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

Aggragrating Nested Transformer Official Jax Implementation

Adaptive Graph Convolution for Point Cloud Analysis

Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR

Joint parameterization and fitting of stroke clusters

Mesh Graphormer is a new transformer-based method for human pose and mesh reconsruction from an input image

MTCNN face detection implementation for TensorFlow, as a PIP package.

source code of Adversarial Feedback Loop Paper

A time series processing library

Fuzzy Overclustering (FOC)

Forecasting with Gradient Boosted Time Series Decomposition

Code for the paper "Generative design of breakwaters usign deep convolutional neural network as a surrogate model"

Credit fraud detection in Python using a Jupyter Notebook

Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"

A simple version for graphfpn

A general and strong 3D object detection codebase that supports more methods, datasets and tools (debugging, recording and analysis).

PIXIE: Collaborative Regression of Expressive Bodies

object detection; robust detection; ACM MM21 grand challenge; Security AI Challenger Phase VII

Real Time Object Detection and Classification using Yolo Algorithm.

Contains a bunch of different python programm tasks

Implementation of paper "DCS-Net: Deep Complex Subtractive Neural Network for Monaural Speech Enhancement"