377 and -0.580 across three metrics. This suggests that a method which improves class-conditional ImageNet FID may show no corresponding improvement on T2I, clearly indicating the necessity of evaluating DiTs on both tasks. To this end, we summarize ImageNet and text-to-image results, which yields DiffusionBench, a holistic benchmark for DiT research. We recommend reporting DiffusionBench in place of ImageNet alone: methods that improve DiffusionBench are more likely to reflect broader progress.