# fast-neural-doodle

**Repository Path**: mirrors_DmitryUlyanov/fast-neural-doodle

## Basic Information

- **Project Name**: fast-neural-doodle
- **Description**: Faster neural doodle
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-11
- **Last Updated**: 2025-10-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Faster neural doodle

This is my try on drawing with neural networks, which is faster than [Alex J. Champandard's version](https://github.com/alexjc/neural-doodle), and similar in quality. This approach is based on [neural artistic style method](http://arxiv.org/abs/1508.06576) (L. Gatys), whereas Alex's version uses [CNN+MRF approach](http://arxiv.org/abs/1601.04589) of Chuan Li.

It takes several minutes to redraw `Renoir` example using GPU and it will easily fit in 4GB GPUs. If you were able to work with [Justin Johnson's code for artistic style](https://github.com/jcjohnson/neural-style) then this code should work for you too. 

You can find even faster version [here](https://github.com/DmitryUlyanov/online-neural-doodle).

## Requirements
- torch
- torch.cudnn (optional)
- [torch-hdf5](https://github.com/deepmind/torch-hdf5)
- python + numpy + scipy + h5py + sklearn

Tested with python2.7 and latest `conda` packages.
## Do it yourself

First download VGG-19.
```
cd data/pretrained && bash download_models.sh && cd ../..
```

Use this script to get intermediate representations for masks. 
```
python get_mask_hdf5.py --n_colors=4 --style_image=data/Renoir/style.png --style_mask=data/Renoir/style_mask.png --target_mask=data/Renoir/target_mask.png
```

Now run doodle.
```
th fast_neural_doodle.lua -masks_hdf5 masks.hdf5
```

And here is the result.
![Renoir](data/Renoir/grid.png)
First row: original, second -- result.

And Monet.
![Monet](data/Monet/grid.png)

## Multiscale

Processing the image at low resolution first can provide a significant speed-up. You can pass a list of resolutions to use when processing. Passing `256` means that the images and masks should be resized to `256x256` resolution. With `0` passed no resizing is done. Here is an example for cmd parameters: 
- `-num_iterations 450,100 -resolutions 256,0`
Which means: work for 450 iterations at `256x256` resolution and 100 iterations at original. 

`Monet` and `Renoir` examples take ~1.5 min to process with these options. 

## Style transfer

You can also provide target image to use in content loss (in the same way as in neural artisctic style algorithm) via `--target_image` option of `get\_mask\_hdf5.py` script.

Example:
```
python get_mask_hdf5.py --n_colors=4 --style_image=data/Renoir/style.png --style_mask=data/Renoir/style_mask.png --target_mask=data/Renoir/creek_mask.jpg --target_image=data/Renoir/creek.jpg
th fast_neural_doodle.lua -masks_hdf5 masks.hdf5
```

![Renoir](data/Renoir/comparison.jpg)
Upper left: target image. Upper right: neural doodle with target image, i.e. both the masks and content loss were used. Lower left: regular neural doodle without content loss. Lower right: stylization without masks, with high style weight, obtained via [neural style code](https://github.com/jcjohnson/neural-style). With high style weight, stylization tends to mix unrelated parts of image, such as patches of grass floating in the sky on last picture. Neural doodle with content loss allows to generate highly stylized images without this problem.

## Misc
- Supported backends: 
	- nn (CPU/GPU mode)
	- cudnn
	- clnn (not tested yet..)
 
- When using `-backend cudnn` do not forget to switch `-cudnn_autotune`.

## Acknowledgement

The code is heavily based on [Justin Johnson's great code](https://github.com/jcjohnson/neural-style) for artistic style.

## Citation

If you use this code for your research please cite [neural-style](https://github.com/jcjohnson/neural-style) and this repository.

```
@misc{Ulyanov2016fastdoodle,
  author = {Ulyanov, Dmitry},
  title = {Fast Neural Doodle},
  year = {2016},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/DmitryUlyanov/fast-neural-doodle}},
}
```