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[ACM MM 2023] - LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On
| Date | Stars |
|---|---|
| 2026-07-24 | 466 |
| 2026-07-25 | 466 |
| 2026-07-28 | 466 |
| 2026-07-30 | 466 |
| 2026-08-06 | 466 |
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# LaDI-VTON (ACM Multimedia 2023)
### Latent Diffusion Textual-Inversion Enhanced Virtual Try-On
[**Davide Morelli**](https://scholar.google.com/citations?user=UJ4D3rYAAAAJ&hl=en)**\***,
[**Alberto Baldrati**](https://scholar.google.com/citations?hl=en&user=I1jaZecAAAAJ)**\***,
[**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/2305.13501)
[](https://github.com/miccunifi/ladi-vton)
🔥🔥 **[05/09/2023] Release of the training code**
This is the **official repository** for the [**paper**](https://arxiv.org/abs/2305.13501) "*LaDI-VTON: Latent Diffusion
Textual-Inversion Enhanced Virtual Try-On*".
## Overview
> **Abstract**: <br>
> The rapidly evolving fields of e-commerce and metaverse continue to seek innovative approaches to enhance the consumer
> experience. At the same time, recent advancements in the development of diffusion models have enabled generative
> networks to create remarkably realistic images. In this context, image-based virtual try-on, which consists in
> generating a novel image of a target model wearing a given in-shop garment, has yet to capitalize on the potential of
> these powerful generative solutions. This work introduces LaDI-VTON, the first Latent Diffusion textual
> Inversion-enhanced model for the Virtual Try-ON task. The proposed architecture relies on a latent diffusion model
> extended with a novel additional autoencoder module that exploits learnable skip connections to enhance the generation
> process preserving the model's characteristics. To effectively maintain the texture and details of the in-shop
> garment,
> we propose a textual inversion component that can map the visual features of the garment to the CLIP token embedding
> space and thus generate a set of pseudo-word token embeddings capable of conditioning the generation process.
> Experimental results on Dress Code and VITON-HD datasets demonstrate that our approach outperforms the competitors by
> a consistent margin, achieving a significant milestone for the task
## Citation
If you make use of our work, please cite our paper:
```bibtex
@inproceedings{morelli2023ladi,
title={{LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On}},
author={Morelli, Davide and Baldrati, Alberto and Cartella, Giuseppe and Cornia, Marcella and Bertini, Marco and Cucchiara, Rita},
booktitle={Proceedings of the ACM International Conference on Multimedia},
year={2023}
}
```
<details>
<summary><h2>Getting Started</h2></summary>
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/miccunifi/ladi-vton
```
2. Install Python dependencies
```sh
conda env create -n ladi-vton -f environment.yml
conda activate ladi-vton
```
Alternatively, you can create a new conda environment and install the required packages manually:
```sh
conda create -n ladi-vton -y python=3.10
conda activate ladi-vton
pip install torch==2.0.1 torchvision==0.15.2 opencv-python==4.7.0.72 diffusers==0.14.0 transformers==4.27.3 accelerate==0.18.0 clean-fid==0.1.35 torchmetrics[image]==0.11.4 wandb==0.14.0 matplotlib==3.7.1 tqdm xformers
```
### Data Preparation
#### DressCode
1. Download the [DressCode](https://github.com/aimagelab/dreExcerpt of 30,737 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:680e2479377c0800, topic:stable-diffusion, readme:diffusion model, desc:latent diffusion