# Cross-Cultural Fashion Design via Interactive Large Language Models and Diffusion Models

arXiv: https://arxiv.org/abs/2501.15571 · HTML: https://mlsanity.com/paper/2501.15571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 25 of 25)

- [Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models](https://mlsanity.com/paper/2312.04410/) (2023)
- [Rethinking Visual Dependency in Long-Context Reasoning for Large Vision-Language Models](https://mlsanity.com/paper/2410.19732/) (2024)
- [Lost in Translation: Large Language Models in Non-English Content Analysis](https://mlsanity.com/paper/2306.07377/) (2023)
- [Diffusion Models for Generative Outfit Recommendation](https://mlsanity.com/paper/2402.17279/) (2024)
- [Goldfish: Monolingual Language Models for 350 Languages](https://mlsanity.com/paper/2408.10441/) (2024)
- [On the Robustness of Latent Diffusion Models](https://mlsanity.com/paper/2306.08257/) (2023)
- [Cedille: A large autoregressive French language model](https://mlsanity.com/paper/2202.03371/) (2022)
- [Hyperbolic Geometric Latent Diffusion Model for Graph Generation](https://mlsanity.com/paper/2405.03188/) (2024)
- [UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and Generation](https://mlsanity.com/paper/2408.11305/) (2024)
- [InsectMamba: Insect Pest Classification with State Space Model](https://mlsanity.com/paper/2404.03611/) (2024)
- [Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping](https://mlsanity.com/paper/1609.02489/) (2016)
- [Diffusion Model with Representation Alignment for Protein Inverse Folding](https://mlsanity.com/paper/2412.09380/) (2024)
- [How Good are Commercial Large Language Models on African Languages?](https://mlsanity.com/paper/2305.06530/) (2023)
- [A Survey of Large Language Models for European Languages](https://mlsanity.com/paper/2408.15040/) (2024)
- [A Precis of Language Models are not Models of Language](https://mlsanity.com/paper/2205.07634/) (2022)
- [Planning with Logical Graph-based Language Model for Instruction Generation](https://mlsanity.com/paper/2308.13782/) (2023)
- [FIRST: A Million-Entry Dataset for Text-Driven Fashion Synthesis and Design](https://mlsanity.com/paper/2311.07414/) (2023)
- [Towards diffusion models for large-scale sea-ice modelling](https://mlsanity.com/paper/2406.18417/) (2024)
- [Interactive Fashion Content Generation Using LLMs and Latent Diffusion Models](https://mlsanity.com/paper/2306.05182/) (2023)
- [Modelling Language using Large Language Models](https://mlsanity.com/paper/2404.09579/) (2024)
- [LLaMA-Reg: Using LLaMA 2 for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2405.18774/) (2024)
- ["Woman-Metal-White vs Man-Dress-Shorts": Combining Social, Temporal and Image Signals to Understand Popularity of Pinterest Fashion Boards](https://mlsanity.com/paper/1812.07759/) (2018)
- [SnapMode: An Intelligent and Distributed Large-Scale Fashion Image Retrieval Platform Based On Big Data and Deep Generative Adversarial Network Technologies](https://mlsanity.com/paper/2204.03998/) (2022)
- [Beyond the limitations of any imaginable mechanism: large language models and psycholinguistics](https://mlsanity.com/paper/2303.00077/) (2023)
- [Advancing bioinformatics with large language models: components, applications and perspectives](https://mlsanity.com/paper/2401.04155/) (2024)

## Abstract (arXiv)

Fashion content generation is an emerging area at the intersection of artificial intelligence and creative design, with applications ranging from virtual try-on to culturally diverse design prototyping. Existing methods often struggle with cultural bias, limited scalability, and alignment between textual prompts and generated visuals, particularly under weak supervision. In this work, we propose a novel framework that integrates Large Language Models (LLMs) with Latent Diffusion Models (LDMs) to address these challenges. Our method leverages LLMs for semantic refinement of textual prompts and introduces a weak supervision filtering module to effectively utilize noisy or weakly labeled data. By fine-tuning the LDM on an enhanced DeepFashion+ dataset enriched with global fashion styles, the proposed approach achieves state-of-the-art performance. Experimental results demonstrate that our method significantly outperforms baselines, achieving lower Frechet Inception Distance (FID) and higher Inception Scores (IS), while human evaluations confirm its ability to generate culturally diverse and semantically relevant fashion content. These results highlight the potential of LLM-guided diffusion models in driving scalable and inclusive AI-driven fashion innovation.