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FastGen Methods

Training methods for fast single-step or few-step generation from diffusion models.

Methods Overview

Category README Method Class Description Reference
Consistency README CM CMModel Consistency model (tuning/distillation) Song et al., 2023, Geng et al., 2024
sCM SCMModel Continuous-time CM with TrigFlow Lu & Song, 2024
TCM TCMModel Two-stage CM with boundary loss Lee et al., 2024
MeanFlow MeanFlowModel Mean velocity prediction Geng et al., 2025, Sabour et al., 2025
Distribution Matching README DMD2 DMD2Model VSD + GAN distillation Yin et al., 2024
f-distill FdistillModel f-divergence weighted DMD2 Xu et al., 2025
LADD LADDModel Pure adversarial distillation Sauer et al., 2024
CausVid CausVidModel Causal DMD2 with diffusion forcing Yin et al., 2024
Self-Forcing SelfForcingModel Causal DMD2 with self-forcing Huang et al., 2025
Fine-Tuning README SFT SFTModel Finetuning with denoising score matching Ho et al., 2020, Song et al., 2020, Lipman et al., 2022, Albergo et al., 2023
CausalSFT CausalSFTModel Causal version of SFT Chen et al., 2024
Knowledge Distillation README KD KDModel Learn from pre-computed trajectories Luhman & Luhman, 2021
CausalKD CausalKDModel Causal version of KD Yin et al., 2024