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We show that continuous bitstream diffusion (CoBit), originally developed for language modeling, provides a unified framework for joint and conditional multimodal generation using the same training and sampling protocol. Across image-text, speech-text and protein sequence-structure modeling, models are trained separate...
Diffusion models rely on numerical solvers requiring time-discretization, which has a large influence on the tradeoff between sampling cost and quality. However, the computational difficulty of the reverse process varies along the sampling trajectory and across data distributions, making the choice of discretization im...
This work hypothesizes that generative image editing can simulate a controlled domain shift in training data, effectively bridging the gap between source and target domains, and studies camouflaged military vehicle detection as a challenging domain shift scenario.
A general statistical framework for studying asymptotically optimal noise-level allocation in diffusion training and an idealized independent-learner regime, intended to model temporal specialization in neural networks are developed.
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