High-quality, diverse, and photorealistic images can now be generated by unconditional GANs (e.g., StyleGAN). However, limited options exist to control the generation process using (semantic) attributes while still preserving the quality of the output. Further, due to the entangled nature of the GAN latent space, performing edits along one attribute can easily result in unwanted changes along other attributes. In this article, in the context of conditional exploration of entangled latent spaces, we investigate the two sub-problems of attribute-conditioned sampling and attribute-controlled editing. We present StyleFlow as a simple, effective, and robust solution to both the sub-problems by formulating conditional exploration as an instance of conditional continuous normalizing flows in the GAN latent space conditioned by attribute features. We evaluate our method using the face and the car latent space of StyleGAN, and demonstrate fine-grained disentangled edits along various attributes on both real photographs and StyleGAN generated images. For example, for faces, we vary camera pose, illumination variation, expression, facial hair, gender, and age. Finally, via extensive qualitative and quantitative comparisons, we demonstrate the superiority of StyleFlow over prior and several concurrent works. Project Page and Video: https://rameenabdal.github.io/StyleFlow .

中文翻译：

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StyleFlow：使用条件连续归一化流对 StyleGAN 生成的图像进行属性条件探索

现在可以通过无条件 GAN（例如 StyleGAN）生成高质量、多样化和逼真的图像。但是，在使用（语义）属性控制生成过程的同时仍保持输出质量的选项有限。此外，由于 GAN 潜在空间的纠缠性质，沿一个属性执行编辑很容易导致沿其他属性的不需要的更改。在本文中，在条件探索对于纠缠的潜在空间，我们研究了属性条件采样和属性控制编辑的两个子问题。我们通过将条件探索公式化为 GAN 潜在空间中由属性特征调节的条件连续归一化流的实例，将 StyleFlow 呈现为这两个子问题的简单、有效且稳健的解决方案。我们使用 StyleGAN 的面部和汽车潜在空间评估我们的方法，并在真实照片和 StyleGAN 生成的图像上展示沿着各种属性的细粒度分离编辑。例如，对于面部，我们会改变相机姿势、光照变化、表情、面部毛发、性别和年龄。最后，通过广泛的定性和定量比较，我们证明了 StyleFlow 优于先前和几个并发工作的优势。https://rameenabdal.github.io/StyleFlow.