Train and deploy real-time artistic style transfer in mobile apps with Fritz Style Transfer.
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Code for training artistic style transfer models with Keras and converting them to Core ML.

Add style transfer to your app in minutes with Fritz

If you're looking to add style transfer to your app quickly, check out Fritz. The Fritz SDK provides 11 pre-trained style transfer models along with all the code you need to apply them images or live video. If you want to train your own model, keep reading.


If you're not installing using a package manager like pip, make sure the root directory is on your PYTHONPATH:


Preprocessing Training Data

The training data comes from the COCO Training data set. It consists of ~80,000 images and labels, although the labels arent used here.

the script will download and unzip this data then process images to create an h5 dataset used by the style transfer network trainer. You can run this with the command below. Note the first time you run this you will need to download and unzip 13GB worth of data and it can take a while. The command only processes the first 10 images to make sure things are working, but you can modify --num-images to process more.

python \
--output example/training_images.tfrecord \
--image-dir path/to/coco/ \
--num-images 10

Note that if you have already downloaded and extracted a set of images to use for training, that directory needs to be called train2014/ and you need to point --coco-image-dir to the parent directory that contains that folder. Otherwise you can use the --download flag.

Training a Style Transfer Model

To train the model from scratch for 100 iterations:

python style_transfer/ \
--training-image-dset example/training_images.tfrecord \
--style-images example/starry_night.jpg \
--model-checkpoint example/starry_night.h5 \
--image-size 256,256 \
--alpha 0.25 \
--log-interval 1 \
--num-iterations 10

If everything looks good, we can pick up where we left off and keep training the same model.

python style_transfer/ \
--training-image-dset example/training_images.tfrecord \
--style-images example/starry_night.jpg \
--model-checkpoint example/starry_night.h5 \
--image-size 256,256 \
--alpha 0.25 \
--num-iterations 1000 \
--fine-tune-checkpoint example/starry_night.h5

If you're using the full COCO dataset, you'll need around 20,000 iterations to train a model from scratch with a batch size of 24. If you're starting from a pre-trained model checkpoint, 5,000 steps should work. A model pre-trained on Starry Night is provided in the example/ folder.

For styles that are abstract with strong geometric patters, try higher values for --content-weight like 3 or 10. For styles that are more photo-realistic images with smaller details, boost the --style-weight to 0.001 or more.

Finally, note that for training, we resize images to be 256x256px. This is for training only. Final models can be set to take images of any size.

Training models for mobile

By default, the style transfer networks produced here are roughly 7mb in size and contain 7 million parameters. They can create a stylized image in ~500ms on high end mobile phones, and 5s on lower end phones. To make the model's faster, we've included a width-multiplier parameter similar to the one introduced by Google in their MobileNet architecture. The value alpha can be set between 0 and 1 and will control how many filters are included in each layer. Lower alpha means fewer filters, fewer parameters, faster models, with slightly worse style transfer abilities. In testing, alpha=0.25 produced models that ran at 17fps on an iPhone X, while still transfering styles well.

Finally, for models that are intended to be used in real-time on a CPU only, you can use the --use-small-network flag to train a model architecture that has been heavily pruned. The style transfer itself isn't quite as good, but the results are usable and the models are incredible small.

Stylizing Images

To stylize an image with a trained model you can run:

python \
--input-image example/dog.jpg \
--output-image example/stylized_dog.jpg \
--model-checkpoint example/starry_night_256x256_025.h5

Convert to Mobile

Style transfer models can be converted to both Core ML and TensorFlow Mobile formats.

Convert to Core ML

Use the converter script to convert to Core ML.

This converter is a slight modification of Apple's keras converter that allows the user to define custom conversions between Keras layers and core ml layers. This allows us to convert the Instance Normalization and Deprocessing layers.

python \
--keras-checkpoint example/starry_night_256x256_025.h5 \
--alpha 0.25 \
--image-size 640,480 \
--coreml-model example/starry_night_640x480_025.mlmodel

Convert to TensorFlow Mobile

Models cannot be converted to TFLite because some operations are not supported, but TensorFlow Mobile works fine. To convert your model to an optimized frozen graph, run:

python \
--keras-checkpoint example/starry_night_256x256_025.h5 \
--alpha 0.25 \
--image-size 640,480 \
--output-dir example/

This produces a number of TensorFlow graph formats. The *_optimized.pb graph file is the one you want to use with your app. Note that the input node name is input_1 and the output node name is deprocess_stylized_image_1/mul.

Train on Google Cloud ML

This library is designed to work with certain configurations on Google Cloud ML so you can train styles in parallel and take advantage GPUs. Assuming you have Google Cloud ML and Google Cloud Storage set up, the following commands will get you training new models in just a few hours.

Set up your Google Cloud Storage bucket.

This repo assumes the structure on Google Cloud is

    |-- data/
        |-- training_images.tfrecord
        |-- starry_night_256x256_025.h5
        |-- style_images/
            |-- style_1.jpg
            |-- style_2.jpg
    |-- dist/
    |-- train/
        |-- pretrained_model.h5
        |-- output_model.h5

To make things easier, start by setting some environmental variables.

export YOUR_GCS_BUCKET=your_gcs_bucket
export FRITZ_STYLE_TRANSFER_PATH=/path/to/fritz-style-transfer
export KERAS_CONTRIB_PATH=/path/to/keras-contrib
export STYLE_NAME=style_name

Note that STYLE_NAME should be the filename of the style image (without the extension).

Create the GCS bucket if you haven't already:

gsutil mb gs://${YOUR_GCS_BUCKET}

Copy training data to GCS, pre-trained checkpoints, and style image to:

gsutil cp example/training_images.tfrecord gs://${YOUR_GCS_BUCKET}/data
gsutil cp example/${STYLE_NAME}.jpg gs://${YOUR_GCS_BUCKET}/data/style_images/
gsutil cp example/starry_night_256x256_025.h5 gs://${YOUR_GCS_BUCKET}/data/

Package up libraries.

Zip up all of the local files to send up to Google Cloud.

python sdist

Zip up keras_contrib so it's available to the library as well.

python sdist
cp dist/* ${FRITZ_STYLE_TRANSFER_PATH}/dist/

Start the training job

The following command will start training a new style transfer models from a pre-trained checkpoint. This configuration trains on 256x256 images and has --alpha=0.25 making it suitable for real-time use in mobile apps.

gcloud ml-engine jobs submit training `whoami`_style_transfer`date +%s` \
    --runtime-version 1.8 \
    --job-dir=gs://${YOUR_GCS_BUCKET} \
    --packages dist/style_transfer-1.0.tar.gz,dist/keras_contrib-2.0.8.tar.gz \
    --module-name style_transfer.train \
    --region us-east1 \
    --scale-tier basic_gpu \
    -- \
    --training-image-dset gs://${YOUR_GCS_BUCKET}/data/test_training_images.tfrecord \
    --style-images gs://${YOUR_GCS_BUCKET}/data/style_images/${STYLE_NAME}.jpg \
    --model-checkpoint ${STYLE_NAME}_256x256_025.h5 \
    --image-size 256,256 \
    --alpha 0.25 \
    --num-iterations 5000 \
    --batch-size 24 \
    --content-weight 1 \
    --style-weight .0001 \
    --gcs-bucket gs://${YOUR_GCS_BUCKET}/train \
    --fine-tune-checkpoint gs://${YOUR_GCS_BUCKET}/data/starry_night_256x256_025.h5

Distributed training and TPUs are not yet supported.

Add the model to your app with Fritz

Now that you have a style transfer model that works for both iOS and Android, head over to for tools to help you integrate it into your app and manage it over time.