Sketch Before Pixel: Pixel-Space Structure Forcing for Real-World Super-Resolution

Pixel-space diffusion model offers a promising alternative for Real-world Image Super-Resolution (Real-SR), enabling end-to-end restoration without lossy VAE compression. However, existing pixel-space generation methods typically couple structural reconstruction and detail generation along a single denoising trajectory, making joint optimization more challenging for Real-SR. To address these limitations, we propose Sketch2Pix, a VAE-free Pixel-Space Diffusion Transformer that restores images from structural sketches to fine-grained details. Specifically, we first discover that performing moderate structural restoration before image refinement can improve the restoration quality. Based on this discovery, we design a Dual-Time Joint Diffusion framework that leverages the low-dimensional manifold hypothesis of natural images to jointly predict clean low-frequency structure and image, thereby enabling end-to-end pixel-space restoration. Furthermore, to improve the quality of the recovery, we devise a Structure-Forcing Schedule strategy that reorders the structure and pixel denoising trajectories to explicitly control the recovery order, allowing structure to emerge earlier and guide subsequent pixel generation. Benefiting from the above designs, our Sketch2Pix preserves complete pixel information while transforming coupled restoration into an ordered structure-to-detail process. Extensive experiments on Real-SR benchmarks illustrate that our Sketch2Pix restores perceptually realistic details while faithfully preserving structural content and enabling efficient inference.