OrbitQuant is a data-agnostic PTQ technique for DiTs that uses RPBH rotation in a normalized basis to enable a single codebook across all inputs, achieving SOTA low-bit performance on FLUX.1, CogVideoX and similar models.
arXiv preprint arXiv:2601.19675 , year=
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C²R framework for robust dataset distillation prioritizes small-margin adversaries via a derived perturbation score and widens class boundaries with contrastive loss, yielding 2.8% average robust accuracy gains on CIFAR and ImageNet benchmarks.
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OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers
OrbitQuant is a data-agnostic PTQ technique for DiTs that uses RPBH rotation in a normalized basis to enable a single codebook across all inputs, achieving SOTA low-bit performance on FLUX.1, CogVideoX and similar models.
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Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?
C²R framework for robust dataset distillation prioritizes small-margin adversaries via a derived perturbation score and widens class boundaries with contrastive loss, yielding 2.8% average robust accuracy gains on CIFAR and ImageNet benchmarks.