发现生成与编码共享高斯潜空间，实现无需修改结构的可控编辑。
The Universal Normal Embedding
- 提出统一高斯嵌入假说，认为生成噪声与编码嵌入同源。
- 在CelebA上线性探测显示两者属性预测性能强且对齐。
- 简单正交化即可实现语义清晰的可控编辑，适合图像生成研究者。
生成模型与视觉编码器长期独立发展，目标与数学基础各异，但共享一个核心特性：潜空间的高斯性。生成模型将高斯噪声映射为图像，而编码器将图像映射为语义嵌入，其坐标经验上表现为高斯分布。我们提出二者均为同一潜在源——通用高斯嵌入（UNE）的观测：一个近似高斯的潜空间，其中编码嵌入与DDIM反演噪声作为有噪线性投影产生。为验证该假设，引入NoiseZoo数据集，包含每张图像的DDIM反演扩散噪声与对应编码表示（CLIP、DINO）。在CelebA上，两个空间的线性探测均表现出强且对齐的属性预测能力，表明生成噪声编码了有意义的语义信息。这些方向可直接用于无架构修改的精准可控编辑（如微笑、性别、年龄），简单的正交化操作有效缓解冗余纠缠。结果为UNE假说提供实证支持，并揭示编码与生成间的共享高斯几何结构。代码与数据见https://rbetser.github.io/UNE/
原文摘要 · Abstract (English)
Generative models and vision encoders have largely advanced on separate tracks, optimized for different goals and grounded in different mathematical principles. Yet, they share a fundamental property: latent space Gaussianity. Generative models map Gaussian noise to images, while encoders map images to semantic embeddings whose coordinates empirically behave as Gaussian. We hypothesize that both are views of a shared latent source, the Universal Normal Embedding (UNE): an approximately Gaussian latent space from which encoder embeddings and DDIM-inverted noise arise as noisy linear projections. To test our hypothesis, we introduce NoiseZoo, a dataset of per-image latents comprising DDIM-inverted diffusion noise and matching encoder representations (CLIP, DINO). On CelebA, linear probes in both spaces yield strong, aligned attribute predictions, indicating that generative noise encodes meaningful semantics along linear directions. These directions further enable faithful, controllable edits (e.g., smile, gender, age) without architectural changes, where simple orthogonalization mitigates spurious entanglements. Taken together, our results provide empirical support for the UNE hypothesis and reveal a shared Gaussian-like latent geometry that concretely links encoding and generation. Code and data are available https://rbetser.github.io/UNE/
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