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| title: Composable-Diffusion | |
| sdk: gradio | |
| sdk_version: 3.12.0 | |
| app_file: app.py | |
| pinned: true | |
| # Composable Diffusion | |
| **Compositional Visual Generation with Composable Diffusion Models (ECCV 2022)** | |
| **[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)** | |
| ## Overview | |
| 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: | |
| [Compositional Visual Generation with Composable Diffusion Models](https://arxiv.org/abs/2206.01714).<br> | |
| [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** | |
| ## Citation | |
| If you find our paper useful in your research, please cite the following paper: | |
| ``` latex | |
| @article{liu2022compositional, | |
| title={Compositional Visual Generation with Composable Diffusion Models}, | |
| author={Liu, Nan and Li, Shuang and Du, Yilun and Torralba, Antonio and Tenenbaum, Joshua B}, | |
| journal={arXiv preprint arXiv:2206.01714}, | |
| year={2022} | |
| } | |
| ``` | |