V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising

Pixel-space diffusion has recently re-emerged as a strong al-ternative to latent diffusion, enabling high-quality generation withoutpretrained autoencoders. However, standard pixel-space diffusion mod-els receive relatively weak semantic supervision and are not explicitlydesigned to capture high-level visual structure. Recent representation-alignment methods (e.g., REPA) suggest that pretrained visual featurescan substantially improve diffusion training, and visual co-denoising hasemerged as a promising direction for incorporating such features intothe generative process. However, existing co-denoising approaches of-ten entangle multiple design choices, making it unclear which are trulyessential. We therefore present V-Co, a systematic study of visual co-denoising in a unified JiT-based framework. This controlled setting allowsus to isolate the ingredients that make visual co-denoising effective. Ourstudy reveals two main ingredients. First, co-denoising benefits from pre-serving feature-specific computation while enabling flexible cross-streaminteraction, which leads to a fully dual-stream architecture together witha structurally defined unconditional prediction for classifier-free guid-ance. Second, it requires both stronger semantic supervision and propercross-stream calibration, which we realize through a perceptual-driftinghybrid loss and RMS-based feature rescaling. Together, these findingsyield a simple recipe for visual co-denoising. Experiments on ImageNet-256 show that, at comparable model sizes, V-Co outperforms the un-derlying pixel-space diffusion baseline and strong prior pixel-diffusionmethods while using fewer training epochs, offering practical guidancefor future representation-aligned generative models.