Spaces:
Sleeping
Sleeping
File size: 1,802 Bytes
901a2ca 883a623 901a2ca e015b78 901a2ca |
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 |
import gradio as gr
import torch
from models import create_model
from options.test_options import TestOptions
from PIL import Image
from torchvision import transforms
import os
# Set options
opt = TestOptions().parse(use_cmd_line=False)
opt.model = 'pix2pix'
opt.netG = 'unet_256'
opt.dataset_mode = 'single'
opt.norm = 'batch'
opt.no_dropout = True
opt.init_type = 'normal'
opt.init_gain = 0.02
opt.dataroot = './dummy_path' # This is just a placeholder since it's required
opt.checkpoints_dir = './checkpoints'
opt.name = 'artgan_pix2pix'
# Load model
model = create_model(opt)
model.setup(opt)
model.eval()
# Specify the path to the model weights
model_path = './checkpoints/artgan_pix2pix/latest_net_G.pth'
# Ensure the file exists
if os.path.isfile(model_path):
# Load the weights into the model's generator
model.netG.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
print("Weights loaded successfully from:", model_path)
else:
raise ValueError(f"No file found at {model_path}. Please check the path.")
# Define preprocessing transformations
transform = transforms.Compose([
transforms.Resize(290),
transforms.CenterCrop(256),
transforms.ToTensor()
])
def generate_art(input_image):
# Convert the input_image into a format suitable for your model
input_tensor = transform(input_image).unsqueeze(0)
with torch.no_grad():
output = model.netG(input_tensor)
print(output[0].min(), output[0].max(), output[0].mean())
output_image = transforms.ToPILImage()(output[0])
return output_image
# Define the Gradio Interface
gr.Interface(
generate_art,
inputs=gr.Image(label="Upload 5x5 vector map", type="pil"),
outputs=gr.Image(type="pil"),
title="ArtGAN Generator",
).launch()
|