Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
Inference Endpoints
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@@ -33,7 +33,7 @@ extra_gated_heading: Please read the LICENSE to access this model
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  # BK-SDM Model Card
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- Block-Removed Knowledge-Distilled Stable Diffusion Model (BK-SDM) is a compressed SDM for efficient general-purpose text-to-image synthesis. This model is bulit with (i) removing several residual and attention blocks from the U-Net of [Stable Diffusion v1.4]( https://huggingface.co/CompVis/stable-diffusion-v1-4) and (ii) distillation pretraining on only 0.22M LAION pairs (fewer than 0.1% of the full training set). Despite being trained with very limited resources, our compact model can imitate the original SDM by benefiting from transferred knowledge.
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  - **Resources for more information**: [Paper](https://arxiv.org/abs/2305.15798), [Demo]( https://huggingface.co/spaces/nota-ai/compressed-stable-diffusion).
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  title={On Architectural Compression of Text-to-Image Diffusion Models},
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  author={Kim, Bo-Kyeong and Song, Hyoung-Kyu and Castells, Thibault and Choi, Shinkook},
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  journal={arXiv preprint arXiv:2305.15798},
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- year={2023}
 
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  }
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  ```
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  ```bibtex
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  title={BK-SDM: Architecturally Compressed Stable Diffusion for Efficient Text-to-Image Generation},
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  author={Kim, Bo-Kyeong and Song, Hyoung-Kyu and Castells, Thibault and Choi, Shinkook},
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  journal={ICML Workshop on Efficient Systems for Foundation Models (ES-FoMo)},
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- year={2023}
 
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  }
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- ```
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  *This model card was written by Bo-Kyeong Kim and is based on the [Stable Diffusion v1 model card]( https://huggingface.co/CompVis/stable-diffusion-v1-4).*
 
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  # BK-SDM Model Card
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+ Block-removed Knowledge-distilled Stable Diffusion Model (BK-SDM) is an architecturally compressed SDM for efficient general-purpose text-to-image synthesis. This model is bulit with (i) removing several residual and attention blocks from the U-Net of [Stable Diffusion v1.4]( https://huggingface.co/CompVis/stable-diffusion-v1-4) and (ii) distillation pretraining on only 0.22M LAION pairs (fewer than 0.1% of the full training set). Despite being trained with very limited resources, our compact model can imitate the original SDM by benefiting from transferred knowledge.
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  - **Resources for more information**: [Paper](https://arxiv.org/abs/2305.15798), [Demo]( https://huggingface.co/spaces/nota-ai/compressed-stable-diffusion).
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  title={On Architectural Compression of Text-to-Image Diffusion Models},
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  author={Kim, Bo-Kyeong and Song, Hyoung-Kyu and Castells, Thibault and Choi, Shinkook},
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  journal={arXiv preprint arXiv:2305.15798},
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+ year={2023},
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+ url={https://arxiv.org/abs/2305.15798}
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  }
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  ```
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  ```bibtex
 
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  title={BK-SDM: Architecturally Compressed Stable Diffusion for Efficient Text-to-Image Generation},
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  author={Kim, Bo-Kyeong and Song, Hyoung-Kyu and Castells, Thibault and Choi, Shinkook},
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  journal={ICML Workshop on Efficient Systems for Foundation Models (ES-FoMo)},
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+ year={2023},
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+ url={https://openreview.net/forum?id=bOVydU0XKC}
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  }
 
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  *This model card was written by Bo-Kyeong Kim and is based on the [Stable Diffusion v1 model card]( https://huggingface.co/CompVis/stable-diffusion-v1-4).*