Official implementation of "MambaPainter: Neural Stroke-Based Rendering in a Single Step."

Paper | Code

• Install dependencies. We assume that torch and torchvision is already installed. Checked that torch versions 2.3.x, 2.4.0 is moving.

  pip install -r requirements.txt

• Install the selective_scan module in MzeroMiko/VMamba.

  You can optionally erase the VMamba repository after installing because we will not be using it.

  git clone https://github.com/MzeroMiko/VMamba.git
  cd VMamba
  git switch -d 0a74a29eefb9223efc1a399000e22a723390defd
  cd kernels/selective_scan
  pip install .

The configuration is done using the hydra package.

1. Train neural renderer.

   Edit the configuration file.

   torchrun train_1_neural_renderer.py

2. Train MambaPainter.

   Edit the configuration file.

   The values in <> must be edited.

   torchrun train_2_stroke_predictor.py \
       data.image_dir=<path/to/dataset> \
       renderer.config_file=<path/to/renderer/config.yaml> \
       renderer.pretrained=<path/to/renderer/weights.pt>

Use the checkpoint folder created above as <path/to/trained/folder> in the command below.

You can also download pretrained files from GDrive or Huggingface. Create a folder and place the downloaded config.yaml and last-model.pt inside. Use the created folder as <path/to/trained/folder> in the command below.

python multi_patch_inference_fast.py \
    <path/to/trained/folder> \
    <path/to/image.jpg> <mutliple/images/can/be/provided.png> \
    --output . \
    --image-size 512 \
    --patch-size 64 \
    --overlap-pixels 32 \
    --stroke-padding 32

The script will automatically create the translated image and a JSON file containing the command line arguments. You can add the --save-all option to save an image of patches used in the translation, predicted stroke parameters, and a timelapse GIF.

We use Mamba2 layers, which heavily relies on triton. Thus, when translating only one image you will encounter slow translation speed. You will see the proper speed after the second image when translating multiple images with one command. For a reference, we included the source code used to calculate the scores reported in our paper here.

$ python multi_patch_inference_fast.py --help
usage: multi_patch_inference_fast.py [-h] [--output OUTPUT] [--params PARAMS] [--image-size IMAGE_SIZE] [--patch-size PATCH_SIZE] [--overlap-pixels OVERLAP_PIXELS]
                                     [--stroke-padding STROKE_PADDING] [--batch-size BATCH_SIZE] [--merge-every MERGE_EVERY] [--save-timelapse] [--gif-optimize]
                                     [--gif-duration GIF_DURATION] [--gif-loop GIF_LOOP] [--save-parameters] [--save-patches] [--save-all]
                                     model_folder input

positional arguments:
  model_folder          Path to the folder of saved checkpoints.
  input                 Input filename.

options:
  -h, --help            show this help message and exit
  --output OUTPUT, -o OUTPUT
                        Output results to.
  --params PARAMS       Path to saved parameters.
  --image-size IMAGE_SIZE, -is IMAGE_SIZE
                        Output image size.
  --patch-size PATCH_SIZE, -ps PATCH_SIZE
                        Size of each image patch. The patch size is `--patch-size + --overlap-pixels`
  --overlap-pixels OVERLAP_PIXELS, -op OVERLAP_PIXELS
                        Overlapping pixels. The patch size is `--patch-size + --overlap-pixels`
  --stroke-padding STROKE_PADDING, -sp STROKE_PADDING
                        Number of pixels to pad to the rendering image size.
  --batch-size BATCH_SIZE, -bs BATCH_SIZE
                        Batch size.
  --merge-every MERGE_EVERY
                        Render n strokes to an image per merging.
  --save-timelapse      Save a timelapse as a GIF file.
  --gif-optimize
  --gif-duration GIF_DURATION
  --gif-loop GIF_LOOP
  --save-parameters     Save predicted parameters. Useful when you want to quickly recreate the timelapse GIF.
  --save-patches        Save image patches used to render the output.
  --save-all            Trigger all saving flags, for peaple who are too lazy.

@inproceedings{10.1145/3681756.3697906,
    author    = {Sawada, Tomoya and Katsurai, Marie},
    title     = {MambaPainter: Neural Stroke-Based Rendering in a Single Step},
    year      = {2024},
    isbn      = {9798400711381},
    publisher = {Association for Computing Machinery},
    address   = {New York, NY, USA},
    url       = {https://doi.org/10.1145/3681756.3697906},
    doi       = {10.1145/3681756.3697906},
    booktitle = {SIGGRAPH Asia 2024 Posters},
    articleno = {1},
    numpages  = {2},
    location  = {Tokyo, Japan},
    series    = {SA Posters ’24},
}

We thank the contributors of mamba/mamba2 and VMamba for publishing their work. We also thank the contributors of Compositional Neural Painter and PaintTransformer, which we heavily referenced to implement the parametric rendering of stroke parameters.

Tomoya Sawada