Generating Print-Ready Personalized AI Art Products from Minimal User Inputs
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Abstract (arXiv)
We present a novel framework to advance generative artificial intelligence (AI) applications in the realm of printed art products, specifically addressing large-format products that require high-resolution artworks. The framework consists of a pipeline that addresses two major challenges in the domain: the high complexity of generating effective prompts, and the low native resolution of images produced by diffusion models. By integrating AI-enhanced prompt generations with AI-powered upscaling techniques, our framework can efficiently produce high-quality, diverse artistic images suitable for many new commercial use cases. Our work represents a significant step towards democratizing high-quality AI art, opening new avenues for consumers, artists, designers, and businesses.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 8 references that are in the map. First posted on arXiv 28 Mar 2024.
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References · 5
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2021Zero-Shot Text-to-Image Generationcited by 4,288
- 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
- 2022Activating More Pixels in Image Super-Resolution Transformercited by 438
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- 2025Generative AI for Vision: A Comprehensive Study of Frameworks and Applications
- 2024KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion Models