AnyStyleDiffusion: Flexible Style Transfer with Consistent Content Adaptation Across Diffusion Models

Recent advances in text-to-image diffusion models have demonstrated remarkable capabilities in generating high-quality visual content with style and feature controlled. A fundamental challenge remains in simultaneously maintaining three critical properties of generated image sequences: (1) fine-grained style control, (2) strict image-prompt alignment, and (3) cross-image content coherence. To overcome the challenge, we leverage AnyStyleDiffusion to overcome the challenge. Specifically, we interpret any artistic style required by users on generated image as a feature in models' weight space. Interpolation between weight space obtains models expressing middle styles with linear transition. Hyper-receptive Motion Layers is proposed to align outputs of diverse weight spaces, operating as adaptive style modulators. These HRMLs are separated from interpolated diffusion models, leveraging zero-shot compatibility with existing model checkpoints. By employing Homogeneous Stable Diffusion, direct interpolation on weight space is avoided to improve synthesis efficiency. Comprehensive evaluations across personalized models demonstrate our method's superiority in generating content-coherent sequences with dynamic style transformations. Code will be released at https://github.com/shermandozer/AnyStyleDiffusion.git.