# Navigating the Open-Source Model Ecosystem: An Empirical Study of Creator Practices in Artistic Image Generation

arXiv: https://arxiv.org/abs/2607.10538 · HTML: https://mlsanity.com/paper/2607.10538/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 76 references that are in the map.
- First posted on arXiv 12 Jul 2026.

## Builds on (most cited first, 17 of 17)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2305.01569/) (2023)
- [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023)
- [A Survey on Generative Diffusion Model](https://mlsanity.com/paper/2209.02646/) (2022)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021)
- [JourneyDB: A Benchmark for Generative Image Understanding](https://mlsanity.com/paper/2307.00716/) (2023)
- [GenAI Arena: An Open Evaluation Platform for Generative Models](https://mlsanity.com/paper/2406.04485/) (2024)
- [Deepfakes on Demand: the rise of accessible non-consensual deepfake image generators](https://mlsanity.com/paper/2505.03859/) (2025)
- [Understanding the Impact of AI Generated Content on Social Media: The Pixiv Case](https://mlsanity.com/paper/2402.18463/) (2024)
- [ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting](https://mlsanity.com/paper/2411.17176/) (2024)
- [Exploring Language Patterns of Prompts in Text-to-Image Generation and Their Impact on Visual Diversity](https://mlsanity.com/paper/2504.14125/) (2025)
- [Human vs. AI: A Novel Benchmark and a Comparative Study on the Detection of Generated Images and the Impact of Prompts](https://mlsanity.com/paper/2412.09715/) (2024)
- [ImageGem: In-the-wild Generative Image Interaction Dataset for Generative Model Personalization](https://mlsanity.com/paper/2510.18433/) (2025)
- [A Marketplace for AI-Generated Adult Content and Deepfakes](https://mlsanity.com/paper/2601.09117/) (2026)

## Abstract (arXiv)

The open-sourcing of powerful image generation models has created a vibrant ecosystem where creators curate and combine a vast array of community-contributed models. This practice stands in sharp contrast to using closed-source tools like Midjourney. Yet, little is known about these emerging creative workflows. To bridge this gap, this paper presents the first large-scale empirical study of creator model usage behavior within this open-source image generation ecosystem. We construct a novel dataset of 6 million images with their embedded generation metadata -- a detailed recipe of the creation process, including the models used and the prompts. By linking the usage of 22.4K base models and 154K LoRA models to the images, our findings underscore the ecosystem's unique strengths and its inherent obstacles. This provides valuable insights for making this ecosystem more sustainable and innovative. Moreover, we make our dataset publicly available, providing creators with practical references for producing better artworks and researchers to facilitate further studies.