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# DiffSketch: Plug-and-Play Diffusion Features for Zero-Shot Line-Drawing-to-Sketch Synthesis
> *Stable Diffusion* Implementation, our method is built on PnP-Diffusion
**To generate Facial Sketch given line drawings, please follow these steps:**
1. [Setup](#setup)
2. [Running synthesis](#running-synthesis)
3. [Gather Results](#gather-results)
## Setup
Our codebase is built on [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion)
and has shared dependencies and model architecture.
### Creating a Conda Environment
```
conda env create -f environment.yaml
conda activate pnp-diffusion
```
### Downloading StableDiffusion Weights
Download the StableDiffusion weights from the [CompVis organization at Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
(download the `sd-v1-4.ckpt` file), and link them:
```
mkdir -p models/pnp_ldm/stable-diffusion-v1/
ln -s <path/to/model.ckpt> models/pnp_ldm/stable-diffusion-v1/model.ckpt
```
### Setting Experiment Root Path
The data of all the experiments is stored in a root directory.
The path of this directory is specified in `configs/pnp/setup.yaml`, under the `config.exp_path_root` key.
### Hardware
The code was tested with a single GPU (NVIDIA GeForce RTX 3090) with 24GB of memory.
## Running Synthesis
Note that the data for testing is already provided in the repository (in `data` directory).
### Running on a Single Image
To run the model on a single image, and test with hyperparameters, run the notebook `pnp_sketch.ipynb`.
Some important parameters to set: `self_attn_output_block_indices`, `out_layers_output_block_indices`, to specify the layers to use for the self-attention and the output layers, respectively; `prompts`, used to specify the prompts to use for the diffusion process; `img_path`, the path to the input image; `exp_config.scale`, used to specify the scale of unconditional guidance (a small value encourage the model to generate a sketch that is close to the input image). More parameters can be found in `configs/pnp_refine_all.yaml`.
### Running on a Dataset
To run the model on a dataset, run the python script `pnp_sketch.py`.
```bash
python pnp_sketch.py
```
All required hyperparameters are specified in `configs/pnp_refine_all.yaml`.
## Gather Results
To gather the results, run the notebook `gather.ipynb`.
This notebook will create a directory `result` in the root directory, and will gather the results of the experiments in this directory for submission.
## Additional Results
We provide the comparison with [DiffStyle](https://github.com/Junyi42/DiffStyle) and the ablation study as below.
## Citation
```
@article{pnpDiffusion2022,
title={Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation},
author={Tumanyan, Narek and Geyer, Michal and Bagon, Shai and Dekel, Tali},
journal={arXiv preprint arXiv:2211.12572},
year={2022}
}
```
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