Memory-Efficient Personalization of Text-to-Image Diffusion Models via Selective Optimization Strategies

• URL: http://arxiv.org/abs/2507.10029v1
• Date: Mon, 14 Jul 2025 08:08:55 GMT
• Title: Memory-Efficient Personalization of Text-to-Image Diffusion Models via Selective Optimization Strategies
• Authors: Seokeon Choi, Sunghyun Park, Hyoungwoo Park, Jeongho Kim, Sungrack Yun,
• Abstract summary: We propose a selective optimization framework that adaptively chooses between backpropagation on low-resolution images (BP-low) and zeroth-order optimization on high-resolution images (ZO-high)<n>Our method achieves competitive performance while significantly reducing memory consumption, enabling scalable, high-quality on-device personalization without increasing latency inference.
• Score: 18.692036523182594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Memory-efficient personalization is critical for adapting text-to-image diffusion models while preserving user privacy and operating within the limited computational resources of edge devices. To this end, we propose a selective optimization framework that adaptively chooses between backpropagation on low-resolution images (BP-low) and zeroth-order optimization on high-resolution images (ZO-high), guided by the characteristics of the diffusion process. As observed in our experiments, BP-low efficiently adapts the model to target-specific features, but suffers from structural distortions due to resolution mismatch. Conversely, ZO-high refines high-resolution details with minimal memory overhead but faces slow convergence when applied without prior adaptation. By complementing both methods, our framework leverages BP-low for effective personalization while using ZO-high to maintain structural consistency, achieving memory-efficient and high-quality fine-tuning. To maximize the efficacy of both BP-low and ZO-high, we introduce a timestep-aware probabilistic function that dynamically selects the appropriate optimization strategy based on diffusion timesteps. This function mitigates the overfitting from BP-low at high timesteps, where structural information is critical, while ensuring ZO-high is applied more effectively as training progresses. Experimental results demonstrate that our method achieves competitive performance while significantly reducing memory consumption, enabling scalable, high-quality on-device personalization without increasing inference latency.

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