update py
27
Dockerfile
|
@ -2,20 +2,29 @@
|
|||
|
||||
FROM public.ecr.aws/iflytek-open/aiges-gpu:11.6-1.17-3.9.13-ubuntu1804-v2.0.0-rc6
|
||||
|
||||
RUN apt-get install -y unzip
|
||||
|
||||
RUN mkdir /app
|
||||
RUN mkdir /app/hub/
|
||||
RUN mkdir /app/hub/checkpoints
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
# do this if you are on the chinese server.
|
||||
RUN pip3 config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple/
|
||||
|
||||
RUN mkdir -p requirements
|
||||
# Install packages
|
||||
RUN pip install gradio torch torchvision Pillow gdown numpy scipy cmake onnxruntime-gpu opencv-python-headless
|
||||
|
||||
ADD requirements.txt /requirements
|
||||
# Download images
|
||||
ADD https://github.com/gradio-app/gradio/raw/main/demo/animeganv2/gongyoo.jpeg /app
|
||||
ADD https://github.com/gradio-app/gradio/raw/main/demo/animeganv2/groot.jpeg /app
|
||||
ADD https://github.com/AK391/animegan2-pytorch/archive/main.zip /app/hub
|
||||
ADD https://github.com/bryandlee/animegan2-pytorch/raw/main/weights/face_paint_512_v2.pt /app/hub/checkpoints/
|
||||
ADD https://github.com/bryandlee/animegan2-pytorch/raw/main/weights/face_paint_512_v1.pt /app/hub/checkpoints/
|
||||
|
||||
RUN pip install -r /requirements/requirements.txt
|
||||
RUN unzip main.zip
|
||||
|
||||
RUN mkdir -p /app/model && \
|
||||
wget https://github.com/AK391/animegan2-pytorch/zipball/main /app/model/
|
||||
COPY animeganv2.py /app
|
||||
|
||||
RUN mkdir -p /app/model/checkpoints && \
|
||||
wget https://github.com/bryandlee/animegan2-pytorch/raw/main/weights/face_paint_512_v2.pt /app/model/checkpoints/face_paint_512_v2.pt
|
||||
|
||||
RUN wget https://github.com/bryandlee/animegan2-pytorch/raw/main/weights/face_paint_512_v1.pt /app/model/checkpoints/face_paint_512_v1.pt
|
||||
CMD ["python3", "animeganv2.py"]
|
|
@ -2,16 +2,21 @@ import gradio as gr
|
|||
from PIL import Image
|
||||
import torch
|
||||
|
||||
model_dir="./app/hub/animegan2-pytorch-main"
|
||||
model_dir_weight="./app/hub/checkpoints/face_paint_512_v1.pt"
|
||||
|
||||
model2 = torch.hub.load(
|
||||
"AK391/animegan2-pytorch:main",
|
||||
model_dir,
|
||||
"generator",
|
||||
pretrained=True,
|
||||
progress=False
|
||||
progress=False,
|
||||
source="local"
|
||||
)
|
||||
model1 = torch.hub.load("AK391/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1")
|
||||
model1 = torch.load(model_dir_weight)
|
||||
face2paint = torch.hub.load(
|
||||
'AK391/animegan2-pytorch:main', 'face2paint',
|
||||
size=512,side_by_side=False
|
||||
model_dir, 'face2paint',
|
||||
size=512,side_by_side=False,
|
||||
source="local"
|
||||
)
|
||||
|
||||
def inference(img, ver):
|
||||
|
|
After Width: | Height: | Size: 18 KiB |
After Width: | Height: | Size: 342 KiB |
|
@ -0,0 +1,129 @@
|
|||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
pip-wheel-metadata/
|
||||
share/python-wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
MANIFEST
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.nox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
