Artistic Intelligence: A Diffusion-Based Framework for High-Fidelity Landscape Painting Synthesis
This work advances AI-generated art for applications in creative industries, though it is incremental as it builds on existing diffusion models with domain-specific improvements.
The paper tackles the challenge of generating high-fidelity landscape paintings by introducing LPGen, a diffusion-based model with a decoupled cross-attention mechanism and structural controller, which surpasses state-of-the-art models in producing structurally accurate and stylistically coherent paintings.
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.