Gligen Xl 1024

🚀 IGLIGEN-XL

This project aims to support a SDXL version of GLIGEN adapters with a Hugging Face-style pipeline, contributing to the creation of InteractDiffusion XL.

📦 Information

🚀 Quick Start

This project aims to support a SDXL version of GLIGEN adapters, with a Hugging Face-style pipeline. The project is part of the effort in creating InteractDiffusion XL. More details can be found at the Github Repo.

✨ Features

IGLIGEN reproduces GLIGEN on diffusers frameworks and simplifies the training procedure. They have released the code and pretrained weights for SD v1.4/v1.5, SD v2.0/v2.1, but the support for SDXL is highly anticipated. This repo open-sources the pretrained weights of the GLIGEN adapter for SDXL, along with the diffusers pipeline and training code. We appreciate the work of the authors of GLIGEN and IGLIGEN.

💻 Usage Examples

Basic Usage

import torch
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "jiuntian/gligen-xl-1024", trust_remote_code=True, torch_dtype=torch.float16
).to("cuda")

prompt = "An image of grassland with a dog."

# Image generation with GLIGEN
output_images = pipeline(
    prompt,
    num_inference_steps=50,
    height=1024, width=1024,
    gligen_scheduled_sampling_beta=0.4,
    gligen_boxes=[[0.1, 0.6, 0.3, 0.8]],
    gligen_phrases=["a dog"],
    num_images_per_prompt=1,
    output_type="pt"
).images

📄 License

This project is licensed under the Apache-2.0 license.

📚 Documentation

Citation

The authors of this repo (IGLIGEN-XL) are not affiliated with the authors of GLIGEN and IGLIGEN. Since IGLIGEN-XL is based on GLIGEN and IGLIGEN, if you use the IGLIGEN-XL code or adapters, please kindly consider citing the original GLIGEN and IGLIGEN paper:

@article{li2023gligen,
  title={GLIGEN: Open-Set Grounded Text-to-Image Generation},
  author={Li, Yuheng and Liu, Haotian and Wu, Qingyang and Mu, Fangzhou and Yang, Jianwei and Gao, Jianfeng and Li, Chunyuan and Lee, Yong Jae},
  journal={CVPR},
  year={2023}
}
@article{lian2023llmgrounded,
  title={Llm-grounded diffusion: Enhancing prompt understanding of text-to-image diffusion models with large language models},
  author={Lian, Long and Li, Boyi and Yala, Adam and Darrell, Trevor},
  journal={arXiv preprint arXiv:2305.13655},
  year={2023}
}

The project is part of the effort in creating InteractDiffusion XL.

Please kindly consider citing InteractDiffusion if you use IGLIGEN-XL code/trained weights.

@inproceedings{hoe2023interactdiffusion,
  title={InteractDiffusion: Interaction Control in Text-to-Image Diffusion Models}, 
  author={Jiun Tian Hoe and Xudong Jiang and Chee Seng Chan and Yap-Peng Tan and Weipeng Hu},
  year={2024},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
}