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  # Composable Diffusion
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  **Compositional Visual Generation with Composable Diffusion Models (ECCV 2022)**
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- [Webpage](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) | [GitHub](https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch)
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  ## Overview
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- We propose to use **conjunction and negation** (negative prompts) operators for **compositional generation with conditional diffusion models in test time without any training**. For more details, please refer to our paper:
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- [Compositional Visual Generation with Composable Diffusion Models](https://arxiv.org/abs/2206.01714)
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- [Nan Liu](https://nanliu.io)*\*, [Shuang Li](https://people.csail.mit.edu/lishuang)*\*, [Yilun Du](https://yilundu.github.io)*\*, [Antonio Torralba](https://groups.csail.mit.edu/vision/torralbalab/), [Joshua B. Tenenbaum](https://mitibmwatsonailab.mit.edu/people/joshua-tenenbaum/), **ECCV 2022**
 
 
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  ## Citation
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  # Composable Diffusion
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  **Compositional Visual Generation with Composable Diffusion Models (ECCV 2022)**
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+ **[Webpage](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) | [GitHub](https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch)**
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  ## Overview
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+ We propose to use **conjunction and negation** (negative prompts) operators for **compositional generation with conditional diffusion models in test time without any training**.
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+ For more details, please refer to our paper:
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+ [Compositional Visual Generation with Composable Diffusion Models](https://arxiv.org/abs/2206.01714).<br>
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+ [Nan Liu](https://nanliu.io)*\, [Shuang Li](https://people.csail.mit.edu/lishuang)*\, [Yilun Du](https://yilundu.github.io)*\, [Antonio Torralba](https://groups.csail.mit.edu/vision/torralbalab/), [Joshua B. Tenenbaum](https://mitibmwatsonailab.mit.edu/people/joshua-tenenbaum/), **ECCV 2022**
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  ## Citation
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