RiT: Vanilla Diffusion Transformers Are Enough in Representation Space

Flow matching with -prediction—regressing the clean data point rather than the ambient velocity—is known to exploit low-dimensional manifold structure effectively in pixel space. We ask whether a pretrained representation space, while containing a low-dimensional data manifold of comparable intrinsic dimensionality, offers a distribution more favorable for flow-matching learning. Comparing pixel, SD-VAE, and DINOv2 features along four geometric axes, we find that pixel and DINOv2 share nearly identical intrinsic dimensionalities (both ) yet DINOv2 exhibits higher effective rank, better covariance conditioning, lower excess kurtosis, and lower on-manifold interpolation error; SD-VAE latents are consistently intermediate, indicating that the advantage stems from representation-learning objectives rather than mere compression. These statistical properties render the flow-matching regression well-conditioned and remove the need for the specialized prediction heads or Riemannian transport used by prior DINOv2 diffusion methods. We propose the Representation Image Transformer (RiT): a vanilla Diffusion Transformer trained by -prediction on frozen DINOv2 features, augmented only by a dimension-aware noise schedule and joint [CLS]-patch modeling. On ImageNet , RiT attains FID 1.45 without guidance and 1.14 with classifier-free guidance, outperforming DiT -XL with fewer parameters (676M vs. 839M). The resulting ODE is efficiently solvable at coarse discretizations: Heun steps already reach FID 2.0 and steps reach 1.25, without distillation or consistency training. Code and model weights will be released.