File size: 1,884 Bytes
b021ace 3a1adec b021ace 0b56c75 b021ace |
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 |
import subprocess
import shutil
import os
import gradio as gr
import torchvision.transforms as T
import spaces
subprocess.run(["git", "clone", "https://github.com/AIRI-Institute/HairFastGAN"], check=True)
os.chdir("HairFastGAN")
subprocess.run(["git", "clone", "https://huggingface.co/AIRI-Institute/HairFastGAN"], check=True)
os.chdir("HairFastGAN")
subprocess.run(["git", "lfs", "pull"], check=True)
os.chdir("..")
shutil.move("HairFastGAN/pretrained_models", "pretrained_models")
shutil.move("HairFastGAN/input", "input")
shutil.rmtree("HairFastGAN")
directory_path = 'pretrained_models'
# Iterate over each entry in the directory
for entry in os.listdir(directory_path):
# Create full path to the entry
full_path = os.path.join(directory_path, entry)
# Check if it's a file
if os.path.isfile(full_path):
# Get the file size
size = os.path.getsize(full_path)
# Print file name and its size in bytes
print(f"{entry}: {size} bytes")
from HairFastGAN.hair_swap import HairFast, get_parser
hair_fast = HairFast(get_parser().parse_args([]))
@spaces.GPU
def swap_hair(source, target_1, target_2):
result = hair_fast(face_img, shape_img, color_img)
final_image = hair_fast.swap(face_path, shape_path, color_path)
return T.functional.to_pil_image(final_image)
with gr.Blocks() as demo:
gr.Markdown("Start typing below and then click **Run** to see the output.")
with gr.Row():
source = gr.Image(label="Photo that you want to replace the hair", type="filepath")
target_1 = gr.Image(label="Reference hair you want to get", type="filepath")
target_2 = gr.Image(label="Reference color hair you want to get (optional)", type="filepath")
btn = gr.Button("Get the haircut")
output = gr.Image(label="Your result")
btn.click(fn=update, inputs=inp, outputs=out)
demo.launch() |