Rod flow models for Adam and related optimizers track discrete iterates at the edge of stability more accurately than standard stable flows across tested ML architectures.
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RDM trains one-step generators via MMD on large batches and multi-encoder representations, achieving SOTA SW_r14 of 1.30 on ImageNet and distilling FLUX.2 to one-step with gains on GenEval and PickScore.
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A Rod Flow Model for Adam at the Edge of Stability
Rod flow models for Adam and related optimizers track discrete iterates at the edge of stability more accurately than standard stable flows across tested ML architectures.
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Representation Distribution Matching for One-Step Visual Generation
RDM trains one-step generators via MMD on large batches and multi-encoder representations, achieving SOTA SW_r14 of 1.30 on ImageNet and distilling FLUX.2 to one-step with gains on GenEval and PickScore.