EquiFashionModel

👗Hybrid GAN–Diffusion Balancing Diversity–Fidelity for Fashion Design Generation

Authors:
Nguyen Dinh Hieu [0009-0002-6683-8036], Tran Minh Khuong, Phan Duy Hung [0000-0002-6033-6484] Institution: FPT University, Hanoi, Vietnam / Quantum AI & Cyber Security Institute, FPT Corporation, Hanoi, Vietnam 📧 [email protected] / [email protected] / [email protected]

Full code at: https://github.com/nguyendinhhieu1309/EquiFashion.git

🧩 Overview

EquiFashion is a hybrid GAN–Diffusion framework that reconciles the long-standing trade-off between stylistic diversity and photorealistic fidelity in generative fashion design.
It integrates a GAN-based ideation branch for creative exploration and a diffusion-based refinement branch for faithful reconstruction, enabling high-quality, diverse, and robust fashion image generation.

🎨 Try the live demo here:
👉 EquiFashion Demo on Hugging Face Spaces

🎯 Motivation

Fashion design requires models that are simultaneously creative, robust, and trustworthy.
While GANs generate diverse styles but lack stability, and Diffusion Models produce realism but constrain creativity, EquiFashion bridges both worlds—achieving controlled diversity, semantic alignment, and realistic garment rendering.

🧱 Architecture Overview

📂 Dataset Access EquiFashion-DB

The dataset used for training and evaluation is available on Hugging Face:

➡️ NguyenDinhHieu/EquiFashion-DB

You can load it directly using the datasets library:

from datasets import load_dataset

dataset = load_dataset("NguyenDinhHieu/EquiFashion-DB")
print(dataset)

🧮 Training Configuration

🖼️ Visual Results

🚀 Usage Example

from huggingface_hub import hf_hub_download
from cldm.model import create_model, load_state_dict
import torch

# Download checkpoint
ckpt = hf_hub_download("NguyenDinhHieu/EquiFashionModel", filename="eqf_final.ckpt")

# Load model
model = create_model("utils/configs/cldm_v2.yaml").to("cuda")
model.load_state_dict(load_state_dict(ckpt, location="cuda"))
model.eval()

prompt = "long-sleeve floral dress with tied waist, elegant, 8k detail"

💡 Citation

If you use this model or dataset, please cite:

@inproceedings{nguyen2026equifashion,
  title={EquiFashion: Hybrid GAN–Diffusion Balancing Diversity–Fidelity for Fashion Design Generation},
  author={Nguyen Dinh Hieu and Tran Minh Khuong and Phan Duy Hung},
  booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
  year={2026},
  organization={FPT University, VietNam / Quantum AI & Cyber Security Institute, FPT Corporation, Vietnam}
}

🧩 File Descriptions

📚 References

1. Zhu et al. Be Your Own Prada (ICCV 2017)
2. Chen et al. TailorGAN (WACV 2020)
3. Li et al. BC-GAN (CVPR 2019)
4. Xu et al. AttnGAN (CVPR 2018)
5. Karras et al. StyleGAN (CVPR 2019)
6. Zhang et al. DiffCloth (ICCV 2023)
7. Xie et al. HieraFashDiff (AAAI 2025)
8. Kim et al. FashionSD-X (arXiv 2024)
9. Baldrati et al. Multimodal Garment Designer (ICCV 2023)
10. Rombach et al. Latent Diffusion Models (CVPR 2022)

🪪 License

Released under the MIT License.
You may use, modify, and distribute the model and dataset with attribution.

🧩 Acknowledgment

Developed by FPT University AI Research Group, Hanoi, Vietnam
as part of the EquiAI Research Suite on fairness, robustness, and trustworthy generative AI.