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This paper introduces SGFlow, a method for learning flow maps for diffusion models that avoids invertibility constraints and backpropagation through model iterations, achieving competitive FID scores on CIFAR with a proven stationary-point guarantee.
Proposes Flow-Map GRPO, an online RL post-training framework for deterministic few-step flow-map generators, introducing Anchored Stochastic Flow Map Composition (ASFMC) to enable stochastic optimization without altering original model parameterization. Experiments on FLUX-based MeanFlow and sCM show improvement across reward-based, perceptual, and task-level metrics.
This paper introduces GCCM, a graph contrastive consistency model that improves generative graph prediction by mitigating shortcut solutions in consistency training through negative pairs and feature perturbation.
OpenAI presents sCM (simplified continuous-time consistency models), a new approach that scales consistency models to 1.5B parameters and achieves ~50x speedup over diffusion models by generating high-quality samples in just 2 steps. The method demonstrates comparable sample quality to state-of-the-art diffusion models while using less than 10% of the effective sampling compute.
OpenAI presents improved techniques for training consistency models that enable high-quality single-step image generation without distillation, achieving significant FID improvements on CIFAR-10 and ImageNet 64×64 through novel loss functions and training strategies.