Spaces:
Build error
Build error
File size: 2,710 Bytes
146d3ac c39e45e 14567ed 146d3ac dffcfb5 f60da0f 146d3ac ab42d96 146d3ac dffcfb5 146d3ac dffcfb5 146d3ac 72c2d65 146d3ac 72c2d65 146d3ac 03ef60c 14567ed 03ef60c 146d3ac 03ef60c 146d3ac 8e8807d 146d3ac 70ce17b 03ef60c 146d3ac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
import os
os.system("pip freeze")
import torch
import PIL
import gradio as gr
import torch
from utils import align_face
from torchvision import transforms
from huggingface_hub import hf_hub_download
device = "cuda:0" if torch.cuda.is_available() else "cpu"
image_size = 512
transform_size = 1024
means = [0.5, 0.5, 0.5]
stds = [0.5, 0.5, 0.5]
img_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(means, stds)])
model_path = hf_hub_download(repo_id="jjeamin/ArcaneStyleTransfer", filename="pytorch_model.bin")
if 'cuda' in device:
style_transfer = torch.jit.load(model_path).eval().cuda().half()
t_stds = torch.tensor(stds).cuda().half()[:,None,None]
t_means = torch.tensor(means).cuda().half()[:,None,None]
else:
style_transfer = torch.jit.load(model_path).eval().cpu()
t_stds = torch.tensor(stds).cpu()[:,None,None]
t_means = torch.tensor(means).cpu()[:,None,None]
def tensor2im(var):
return var.mul(t_stds).add(t_means).mul(255.).clamp(0,255).permute(1,2,0)
def proc_pil_img(input_image):
if 'cuda' in device:
transformed_image = img_transforms(input_image)[None,...].cuda().half()
else:
transformed_image = img_transforms(input_image)[None,...].cpu()
with torch.no_grad():
result_image = style_transfer(transformed_image)[0]
output_image = tensor2im(result_image)
output_image = output_image.detach().cpu().numpy().astype('uint8')
output_image = PIL.Image.fromarray(output_image)
return output_image
def process(im, is_align):
im = PIL.ImageOps.exif_transpose(im)
if is_align == 'True':
im = align_face(im, output_size=image_size, transform_size=transform_size)
else:
pass
res = proc_pil_img(im)
return res
gr.Interface(
process,
inputs=[gr.inputs.Image(type="pil", label="Input", shape=(image_size, image_size)), gr.inputs.Radio(['True','False'], type="value", default='True', label='face align')],
outputs=gr.outputs.Image(type="pil", label="Output"),
title="Arcane Style Transfer",
description="Gradio demo for Arcane Style Transfer",
article = "<p style='text-align: center'><a href='https://github.com/jjeamin/anime_style_transfer_pytorch' target='_blank'>Github Repo by jjeamin</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=jjeamin_arcane_st' alt='visitor badge'></center></p>",
examples=[['billie.png', 'True'], ['gongyoo.jpeg', 'True'], ['IU.png', 'True'], ['elon.png', 'True']],
enable_queue=True,
allow_flagging=False,
allow_screenshot=False
).launch(enable_queue=True,cache_examples=True)
|