Addressing Detail Bottlenecks in Latent Diffusion for RGB-to-SWIR Image Translation
For autonomous driving and remote sensing applications requiring RGB-to-SWIR translation, this work addresses a critical bottleneck in latent diffusion models that limits downstream perception performance.
Latent diffusion models for RGB-to-SWIR image translation lose fine spatial details, degrading downstream perception. The proposed Source-Conditioned Autoencoder and Learnable Guidance Encoder improve detection mAP by up to 2x over the baseline, with up to 3.4x gains on small objects, while achieving state-of-the-art FID.
Latent diffusion models (LDMs) enable efficient image-to-image translation but discard fine spatial details during compression, degrading downstream perception tasks. We identify two bottlenecks: the autoencoder, which loses spatial information, and the conditioning pathway, which further degrades the source signal through naive downsampling. We propose two lightweight, backbone-agnostic fixes: a Source-Conditioned Autoencoder (SCAE) that injects high-resolution source features into the decoder via skip connections, and a Learnable Guidance Encoder (LGE) that replaces naive downsampling with a learned conditioning signal. Evaluated on RGB-to-SWIR translation for driving scenes with two denoiser backbones (U-Net and DiT), our approach improves detection mAP by up to 2x over the latent diffusion baseline, with up to 3.4x gains on small objects (COCO-small, <32^2 px^2), while achieving state-of-the-art FID. We further show that FID and detection performance are poorly correlated, motivating multi-axis evaluation. Results generalise zero-shot to the public RASMD benchmark. We will publicly release test data with annotations, all checkpoints, and training code.