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.

翻译：像素空间扩散模型近期重新成为潜空间扩散模型的有力替代方案，无需预训练自编码器即可实现高质量生成。然而，标准像素空间扩散模型获得的语义监督相对较弱，且未明确设计用于捕获高层视觉结构。近期表示对齐方法（如REPA）表明，预训练视觉特征可显著改进扩散训练，而视觉协同去噪已成为将此类特征融入生成过程的前沿方向。但现有协同去噪方法常耦合多种设计选择，难以厘清何种设计选择真正关键。为此，我们提出V-Co——在统一的即时训练框架下对视觉协同去噪进行的系统性研究。这种受控设置使我们能分离出影响视觉协同去噪效果的核心要素。研究发现有效视觉协同去噪需具备四个关键要素：首先，保持特征特异性计算并实现灵活跨流交互需要完全双流架构；其次，有效的无分类器引导需要结构化的无条件预测机制；第三，更强的语义监督需通过感知漂移混合损失实现；第四，稳定的协同去噪还需适当的跨流校准，我们通过基于RMS的特征重缩放实现。这些发现共同构成了视觉协同去噪的简明方案。在ImageNet-256上的实验表明，在模型规模相当的条件下，V-Co不仅优于基础像素空间扩散基线及现有强像素扩散方法，且训练周期更短，为未来表示对齐生成模型提供了实用指导。