Summary of Differentially Private Adaptation Of Diffusion Models Via Noisy Aggregated Embeddings, by Pura Peetathawatchai et al.
Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings
by Pura Peetathawatchai, Wei-Ning Chen, Berivan Isik, Sanmi Koyejo, Albert No
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We present a novel method for adapting diffusion models under differential privacy (DP) constraints, enabling private style and content transfer without fine-tuning model weights. Our approach leverages an embedding-based technique derived from Textual Inversion (TI), adapted with differentially private mechanisms to overcome the limitations of traditional approaches like DP-SGD. We demonstrate the effectiveness of our method using Stable Diffusion for style adaptation on two private datasets: a collection of artworks by a single artist and pictograms from the Paris 2024 Olympics. Our results show that TI-based adaptation achieves superior fidelity in style transfer, even under strong privacy guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’ve developed a new way to adapt computer models for art and pictures while keeping personal information private. Traditional methods can be slow and noisy when used on big models or small datasets. Our approach uses a special technique called Textual Inversion (TI) to adapt the model without changing its underlying structure. We tested our method using Stable Diffusion, a popular model for generating art, and two private datasets: artworks by a single artist and Olympic pictograms from 2024. The results show that our method is better at transferring styles while keeping personal information safe. |
Keywords
» Artificial intelligence » Diffusion » Embedding » Fine tuning » Style transfer
