PyTorch Implementation of AnimeGANv2

Last update: Jan 07, 2023

Overview

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.

See demo:
2021-11-07 Thanks to xhlulu, the torch.hub model is now available. See Torch Hub Usage.
2021-11-07 Add FacePortraitV2 style demo to a telegram bot. See @face2stickerbot by sxela

Basic Usage

Weight Conversion from the Original Repo (Requires TensorFlow 1.x)

git clone https://github.com/TachibanaYoshino/AnimeGANv2
python convert_weights.py

Inference

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

Results from converted [Paprika] style model

(input image, original tensorflow result, pytorch result from left to right)

Note: Training code not included / Results from converted weights slightly different due to the bilinear upsample issue

Additional Model Weights

Webtoon Face [ckpt]

samples

Trained on 256x256 face images. Distilled from webtoon face model with L2 + VGG + GAN Loss and CelebA-HQ images. See test_faces.ipynb for details.

Face Portrait v1 [ckpt]

samples

Trained on 512x512 face images.

📺

Face Portrait v2 [ckpt]

samples

Trained on 512x512 face images. Compared to v1, 🔻beautify 🔺robustness

🦑 🎮 🔥

Torch Hub Usage

You can load Animegan v2 via torch.hub:

import torch
model = torch.hub.load('bryandlee/animegan2-pytorch', 'generator').eval()
# convert your image into tensor here
out = model(img_tensor)

You can load with various configs (more details in the torch docs):

model = torch.hub.load(
    "bryandlee/animegan2-pytorch:main",
    "generator",
    pretrained=True, # or give URL to a pretrained model
    device="cuda", # or "cpu" if you don't have a GPU
    progress=True, # show progress
)

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("bryandlee/animegan2-pytorch:main", "generator", pretrained="paprika")

You can also load the face2paint util function. First, install dependencies:

pip install torchvision Pillow numpy

Then, import the function using torch.hub:

face2paint = torch.hub.load(
    'bryandlee/animegan2-pytorch:main', 'face2paint', 
    size=512, device="cpu"
)

img = Image.open(...).convert("RGB")
out = face2paint(model, img)

PyTorch Implementation of AnimeGANv2

Related tags

Overview

PyTorch Implementation of AnimeGANv2

Basic Usage

Additional Model Weights

Torch Hub Usage

Owner

This repository is the official implementation of the Hybrid Self-Attention NEAT algorithm.

AirCode: A Robust Object Encoding Method

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

Code for "Reconstructing 3D Human Pose by Watching Humans in the Mirror", CVPR 2021 oral

A Python framework for conversational search

Image Super-Resolution by Neural Texture Transfer

Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

Rafael Project- Classifying rockets to different types using data science algorithms.

Code and data for "Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning" (EMNLP 2021).

A Streamlit component to render ECharts.

MPI-IS Mesh Processing Library

CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework

Pytorch implementation of AREL

This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

Context-Sensitive Misspelling Correction of Clinical Text via Conditional Independence, CHIL 2022

Generic U-Net Tensorflow implementation for image segmentation

Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

DIT is a DTLS MitM proxy implemented in Python 3. It can intercept, manipulate and suppress datagrams between two DTLS endpoints and supports psk-based and certificate-based authentication schemes (RSA + ECC).

Official Chainer implementation of GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral)

WSDM2022 Challenge - Large scale temporal graph link prediction