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@@ -6,7 +6,6 @@ library_name: diffusers
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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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- 🤗 <a href="https://huggingface.co/OPPOer/TLCM" target="_blank">Checkpoints</a>
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  </p>
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  <!-- **TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps** -->
@@ -25,4 +24,4 @@ global consistency across inter-segments through data-free consistency distillat
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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.
 
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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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  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.