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Introducing ConceptAttention, an approach to interpreting diffusion transformer models! Write a prompt, choose some concepts, generate an image, and get high-quality heatmaps of text concepts. Our method outperforms existing methods like cross attention. Link to demo 👇

36,631 Aufrufe • vor 1 Jahr •via X (Twitter)

11 Kommentare

Profilbild von Alec Helbling
Alec Helblingvor 1 Jahr

We have a live interactive demo hosted on Huggingface Spaces:

Profilbild von Alec Helbling
Alec Helblingvor 1 Jahr

Check out the code here:

Profilbild von Alec Helbling
Alec Helblingvor 1 Jahr

We repurpose the parameters of multi-modal DiT models (i.e. Flux) without training to create rich contextualized embeddings of text concepts. This allows us to create high quality saliency maps. We wrote a paper about our method:

Profilbild von Rainmaker
Rainmakervor 2 Jahren

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Profilbild von Rishi
Rishivor 1 Jahr

Very Nice Idea, Explainable AI as a field is not that much explored so nice to see good work in that domain

Profilbild von Rishi
Rishivor 1 Jahr

Will this work for not non Diffusion based models ?

Profilbild von Minh Nhat Nguyen
Minh Nhat Nguyenvor 1 Jahr

i am ... going to see how well this works for video

Profilbild von  007
 007vor 1 Jahr

Cool

Profilbild von Julien Blanchon 🇺🇦
Julien Blanchon 🇺🇦vor 1 Jahr

Curious about what your intuition about the entanglement between dog and cat features ?

Profilbild von Alec Helbling
Alec Helblingvor 1 Jahr

Absolutely. Our observations have been that this approach works very well for discerning distinct features (like dog and background) but struggles with examples like you show where you have two very similar concepts. There is clearly some more complex mechanism that allows the model to differentiate between these concepts that unfortunately our approach alone is not able to discern. It is worth noting this limitation is also at play in cross attention mechanisms, and poor object attribute assignment is a known limitation of current diffusion models.

Profilbild von Latent Spacer
Latent Spacervor 1 Jahr

I learned a lot from the paper, great work 👏

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