Artistic Intelligence: A Diffusion-Based Framework for High-Fidelity Landscape Painting Synthesis

• URL: http://arxiv.org/abs/2407.17229v4
• Date: Fri, 11 Oct 2024 08:48:03 GMT
• Title: Artistic Intelligence: A Diffusion-Based Framework for High-Fidelity Landscape Painting Synthesis
• Authors: Wanggong Yang, Yifei Zhao,
• Abstract summary: LPGen is a novel diffusion-based model specifically designed for landscape painting generation. LPGen introduces a decoupled cross-attention mechanism that independently processes structural and stylistic features. The model is pre-trained on a curated dataset of high-resolution landscape images, categorized by distinct artistic styles, and then fine-tuned to ensure detailed and consistent output.
• Score: 2.205829309604458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating high-fidelity landscape paintings remains a challenging task that requires precise control over both structure and style. In this paper, we present LPGen, a novel diffusion-based model specifically designed for landscape painting generation. LPGen introduces a decoupled cross-attention mechanism that independently processes structural and stylistic features, effectively mimicking the layered approach of traditional painting techniques. Additionally, LPGen proposes a structural controller, a multi-scale encoder designed to control the layout of landscape paintings, striking a balance between aesthetics and composition. Besides, the model is pre-trained on a curated dataset of high-resolution landscape images, categorized by distinct artistic styles, and then fine-tuned to ensure detailed and consistent output. Through extensive evaluations, LPGen demonstrates superior performance in producing paintings that are not only structurally accurate but also stylistically coherent, surpassing current state-of-the-art models. This work advances AI-generated art and offers new avenues for exploring the intersection of technology and traditional artistic practices. Our code, dataset, and model weights will be publicly available.

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