用自监督模型表示条件控制图像生成，提升可控性与质量
Towards Controllable Image Generation through Representation-Conditioned Diffusion Models
- 用预训练自监督模型的表征作为扩散模型的条件
- 生成图像质量提升，且表征空间具平滑与解耦特性
- 适合研究可控生成与无标注数据条件下的图像合成
扩散模型已成为高质量图像生成与编辑的强大工具，但引导其生成特定输出仍具挑战。传统方法依赖文本提示或语义图等条件机制，需大量标注数据。本文初步探索了基于预训练自监督模型表征的扩散模型条件化方法。自条件机制不仅提升了无条件图像生成的质量，还构建了一个可用于控制生成的表征空间。通过识别变化方向，实验表明该空间具有良好的平滑性与解耦性，展现出可控生成的潜力。
原文摘要 · Abstract (English)
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.
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