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20142026
most citedFedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data

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cs.CV2026

Token Pruning for In-Context Generation in Diffusion Transformers

Junqing Lin, Xingyu Zheng, Pei Cheng +3

In-context generation significantly enhances Diffusion Transformers (DiTs) by enabling controllable image-to-image generation through reference examples. However, the resulting inp…