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This is the official repository for the paper "Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing". ICCV 2023
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| 2026-07-24 | 445 |
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| 2026-08-06 | 445 |
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# Multimodal Garment Designer (ICCV 2023)
### Human-Centric Latent Diffusion Models for Fashion Image Editing
[**Alberto Baldrati**](https://scholar.google.com/citations?hl=en&user=I1jaZecAAAAJ)**\***,
[**Davide Morelli**](https://scholar.google.com/citations?user=UJ4D3rYAAAAJ&hl=en)**\***,
[**Giuseppe Cartella**](https://scholar.google.com/citations?hl=en&user=0sJ4VCcAAAAJ),
[**Marcella Cornia**](https://scholar.google.com/citations?hl=en&user=DzgmSJEAAAAJ),
[**Marco Bertini**](https://scholar.google.com/citations?user=SBm9ZpYAAAAJ&hl=en),
[**Rita Cucchiara**](https://scholar.google.com/citations?hl=en&user=OM3sZEoAAAAJ)
**\*** Equal contribution.
[](https://arxiv.org/abs/2304.02051)
[](https://github.com/aimagelab/multimodal-garment-designer)
This is the **official repository** for the [**paper**](https://arxiv.org/abs/2304.02051) "*Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing*".
🔥🔥 **[21/03/2024] If you are interested in multimodal fashion image editing take a look at our most recent work [Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing](https://arxiv.org/abs/2403.14828). We introduce Ti-MGD a novel approach that also integrates fabric texture conditioning [](https://github.com/aimagelab/Ti-MGD)**
## Overview
<p align="center">
<img src="images/1.gif" style="max-width:500px">
</p>
>**Abstract**: <br>
> Fashion illustration is used by designers to communicate their vision and to bring the design idea from conceptualization to realization, showing how clothes interact with the human body. In this context, computer vision can thus be used to improve the fashion design process. Differently from previous works that mainly focused on the virtual try-on of garments, we propose the task of multimodal-conditioned fashion image editing, guiding the generation of human-centric fashion images by following multimodal prompts, such as text, human body poses, and garment sketches. We tackle this problem by proposing a new architecture based on latent diffusion models, an approach that has not been used before in the fashion domain. Given the lack of existing datasets suitable for the task, we also extend two existing fashion datasets, namely Dress Code and VITON-HD, with multimodal annotations collected in a semi-automatic manner. Experimental results on these new datasets demonstrate the effectiveness of our proposal, both in terms of realism and coherence with the given multimodal inputs.
## Citation
If you make use of our work, please cite our paper:
```bibtex
@inproceedings{baldrati2023multimodal,
title={Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing},
author={Baldrati, Alberto and Morelli, Davide and Cartella, Giuseppe and Cornia, Marcella and Bertini, Marco and Cucchiara, Rita},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
year={2023}
}
```
## Getting Started
We recommend using the [**Anaconda**](https://www.anaconda.com/) package manager to avoid dependency/reproducibility
problems.
For Linux systems, you can find a conda installation
guide [here](https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html).
### Installation
1. Clone the repository
```sh
git clone https://github.com/aimagelab/multimodal-garment-designer
```
2. Install Python dependencies
```sh
conda env create -n mgd -f environment.yml
conda activate mgd
```
Alternatively, you can create a new conda environment and install the required packages manually:
```sh
conda create -n mgd -y python=3.9
conda activate mgd
pip install torch==1.12.1 torchmetrics==0.11.0 opencv-python==4.7.0.68 diffusers==0.12.0 transformers==4.25.1 accelerate==0.15.0 Excerpt of 9,399 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ee8c32cec034d98b, topic:stable-diffusion, topic:diffusion-models, desc:latent diffusion