CreativeSynth: Cross-Art-Attention for Artistic Image Synthesis with Multimodal Diffusion

Abstract: Although remarkable progress has been made in image style transfer, style is just one of the components of artistic paintings. Directly transferring extracted style features to natural images often results in outputs with obvious synthetic traces. This is because key painting attributes including layout, perspective, shape, and semantics often cannot be conveyed and expressed through style transfer. Large-scale pretrained text-to-image generation models have demonstrated their capability to synthesize a vast amount of high-quality images. However, even with extensive textual descriptions, it is challenging to fully express the unique visual properties and details of paintings. Moreover, generic models often disrupt the overall artistic effect when modifying specific areas, making it more complicated to achieve a unified aesthetic in artworks. Our main novel idea is to integrate multimodal semantic information as a synthesis guide into artworks, rather than transferring style to the real world. We also aim to reduce the disruption to the harmony of artworks while simplifying the guidance conditions. Specifically, we propose an innovative multi-task unified framework called CreativeSynth, based on the diffusion model with the ability to coordinate multimodal inputs. CreativeSynth combines multimodal features with customized attention mechanisms to seamlessly integrate real-world semantic content into the art domain through Cross-Art-Attention for aesthetic maintenance and semantic fusion. We demonstrate the results of our method across a wide range of different art categories, proving that CreativeSynth bridges the gap between generative models and artistic expression. Code and results are available at https://github.com/haha-lisa/CreativeSynth.

• The paper introduces CreativeSynth, a novel framework that fuses multimodal diffusion and custom attention to preserve artistic style during digital art synthesis.
• Its methodology employs aesthetic maintenance and semantic fusion stages, enabling high control in editing while ensuring fidelity to original art.
• Quantitative and qualitative experiments show CreativeSynth outperforms legacy models on aesthetic metrics and style consistency in image blending.

Overview

CreativeSynth marks a pioneering advancement in the field of digital art synthesis. Carved out by researchers specializing in artificial intelligence, this framework seamlessly integrates multimodal prompts into the fabric of existing digital artworks, revolutionizing the way we approach the editing and generation of artistic images. It preserves the salient attributes of reference artworks like conceptual underpinnings, stylistic details, and visual symbolism. The researchers accentuate the method's proficiency through comprehensive qualitative and quantitative assessments, establishing its capability to generate images that are not only realistic but also maintain aesthetic authenticity true to the original artwork's style and intent.

Methodology

CreativeSynth's cornerstone is its capability to foster high level of control while editing artistic images, addressing issues that legacy models face, such as interpreting intricate visual elements and ensuring style consistency throughout an artwork. The paper delineates how adapting large-scale text-to-image generative models to the nuanced field of artistic image editing presents notable challenges. This is where CreativeSynth steps in, leveraging custom attention mechanisms and multimodal feature processing.

Breaking it down, the framework involves a couple of crucial stages - aesthetic maintenance mechanism and semantic fusion. These stages are adept at retaining the artist's original vision, ensuring high fidelity in style transfer and textual description adherence. Furthermore, an innovative approach called image prompt adapter enhances the model's versatility, making way for a more realistic entwining of visual and textual elements.

Experimental Results

CreativeSynth is rigorously tested against a swath of contemporary methods like Image Mixer and ProSpect, with a focus on tasks such as image fusion, editing, and realistic detail generation. Through a combination of aesthetic scores, CLIP-T, and CLIP-I metrics, the results underscore its superior performance. CreativeSynth excels in blending images without undermining their inherent artistic expression and manages to synthesize visuals which are consistent with the textual prompts guiding them.

Conclusion and Future Direction

The paper wraps up with an affirmative nod to the practicality and revolutionary potential of CreativeSynth. Not only does it fare well in qualitative domain, but it is also backed by a strong quantitative standing, setting the stage for future applications not just in still images but potentially across other media forms including video. The undeniable prowess of CreativeSynth to retain and amplify the semantic and aesthetic integrity of digital artworks underscores a significant leap for creators aiming at personalized digital art creation.