*.py,cover
|
||||
.hypothesis/
|
||||
.pytest_cache/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
db.sqlite3
|
||||
db.sqlite3-journal
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# IPython
|
||||
profile_default/
|
||||
ipython_config.py
|
||||
|
||||
# pyenv
|
||||
.python-version
|
||||
|
||||
# pipenv
|
||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
|
||||
# install all needed dependencies.
|
||||
#Pipfile.lock
|
||||
|
||||
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
|
||||
__pypackages__/
|
||||
|
||||
# Celery stuff
|
||||
celerybeat-schedule
|
||||
celerybeat.pid
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# Environments
|
||||
.env
|
||||
.venv
|
||||
env/
|
||||
venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
.dmypy.json
|
||||
dmypy.json
|
||||
|
||||
# Pyre type checker
|
||||
.pyre/
|
|
@ -0,0 +1,21 @@
|
|||
MIT License
|
||||
|
||||
Copyright (c) 2021 Bryan Lee
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
|
@ -0,0 +1,140 @@
|
|||
## PyTorch Implementation of [AnimeGANv2](https://github.com/TachibanaYoshino/AnimeGANv2)
|
||||
|
||||
|
||||
**Updates**
|
||||
|
||||
* `2021-10-17` Add weights for [FacePortraitV2](#additional-model-weights)
|
||||
* `2021-11-07` Thanks to [ak92501](https://twitter.com/ak92501), a web demo is integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio).
|
||||
|
||||
See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/AnimeGANv2)
|
||||
|
||||
* `2021-11-07` Thanks to [xhlulu](https://github.com/xhlulu), the `torch.hub` model is now available. See [Torch Hub Usage](#torch-hub-usage).
|
||||
* `2021-11-07` Add FacePortraitV2 style demo to a telegram bot. See [@face2stickerbot](https://t.me/face2stickerbot) by [sxela](https://github.com/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]](https://drive.google.com/file/d/1K_xN32uoQKI8XmNYNLTX5gDn1UnQVe5I/view?usp=sharing) style model**
|
||||
|
||||
(input image, original tensorflow result, pytorch result from left to right)
|
||||
|
||||
<img src="./samples/compare/1.jpg" width="960">
|
||||
<img src="./samples/compare/2.jpg" width="960">
|
||||
<img src="./samples/compare/3.jpg" width="960">
|
||||
|
||||
**Note:** Training code not included / Results from converted weights slightly different due to the [bilinear upsample issue](https://github.com/pytorch/pytorch/issues/10604)
|
||||
|
||||
|
||||
|
||||
|
||||
## Additional Model Weights
|
||||
|
||||
**Webtoon Face** [[ckpt]](https://drive.google.com/file/d/10T6F3-_RFOCJn6lMb-6mRmcISuYWJXGc)
|
||||
|
||||
<details>
|
||||
<summary>samples</summary>
|
||||
|
||||
Trained on <b>256x256</b> face images. Distilled from [webtoon face model](https://github.com/bryandlee/naver-webtoon-faces/blob/master/README.md#face2webtoon) with L2 + VGG + GAN Loss and CelebA-HQ images. See `test_faces.ipynb` for details.
|
||||
|
||||
<img src="./samples/face_results.jpg" width="512">
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
**Face Portrait v1** [[ckpt]](https://drive.google.com/file/d/1WK5Mdt6mwlcsqCZMHkCUSDJxN1UyFi0-)
|
||||
|
||||
<details>
|
||||
<summary>samples</summary>
|
||||
|
||||
Trained on <b>512x512</b> face images.
