# Delta-SVD: Efficient Compression for Personalized Text-to-Image Models

arXiv: https://arxiv.org/abs/2508.16863 · HTML: https://mlsanity.com/paper/2508.16863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 18 references that are in the map.
- First posted on arXiv 23 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Relational Knowledge Distillation](https://mlsanity.com/paper/1904.05068/) (2019)
- [Sequence-Level Knowledge Distillation](https://mlsanity.com/paper/1606.07947/) (2016)
- [Decoupled Knowledge Distillation](https://mlsanity.com/paper/2203.08679/) (2022)
- [Structured Pruning of Large Language Models](https://mlsanity.com/paper/1910.04732/) (2019)
- [DepGraph: Towards Any Structural Pruning](https://mlsanity.com/paper/2301.12900/) (2023)
- [Quantization Networks](https://mlsanity.com/paper/1911.09464/) (2019)
- [Dynamic Model Pruning with Feedback](https://mlsanity.com/paper/2006.07253/) (2020)
- [Transform Quantization for CNN (Convolutional Neural Network) Compression](https://mlsanity.com/paper/2009.01174/) (2020)

## Abstract (arXiv)

Personalized text-to-image models such as DreamBooth require fine-tuning large-scale diffusion backbones, resulting in significant storage overhead when maintaining many subject-specific models. We present Delta-SVD, a post-hoc, training-free compression method that targets the parameter weights update induced by DreamBooth fine-tuning. Our key observation is that these delta weights exhibit strong low-rank structure due to the sparse and localized nature of personalization. Delta-SVD first applies Singular Value Decomposition (SVD) to factorize the weight deltas, followed by an energy-based rank truncation strategy to balance compression efficiency and reconstruction fidelity. The resulting compressed models are fully plug-and-play and can be re-constructed on-the-fly during inference. Notably, the proposed approach is simple, efficient, and preserves the original model architecture. Experiments on a multiple subject dataset demonstrate that Delta-SVD achieves substantial compression with negligible loss in generation quality measured by CLIP score, SSIM and FID. Our method enables scalable and efficient deployment of personalized diffusion models, making it a practical solution for real-world applications that require storing and deploying large-scale subject customizations.