  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 visual 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 directly. Instead, we propose to
implicitly represent a concept in this space by optimizing a small neural
mapper that receives the current time and space parameters and outputs the
matching token embedding. In doing so, the entire personalized concept is
represented by the parameters of the learned mapper, resulting in a compact,
yet expressive, representation. Similarly to other personalization methods, the
output of our neural mapper resides in the input space of the text encoder. We
observe that one can significantly improve the convergence and visual fidelity
of the concept by introducing a textual bypass, where our neural mapper
additionally outputs a residual that is added to the output of the text
encoder. Finally, we show how one can impose an importance-based ordering over
our implicit representation, providing users control over the reconstruction
and editability of the learned concept using a single trained model. We
demonstrate the effectiveness of our approach over a range of concepts and
prompts, showing our method's ability to generate high-quality and controllable
compositions without fine-tuning any parameters of the generative model itself.
