What is AnimeGANV2?

AnimeGANv2, the improved version of AnimeGAN.
AnimeGAN is a lightweight GAN for a photo animation.

In brief, people can generate a photo that looks like an animation's scene from an image.

You can try AnimeGAN easily with this.
https://animegan.js.org/

repos

Original repo

TachibanaYoshino / AnimeGANv2

[Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime

AnimeGANv2

The improved version of AnimeGAN.

Project Page | Landscape photos / videos to anime

News

(2022.08.03) Added the AnimeGANv2 Colab: 🖼️ Photos | 🎞️ Videos
(2021.12.25) AnimeGANv3 has been released. 🎄
(2021.02.21) The pytorch version of AnimeGANv2 has been released, Be grateful to @bryandlee for his contribution.
(2020.12.25) AnimeGANv3 will be released along with its paper in the spring of 2021.

Focus:

Anime style	Film	Picture Number	Quality	Download Style Dataset
Miyazaki Hayao	The Wind Rises	1752	1080p	Link
Makoto Shinkai	Your Name & Weathering with you	1445	BD
Kon Satoshi	Paprika	1284	BDRip

News:

The improvement directions of AnimeGANv2 mainly include the following 4 points:

1. Solve the problem of high-frequency artifacts in the generated image.
2. It is easy to train and directly achieve the effects in the paper.
3. Further reduce the number of parameters of the generator network. (generator size: 8.17 Mb)…

View on GitHub

PyTorch Implementation

bryandlee / animegan2-pytorch

PyTorch implementation of AnimeGANv2

PyTorch Implementation of AnimeGANv2

Updates

2021-10-17 Add weights for FacePortraitV2.
2021-11-07 Thanks to ak92501, a web demo is integrated to Huggingface Spaces with Gradio.
2021-11-07 Thanks to xhlulu, the torch.hub model is now available. See Torch Hub Usage.

Basic Usage

Inference

python test.py --input_dir [image_folder_path] --device [cpu/cuda]

Torch Hub Usage

You can load the model via torch.hub:

import torch model = torch.hub.load("bryandlee/animegan2-pytorch", "generator").eval() out = model(img_tensor) # BCHW tensor

Currently, the following pretrained shorthands are available:

model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="celeba_distill") model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1") model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v2") model = torch.hub.load

…

View on GitHub

steps

Create a new note on Google Colab

Upload an input image

Run the following code

from PIL import Image import torch import IPython from IPython.display import display # https://github.com/bryandlee/animegan2-pytorch # load models model_celeba = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="celeba_distill") model_facev1 = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1") model_facev2 = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v2") model_paprika = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="paprika") face2paint = torch.hub.load("bryandlee/animegan2-pytorch:main", "face2paint", size=512) INPUT_IMG = "sg.jpg" # input_image jpg/png img = Image.open(INPUT_IMG).convert("RGB") out_celeba = face2paint(model_celeba, img) out_facev1 = face2paint(model_facev1, img) out_facev2 = face2paint(model_facev2, img) out_paprika = face2paint(model_paprika, img) # save images out_celeba.save("out_celeba.jpg") out_facev1.save("out_facev1.jpg") out_facev2.save("out_facev2.jpg") out_paprika.save("out_paprika.jpg") # display images display(img) display(out_celeba) display(out_facev1) display(out_facev2) display(out_paprika)