Quantitative Comparison of Fine-Tuning Techniques for Pretrained Latent Diffusion Models in the Generation of Unseen SAR Image Concepts

• URL: http://arxiv.org/abs/2506.13307v1
• Date: Mon, 16 Jun 2025 09:48:01 GMT
• Title: Quantitative Comparison of Fine-Tuning Techniques for Pretrained Latent Diffusion Models in the Generation of Unseen SAR Image Concepts
• Authors: Solène Debuysère, Nicolas Trouvé, Nathan Letheule, Olivier Lévêque, Elise Colin,
• Abstract summary: This work investigates the adaptation of large pre-trained latent diffusion models to a radically new imaging domain: Synthetic Aperture Radar (SAR)<n>We explore and compare multiple fine-tuning strategies, including full model fine-tuning and parameter-efficient approaches like Low-Rank Adaptation (LoRA)<n>Our results show that a hybrid tuning strategy yields the best performance, while LoRA-based partial tuning of the text encoder, combined with embedding learning of the SAR> token, suffices to preserve prompt alignment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work investigates the adaptation of large pre-trained latent diffusion models to a radically new imaging domain: Synthetic Aperture Radar (SAR). While these generative models, originally trained on natural images, demonstrate impressive capabilities in text-to-image synthesis, they are not natively adapted to represent SAR data, which involves different physics, statistical distributions, and visual characteristics. Using a sizeable SAR dataset (on the order of 100,000 to 1 million images), we address the fundamental question of fine-tuning such models for this unseen modality. We explore and compare multiple fine-tuning strategies, including full model fine-tuning and parameter-efficient approaches like Low-Rank Adaptation (LoRA), focusing separately on the UNet diffusion backbone and the text encoder components. To evaluate generative quality, we combine several metrics: statistical distance from real SAR distributions, textural similarity via GLCM descriptors, and semantic alignment assessed with a CLIP model fine-tuned on SAR data. Our results show that a hybrid tuning strategy yields the best performance: full fine-tuning of the UNet is better at capturing low-level SAR-specific patterns, while LoRA-based partial tuning of the text encoder, combined with embedding learning of the <SAR> token, suffices to preserve prompt alignment. This work provides a methodical strategy for adapting foundation models to unconventional imaging modalities beyond natural image domains.

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