2

𝑧!𝑧!"#𝑡ℓ$𝑡..  .ℓ%ℓ&𝑡-1NeTI𝒫∗Space𝑧!𝑧!"#𝑧!𝑧!"#Textual Inversion𝒫SpaceExtended Textual Inversion𝒫+SpaceWe demonstrate the effectiveness of our P∗ space and
NeTI over a range of concepts and prompts when com-
pared to existing personalization approaches. We addition-
ally analyze P∗ and the attributes learned at different de-
noising timesteps. Finally, we demonstrate the appealing
properties of our ordered representations for controlling the
reconstruction-editability tradeoff at inference time.

2. Related Work
Text-Guided Synthesis. Recent advancements in large-
scale autoregressive models [28, 44] and diffusion mod-
els [8, 12, 21] have resulted in unprecedented diversity and
fidelity in visual content creation guided by a free-form text
prompt [4,20,27,31,33]. While extremely expressive, these
methods do not directly support the usage of user-specified
concepts, resulting in research on inversion and personal-
ization for diffusion models.
A Neural Space-Time Representation for Text-to-Image Personalization