mlsanityData to 14 Oct 2026

Generating Print-Ready Personalized AI Art Products from Minimal User Inputs

arXiv 2405.18247 ↗First posted 28 Mar 2024cs.CV eess.IVcited by 0

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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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5 of 8 references.

References · 5

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  4. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
  5. 2022Activating More Pixels in Image Super-Resolution Transformercited by 438

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  1. 2024No Longer Trending on Artstation: Prompt Analysis of Generative AI Art
  2. 2025Style Transfer: A Decade Survey
  3. 2024Alfie: Democratising RGBA Image Generation With No
  4. 2022Best Prompts for Text-to-Image Models and How to Find Them
  5. 2024Generative AI in Vision: A Survey on Models, Metrics and Applications
  6. 2023From paintbrush to pixel: A review of deep neural networks in AI-generated art
  7. 2025Generative AI for Vision: A Comprehensive Study of Frameworks and Applications
  8. 2024KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion Models