few-step-generation

This paper introduces untied self-conditioning to correct train-inference mismatch in flow-matching language models, improving few-step generation quality with lower perplexity and no retraining.

This paper proposes MeanFlow-Transfer (MF-T) and Continuous Adversarial MeanFlow (CAMF) to unify the adaptation and acceleration of pretrained diffusion and flow models, enabling high-quality few-step generation on new domains with limited data.

The paper introduces Latent-Kernel Discrete Flow Maps (LKF), a flow-map kernel for discrete diffusion models that captures correlations between positions via a shared latent, enabling few-step generation without distillation and improving text generation perplexity.

Perceptual Flow Matching supervises flow matching in perceptual feature space, enabling high-quality few-step generation with 4-8 sampling steps instead of 35-50, without needing teacher models.

The paper identifies why deterministic few-step generation fails for text while succeeding for images: the sharp categorical readout in text decoders amplifies small errors, causing token flips, whereas continuous image decoders are smooth. It proposes diagnostics (DABI, CCI) and escape mechanisms such as categorical commitment and stochastic re-injection.

VESFlow is a training-free safety method for flow matching-based text-to-image generation that edits velocity fields to ensure safe output while maintaining prompt integrity.

RTDMD is a two-stage framework combining distribution matching distillation with reward-guided reinforcement learning to improve few-step image generation alignment with human preferences. It achieves state-of-the-art results on multiple models with only 4 inference steps.

FlowLM introduces a flow matching language model derived from pre-trained diffusion models via efficient fine-tuning, enabling high-quality few-step text generation that rivals 2,000-step diffusion sampling with far fewer training epochs.