做图像生成和编辑的研究者可以关注——自监督表示条件化方法有望减少对标注数据的依赖，且提供更平滑、解耦的控制空间，值得深入探索。
本文提出一种基于预训练自监督模型表示的条件扩散模型，用于可控图像生成。传统方法依赖文本提示或语义图等条件机制，需要大量标注数据。该工作通过自监督表示作为条件，不仅提升了无条件图像生成的质量，还提供了一个可操控的表示空间。研究者通过识别变化方向探索该条件空间，展示了平滑性和解耦性等有前景的特性。这项工作为减少对标注数据的依赖、实现更灵活的图像生成控制提供了新思路。
Towards Controllable Image Generation through Representation-Conditioned Diffusion Models
Diffusion models have emerged as powerful tools for high-quality image generation and editing, but guiding these models to produce specific outputs remains a challenge. Conventional approaches rely on conditioning mechanisms, such as text prompts or semantic maps, which require extensively annotated datasets. In this preliminary work, we explore diffusion models conditioned on representations from a pre-trained self-supervised model. The self-conditioning mechanism not only improves the quality of unconditional image generation, but also provides a representation space that can be used to control the generation. We explore this conditioning space by identifying directions of variations, and demonstrate promising properties in terms of smoothness and disentanglement.