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README.md

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 license: apache-2.0
 library_name: diffusers
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+---
+# TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps
+
+<p align="center">
+   📃 <a href="https://arxiv.org/html/2406.05768v5" target="_blank">Paper</a> • 
+   🤗 <a href="https://huggingface.co/OPPOer/TLCM" target="_blank">Checkpoints</a> 
+</p>
+
+<!-- **TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps** -->
+
+<!-- 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. -->
+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 SDXL-base LoRA.