|
||||
|
||||
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1jCqcKekdtKzW7cxiw_bjbbfLsPh-dEds?usp=sharing)
|
||||
|
||||
![samples](https://user-images.githubusercontent.com/26464535/127134790-93595da2-4f8b-4aca-a9d7-98699c5e6914.jpg)
|
||||
|
||||
[📺](https://youtu.be/CbMfI-HNCzw?t=317)
|
||||
|
||||
![sample](https://user-images.githubusercontent.com/26464535/129888683-98bb6283-7bb8-4d1a-a04a-e795f5858dcf.gif)
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
**Face Portrait v2** [[ckpt]](https://drive.google.com/uc?id=18H3iK09_d54qEDoWIc82SyWB2xun4gjU)
|
||||
|
||||
<details>
|
||||
<summary>samples</summary>
|
||||
|
||||
Trained on <b>512x512</b> face images. Compared to v1, `🔻beautify` `🔺robustness`
|
||||
|
||||
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1jCqcKekdtKzW7cxiw_bjbbfLsPh-dEds?usp=sharing)
|
||||
|
||||
![face_portrait_v2_0](https://user-images.githubusercontent.com/26464535/137619176-59620b59-4e20-4d98-9559-a424f86b7f24.jpg)
|
||||
|
||||
![face_portrait_v2_1](https://user-images.githubusercontent.com/26464535/137619181-a45c9230-f5e7-4f3c-8002-7c266f89de45.jpg)
|
||||
|
||||
🦑 🎮 🔥
|
||||
|
||||
![face_portrait_v2_squid_game](https://user-images.githubusercontent.com/26464535/137619183-20e94f11-7a8e-4c3e-9b45-378ab63827ca.jpg)
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
## Torch Hub Usage
|
||||
|
||||
You can load Animegan v2 via `torch.hub`:
|
||||
|
||||
```python
|
||||
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](https://pytorch.org/docs/stable/hub.html)):
|
||||
```python
|
||||
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:
|
||||
```python
|
||||
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`:
|
||||
```python
|
||||
face2paint = torch.hub.load(
|
||||
'bryandlee/animegan2-pytorch:main', 'face2paint',
|
||||
size=512, device="cpu"
|
||||
)
|
||||
|
||||
img = Image.open(...).convert("RGB")
|
||||
out = face2paint(model, img)
|
||||
```
|
|
@ -0,0 +1,140 @@
|
|||
import argparse
|
||||
|
||||
import numpy as np
|
||||
import os
|
||||
|
||||
import tensorflow as tf
|
||||
from AnimeGANv2.net import generator as tf_generator
|
||||
|
||||
import torch
|
||||
from model import Generator
|
||||
|
||||
|
||||
def load_tf_weights(tf_path):
|
||||
test_real = tf.placeholder(tf.float32, [1, None, None, 3], name='test')
|
||||
with tf.variable_scope("generator", reuse=False):
|
||||
test_generated = tf_generator.G_net(test_real).fake
|
||||
|
||||
saver = tf.train.Saver()
|
||||
|
||||
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, device_count = {'GPU': 0})) as sess:
|
||||
ckpt = tf.train.get_checkpoint_state(tf_path)
|
||||
|
||||
assert ckpt is not None and ckpt.model_checkpoint_path is not None, f"Failed to load checkpoint {tf_path}"
|
||||
|
||||
saver.restore(sess, ckpt.model_checkpoint_path)
|
||||
print(f"Tensorflow model checkpoint {ckpt.model_checkpoint_path} loaded")
|
||||
|
||||
tf_weights = {}
|
||||
for v in tf.trainable_variables():
|
||||
tf_weights[v.name] = v.eval()
|
||||
|
||||
return tf_weights
|
||||
|
||||
|
||||
def convert_keys(k):
|
||||
|
||||
# 1. divide tf weight name in three parts [block_idx, layer_idx, weight/bias]
|
||||
# 2. handle each part & merge into a pytorch model keys
|
||||
|
||||
k = k.replace("Conv/", "Conv_0/").replace("LayerNorm/", "LayerNorm_0/")
|
||||
keys = k.split("/")[2:]
