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

TL;DR

V-Co systematically studies visual co-denoising in pixel-space diffusion models, identifying four key ingredients that improve semantic alignment and outperform baseline methods on ImageNet-256.

Contribution

This paper introduces a unified framework for visual co-denoising, clarifies essential design choices, and provides a practical recipe for enhancing diffusion models with visual feature alignment.

Findings

V-Co outperforms baseline pixel-space diffusion models on ImageNet-256.

Four key ingredients are identified for effective visual co-denoising.

V-Co achieves better results with fewer training epochs.

Abstract

Pixel-space diffusion has recently re-emerged as a strong alternative to latent diffusion, enabling high-quality generation without pretrained autoencoders. However, standard pixel-space diffusion models receive relatively weak semantic supervision and are not explicitly designed to capture high-level visual structure. Recent representation-alignment methods (e.g., REPA) suggest that pretrained visual features can substantially improve diffusion training, and visual co-denoising has emerged as a promising direction for incorporating such features into the generative process. However, existing co-denoising approaches often entangle multiple design choices, making it unclear which design choices are truly essential. Therefore, we present V-Co, a systematic study of visual co-denoising in a unified JiT-based framework. This controlled setting allows us to isolate the ingredients that make visual co-denoising effective. Our study reveals four key ingredients for effective visual co-denoising. First, preserving feature-specific computation while enabling flexible cross-stream interaction motivates a fully dual-stream architecture. Second, effective classifier-free guidance (CFG) requires a structurally defined unconditional prediction. Third, stronger semantic supervision is best provided by a perceptual-drifting hybrid loss. Fourth, stable co-denoising further requires proper cross-stream calibration, which we realize through RMS-based feature rescaling. Together, these findings yield a simple recipe for visual co-denoising. Experiments on ImageNet-256 show that, at comparable model sizes, V-Co outperforms the underlying pixel-space diffusion baseline and strong prior pixel-diffusion methods while using fewer training epochs, offering practical guidance for future representation-aligned generative models.