Merge pull request #9 from xhlulu/main

Add torch hub support
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bryandlee 2021-11-07 16:30:55 +09:00 committed by GitHub
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## PyTorch Implementation of [AnimeGANv2](https://github.com/TachibanaYoshino/AnimeGANv2)
**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",
"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", "generator", pretrained="celeba_distill")
model = torch.hub.load("bryandlee/animegan2-pytorch", "generator", pretrained="face_paint_512_v1")
model = torch.hub.load("bryandlee/animegan2-pytorch", "generator", pretrained="face_paint_512_v2")
model = torch.hub.load("bryandlee/animegan2-pytorch", "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', 'face2paint',
size=512, device="cpu"
)
img = Image.open(...).convert("RGB")
out = face2paint(model, img)
```
**Updates**
* `2021-10-17` Add weights for [face portrait v2](https://github.com/bryandlee/animegan2-pytorch#additional-models)
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See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/AnimeGANv2)
**Weight Conversion from the Original Repo (Requires TensorFlow 1.x)**
```
git clone https://github.com/TachibanaYoshino/AnimeGANv2
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</details>

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hubconf.py Normal file
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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):
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 = True,
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

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