Suggestions to convert animeGAN v1 to bryandlee / animegan2-pytorch

AnimeGAN Pytorch

Pytorch implementation of AnimeGAN for fast photo animation

Paper: AnimeGAN: a novel lightweight GAN for photo animation - Semantic scholar or from Yoshino repo
Original implementation in Tensorflow by Tachibana Yoshino
Demo and Docker image on Replicate
Sample anime video: https://www.youtube.com/watch?v=45ASFOR3rNU

Input	Animation

23/04/2024: Added DDP training.
16/04/2024: AnimeGANv2 (Hayao style) is released with training code

Quick start

git clone https://github.com/ptran1203/pytorch-animeGAN.git
cd pytorch-animeGAN

Run Inference on your local machine

--src can be directory or image file

python3 inference.py --weight hayao:v2 --src /your/path/to/image_dir --out /path/to/output_dir

Python code

from inference import Predictor

predictor= Predictor('hayao:v2')

url = 'https://github.com/ptran1203/pytorch-animeGAN/blob/master/example/result/real/1%20(20).jpg?raw=true'

predictor.transform_file(url, "anime.jpg")

Pretrained weight

Model name	Model	Dataset	Weight
Hayao:v2	AnimeGANv2	Google Landmark v2 + Hayao style	GeneratorV2_gldv2_Hayao.pt
Hayao	AnimeGAN	train_photo + Hayao style	generator_hayao.pt
Shinkai	AnimeGAN	train_photo + Shinkai style	generator_shinkai.pt

Documentation

Training notebook on Google colab
Inference notebook on Google colab

1. Prepare dataset

1.1 To download dataset from the paper, run below command

wget -O anime-gan.zip https://github.com/ptran1203/pytorch-animeGAN/releases/download/v1.0/dataset_v1.zip
unzip anime-gan.zip

=> The dataset folder can be found in your current folder with named dataset

1.2 Create custom data from anime video

You need to have a video file located on your machine.

Step 1. Create anime images from the video

python3 script/video_to_images.py --video-path /path/to/your_video.mp4\
                                --save-path dataset/MyCustomData/style\
                                --image-size 256\

Step 2. Create edge-smooth version of dataset from Step 1.

python3 script/edge_smooth.py --dataset MyCustomData --image-size 256

2. Train animeGAN

To train the animeGAN from command line, you can run train.py as the following:

python3 train.py --anime_image_dir dataset/Hayao \
                --real_image_dir dataset/photo_train \
                --model v2 \                 # animeGAN version, can be v1 or v2
                --batch 8 \
                --amp \                      # Turn on Automatic Mixed Precision training
                --init_epochs 10 \
                --exp_dir runs \
                --save-interval 1 \
                --gan-loss lsgan \           # one of [lsgan, hinge, bce]
                --init-lr 1e-4 \
                --lr-g 2e-5 \
                --lr-d 4e-5 \
                --wadvd 300.0\               # Aversarial loss weight for D
                --wadvg 300.0\               # Aversarial loss weight for G
                --wcon 1.5\                  # Content loss weight
                --wgra 3.0\                  # Gram loss weight
                --wcol 30.0\                 # Color loss weight
                --use_sn\                    # If set, use spectral normalization, default is False

3. Transform images

To convert images in a folder or single image, run inference.py, for example:

--src and --out can be a directory or a file

python3 inference.py --weight path/to/Generator.pt \
                     --src dataset/test/HR_photo \
                     --out inference_images

4. Transform video

To convert a video to anime version:

Be careful when choosing --batch-size, it might lead to CUDA memory error if the resolution of the video is too large

python3 inference.py --weight hayao:v2\
                        --src test_vid_3.mp4\
                        --out test_vid_3_anime.mp4\
                        --batch-size 4