Awesome

MAD: Semantically Coherent Montages by Merging and Splitting Diffusion Paths

Official PyTorch implementation for "Merging and Splitting Diffusion Paths for Semantically Coherent Panoramas", presenting the Merge-Attend-Diffuse operator.

The code is tested on Python 3.11.7, CUDA 12.1, and PyTorch 2.1.2

If you find it useful, please cite it as:

@inproceedings{quattrini2024merging,
  title={{Merging and Splitting Diffusion Paths for Semantically Coherent Panoramas}},
  author={Quattrini, Fabio and Pippi, Vittorio and Cascianelli, Silvia and Cucchiara, Rita},
  booktitle={Proceedings of the European Conference on Computer Vision},
  year={2024},
  organization={Springer}
}

Installation

This is the list of python packages that we need to run inference

conda create --name mad python=3.11.7
pip install -r requirements.txt

Inference with Stable Diffusion

Basic code to run inference with the default parameters

python sample_panorama_stable_diffusion.py

Some suggestions:

python sample_panorama_stable_diffusion.py --prompt "A shelf full of colorful books"

python sample_panorama_stable_diffusion.py --prompt "Tube map of London"

python sample_panorama_stable_diffusion.py --prompt "A whole shepherd pie"

Inference with LCM

Basic code to run inference with the default parameters

python sample_panorama_lcm.py

Some suggestions:

python sample_panorama_lcm.py --prompt "A pride concert full of colorful fireworks"

python sample_panorama_lcm.py --prompt "Top-view of a square pizza"

Some suggestions of vertical images:

python sample_panorama_lcm.py --prompt "A tower in a colorful sky" --W 512 --H 2048

python sample_panorama_lcm.py --prompt "A view of a river inside a canyon" --W 512 --H 2048

Acknowledgements

Our code is heavily based on the implementation of MultiDiffusion