Discrete MeanFlow parameterizes CTMC conditional transition kernels with a boundary-by-construction design to enable exact one-step generation in discrete state spaces.
AlphaFlow: Understanding and improving MeanFlow models
7 Pith papers cite this work. Polarity classification is still indexing.
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W-Flow achieves state-of-the-art one-step ImageNet 256x256 generation at 1.29 FID by training a static neural network to follow a Wasserstein gradient flow that minimizes Sinkhorn divergence, delivering roughly 100x faster sampling than comparable multi-step models.
Tyche achieves competitive probabilistic weather forecasting skill and calibration using a single-step flow model with JVP-regularized training and rollout finetuning.
By requiring and using highly discriminative LLM text features, the work enables the first effective one-step text-conditioned image generation with MeanFlow.
SnapFlow compresses multi-step denoising in flow-matching VLAs into one step via progressive self-distillation using two-step Euler shortcuts from marginal velocities, matching 10-step teacher success rates with 9.6x speedup on pi0.5.
SubFlow restores full mode coverage in one-step flow matching by conditioning on sub-modes from semantic clustering, yielding higher diversity on ImageNet-256 while preserving FID.
A DMF curriculum initialized from pretrained flow models achieves one-step FID 3.36 on CIFAR-10 after only 2000 epochs by exploiting a discretized consistency property in the Meanflow objective.
citing papers explorer
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Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation
By requiring and using highly discriminative LLM text features, the work enables the first effective one-step text-conditioned image generation with MeanFlow.
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SnapFlow: One-Step Action Generation for Flow-Matching VLAs via Progressive Self-Distillation
SnapFlow compresses multi-step denoising in flow-matching VLAs into one step via progressive self-distillation using two-step Euler shortcuts from marginal velocities, matching 10-step teacher success rates with 9.6x speedup on pi0.5.