用扩散模型分离图像共性与独特特征，生成质量更高。
Diff-CA: Separating Common and Salient Factors with Diffusion Models
- 基于扩散模型设计无提示条件框架，实现因子分解
- 在保持高质量生成的前提下，成功分离共性与显著特征
- 适合需要精准图像编辑的场景，如风格迁移或内容替换
对比分析旨在分离两个数据分布之间的共性因素与仅属于其中一个的显著因素。现有对比方法依赖生成模型（如VAE或GAN），常受限于重建能力与图像质量，阻碍了有效潜在因子分离，并限制其在高保真图像生成与编辑中的应用。我们提出一种新型扩散模型条件框架，可在不牺牲生成质量的情况下实现对比分解。首先训练一个无提示、图像条件的扩散模型，随后利用弱监督学习将条件分解为共性与显著因子。我们证明，在温和条件下，加性对比因子分解是可识别的。该分解支持仅交换或插值显著因子，实现精准操作。
原文摘要 · Abstract (English)
Contrastive Analysis aims to separate factors that are common between two data distributions from those that are salient to only one of them. Existing contrastive methods are based on generative models (e.g., VAEs or GANs) that often suffer from limited reconstruction and image quality, which hampers effective latent factor separation and limits their applicability to high-fidelity image generation and edition. We propose a novel conditioning framework for diffusion models that enables contrastive decomposition without compromising generation quality. We first train a prompt-free, image-conditioned diffusion model, and then learn to decompose the conditioning into a common and a salient factor, using weak supervision. We prove that the additive contrastive factorization, commonly assumed in prior work, is identifiable under mild conditions. This factorization enables targeted operations by swapping or interpolating only the salient factor.
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