# Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works

arXiv: https://arxiv.org/abs/2210.08477 · HTML: https://mlsanity.com/paper/2210.08477/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2022.

## Built on by (most cited first, 30 of 61)

- [A Survey on Multimodal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.12320/) (2023)
- [NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360deg Views](https://mlsanity.com/paper/2211.16431/) (2022)
- [Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models](https://mlsanity.com/paper/2304.09337/) (2023)
- [PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement](https://mlsanity.com/paper/2403.04014/) (2024)
- [Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers](https://mlsanity.com/paper/2309.12570/) (2023)
- [OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation](https://mlsanity.com/paper/2411.18499/) (2024)
- [MagicQuill: An Intelligent Interactive Image Editing System](https://mlsanity.com/paper/2411.09703/) (2024)
- [From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape](https://mlsanity.com/paper/2312.10868/) (2023)
- [Diffusion-Based Visual Art Creation: A Survey and New Perspectives](https://mlsanity.com/paper/2408.12128/) (2024)
- [Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image Models](https://mlsanity.com/paper/2310.01929/) (2023)
- [Deepfakes on Demand: the rise of accessible non-consensual deepfake image generators](https://mlsanity.com/paper/2505.03859/) (2025)
- ["An Adapt-or-Die Type of Situation": Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionals](https://mlsanity.com/paper/2302.12601/) (2023)
- [Imperceptible Protection against Style Imitation from Diffusion Models](https://mlsanity.com/paper/2403.19254/) (2024)
- [PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation](https://mlsanity.com/paper/2410.16547/) (2024)
- [PosterMate: Audience-driven Collaborative Persona Agents for Poster Design](https://mlsanity.com/paper/2507.18572/) (2025)
- [Block-wise LoRA: Revisiting Fine-grained LoRA for Effective Personalization and Stylization in Text-to-Image Generation](https://mlsanity.com/paper/2403.07500/) (2024)
- [PromptMap: An Alternative Interaction Style for AI-Based Image Generation](https://mlsanity.com/paper/2503.09436/) (2025)
- [A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis](https://mlsanity.com/paper/2402.12760/) (2024)
- [Towards Mode Balancing of Generative Models via Diversity Weights](https://mlsanity.com/paper/2304.11961/) (2023)
- [From Paper to Card: Transforming Design Implications with Generative AI](https://mlsanity.com/paper/2403.08137/) (2024)
- [The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks](https://mlsanity.com/paper/2410.07391/) (2024)
- [SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image Generation with Region-Based Sketches](https://mlsanity.com/paper/2502.07556/) (2025)
- [POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation](https://mlsanity.com/paper/2504.13392/) (2025)
- [Controllable Human Image Generation with Personalized Multi-Garments](https://mlsanity.com/paper/2411.16801/) (2024)
- [Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software](https://mlsanity.com/paper/2504.15549/) (2025)
- [GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design](https://mlsanity.com/paper/2508.15227/) (2025)
- [LongT2IBench: A Benchmark for Evaluating Long Text-to-Image Generation with Graph-structured Annotations](https://mlsanity.com/paper/2512.09271/) (2025)
- [Wan-Weaver: Interleaved Multi-modal Generation via Decoupled Training](https://mlsanity.com/paper/2603.25706/) (2026)
- [AI-Assisted Causal Pathway Diagram for Human-Centered Design](https://mlsanity.com/paper/2403.08111/) (2024)
- [ECOR: Explainable CLIP for Object Recognition](https://mlsanity.com/paper/2404.12839/) (2024)

## Abstract (arXiv)

Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating high-quality open-domain images from multi-modal input. Although they can even produce anthropomorphized versions of objects and animals, combine irrelevant concepts in reasonable ways, and give variation to any user-provided images, we witnessed such rapid technological advancement left many visual artists disoriented in leveraging LTGMs more actively in their creative works. Our goal in this work is to understand how visual artists would adopt LTGMs to support their creative works. To this end, we conducted an interview study as well as a systematic literature review of 72 system/application papers for a thorough examination. A total of 28 visual artists covering 35 distinct visual art domains acknowledged LTGMs' versatile roles with high usability to support creative works in automating the creation process (i.e., automation), expanding their ideas (i.e., exploration), and facilitating or arbitrating in communication (i.e., mediation). We conclude by providing four design guidelines that future researchers can refer to in making intelligent user interfaces using LTGMs.