MythraGen: Two-Stage Retrieval Augmented Art Generation Framework
Text-to-image generation has seen rapid advancements, especially with the development of generative models. However, challenges remain in achieving high-quality, contextually accurate image outputs that faithfully match the provided textual descriptions, especially in artistic generation. In this paper, we present a simple yet efficient retrieval augmented generation framework, namely MythraGen, for text-to-artistic image generation by integrating an art retrieval mechanism with LoRA-based model fine-tuning. Our method extracts features from a large-scale art dataset, optimizing the generation process by combining artist-specific styles and content. Particularly, retrieved images from an ...
| Authors | Quang-Khai Le, Cong-Long Nguyen, Minh-Triet Tran et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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