Positional Encoding To introduce an inductive bias with
respect to the timestep and U-Net layer, we apply a posi-
tional encoding on the input (t, ℓ). Specifically, each in-
put (t, ℓ) is encoded with Random Fourier Features [25,35]

4

𝑡ℓPositional EncodingFully ConnectedLayersNested Dropoutℎ𝑖>𝑡=0𝑡~𝑈[0,𝑑!]Fully Connected & RescalingAPhotoofa𝑆∗𝑡ℓ𝑣"𝑣#$%&%𝑣"𝑣%’𝑣&,ℓText Encoder𝑐𝑦,ℳ𝑡,ℓU-NetLayerℓℳNeural Mapper𝑣!,ℓ∈𝒫w/o rescale w/ rescale w/o rescale w/ rescale

Real

“An app icon...”

“A watercolor painting...”

Real

“S∗ on a beach”
A Neural Space-Time Representation for Text-to-Image Personalization