### Clone Repository and Install Requirements

Source: https://github.com/aminshabani/house_diffusion/blob/main/README.md

Clone the HouseDiffusion repository and install its dependencies using pip. Ensure you have Python and pip installed.

```bash
git clone https://github.com/aminshabani/house_diffusion.git
cd house_diffusion
pip install -r requirements.txt
pip install -e .
```

--------------------------------

### Sample Floorplans with HouseDiffusion

Source: https://github.com/aminshabani/house_diffusion/blob/main/README.md

Command to sample floorplans using a trained HouseDiffusion model. Requires specifying dataset, batch size, set name, target set, model path, and number of samples.

```bash
python image_sample.py --dataset rplan --batch_size 32 --set_name eval --target_set 8 --model_path ckpts/exp/model250000.pt --num_samples 64
```

--------------------------------

### Dataset Directory Structure

Source: https://github.com/aminshabani/house_diffusion/blob/main/README.md

Illustrates the expected directory structure for the datasets. Place processed files from downloaded datasets into the 'datasets' folder.

```bash
house_diffusion
├── datasets
│   ├── rplan
|   |   └── 0.json
|   |   └── 1.json
|   |   └── ...
|   └── ...
└── guided_diffusion
└── scripts
└── ...
```

--------------------------------

### Train HouseDiffusion Model

Source: https://github.com/aminshabani/house_diffusion/blob/main/README.md

Command to run a single training experiment for the HouseDiffusion model. Specify dataset, batch size, set name, and target set.

```bash
python image_train.py --dataset rplan --batch_size 32 --set_name train --target_set 8
```

--------------------------------

### HouseDiffusion Citation

Source: https://github.com/aminshabani/house_diffusion/blob/main/README.md

BibTeX entry for citing the HouseDiffusion paper. Use this in academic publications.

```bibtex
@article{shabani2022housediffusion,
  title={HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising},
  author={Shabani, Mohammad Amin and Hosseini, Sepidehsadat and Furukawa, Yasutaka},
  journal={arXiv preprint arXiv:2211.13287},
  year={2022}
}
```

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