AI Imagery and the Overton Window
On the map: Diffusion models → Image generation, text-to-image and editing
Abstract (arXiv)
AI-based text-to-image generation has undergone a significant leap in the production of visually comprehensive and aesthetic imagery over the past year, to the point where differentiating between a man-made piece of art and an AI-generated image is becoming more difficult. Generative Models such as Stable Diffusion, Midjourney and others are expected to affect several major industries in technological and ethical aspects. Striking the balance between raising human standard of life and work vs exploiting one group of people to enrich another is a complex and crucial part of the discussion. Due to the rapid growth of this technology, the way in which its models operate, and gray area legalities, visual and artistic domains - including the video game industry, are at risk of being taken over from creators by AI infrastructure owners. This paper is a literature review examining the concerns facing both AI developers and users today, including identity theft, data laundering and more. It discusses legalization challenges and ethical concerns, and concludes with how AI generative models can be tremendously useful in streamlining the process of visual creativity in both static and interactive media given proper regulation. Keywords: AI text-to-image generation, Midjourney, Stable Diffusion, AI Ethics, Game Design, Digital Art, Data Laundering
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 15 of its 115 references that are in the map. First posted on arXiv 31 May 2023.
Builds on
15 of 115 references.
Closest ancestors · 3
Same direction, newest first
- 2023Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models8 Feb
- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation25 Aug
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latents13 Apr
Other references · 12
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2021Zero-Shot Text-to-Image Generationcited by 4,276
- 2014Deep Learning Face Attributes in the Wildcited by 3,679
- 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 773
- 2015A Neural Algorithm of Artistic Stylecited by 480
- 2021Multimodal datasets: misogyny, pornography, and malignant stereotypescited by 326
- 2022Diffusion-GAN: Training GANs with Diffusioncited by 203
- 2023Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examplescited by 163
- 2022Can There be Art Without an Artist?cited by 10
- 2022Acceleration AI Ethics, the Debate between Innovation and Safety, and Stability AI's Diffusion versus OpenAI's Dall-Ecited by 5
- 2023Is AI Art Another Industrial Revolution in the Making?cited by 5
Built on by
Citing papers per year: 2024: 3.
3 papers.
Similar, not cited
Close in wording, no citation either way
- 2024AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks
- 2024At the edge of a generative cultural precipice
- 2025Could AI Trace and Explain the Origins of AI-Generated Images and Text?
- 2023On Copyright Risks of Text-to-Image Diffusion Models
- 2025SafeGen: Embedding Ethical Safeguards in Text-to-Image Generation
- 2025Advances in Artificial Intelligence: A Review for the Creative Industries
- 2023Measuring the Success of Diffusion Models at Imitating Human Artists
- 2024Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?