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It is demonstrated that for uniform-state diffusion LMs, where a block's denoising progress is not visible in its intermediate tokens, concurrent decoding is advantageous if the next block's tokens are promoted together with the corresponding sampler state, including the self-conditioning signal and the denoising time.
Diffusion LAIR is proposed, a reward-aware listwise preference optimization method for diffusion models that outperforms strong preference optimization baselines on SD1.5 and SDXL across text-to-image generation, compositional generation, and image editing benchmarks.
This work proposes ABC: Any-Subset Autoregressive Models via Non-Markovian Diffusion Bridges in Continuous Time and Space, and derives SDE dynamics via changes-of-measure on path space, yielding another advantage: path-dependent conditioning on arbitrary subsets of the state history and/or future.
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