A vanilla Diffusion Transformer trained via x-prediction on frozen DINOv2 features reaches FID 1.14 on ImageNet 256x256 with fewer parameters and faster sampling than prior DiT variants.
simple diffusion: End-to-end diffusion for high resolution images
2 Pith papers cite this work. Polarity classification is still indexing.
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HyperDiT achieves FID 1.56 on ImageNet 256x256 in pixel space via hyper-connected cross-scale interactions, cross-attention, SA-RoPE, and VFM registers.
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RiT: Vanilla Diffusion Transformers Suffice in Representation Space
A vanilla Diffusion Transformer trained via x-prediction on frozen DINOv2 features reaches FID 1.14 on ImageNet 256x256 with fewer parameters and faster sampling than prior DiT variants.
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HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion
HyperDiT achieves FID 1.56 on ImageNet 256x256 in pixel space via hyper-connected cross-scale interactions, cross-attention, SA-RoPE, and VFM registers.