Artistic Intelligence: A Diffusion-Based Framework for High-Fidelity Landscape Painting Synthesis
Generating high-fidelity landscape paintings presents significant challenges, necessitating precise control over both structure and style. This paper introduces LPGen, a novel diffusion-based model specifically designed for landscape painting generation. LPGen incorporates a decoupled cross-attentio...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10804083/ |
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| Summary: | Generating high-fidelity landscape paintings presents significant challenges, necessitating precise control over both structure and style. This paper introduces LPGen, a novel diffusion-based model specifically designed for landscape painting generation. LPGen incorporates a decoupled cross-attention mechanism that serves as a style controller, independently processing stylistic features to manage the style of the generated images. Furthermore, LPGen incorporates a structural controller—a multi-scale encoder that governs the layout of landscape paintings, achieving a balance between aesthetics and composition. The model is pre-trained on a curated dataset of high-resolution landscape images categorized by distinct artistic styles, followed by fine-tuning to ensure detailed and consistent outputs. Extensive evaluations demonstrate that LPGen outperforms current state-of-the-art models in producing structurally accurate and stylistically coherent paintings. This work advances AI-generated art and opens new avenues for exploring the intersection of technology and traditional artistic practices. Our code, dataset, and model weights will be made publicly available. |
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| ISSN: | 2169-3536 |