📃 Paper • 🤗 Checkpoints 📰 Github
Our method accelerates LDMs via data-free multistep latent consistency distillation (MLCD), and data-free latent consistency distillation is proposed to efficiently guarantee the inter-segment consistency in MLCD. Furthermore, we introduce bags of techniques, e.g., distribution matching, adversarial learning, and preference learning, to enhance TLCM’s performance at few-step inference without any real data. 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. Details are presented in the [paper](https://arxiv.org/html/2406.05768v5) and [Github](https://github.com/OPPO-Mente-Lab/TLCM). ## Art Gallery Here we present some examples with different sampling steps.
2-Steps Sampling.
3-Steps Sampling.
4-Steps Sampling.
8-Steps Sampling.
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