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import torch
import matplotlib.pyplot as plt
from PIL import Image
import cv2
import numpy as np
from torchvision.transforms import ToPILImage
from torchvision import datasets, transforms
from torchvision.utils import save_image
import os
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img = Image.open("model10211041.jpg").convert("RGB")
size = 512
plt.imshow(img)
plt.show()
face2paint = torch.hub.load("bryandlee/animegan2-pytorch:main", "face2paint", size=size, device="cuda")
print("default(face_paint_512_v2)")
model = torch.hub.load("bryandlee/animegan2-pytorch", "generator", device="cuda").eval()
pilImg = face2paint(model, img)
plt.imshow(pilImg)
plt.show()
pilImg.save("default.png")
print("celeba_distill")
model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="celeba_distill", device="cuda").eval()
pilImg = face2paint(model, img)
plt.imshow(pilImg)
plt.show()
pilImg.save("celeba_distill.png")
print("face_paint_512_v1")
model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1", device="cuda").eval()
pilImg = face2paint(model, img)
plt.imshow(pilImg)
plt.show()
pilImg.save("face_paint_512_v1.png")
print("face_paint_512_v2")
model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v2", device="cuda").eval()
pilImg = face2paint(model, img)
plt.imshow(pilImg)
plt.show()
pilImg.save("face_paint_512_v2.png")
print("paprika")
model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="paprika", device="cuda").eval()
pilImg = face2paint(model, img)
plt.imshow(pilImg)
plt.show()
pilImg.save("paprika.png")
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