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@@ -10,7 +10,7 @@ I regularly update this codebase. Please submit an issue if you have any questio
In [our paper](https://aclanthology.org/2023.acl-long.310), we propose diffusion attentive attribution maps (DAAM), a cross attention-based approach for interpreting Stable Diffusion.
Check out our demo: https://huggingface.co/spaces/tetrisd/Diffusion-Attentive-Attribution-Maps.
See our [documentation](https://castorini.github.io/daam/), hosted by GitHub pages, and [our Colab notebook](https://colab.research.google.com/drive/1miGauqa07uHnDoe81NmbmtTtnupmlipv?usp=sharing), updated for v0.0.11.
See our [documentation](https://castorini.github.io/daam/), hosted by GitHub pages, and [our Colab notebook](https://colab.research.google.com/drive/1miGauqa07uHnDoe81NmbmtTtnupmlipv?usp=sharing), updated for v0.1.0.
## Getting Started
First, install [PyTorch](https://pytorch.org) for your platform.
