OPPOer/TLCMFlux

TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps

📃 Paper •

we propose an innovative two-stage data-free consistency distillation (TDCD) approach to accelerate latent consistency model. The first stage improves consistency constraint by data-free sub-segment consistency distillation (DSCD). The second stage enforces the global consistency across inter-segments through data-free consistency distillation (DCD). Besides, we explore various techniques to promote TLCM’s performance in data-free manner, forming Training-efficient Latent Consistency Model (TLCM) with 2-8 step inference.

TLCM demonstrates a high level of flexibility by enabling adjustment of sampling steps within the range of 2 to 8 while still producing competitive outputs compared to full-step approaches.

This is for Flux-base LoRA.