|
import pystuck
|
|
pystuck.run_server()
|
|
import os
|
|
os.system("pip install gradio==2.5.3")
|
|
os.system("wget https://github.com/Sxela/ArcaneGAN/releases/download/v0.4/ArcaneGANv0.4.jit")
|
|
os.system("wget https://github.com/Sxela/ArcaneGAN/releases/download/v0.3/ArcaneGANv0.3.jit")
|
|
os.system("wget https://github.com/Sxela/ArcaneGAN/releases/download/v0.2/ArcaneGANv0.2.jit")
|
|
os.system("pip -qq install facenet_pytorch")
|
|
from facenet_pytorch import MTCNN
|
|
from torchvision import transforms
|
|
import torch, PIL
|
|
torch.hub.download_url_to_file('https://hf.space/gradioiframe/akhaliq/AnimeGANv2/file/bill.png', 'bill.png')
|
|
from tqdm.notebook import tqdm
|
|
import gradio as gr
|
|
import torch
|
|
mtcnn = MTCNN(image_size=256, margin=80)
|
|
|
|
def detect(img):
|
|
|
|
|
|
batch_boxes, batch_probs, batch_points = mtcnn.detect(img, landmarks=True)
|
|
|
|
if not mtcnn.keep_all:
|
|
batch_boxes, batch_probs, batch_points = mtcnn.select_boxes(
|
|
batch_boxes, batch_probs, batch_points, img, method=mtcnn.selection_method
|
|
)
|
|
|
|
return batch_boxes, batch_points
|
|
|
|
def makeEven(_x):
|
|
return _x if (_x % 2 == 0) else _x+1
|
|
|
|
def scale(boxes, _img, max_res=1_500_000, target_face=256, fixed_ratio=0, max_upscale=2, VERBOSE=False):
|
|
|
|
x, y = _img.size
|
|
|
|
ratio = 2
|
|
|
|
|
|
if (boxes is not None):
|
|
if len(boxes)>0:
|
|
ratio = target_face/max(boxes[0][2:]-boxes[0][:2]);
|
|
ratio = min(ratio, max_upscale)
|
|
if VERBOSE: print('up by', ratio)
|
|
if fixed_ratio>0:
|
|
if VERBOSE: print('fixed ratio')
|
|
ratio = fixed_ratio
|
|
|
|
x*=ratio
|
|
y*=ratio
|
|
|
|
|
|
res = x*y
|
|
if res > max_res:
|
|
ratio = pow(res/max_res,1/2);
|
|
if VERBOSE: print(ratio)
|
|
x=int(x/ratio)
|
|
y=int(y/ratio)
|
|
|
|
|
|
x = makeEven(int(x))
|
|
y = makeEven(int(y))
|
|
|
|
size = (x, y)
|
|
return _img.resize(size)
|
|
"""
|
|
A useful scaler algorithm, based on face detection.
|
|
Takes PIL.Image, returns a uniformly scaled PIL.Image
|
|
boxes: a list of detected bboxes
|
|
_img: PIL.Image
|
|
max_res: maximum pixel area to fit into. Use to stay below the VRAM limits of your GPU.
|
|
target_face: desired face size. Upscale or downscale the whole image to fit the detected face into that dimension.
|
|
fixed_ratio: fixed scale. Ignores the face size, but doesn't ignore the max_res limit.
|
|
max_upscale: maximum upscale ratio. Prevents from scaling images with tiny faces to a blurry mess.
|
|
"""
|
|
def scale_by_face_size(_img, max_res=1_500_000, target_face=256, fix_ratio=0, max_upscale=2, VERBOSE=False):
|
|
boxes = None
|
|
boxes, _ = detect(_img)
|
|
if VERBOSE: print('boxes',boxes)
|
|
img_resized = scale(boxes, _img, max_res, target_face, fix_ratio, max_upscale, VERBOSE)
|
|
return img_resized
|
|
size = 256
|
|
means = [0.485, 0.456, 0.406]
|
|
stds = [0.229, 0.224, 0.225]
|
|
t_stds = torch.tensor(stds).cuda().half()[:,None,None]
|
|
t_means = torch.tensor(means).cuda().half()[:,None,None]
|
|
def makeEven(_x):
|
|
return int(_x) if (_x % 2 == 0) else int(_x+1)
|
|
img_transforms = transforms.Compose([
|
|
transforms.ToTensor(),
|
|
transforms.Normalize(means,stds)])
|
|
|
|
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, model):
|
|
transformed_image = img_transforms(input_image)[None,...].cuda().half()
|
|
|
|
with torch.no_grad():
|
|
result_image = model(transformed_image)[0]; print(result_image.shape)
|
|
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 fit(img,maxsize=512):
|
|
maxdim = max(*img.size)
|
|
if maxdim>maxsize:
|
|
ratio = maxsize/maxdim
|
|
x,y = img.size
|
|
size = (int(x*ratio),int(y*ratio))
|
|
img = img.resize(size)
|
|
return img
|
|
|
|
modelv4 = torch.jit.load('./ArcaneGANv0.4.jit').eval().cuda().half()
|
|
modelv3 = torch.jit.load('./ArcaneGANv0.3.jit').eval().cuda().half()
|
|
modelv2 = torch.jit.load('./ArcaneGANv0.2.jit').eval().cuda().half()
|
|
def process(im, version):
|
|
if version == 'version 0.4':
|
|
im = scale_by_face_size(im, target_face=300, max_res=1_500_000, max_upscale=2)
|
|
res = proc_pil_img(im, modelv4)
|
|
elif version == 'version 0.3':
|
|
im = scale_by_face_size(im, target_face=300, max_res=1_500_000, max_upscale=2)
|
|
res = proc_pil_img(im, modelv3)
|
|
else:
|
|
im = scale_by_face_size(im, target_face=300, max_res=1_500_000, max_upscale=2)
|
|
res = proc_pil_img(im, modelv2)
|
|
return res
|
|
|
|
title = "ArcaneGAN"
|
|
description = "Gradio demo for ArcaneGAN, portrait to Arcane style. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
|
|
article = "<div style='text-align: center;'>ArcaneGan by <a href='https://twitter.com/devdef' target='_blank'>Alexander S</a> | <a href='https://github.com/Sxela/ArcaneGAN' target='_blank'>Github Repo</a> | <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_arcanegan' alt='visitor badge'></center></div>"
|
|
gr.Interface(
|
|
process,
|
|
[gr.inputs.Image(type="pil", label="Input",shape=(256,256)),gr.inputs.Radio(choices=['version 0.2','version 0.3','version 0.4'], type="value", default='version 0.4', label='version')
|
|
],
|
|
gr.outputs.Image(type="pil", label="Output"),
|
|
title=title,
|
|
description=description,
|
|
article=article,
|
|
examples=[['bill.png','version 0.3'],['keanu.png','version 0.4'],['will.jpeg','version 0.4']],
|
|
enable_queue=True
|
|
).launch(debug=True)
|
|
|
