Our neural mapper is illustrated on the right of Fig-
ure 3. The mapper receives as input a pair of scalars
(t, ℓ) denoting the current timestep and U-Net layer. First,
this input is passed through a positional encoding func-
tion f (·), discussed below, to transform the input into a
high-dimensional vector f (t, ℓ), followed by two fully-
connected layers. Next, we optionally apply a Nested
Dropout [30] technique over the hidden representation to
impose an importance-based ordering over the learned rep-
resentation, see Section 4.2. The resulting compressed vec-
tor is passed through a final fully-connected layer to obtain a
768-dimensional vector vt,ℓ ∈ P representing the concept at
the current timestep and layer. In total, the resulting archi-
tecture contains approximately 460, 000 trainable parame-
ters, which amounts to 2MB of disk space. As a reference
to fine-tuning methods, DreamBooth [32] requires several
GBs of disk space per concept with CustomDiffusion [14]
A Neural Space-Time Representation for Text-to-Image Personalization