FILM: Windows Installation Instructions

Anaconda Python 3.9 (Optional)

Install Anaconda3 Python3.9

Go to https://www.anaconda.com/products/individual and click the "Download" button.
Download the Windows 64-Bit or 32-bit Graphical Installer, depending on your system needs.
Run the downloaded (.exe) file to begin the installation.
(Optional) Check the "Add Anaconda3 to my PATH environment variable". You may get a 'red text' warning of its implications, you may ignore it for this setup.

Create a new Anaconda virtual environment

Open a new Terminal
Type the following command:

conda create -n frame_interpolation pip python=3.9

The above command will create a new virtual environment with the name frame_interpolation

Activate the Anaconda virtual environment

Activate the newly created virtual environment by typing in your terminal (Command Prompt or PowerShell)

conda activate frame_interpolation

Once activated, your terminal should look like:

(frame_interpolation) <present working directory> >

NVIDIA GPU Support

Install CUDA Toolkit

Go to https://developer.nvidia.com/cuda-11.2.1-download-archive and select your Windows.
Download and install CUDA Tookit 11.2.1.
Additional CUDA installation information available here.

Install cuDNN

Go to https://developer.nvidia.com/rdp/cudnn-download.
Create a user profile (if needed) and login.
Select cuDNN v8.1.0 (January 26th, 2021), for CUDA 11.0,11.1 and 11.2.
Download cuDNN Library for Widnows (x86).
Extract the contents of the zipped folder (it contains a folder named cuda) into <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\. <INSTALL_PATH> points to the installation directory specified during CUDA Toolkit installation. By default, <INSTAL_PATH> = C:\Program Files.

Environment Setup

Add the following paths to your 'Advanced System Settings' > 'Environment Variables ...' > Edit 'Path', and add:
- \NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin
- \NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvp
- \NVIDIA GPU Computing Toolkit\CUDA\v11.2\include
- \NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64
- \NVIDIA GPU Computing Toolkit\CUDA\v11.2\cuda\bin

Verify Installation

Open a new terminal and type conda activate frame_interpolation.
Install (temporarily) tensorflow and run a simple operation, by typing:

pip install --ignore-installed --upgrade tensorflow==2.6.0
python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

You should see success messages: 'Created device /job:localhost/replica:0/task:0/device:GPU:0'.

FILM Installation

Get Frame Interpolation source codes

git clone https://github.com/google-research/frame-interpolation
cd frame-interpolation

Install dependencies

pip install -r requirements.txt
conda install -c conda-forge ffmpeg

Download pre-traned models, detailed here.

Running the Codes

One mid-frame interpolation. Note: python3 may not be recognized in Windows, so simply drop 3 as below.

python -m eval.interpolator_test --frame1 photos\one.png --frame2 photos\two.png --model_path <pretrained_models>\film_net\Style\saved_model --output_frame photos\output_middle.png

Large resolution mid-frame interpolation: Set block_height and --block_width to subdivide along the height and width to create patches, where the interpolator will be run iteratively, and the resulting interpolated mid-patches will be reconstructed into a final mid-frame. In the example below, will create and run on 4 patches (2*2).

python -m eval.interpolator_test --frame1 photos\one.png --frame2 photos\two.png --block_height 2 --block_wdith 2 --model_path <pretrained_models>\film_net\Style\saved_model --output_frame photos\output_middle.png

Many in-between frames interpolation

python -m eval.interpolator_cli --pattern "photos" --model_path <pretrained_models>\film_net\Style\saved_model --times_to_interpolate 6 --output_video

Acknowledgments

This windows installation guide is heavily based on tensorflow-object-detection-api-tutorial .