Awesome

<div align="center"> <h3>Training-free Composite Scene Generation for Layout-to-Image Synthesis</h3>

Jiaqi Liu<sup>1</sup> Tao Huang<sup>1</sup> Chang Xu<sup>1</sup>

<sup>1</sup> Schoold of Computer Science, Faculty of Engineering, The University of Sydney

</div> <img src="docs/art_demo.png" width="1000">

Image Generation

The Layout-to-Image generation process requires a prompt, bounding boxes, and attending indices, which can be modified in generate.py. The outputs are saved as PNG files in the specified path. Configuration settings are located in config.py.

python generate.py

Evaluation

The method is evaluated using YOLOv7, available here. CLIP score measurement utilizes the model available here. A sample dataset with layouts of two objects, as described in the paper, is provided in the docs folder.

Citation

@misc{liu2024trainingfreecompositescenegeneration,
      title={Training-free Composite Scene Generation for Layout-to-Image Synthesis}, 
      author={Jiaqi Liu and Tao Huang and Chang Xu},
      year={2024},
      eprint={2407.13609},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.13609}, 
}

To Be Provided

Examples for generation

Acknowledgements

This research is supported in part by the Australian Research Council under Projects DP210101859 and FT230100549. This work is inspired by layout-guidance, BoxDiff, and Diffusers.