Localizing and Editing Knowledge in Text-to-Image Generative Models
Text-to-Image Diffusion Models such as Stable-Diffusion and Imagen have
achieved unprecedented quality of photorealism with state-of-the-art FID scores
on MS-COCO and other generation benchmarks. Given a caption, image generation
requires fine-grained knowledge about attributes such as object structure,
style, and viewpoint amongst others. Where does this information reside in
text-to-image generative models? In our paper, we tackle this question and
understand how knowledge corresponding to distinct visual attributes is stored
in large-scale text-to-image diffusion models. We adapt Causal Mediation
Analysis for text-to-image models and trace knowledge about distinct visual
attributes to various (causal) components in the (i) UNet and (ii) text-encoder
of the diffusion model. In particular, we show that unlike generative
large-language models, knowledge about different attributes is not localized in
isolated components, but is instead distributed amongst a set of components in
the conditional UNet. These sets of components are often distinct for different
visual attributes. Remarkably, we find that the CLIP text-encoder in public
text-to-image models such as Stable-Diffusion contains only one causal state
across different visual attributes, and this is the first self-attention layer
corresponding to the last subject token of the attribute in the caption. This
is in stark contrast to the causal states in other language models which are
often the mid-MLP layers. Based on this observation of only one causal state in
the text-encoder, we introduce a fast, data-free model editing method
Diff-QuickFix which can effectively edit concepts in text-to-image models.
DiffQuickFix can edit (ablate) concepts in under a second with a closed-form
update, providing a significant 1000x speedup and comparable editing
performance to existing fine-tuning based editing methods.
65372cfde5ad8b46d5391205
Samyadeep Basu
admin_assigned
2023-10-24T08:57:56.743Z
6384d891cafe4e4ac4060206
/avatars/45eb04bf6e0ed1a4daa14070411b693f.svg
Samyadeep
user
samyadeepbasu
65372cfde5ad8b46d5391206
Nanxuan Zhao
65372cfde5ad8b46d5391207
Vlad Morariu
65372cfde5ad8b46d5391208
Soheil Feizi
admin_assigned
2023-10-24T08:58:14.934Z
64b83f32047fa3db94108a9f
/avatars/c9afdb5c4c9dc3bf15c9edb223428f5f.svg
Soheil Feizi
user
sfeizi
65372cfde5ad8b46d5391209
Varun Manjunatha
admin_assigned
2023-10-24T08:58:20.962Z
64234cafd654afd6931a1b22
/avatars/d0adeab15e5f0eef08e5d4c91d0e18b0.svg
Varun Manjunatha
user
vmanjuna
Localizing and Editing Knowledge in Text-to-Image Generative Models
Text-to-Image Diffusion Models such as Stable-Diffusion and Imagen have
achieved unprecedented quality of photorealism with state-of-the-art FID scores
on MS-COCO and other generation benchmarks. Given a caption, image generation
requires fine-grained knowledge about attributes such as object structure,
style, and viewpoint amongst others. Where does this information reside in
text-to-image generative models? In our paper, we tackle this question and
understand how knowledge corresponding to distinct visual attributes is stored
in large-scale text-to-image diffusion models. We adapt Causal Mediation
Analysis for text-to-image models and trace knowledge about distinct visual
attributes to various (causal) components in the (i) UNet and (ii) text-encoder
of the diffusion model. In particular, we show that unlike generative
large-language models, knowledge about different attributes is not localized in
isolated components, but is instead distributed amongst a set of components in
the conditional UNet. These sets of components are often distinct for different
visual attributes. Remarkably, we find that the CLIP text-encoder in public
text-to-image models such as Stable-Diffusion contains only one causal state
across different visual attributes, and this is the first self-attention layer
corresponding to the last subject token of the attribute in the caption. This
is in stark contrast to the causal states in other language models which are
often the mid-MLP layers. Based on this observation of only one causal state in
the text-encoder, we introduce a fast, data-free model editing method
Diff-QuickFix which can effectively edit concepts in text-to-image models.
DiffQuickFix can edit (ablate) concepts in under a second with a closed-form
update, providing a significant 1000x speedup and comparable editing
performance to existing fine-tuning based editing methods.
Text-to-image diffusion models encode visual attributes across multiple components in the UNet and one causal state in the text-encoder, enabling efficient model editing with Diff-QuickFix.
text-to-image diffusion models Stable-Diffusion Imagen FID scores MS-COCO image generation Causal Mediation Analysis UNet text-encoder generative large-language models self-attention layer attribute editing Diff-QuickFix causal states mid-MLP layers model editing