Frequency-Aligned Latent Adaptation for Text-to-Infrared Image Generation

Text-to-infrared generation provides a controllable way to synthesize infrared images from text, offering a potential approach for expanding diverse infrared data resources. However, adapting pretrained text-to-image diffusion models to infrared imagery remains challenging because latent representations learned from natural images are not tailored to infrared-specific thermal structures and frequency characteristics. Directly modifying the latent space may improve infrared specialization but can disrupt the latent distribution expected by the denoising model, creating a trade-off between adaptation and latent compatibility. To address this issue, we propose Frequency-Aligned Latent Adaptation (FALA), a two-stage framework that learns infrared-aware latent corrections through frequency-aligned encoder adaptation and mean-only posterior refinement, then progressively transfers them to the denoising U-Net for stable adaptation. We further curate a large-scale infrared corpus from 69 datasets for text-to-infrared generation. Extensive experiments demonstrate that FALA consistently improves distribution fidelity and coverage across diverse infrared scenarios while preserving the pretrained diffusion prior without additional inference-time overhead.