3

Figure 3. Method overview. Given a text prompt p containing our concept S∗, we compute the token embedding M(t, ℓ) = vt,ℓ ∈ P
representing the concept at the current timestep t and U-Net layer ℓ. This embedding, along with the embeddings of the other input tokens
are passed to the pretrained text encoder to obtain the conditioning vector c(y,M(t, ℓ)) ∈ RN×D. This conditioning is fed to the U-Net
network at the ℓ-th cross-attention layer. During training, we optimize the parameters of the neural mapper M, shown to the right, using a
reconstruction objective (Equation (3)) while all other components remain fixed.

These token embeddings are then passed to a pretrained
CLIP text encoder [24] which outputs a conditioning vec-
tor c(y) ∈ RN×D where N = 77 is the number of input
tokens and D = 768 is the dimension of each output vector.
A Neural Space-Time Representation for Text-to-Image Personalization