LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing

This is the official implementation of the LOTS adapter from the paper "LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing", published as Oral at ICCV25 in Honolulu.

To access the Sketchy dataset, refer to the HuggingFace repository

Road Map

• Code release
• Weights release
• Platform release

Repository Structure

1. ckpts folder

• Contains the pre-trained weights of the LOTS adapter.

2. scripts folder

• Contains all the scripts for training and inference with LOTS on Sketchy.

3. src folder

• Contains all the source code for the classes, models, and dataloaders used in the scripts.

Installation

We advise creating a Conda environment as follows

• conda create -n lots python=3.12
• conda activate lots
• pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
• pip install -r requirements.txt
• pip install -e .

Unzip the pre-trained weights and config

cd ckpts
unzip lots.zip
cd ..

Training

We provide the script to train LOTS on our Sketchy dataset in scripts/lots/train_lots.py. For an example of usage, check run_train.sh, which contains the default parameters used in our experiments.

Inference

You can test our pre-trained model with the inference script in scripts/lots/inference_lots.py. For an example, check run_inference.sh. This script generates an image for each item in the test split of Sketchy, and saves them in a structured folder, with each item identified by its unique ID.

Citation

If you find our work useful, please cite our work:

@inproceedings{girella2025lots,
  author    = {Girella, Federico and Talon, Davide and Lie, Ziyue and Ruan, Zanxi and Wang, Yiming and Cristani, Marco},
  title     = {LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing},
  journal   = {Proceedings of the International Conference on Computer Vision},
  year      = {2025},
}