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  [TokenCompose_SD14_B](https://mlpc-ucsd.github.io/TokenCompose/) is a [latent text-to-image diffusion model](https://arxiv.org/abs/2112.10752) finetuned from the [**Stable-Diffusion-v1-4**](https://huggingface.co/CompVis/stable-diffusion-v1-4) checkpoint at resolution 512x512 on the [VSR](https://github.com/cambridgeltl/visual-spatial-reasoning) split of [COCO image-caption pairs](https://cocodataset.org/#download) for 24,000 steps with a learning rate of 5e-6. The training objective involves token-level grounding terms in addition to denoising loss for enhanced multi-category instance composition and photorealism. The "_A/B" postfix indicates different finetuning runs of the model using the same above configurations.
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  # 🧨Example Usage
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  We strongly recommend using the [🤗Diffuser](https://github.com/huggingface/diffusers) library to run our model.
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  </table>
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  # 📰 Citation
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- Coming soon!
 
 
 
 
 
 
 
 
 
 
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  [TokenCompose_SD14_B](https://mlpc-ucsd.github.io/TokenCompose/) is a [latent text-to-image diffusion model](https://arxiv.org/abs/2112.10752) finetuned from the [**Stable-Diffusion-v1-4**](https://huggingface.co/CompVis/stable-diffusion-v1-4) checkpoint at resolution 512x512 on the [VSR](https://github.com/cambridgeltl/visual-spatial-reasoning) split of [COCO image-caption pairs](https://cocodataset.org/#download) for 24,000 steps with a learning rate of 5e-6. The training objective involves token-level grounding terms in addition to denoising loss for enhanced multi-category instance composition and photorealism. The "_A/B" postfix indicates different finetuning runs of the model using the same above configurations.
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+ # 📄 Paper
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+ Please follow [this](https://arxiv.org/abs/2312.03626) link.
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  # 🧨Example Usage
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  We strongly recommend using the [🤗Diffuser](https://github.com/huggingface/diffusers) library to run our model.
 
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  </table>
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  # 📰 Citation
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+ ```bibtex
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+ @misc{wang2023tokencompose,
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+ title={TokenCompose: Grounding Diffusion with Token-level Supervision},
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+ author={Zirui Wang and Zhizhou Sha and Zheng Ding and Yilin Wang and Zhuowen Tu},
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+ year={2023},
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+ eprint={2312.03626},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV}
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+ }
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+ ```