A key aspect of text-to-image personalization methods is
the manner in which the target concept is represented within
the generative process. This choice greatly affects the vi-
sual fidelity, downstream editability, and disk space needed
to store the learned concept.
In this paper, we explore a
new text-conditioning space that is dependent on both the
denoising process timestep (time) and the denoising U-Net
layers (space) and showcase its compelling properties. A
single concept in the space-time representation is composed
of hundreds of vectors, one for each combination of time
and space, making this space challenging to optimize di-
rectly. Instead, we propose to implicitly represent a concept
in this space by optimizing a small neural mapper that re-
ceives the current time and space parameters and outputs
the matching token embedding. In doing so, the entire per-
sonalized concept is represented by the parameters of the
learned mapper, resulting in a compact, yet expressive rep-
A Neural Space-Time Representation for Text-to-Image Personalization