|
||||
|
||||
is_dconv = False
|
||||
|
||||
# handle C block..
|
||||
if keys[0] == "C":
|
||||
if keys[1] in ["Conv_1", "LayerNorm_1"]:
|
||||
keys[1] = keys[1].replace("1", "5")
|
||||
|
||||
if len(keys) == 4:
|
||||
assert "r" in keys[1]
|
||||
|
||||
if keys[1] == keys[2]:
|
||||
is_dconv = True
|
||||
keys[2] = "1.1"
|
||||
|
||||
block_c_maps = {
|
||||
"1": "1.2",
|
||||
"Conv_1": "2",
|
||||
"2": "3",
|
||||
}
|
||||
if keys[2] in block_c_maps:
|
||||
keys[2] = block_c_maps[keys[2]]
|
||||
|
||||
keys[1] = keys[1].replace("r", "") + ".layers." + keys[2]
|
||||
keys[2] = keys[3]
|
||||
keys.pop(-1)
|
||||
assert len(keys) == 3
|
||||
|
||||
# handle output block
|
||||
if "out" in keys[0]:
|
||||
keys[1] = "0"
|
||||
|
||||
# first part
|
||||
if keys[0] in ["A", "B", "C", "D", "E"]:
|
||||
keys[0] = "block_" + keys[0].lower()
|
||||
|
||||
# second part
|
||||
if "LayerNorm_" in keys[1]:
|
||||
keys[1] = keys[1].replace("LayerNorm_", "") + ".2"
|
||||
if "Conv_" in keys[1]:
|
||||
keys[1] = keys[1].replace("Conv_", "") + ".1"
|
||||
|
||||
# third part
|
||||
keys[2] = {
|
||||
"weights:0": "weight",
|
||||
"w:0": "weight",
|
||||
"bias:0": "bias",
|
||||
"gamma:0": "weight",
|
||||
"beta:0": "bias",
|
||||
}[keys[2]]
|
||||
|
||||
return ".".join(keys), is_dconv
|
||||
|
||||
|
||||
def convert_and_save(tf_checkpoint_path, save_name):
|
||||
|
||||
tf_weights = load_tf_weights(tf_checkpoint_path)
|
||||
|
||||
torch_net = Generator()
|
||||
torch_weights = torch_net.state_dict()
|
||||
|
||||
torch_converted_weights = {}
|
||||
for k, v in tf_weights.items():
|
||||
torch_k, is_dconv = convert_keys(k)
|
||||
assert torch_k in torch_weights, f"weight name mismatch: {k}"
|
||||
|
||||
converted_weight = torch.from_numpy(v)
|
||||
if len(converted_weight.shape) == 4:
|
||||
if is_dconv:
|
||||
converted_weight = converted_weight.permute(2, 3, 0, 1)
|
||||
else:
|
||||
converted_weight = converted_weight.permute(3, 2, 0, 1)
|
||||
|
||||
assert torch_weights[torch_k].shape == converted_weight.shape, f"shape mismatch: {k}"
|
||||
|
||||
torch_converted_weights[torch_k] = converted_weight
|
||||
|
||||
assert sorted(list(torch_converted_weights)) == sorted(list(torch_weights)), f"some weights are missing"
|
||||
torch_net.load_state_dict(torch_converted_weights)
|
||||
torch.save(torch_net.state_dict(), save_name)
|
||||
print(f"PyTorch model saved at {save_name}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
'--tf_checkpoint_path',
|
||||
type=str,
|
||||
default='AnimeGANv2/checkpoint/generator_Paprika_weight',
|
||||
)
|
||||
parser.add_argument(
|
||||
'--save_name',
|
||||
type=str,
|
||||
default='pytorch_generator_Paprika.pt',
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
convert_and_save(args.tf_checkpoint_path, args.save_name)
|
|
@ -0,0 +1,63 @@
|
|||
import torch
|
||||
|
||||
def generator(pretrained=True, device="cpu", progress=True, check_hash=True):
|
||||
from model import Generator
|
||||
|
||||
release_url = "https://github.com/bryandlee/animegan2-pytorch/raw/main/weights"
|
||||
known = {
|
||||
name: f"{release_url}/{name}.pt"
|
||||
for name in [
|
||||
'celeba_distill', 'face_paint_512_v1', 'face_paint_512_v2', 'paprika'
|
||||
]
|
||||
}
|
||||
|
||||
device = torch.device(device)
