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

arXiv: https://arxiv.org/abs/2407.17229 · HTML: https://mlsanity.com/paper/2407.17229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior](https://mlsanity.com/paper/2308.15070/) (2023)
- [Generative Diffusion Prior for Unified Image Restoration and Enhancement](https://mlsanity.com/paper/2304.01247/) (2023)
- [DCFace: Synthetic Face Generation with Dual Condition Diffusion Model](https://mlsanity.com/paper/2304.07060/) (2023)
- [Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation](https://mlsanity.com/paper/1909.11081/) (2019)
- [End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks](https://mlsanity.com/paper/2011.05552/) (2020)
- [Neural Abstract Style Transfer for Chinese Traditional Painting](https://mlsanity.com/paper/1812.03264/) (2018)
- [SSP-IR: Semantic and Structure Priors for Diffusion-based Realistic Image Restoration](https://mlsanity.com/paper/2407.03635/) (2024)
- [CCLAP: Controllable Chinese Landscape Painting Generation via Latent Diffusion Model](https://mlsanity.com/paper/2304.04156/) (2023)
- [Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning](https://mlsanity.com/paper/2206.02856/) (2022)
- [Chinese Painting Style Transfer Using Deep Generative Models](https://mlsanity.com/paper/2310.09978/) (2023)

## Built on by (most cited first, 1 of 1)

- [Personalized Image Generation with Deep Generative Models: A Decade Survey](https://mlsanity.com/paper/2502.13081/) (2025)

## Abstract (arXiv)

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.