# Personalized Image Generation from an Author Writing Style

arXiv: https://arxiv.org/abs/2507.03313 · HTML: https://mlsanity.com/paper/2507.03313/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (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)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [Personalization of Large Language Models: A Survey](https://mlsanity.com/paper/2411.00027/) (2024)
- [Personalized Prompt Learning for Explainable Recommendation](https://mlsanity.com/paper/2202.07371/) (2022)
- [The Oscars of AI Theater: A Survey on Role-Playing with Language Models](https://mlsanity.com/paper/2407.11484/) (2024)
- [Towards Controllable and Personalized Review Generation](https://mlsanity.com/paper/1910.03506/) (2019)
- [Task Supportive and Personalized Human-Large Language Model Interaction: A User Study](https://mlsanity.com/paper/2402.06170/) (2024)
- [Whose story is it? Personalizing story generation by inferring author styles](https://mlsanity.com/paper/2502.13028/) (2025)

## Abstract (arXiv)

Translating nuanced, textually-defined authorial writing styles into compelling visual representations presents a novel challenge in generative AI. This paper introduces a pipeline that leverages Author Writing Sheets (AWS) - structured summaries of an author's literary characteristics - as input to a Large Language Model (LLM, Claude 3.7 Sonnet). The LLM interprets the AWS to generate three distinct, descriptive text-to-image prompts, which are then rendered by a diffusion model (Stable Diffusion 3.5 Medium). We evaluated our approach using 49 author styles from Reddit data, with human evaluators assessing the stylistic match and visual distinctiveness of the generated images. Results indicate a good perceived alignment between the generated visuals and the textual authorial profiles (mean style match: 4.08/5), with images rated as moderately distinctive. Qualitative analysis further highlighted the pipeline's ability to capture mood and atmosphere, while also identifying challenges in representing highly abstract narrative elements. This work contributes a novel end-to-end methodology for visual authorial style personalization and provides an initial empirical validation, opening avenues for applications in creative assistance and cross-modal understanding.