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

Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth

A knowledge base construction engine for richly formatted data

Transformer Huffman coding - Complete Huffman coding through transformer

A PyTorch implementation of the baseline method in Panoptic Narrative Grounding (ICCV 2021 Oral)

A toolkit for document-level event extraction, containing some SOTA model implementations

A Fast Knowledge Distillation Framework for Visual Recognition

A task-agnostic vision-language architecture as a step towards General Purpose Vision

Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation

Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation

Studying Python release adoptions by looking at PyPI downloads

PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training”

Code base for the paper "Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation"

Simple tutorials on Pytorch DDP training

One Million Scenes for Autonomous Driving

zeus is a Python implementation of the Ensemble Slice Sampling method.

Implementation of Pix2Seq in PyTorch

Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

Facestar dataset. High quality audio-visual recordings of human conversational speech.

This library provides an abstraction to perform Model Versioning using Weight & Biases.

The implementation of "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement"