  In recent years, advances in text-to-image (T2I) diffusion models have
substantially elevated the quality of their generated images. However,
achieving fine-grained control over attributes remains a challenge due to the
limitations of natural language prompts (such as no continuous set of
intermediate descriptions existing between ``person'' and ``old person''). Even
though many methods were introduced that augment the model or generation
process to enable such control, methods that do not require a fixed reference
image are limited to either enabling global fine-grained attribute expression
control or coarse attribute expression control localized to specific subjects,
not both simultaneously. We show that there exist directions in the commonly
used token-level CLIP text embeddings that enable fine-grained subject-specific
control of high-level attributes in text-to-image models. Based on this
observation, we introduce one efficient optimization-free and one robust
optimization-based method to identify these directions for specific attributes
from contrastive text prompts. We demonstrate that these directions can be used
to augment the prompt text input with fine-grained control over attributes of
specific subjects in a compositional manner (control over multiple attributes
of a single subject) without having to adapt the diffusion model. Project page:
https://compvis.github.io/attribute-control. Code is available at
https://github.com/CompVis/attribute-control.
