Controlled scaling shows a 2.3B self-attention U-ViT matches or slightly outperforms SDXL U-Net and larger cross-attention DiT variants, while long captions and larger datasets improve text-image alignment.
[Online; accessed 4-March-2024]
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Efficient Scaling of Diffusion Transformers for Text-to-Image Generation
Controlled scaling shows a 2.3B self-attention U-ViT matches or slightly outperforms SDXL U-Net and larger cross-attention DiT variants, while long captions and larger datasets improve text-image alignment.