Inversion-Based Style Transfer with Diffusion Models (2211.13203v3)

Abstract: The artistic style within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes including semantic elements, object shapes, etc. Previous arbitrary example-guided artistic image generation methods often fail to control shape changes or convey elements. The pre-trained text-to-image synthesis diffusion probabilistic models have achieved remarkable quality, but it often requires extensive textual descriptions to accurately portray attributes of a particular painting. We believe that the uniqueness of an artwork lies precisely in the fact that it cannot be adequately explained with normal language. Our key idea is to learn artistic style directly from a single painting and then guide the synthesis without providing complex textual descriptions. Specifically, we assume style as a learnable textual description of a painting. We propose an inversion-based style transfer method (InST), which can efficiently and accurately learn the key information of an image, thus capturing and transferring the artistic style of a painting. We demonstrate the quality and efficiency of our method on numerous paintings of various artists and styles. Code and models are available at https://github.com/zyxElsa/InST.

Inversion-Based Style Transfer with Diffusion Models

The paper "Inversion-Based Style Transfer with Diffusion Models" by Zhang et al. presents an innovative framework, InST, designed to address challenges in example-guided artistic image generation by integrating inversion techniques within diffusion probabilistic models. This method seeks to enhance the capability of style transfer by learning a painting's style directly, obviating the need for detailed textual descriptions.

Summary of Contributions

The authors focus on leveraging the advancements in diffusion-based models to achieve high-quality artistic style transfer, which remains a challenging task when relying on conventional image synthesis methods. The primary contributions of the paper are threefold:

1. Textual Inversion Technique: The paper introduces an attention-based textual inversion method, which innovatively maps the high-level attributes of an image into a learned textual embedding. The inversion approach allows efficient style representation transfer from reference paintings.
2. Stochastic Inversion: To preserve the content semantics of the source image, the authors adopt a stochastic inversion approach. This technique ensures that when noise is added and subsequently denoised, the essential content of the image remains intact, thus facilitating better content fidelity during style transfer.
3. Practicality and Efficiency: The proposed method promises practical applicability by demonstrating significant improvements over current state-of-the-art style transfer methods in terms of both visual fidelity and computational efficiency.

Key Results

Through empirical evaluations, the InST framework is shown to excel in transferring complex artistic attributes such as brushstrokes, color tones, and even the semantic content from style images. Notably, the attention-based textual inversion reduces the training convergence time substantially compared to existing methods like textual inversion by Gal et al., which relies on direct optimization. The combination of these components in the diffusion models yields superior style transfer results, evidenced by both qualitative visual assessments and quantitative CLIP-based evaluations, where the authors report a notable improvement in style and content consistency.

Implications and Future Directions

From a theoretical standpoint, the integration of diffusion models with inversion techniques offers a new perspective on artistic style transfer, bridging the gap between text-to-image synthesis and example-guided generation. This work effectively addresses limitations posed by the necessity of comprehensive textual prompts, demonstrating a novel mechanism to capture a painting's intrinsically unique style features.

Practically, the framework's adaptability to diverse artistic styles—from classical to abstract—is advantageous, promising broader applicability in creative and commercial digital artwork production. The paper also suggests potential integrations with modern multimodal AI systems, offering speculative pathways toward even more flexible and intuitive creative tools.

Looking forward, further exploration could involve extending the inversion-based framework to other types of generative models for enhanced robustness or refining the system to handle more complex scenarios, involving multiple reference styles or even hybrid content generation tasks.

In conclusion, Zhang et al. present a significant advancement in artistic style transfer through the introduction of InST, a method that paves the way for efficient and precise artistic image synthesis, contributing profoundly to the intersection of artificial intelligence and digital art creation.