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/

Detail
https://tachibanayoshino.github.io/AnimeGANv2/

repos

Original repo

GitHub logo TachibanaYoshino / AnimeGANv2

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

AnimeGANv2

「Open Source」. The improved version of AnimeGAN.
「Project Page」 | Landscape photos/videos to anime

News

  • (2021.12.25) AnimeGANv3 has been released. 🎄
  • (2020.12.25) AnimeGANv3 will be released along with its paper in the spring of 2021.
  • (2021.02.21) The pytorch version of AnimeGANv2 has been released, Be grateful to @bryandlee for his contribution.

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:
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  • 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), The lite version has a smaller generator model.

  • …

View on GitHub

PyTorch Implementation

GitHub logo bryandlee / animegan2-pytorch

PyTorch implementation of AnimeGANv2

PyTorch Implementation of AnimeGANv2

Updates

  • 2021-10-17 Add weights for FacePortraitV2. Colab

    sample

  • 2021-11-07 Thanks to ak92501, a web demo is integrated to Huggingface Spaces with Gradio. Hugging Face Spaces

  • 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
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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
…
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View on GitHub

steps

  1. Create a new note on Google Colab
  2. Upload an input image
  3. 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)
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[original]
original

[celeba_distill]
celeba_distill

[face_paint_512_v1]
face_paint_512_v1

[face_paint_512_v2]
face_paint_512_v2

[paprika]
paprika

# python # pytorch
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