|
||||
model = Generator().to(device)
|
||||
|
||||
if type(pretrained) == str:
|
||||
# Look if a known name is passed, otherwise assume it's a URL
|
||||
ckpt_url = known.get(pretrained, pretrained)
|
||||
pretrained = True
|
||||
else:
|
||||
ckpt_url = known.get('face_paint_512_v2')
|
||||
|
||||
if pretrained is True:
|
||||
state_dict = torch.hub.load_state_dict_from_url(
|
||||
ckpt_url,
|
||||
map_location=device,
|
||||
progress=progress,
|
||||
check_hash=check_hash,
|
||||
)
|
||||
model.load_state_dict(state_dict)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
def face2paint(device="cpu", size=512, side_by_side=False):
|
||||
from PIL import Image
|
||||
from torchvision.transforms.functional import to_tensor, to_pil_image
|
||||
|
||||
def face2paint(
|
||||
model: torch.nn.Module,
|
||||
img: Image.Image,
|
||||
size: int = size,
|
||||
side_by_side: bool = side_by_side,
|
||||
device: str = device,
|
||||
) -> Image.Image:
|
||||
w, h = img.size
|
||||
s = min(w, h)
|
||||
img = img.crop(((w - s) // 2, (h - s) // 2, (w + s) // 2, (h + s) // 2))
|
||||
img = img.resize((size, size), Image.LANCZOS)
|
||||
|
||||
with torch.no_grad():
|
||||
input = to_tensor(img).unsqueeze(0) * 2 - 1
|
||||
output = model(input.to(device)).cpu()[0]
|
||||
|
||||
if side_by_side:
|
||||
output = torch.cat([input[0], output], dim=2)
|
||||
|
||||
output = (output * 0.5 + 0.5).clip(0, 1)
|
||||
|
||||
return to_pil_image(output)
|
||||
|
||||
return face2paint
|
|
@ -0,0 +1,110 @@
|
|||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class ConvNormLReLU(nn.Sequential):
|
||||
def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, padding=1, pad_mode="reflect", groups=1, bias=False):
|
||||
|
||||
pad_layer = {
|
||||
"zero": nn.ZeroPad2d,
|
||||
"same": nn.ReplicationPad2d,
|
||||
"reflect": nn.ReflectionPad2d,
|
||||
}
|
||||
if pad_mode not in pad_layer:
|
||||
raise NotImplementedError
|
||||
|
||||
super(ConvNormLReLU, self).__init__(
|
||||
pad_layer[pad_mode](padding),
|
||||
nn.Conv2d(in_ch, out_ch, kernel_size=kernel_size, stride=stride, padding=0, groups=groups, bias=bias),
|
||||
nn.GroupNorm(num_groups=1, num_channels=out_ch, affine=True),
|
||||
nn.LeakyReLU(0.2, inplace=True)
|
||||
)
|
||||
|
||||
|
||||
class InvertedResBlock(nn.Module):
|
||||
def __init__(self, in_ch, out_ch, expansion_ratio=2):
|
||||
super(InvertedResBlock, self).__init__()
|
||||
|
||||
self.use_res_connect = in_ch == out_ch
|
||||
bottleneck = int(round(in_ch*expansion_ratio))
|
||||
layers = []
|
||||
if expansion_ratio != 1:
|
||||
layers.append(ConvNormLReLU(in_ch, bottleneck, kernel_size=1, padding=0))
|
||||
|
||||
# dw
|
||||
layers.append(ConvNormLReLU(bottleneck, bottleneck, groups=bottleneck, bias=True))
|
||||
# pw
|
||||
layers.append(nn.Conv2d(bottleneck, out_ch, kernel_size=1, padding=0, bias=False))
|
||||
layers.append(nn.GroupNorm(num_groups=1, num_channels=out_ch, affine=True))
|
||||
|
||||
self.layers = nn.Sequential(*layers)
|
||||
|
||||
def forward(self, input):
|
||||
out = self.layers(input)
|
||||
if self.use_res_connect:
|
||||
out = input + out
|
||||
return out
|
||||
|
||||
|
||||
class Generator(nn.Module):
|
||||
def __init__(self, ):
|
||||
super().__init__()
|
||||
|
||||
