Credits for the example in the gif above: JUGZ
conda env create
Also note that some of the scripts depend on ffmpeg.
python -m tcvc.train --dataset-path /path/to/images/ --input-style line_art
or
python -m tcvc.train --dataset-path /path/to/images/ --input-style greyscale
The frame filenames should have zero-padded frame numbers, for example like this:
- frame00001.png, frame00002.png, frame00003.png, ...
If you have multiple sequences of frames (i.e. from different videos/scenes/shots), you can have different prefixes in the frame filenames, like this:
- firstvideo00001.png, firstvideo00002.png, firstvideo00003.png, ..., secondvideo00001.png, secondvideo00002.png, secondvideo00003.png, ...
Alternatively, the different frame sequences can reside in different subfolders. For that to work, you have to use the --include-subfolders argument.
If you have a video file, not a set of PNG frames, you can run python -m tcvc.extract_video_frames --input-path /path/to/video.mp4 to extract the frames as a set of images.
Once you have a folder that contains video frames that you want to colorize, run the following command:
python -m tcvc.apply --input-path /path/to/frames/ --input-style line_art
By default, this command will use a model that is included in this repository. It is trained on Dragonball line art. If you want to specify a different model, you can do that with --model. For example, you could try this model that is trained on Ninjadev demos:
Remember that this Ninjadev demo model expects you to use --input-style greyscale.
tcvc.apply will scale your frames to 256x256. In order to apply the color of the small 256x256 frames to the corresponding higher-resolution original greyscale frames, run python -m tcvc.transfer_colored_frames --input-path /path/to/frames.
Note: --input-path here refers to the path to the folder that contains the greyscale images (frames) and a subfolder named "colored" where the corresponding colored frames reside.
Once you have the set of frames you want in a video, you can run python -m tcvc.convert_images_to_video --framerate 30 --input-path /path/to/frames --audio-path /path/to/original_video_file.mp4 to compile a video file. Alternatively, you can use ffmpeg directly.
There is some experimental code that uses noise2noise techniques to upscale and refine the colored 256x256 outputs, informed by the high-res greyscale originals. See resolution_enhancer
pytest
Credits go to Harry-Thasarathan/TCVC. Changes made to the original can be found in the commit history. See also the licence.
The original paper by Harrish Thasarathan, Kamyar Nazeri and Mehran Ebrahimi (2019) can be read at https://arxiv.org/abs/1904.09527.
- The code is not compatible with CPU mode as of 2019-06-21