V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising
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.
69bade9cba2b62a5fb0b0b2c
Han Lin
69bade9cba2b62a5fb0b0b2d
Xichen Pan
69bade9cba2b62a5fb0b0b2e
Zun Wang
69bade9cba2b62a5fb0b0b2f
Yue Zhang
claimed_verified
2026-03-19T12:57:57.941Z
646cd3cdb91221bd20a43fe5
/avatars/53e47b1549993d8c04f95e9c60d59a7f.svg
Yue Zhang
Yuezhangjoslin
user
Yuezhangjoslin
69bade9cba2b62a5fb0b0b30
Chu Wang
69bade9cba2b62a5fb0b0b31
Jaemin Cho
69bade9cba2b62a5fb0b0b32
Mohit Bansal
V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising
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.
Pixel-space diffusion models can be enhanced through visual co-denoising techniques that incorporate pretrained visual features, with systematic analysis revealing key architectural and training components for improved performance.
pixel-space diffusion latent diffusion representation-alignment methods visual co-denoising classifier-free guidance perceptual-drifting hybrid loss cross-stream calibration dual-stream architecture RMS-based feature rescaling