The Hidden Language of Diffusion Models
Authors: name: Hila Chefer, name: Oran Lang, name: Mor Geva, name: Volodymyr Polosukhin, name: Assaf Shocher, name: Michal Irani, name: Inbar Mosseri, name: Lior Wolf

Text-to-image diffusion models have demonstrated an unparalleled ability to
generate high-quality, diverse images from a textual concept (e.g., "a doctor",
"love"). However, the internal process of mapping text to a rich visual
representation remains an enigma. In this work, we tackle the challenge of
understanding concept representations in text-to-image models by decomposing an
input text prompt into a small set of interpretable elements. This is achieved
by learning a pseudo-token that is a sparse weighted combination of tokens from
the model's vocabulary, with the objective of reconstructing the images
generated for the given concept. Applied over the state-of-the-art Stable
Diffusion model, this decomposition reveals non-trivial and surprising
structures in the representations of concepts. For example, we find that some
concepts such as "a president" or "a composer" are dominated by specific
instances (e.g., "Obama", "Biden") and their interpolations. Other concepts,
such as "happiness" combine associated terms that can be concrete ("family",
"laughter") or abstract ("friendship", "emotion"). In addition to peering into
the inner workings of Stable Diffusion, our method also enables applications
such as single-image decomposition to tokens, bias detection and mitigation,
and semantic image manipulation. Our code will be available at:
https://hila-chefer.github.io/Conceptor/