# Automatic Generation of Fashion Images using Prompting in Generative Machine Learning Models

arXiv: https://arxiv.org/abs/2407.14944 · HTML: https://mlsanity.com/paper/2407.14944/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [The Falcon Series of Open Language Models](https://mlsanity.com/paper/2311.16867/) (2023)
- [Dress Code: High-Resolution Multi-Category Virtual Try-On](https://mlsanity.com/paper/2204.08532/) (2022)
- [Fashion-Gen: The Generative Fashion Dataset and Challenge](https://mlsanity.com/paper/1806.08317/) (2018)
- [Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset](https://mlsanity.com/paper/2004.12276/) (2020)
- [Be Your Own Prada: Fashion Synthesis with Structural Coherence](https://mlsanity.com/paper/1710.07346/) (2017)
- [The iMaterialist Fashion Attribute Dataset](https://mlsanity.com/paper/1906.05750/) (2019)
- [ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations](https://mlsanity.com/paper/1807.01394/) (2018)
- [FIRST: A Million-Entry Dataset for Text-Driven Fashion Synthesis and Design](https://mlsanity.com/paper/2311.07414/) (2023)
- [Chat-to-Design: AI Assisted Personalized Fashion Design](https://mlsanity.com/paper/2207.01058/) (2022)
- [AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method](https://mlsanity.com/paper/2311.09624/) (2023)

## Built on by (most cited first, 2 of 2)

- [Towards Conversational Development Environments: Using Theory-of-Mind and Multi-Agent Architectures for Requirements Refinement](https://mlsanity.com/paper/2505.20973/) (2025)
- [Prompt2Fashion: An automatically generated fashion dataset](https://mlsanity.com/paper/2409.06442/) (2024)

## Abstract (arXiv)

The advent of artificial intelligence has contributed in a groundbreaking transformation of the fashion industry, redefining creativity and innovation in unprecedented ways. This work investigates methodologies for generating tailored fashion descriptions using two distinct Large Language Models and a Stable Diffusion model for fashion image creation. Emphasizing adaptability in AI-driven fashion creativity, we depart from traditional approaches and focus on prompting techniques, such as zero-shot and few-shot learning, as well as Chain-of-Thought (CoT), which results in a variety of colors and textures, enhancing the diversity of the outputs. Central to our methodology is Retrieval-Augmented Generation (RAG), enriching models with insights from fashion sources to ensure contemporary representations. Evaluation combines quantitative metrics such as CLIPscore with qualitative human judgment, highlighting strengths in creativity, coherence, and aesthetic appeal across diverse styles. Among the participants, RAG and few-shot learning techniques are preferred for their ability to produce more relevant and appealing fashion descriptions. Our code is provided at https://github.com/georgiarg/AutoFashion.