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Found 3 papers, 1 papers with code
E-MD3C: Taming Masked Diffusion Transformers for Efficient Zero-Shot Object Customization
We propose E-MD3C ($\underline{E}$fficient $\underline{M}$asked $\underline{D}$iffusion Transformer with Disentangled $\underline{C}$onditions and $\underline{C}$ompact $\underline{C}$ollector), a highly efficient framework for zero-shot object image customization.
Cross-view Masked Diffusion Transformers for Person Image Synthesis
Our compact 33MB model achieves an FID of 7. 42, surpassing a prior Unet latent diffusion approach (FID 8. 07) using only $11\times$ fewer parameters.
Self-Supervised Visual Representation Learning via Residual Momentum
To solve this problem, we propose "residual momentum" to directly reduce this gap to encourage the student to learn the representation as close to that of the teacher as possible, narrow the performance gap with the teacher, and significantly improve the existing SSL.
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