# GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design

arXiv: https://arxiv.org/abs/2508.15227 · HTML: https://mlsanity.com/paper/2508.15227/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 75 of its 149 references that are in the map.
- First posted on arXiv 21 Aug 2025.

## Builds on (most cited first, 30 of 75)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Self-planning Code Generation with Large Language Models](https://mlsanity.com/paper/2303.06689/) (2023)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2411.14432/) (2024)
- [SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing](https://mlsanity.com/paper/2405.04007/) (2024)
- [Improving Text-to-Image Consistency via Automatic Prompt Optimization](https://mlsanity.com/paper/2403.17804/) (2024)
- [Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models](https://mlsanity.com/paper/2304.09337/) (2023)
- ["Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction](https://mlsanity.com/paper/2210.03735/) (2022)
- [PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement](https://mlsanity.com/paper/2403.04014/) (2024)
- [Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works](https://mlsanity.com/paper/2210.08477/) (2022)
- [GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning](https://mlsanity.com/paper/2311.12631/) (2023)
- [PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation](https://mlsanity.com/paper/2307.09036/) (2023)
- [ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models](https://mlsanity.com/paper/2403.01807/) (2024)
- [DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation](https://mlsanity.com/paper/2305.14330/) (2023)
- [Good Seed Makes a Good Crop: Discovering Secret Seeds in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2405.14828/) (2024)
- [RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions](https://mlsanity.com/paper/2302.09466/) (2023)

## Built on by (most cited first, 5 of 5)

- [Exploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models](https://mlsanity.com/paper/2512.18388/) (2025)
- [AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration](https://mlsanity.com/paper/2511.17906/) (2025)
- [From Style Replication to Style Exploration: Enabling Art Style Exploration with Analyze-Experiment-Resituate Framework](https://mlsanity.com/paper/2608.14405/) (2026)
- [TailorCoPilot: Enabling Agentic Pattern Making with Version-Controlled State Tracking](https://mlsanity.com/paper/2608.25462/) (2026)
- [MOONWALK: Mediating Operations with Intent-Evidence-Action Alignment Across Junior-Supervisor Review Workflows in Animation/VFX Pre-Production](https://mlsanity.com/paper/2609.10385/) (2026)

## Abstract (arXiv)

Environment designers in the entertainment industry create imaginative 2D and 3D scenes for games, films, and television, requiring both fine-grained control of specific details and consistent global coherence. Designers have increasingly integrated generative AI into their workflows, often relying on large language models (LLMs) to expand user prompts for text-to-image generation, then iteratively refining those prompts and applying inpainting. However, our formative study with 10 designers surfaced two key challenges: (1) the lengthy LLM-generated prompts make it difficult to understand and isolate the keywords that must be revised for specific visual elements; and (2) while inpainting supports localized edits, it can struggle with global consistency and correctness. Based on these insights, we present GenTune, an approach that enhances human--AI collaboration by clarifying how AI-generated prompts map to image content. Our GenTune system lets designers select any element in a generated image, trace it back to the corresponding prompt labels, and revise those labels to guide precise yet globally consistent image refinement. In a summative study with 20 designers, GenTune significantly improved prompt--image comprehension, refinement quality, and efficiency, and overall satisfaction (all p < .01) compared to current practice. A follow-up field study with two studios further demonstrated its effectiveness in real-world settings.