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license: apache-2.0 |
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library_name: diffusers |
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--- |
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# TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps |
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<p align="center"> |
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📃 <a href="https://arxiv.org/html/2406.05768v5" target="_blank">Paper</a> • |
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</p> |
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<!-- **TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps** --> |
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<!-- 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. |
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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. |
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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 |
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to full-step approaches. --> |
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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 |
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global consistency across inter-segments through data-free consistency distillation (DCD). Besides, we explore various |
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techniques to promote TLCM’s performance in data-free manner, forming Training-efficient Latent Consistency |
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Model (TLCM) with 2-8 step inference. |
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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 |
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to full-step approaches. |
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## This is for SDXL-base LoRA. |