self.block_a = nn.Sequential(
|
||||
ConvNormLReLU(3, 32, kernel_size=7, padding=3),
|
||||
ConvNormLReLU(32, 64, stride=2, padding=(0,1,0,1)),
|
||||
ConvNormLReLU(64, 64)
|
||||
)
|
||||
|
||||
self.block_b = nn.Sequential(
|
||||
ConvNormLReLU(64, 128, stride=2, padding=(0,1,0,1)),
|
||||
ConvNormLReLU(128, 128)
|
||||
)
|
||||
|
||||
self.block_c = nn.Sequential(
|
||||
ConvNormLReLU(128, 128),
|
||||
InvertedResBlock(128, 256, 2),
|
||||
InvertedResBlock(256, 256, 2),
|
||||
InvertedResBlock(256, 256, 2),
|
||||
InvertedResBlock(256, 256, 2),
|
||||
ConvNormLReLU(256, 128),
|
||||
)
|
||||
|
||||
self.block_d = nn.Sequential(
|
||||
ConvNormLReLU(128, 128),
|
||||
ConvNormLReLU(128, 128)
|
||||
)
|
||||
|
||||
self.block_e = nn.Sequential(
|
||||
ConvNormLReLU(128, 64),
|
||||
ConvNormLReLU(64, 64),
|
||||
ConvNormLReLU(64, 32, kernel_size=7, padding=3)
|
||||
)
|
||||
|
||||
self.out_layer = nn.Sequential(
|
||||
nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0, bias=False),
|
||||
nn.Tanh()
|
||||
)
|
||||
|
||||
def forward(self, input, align_corners=True):
|
||||
out = self.block_a(input)
|
||||
half_size = out.size()[-2:]
|
||||
out = self.block_b(out)
|
||||
out = self.block_c(out)
|
||||
|
||||
if align_corners:
|
||||
out = F.interpolate(out, half_size, mode="bilinear", align_corners=True)
|
||||
else:
|
||||
out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
|
||||
out = self.block_d(out)
|
||||
|
||||
if align_corners:
|
||||
out = F.interpolate(out, input.size()[-2:], mode="bilinear", align_corners=True)
|
||||
else:
|
||||
out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
|
||||
out = self.block_e(out)
|
||||
|
||||
out = self.out_layer(out)
|
||||
return out
|
||||
|
After Width: | Height: | Size: 863 KiB |
After Width: | Height: | Size: 1.3 MiB |
After Width: | Height: | Size: 1.4 MiB |
After Width: | Height: | Size: 843 KiB |
After Width: | Height: | Size: 36 KiB |
After Width: | Height: | Size: 26 KiB |
After Width: | Height: | Size: 37 KiB |
After Width: | Height: | Size: 30 KiB |
After Width: | Height: | Size: 62 KiB |
After Width: | Height: | Size: 37 KiB |
After Width: | Height: | Size: 15 KiB |
After Width: | Height: | Size: 39 KiB |
After Width: | Height: | Size: 50 KiB |
After Width: | Height: | Size: 22 KiB |
After Width: | Height: | Size: 35 KiB |
After Width: | Height: | Size: 131 KiB |
After Width: | Height: | Size: 50 KiB |
After Width: | Height: | Size: 37 KiB |
After Width: | Height: | Size: 21 KiB |
After Width: | Height: | Size: 35 KiB |
After Width: | Height: | Size: 719 KiB |
After Width: | Height: | Size: 536 KiB |
After Width: | Height: | Size: 227 KiB |
|
@ -0,0 +1,90 @@
|
|||
import argparse
|
||||
|
||||
import torch
|
||||
import cv2
|
||||
import numpy as np
|
||||
import os
|
||||
|
||||
from model import Generator
|
||||
|
||||
torch.backends.cudnn.enabled = False
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
|
||||
def load_image(image_path, x32=False):
|
||||
img = cv2.imread(image_path).astype(np.float32)
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||||
h, w = img.shape[:2]
|
||||
|
||||
if x32: # resize image to multiple of 32s
|
||||
def to_32s(x):
|
||||
return 256 if x < 256 else x - x%32
|
||||
img = cv2.resize(img, (to_32s(w), to_32s(h)))
