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

Code for "Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo"

Go from graph data to a secure and interactive visual graph app in 15 minutes. Batteries-included self-hosting of graph data apps with Streamlit, Graphistry, RAPIDS, and more!

Pytorch Lightning Implementation of SC-Depth Methods.

This repo contains research materials released by members of the Google Brain team in Tokyo.

Train an RL agent to execute natural language instructions in a 3D Environment (PyTorch)

Py4fi2nd - Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.

Marine debris detection with commercial satellite imagery and deep learning.

PyTorch Implementation for Deep Metric Learning Pipelines

Points2Surf: Learning Implicit Surfaces from Point Clouds (ECCV 2020 Spotlight)

An ML & Correlation platform for transforming disparate data points of interest into usable intelligence.

[Arxiv preprint] Causality-inspired Single-source Domain Generalization for Medical Image Segmentation (code&data-processing pipeline)

Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

Official implementation of "Generating 3D Molecules for Target Protein Binding"

Unofficial PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution

KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control

Prototypical Networks for Few shot Learning in PyTorch

A Gura parser implementation for Python

An SE(3)-invariant autoencoder for generating the periodic structure of materials

Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.

This is the official repository of Music Playlist Title Generation: A Machine-Translation Approach.