DiffusionBench: On Holistic Evaluation of Diffusion Transformers
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. ...
| Authors | Xingjian Leng, Jaskirat Singh, Zhanhao Liang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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