|
||||
|
||||
img = torch.from_numpy(img)
|
||||
img = img/127.5 - 1.0
|
||||
return img
|
||||
|
||||
|
||||
def test(args):
|
||||
device = args.device
|
||||
|
||||
net = Generator()
|
||||
net.load_state_dict(torch.load(args.checkpoint, map_location="cpu"))
|
||||
net.to(device).eval()
|
||||
print(f"model loaded: {args.checkpoint}")
|
||||
|
||||
os.makedirs(args.output_dir, exist_ok=True)
|
||||
|
||||
for image_name in sorted(os.listdir(args.input_dir)):
|
||||
if os.path.splitext(image_name)[-1].lower() not in [".jpg", ".png", ".bmp", ".tiff"]:
|
||||
continue
|
||||
|
||||
image = load_image(os.path.join(args.input_dir, image_name), args.x32)
|
||||
|
||||
with torch.no_grad():
|
||||
input = image.permute(2, 0, 1).unsqueeze(0).to(device)
|
||||
out = net(input, args.upsample_align).squeeze(0).permute(1, 2, 0).cpu().numpy()
|
||||
out = (out + 1)*127.5
|
||||
out = np.clip(out, 0, 255).astype(np.uint8)
|
||||
|
||||
cv2.imwrite(os.path.join(args.output_dir, image_name), cv2.cvtColor(out, cv2.COLOR_BGR2RGB))
|
||||
print(f"image saved: {image_name}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
'--checkpoint',
|
||||
type=str,
|
||||
default='./pytorch_generator_Paprika.pt',
|
||||
)
|
||||
parser.add_argument(
|
||||
'--input_dir',
|
||||
type=str,
|
||||
default='./samples/inputs',
|
||||
)
|
||||
parser.add_argument(
|
||||
'--output_dir',
|
||||
type=str,
|
||||
default='./samples/results',
|
||||
)
|
||||
parser.add_argument(
|
||||
'--device',
|
||||
type=str,
|
||||
default='cuda:0',
|
||||
)
|
||||
parser.add_argument(
|
||||
'--upsample_align',
|
||||
type=bool,
|
||||
default=False,
|
||||
)
|
||||
parser.add_argument(
|
||||
'--x32',
|
||||
action="store_true",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
test(args)
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
import gradio as gr
|
||||
from PIL import Image
|
||||
import torch
|
||||
|
||||
model2 = torch.hub.load(
|
||||
"AK391/animegan2-pytorch:main",
|
||||
"generator",
|
||||
pretrained=True,
|
||||
progress=False
|
||||
)
|
||||
model1 = torch.hub.load("AK391/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1")
|
||||
face2paint = torch.hub.load(
|
||||
'AK391/animegan2-pytorch:main', 'face2paint',
|
||||
size=512,side_by_side=False
|
||||
)
|
||||
|
||||
def inference(img, ver):
|
||||
if ver == 'version 2 (🔺 robustness,🔻 stylization)':
|
||||
out = face2paint(model2, img)
|
||||
else:
|
||||
out = face2paint(model1, img)
|
||||
return out
|
||||
|
||||
title = "AnimeGANv2"
|
||||
description = "Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below."
|
||||
article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
|
||||
examples=[['groot.jpeg','version 2 (🔺 robustness,🔻 stylization)'],['gongyoo.jpeg','version 1 (🔺 stylization, 🔻 robustness)']]
|
||||
|
||||
demo = gr.Interface(
|
||||
fn=inference,
|
||||
inputs=[gr.inputs.Image(type="pil"),gr.inputs.Radio(['version 1 (🔺 stylization, 🔻 robustness)','version 2 (🔺 robustness,🔻 stylization)'], type="value", default='version 2 (🔺 robustness,🔻 stylization)', label='version')],
|
||||
outputs=gr.outputs.Image(type="pil"),
|
||||
title=title,
|
||||
description=description,
|
||||
article=article,
|
||||
examples=examples)
|
||||
|
||||
demo.launch()
|