Papers › Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings
Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings
Pura Peetathawatchai, Wei-Ning Chen, Berivan Isik, Sanmi Koyejo, Albert No
We introduce a novel method for adapting diffusion models under differential privacy (DP) constraints, enabling privacy-preserving style and content transfer without fine-tuning model weights. Traditional approaches to private adaptation, such as DP-SGD, incur significant computational and memory overhead when applied to large, complex models. In addition, when adapting to small-scale specialized datasets, DP-SGD incurs large amount of noise that significantly degrades the performance. Our approach instead leverages an embedding-based technique derived from Textual Inversion (TI) and adapted with differentially private mechanisms. We apply TI to Stable Diffusion for style adaptation using two private datasets: a collection of artworks by a single artist and pictograms from the Paris 2024 Olympics. Experimental results show that the TI-based adaptation achieves superior fidelity in style transfer, even under strong privacy guarantees.
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