StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models

Understand

Content and style (C-S) disentanglement is a fundamental problem and critical challenge of style transfer.

• Existing approaches based on explicit definitions (e.g., Gram matrix) or implicit learning (e.g., GANs) are neither interpretable nor easy to control, resulting in entangled representations and less satisfying results.
• In this paper, we propose a new C-S disentangled framework for style transfer without using previous assumptions.
• The key insight is to explicitly extract the content information and implicitly learn the complementary style information, yielding interpretable and controllable C-S disentanglement and style transfer.