HeadsUp! One-Step High-Fidelity Portrait Image Super-Resolution

Portrait pictures, which typically feature both human subjects and natural backgrounds, are one of the most prevalent forms of photography on social media. Existing image super-resolution (SR) techniques generally focus either on generic real-world images or strictly aligned facial images (i.e., face super-resolution). In practice, separate models are blended to handle portrait photos: the face specialist model handles the face region, and the general model processes the rest. However, these blending approaches inevitably introduce blending or boundary artifacts around the facial regions due to different model training recipes, while human perception is particularly sensitive to facial fidelity. To overcome these limitations, we study the portrait image super-resolution (PortraitSR) problem, and propose HeadsUp, a single-step diffusion model capable of restoring and upscaling portrait images in an end-to-end manner. Specifically, we build a dual-branch diffusion model that processes the face region separately from the whole image, following a pyramidal feature-fusion strategy. We also develop a face supervision mechanism to guide the model in focusing on the facial region. We then integrate a reference-based mechanism to help with identity restoration, reducing face ambiguity in low-quality face restoration. Additionally, we have built a high-quality 4K portrait image super-resolution dataset dubbed PortraitSR-4K, to support model training and benchmarking for portrait images. Extensive experiments show that HeadsUp achieves state-of-the-art performance on the PortraitSR task.