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Published at November 2Hollowed Net for On-Device Personalization of Text-to-Image Diffusion Models
cs.CV
cs.AI
cs.GR
cs.LG
Released Date: November 2, 2024
Authors: Wonguk Cho1, Seokeon Choi1, Debasmit Das1, Matthias Reisser1, Taesup Kim2, Sungrack Yun1, Fatih Porikli1
Aff.: 1Qualcomm AI Research; 2Seoul National University

| Method | # of Parameters | Training Memory | DreamBooth | CustomConcept101 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Base | LoRA | Peak | Comp. w/ Inf. | DINO | CLIP-I | CLIP-T | DINO | CLIP-I | CLIP-T | ||||
| Full FT | 866M | - | 16.62GB | +376% | 0.663 | 0.802 | 0.302 | 0.605 | 0.773 | 0.302 | |||
| LoRA FT (r=128) | 866M | 27M | 5.23GB | +50% | 0.658 | 0.806 | 0.299 | 0.603 | 0.773 | 0.302 | |||
| LoRA FT (r=1) | 866M | 207K | 4.84GB | +39% | 0.516 | 0.738 | 0.314 | 0.522 | 0.737 | 0.305 | |||
| Hollowed Net (Ours) | 527M | 24M | 3.88GB | +11% | 0.660 | 0.805 | 0.300 | 0.603 | 0.773 | 0.302 | |||