At the edge of a generative cultural precipice
On the map: Diffusion models → Image generation, text-to-image and editing
Abstract (arXiv)
Since NFTs and large generative models (such as DALLE2 and Stable Diffusion) have been publicly available, artists have seen their jobs threatened and stolen. While artists depend on sharing their art on online platforms such as Deviantart, Pixiv, and Artstation, many slowed down sharing their work or downright removed their past work therein, especially if these platforms fail to provide certain guarantees regarding the copyright of their uploaded work. Text-to-image (T2I) generative models are trained using human-produced content to better guide the style and themes they can produce. Still, if the trend continues where data found online is generated by a machine instead of a human, this will have vast repercussions in culture. Inspired by recent work in generative models, we wish to tell a cautionary tale and ask what will happen to the visual arts if generative models continue on the path to be (eventually) trained solely on generated content.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 14 of its 24 references that are in the map. First posted on arXiv 30 Apr 2024.
Builds on
14 of 24 references.
Closest ancestors · 2
Same direction, newest first
- 2023Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models8 Feb
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latents13 Apr
Other references · 12
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2021Zero-Shot Text-to-Image Generationcited by 4,276
- 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
- 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,544
- 2023Self-Consuming Generative Models Go MADcited by 239
- 2023Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for nowcited by 61
- 2024AI and the Problem of Knowledge Collapsecited by 34
- 2024Would Deep Generative Models Amplify Bias in Future Models?cited by 24
- 2024Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Artcited by 11
- 2023Studying Artist Sentiments around AI-generated Artworkcited by 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models
- 2023AI Imagery and the Overton Window
- 2023On Copyright Risks of Text-to-Image Diffusion Models
- 2022Language Does More Than Describe: On The Lack Of Figurative Speech in Text-To-Image Models
- 2023Measuring the Success of Diffusion Models at Imitating Human Artists
- 2024Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?
- 2026The Silent Brush: Evaluating Artistic Style Leakage in AI Art Generation
- 2024No Longer Trending on Artstation: Prompt Analysis of Generative AI Art