面向扩散模型属性操纵的一体化滑杆
All-in-One Slider for Attribute Manipulation in Diffusion Models
把文本嵌入空间分解为稀疏语义方向，用一个轻量模块实现多属性连续滑杆控制，并可零样本组合未见属性；该思路可直接迁移到基于扩散模型的图像编辑与个性化生成中，替代逐属性训练滑杆的做法。
原文依据 decomposes the text embedding space into sparse, semantically meaningful attribute directions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论文本到图像（T2I）扩散模型在生成高质量图像方面已取得显著进展。然而，逐步操纵生成图像的某些属性以符合用户期望仍然具有挑战性，尤其是对于细节丰富的内容，例如人脸。已有研究尝试通过训练滑杆模块来解决这一问题。然而，它们采用一对一的方式，即为每个属性训练一个独立的滑杆，每当引入新属性时都需要额外训练。这不仅导致滑杆累积的参数冗余，也限制了实际应用的灵活性以及属性操纵的可扩展性。为解决该问题，我们提出一体化滑杆（All-in-One Slider），这是一个轻量模块，将文本嵌入空间分解为稀疏且具有语义意义的属性方向。一旦训练完成，它便可作为通用滑杆使用，实现对多种属性的可解释、细粒度连续控制。此外，通过重新组合所学到的方向，一体化滑杆支持对未见属性（例如种族和名人）的零样本操纵以及多属性的组合。大量实验表明，我们的方法能够实现准确且可扩展的属性操纵，相比先前方法取得了显著提升。此外，我们的方法可以扩展为与反演框架结合，从而对真实图像执行属性操纵，拓宽了其在各种真实场景中的适用性。代码与训练好的模型将会公开。
英文原文摘要 1643 字符
Text-to-image (T2I) diffusion models have made significant strides in generating high-quality images. However, progressively manipulating certain attributes of generated images to meet the desired user expectations remains challenging, particularly for content with rich details, such as human faces. Some studies have attempted to address this by training slider modules. However, they follow a One-for-One manner, where an independent slider is trained for each attribute, requiring additional training whenever a new attribute is introduced. This not only results in parameter redundancy accumulated by sliders but also restricts the flexibility of practical applications and the scalability of attribute manipulation. To address this issue, we introduce the All-in-One Slider, a lightweight module that decomposes the text embedding space into sparse, semantically meaningful attribute directions. Once trained, it functions as a general-purpose slider, enabling interpretable and fine-grained continuous control over various attributes. Moreover, by recombining the learned directions, the All-in-One Slider supports zero-shot manipulation of unseen attributes (e.g., races and celebrities) and the composition of multiple attributes. Extensive experiments demonstrate that our method enables accurate and scalable attribute manipulation, achieving notable improvements compared to previous methods. Furthermore, our method can be extended to integrate with the inversion framework to perform attribute manipulation on real images, broadening its applicability to various real-world scenarios. The code and trained model will be released.