Instruction-based video editing is commonly built on video-pretrained generative backbones: a video diffusion transformer is adapted, at considerable cost, to condition on a source video and an editing instruction. In this report we explore a different route and show that a strong instruction-based image editing model...
Yun-Peng Bai, Yossi Gandelsman, Michael Gharbi et al.· 3 citations
This work introduces LeSAMP, a framework for learning prompt-conditioned, timestep-varying sampling parameters, and suggests that learned sampling-parameter policies provide a complementary approach to existing post-training methods for improving diffusion model outputs.
The results identify modality-order sensitivity as a circuit-level failure in VLMs and demonstrate that simple, asymmetric test-time adaptation can effectively mitigate it and even improve performance over the baseline.
The proposed DuSPiT uses a compact base branch for efficient global reasoning and a parallel, high-capacity pixel branch, organized into subpatch groups, to preserve detailed appearance, with the two branches interacting through cross-attention.
Yunpeng Bai, Yossi Gandelsman, Michael Gharbi· arXiv.org· 0 citations
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