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0.2400.2430.2450.2480.2500.2530.2550.2580.2600.2630.2650.2680.270 0.71 0.72 0.73 0.74 0.75 0.76 0.77CLIP-Based Image and Text Similarities: CustomDiffusion DatasetText SimilarityImage Similarity500P+1000P+5000500TIDreamBooth500CustomDiffusion500NeTIw/o BypassNeTIw/ Bypass250NeTIw/ Bypass5001000NeTIw/ BypassGeometry

Appearance

Time-Based Mixing Control

Figure 20. Style mixing results obtained with NeTI. Given two concepts, the embeddings of the geometry concept are passed to layers
(16, down,0),(16,down,1),(8,down,0) while the embeddings of the appearance concept are passed to the remaining U-Net
layers. To provide an additional axis of control, we also leverage the time component of P∗ and begin the mixing at various denoising
timesteps. From left to right: t = 600, 700, 800, and 900. As can be seen, starting the mixing later, i.e., a smaller value of t passes more
information from the geometry concept to the output image.

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G. Additional Qualitative Results
A Neural Space-Time Representation for Text-to-Image Personalization