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import gradio as gr
# import torch
# import torch.optim as optim
# import torchvision.models as models
# import torchvision.transforms as transforms
# from PIL import Image
# import numpy as np
# import requests
# from io import BytesIO
# Load VGG19 model
# vgg = models.vgg19(pretrained=True).features
# for param in vgg.parameters():
# param.requires_grad_(False)
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# vgg.to(device)
# # Helper functions (load_image, im_convert, get_features, gram_matrix)
# # ... (Include the helper functions you provided earlier here)
# def style_transfer(content_image, style_image, alpha, beta, conv1_1, conv2_1, conv3_1, conv4_1, conv5_1, steps):
# content = load_image(content_image).to(device)
# style = load_image(style_image, shape=content.shape[-2:]).to(device)
# content_features = get_features(content, vgg)
# style_features = get_features(style, vgg)
# style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features}
# target = content.clone().requires_grad_(True).to(device)
# style_weights = {
# 'conv1_1': conv1_1,
# 'conv2_1': conv2_1,
# 'conv3_1': conv3_1,
# 'conv4_1': conv4_1,
# 'conv5_1': conv5_1
# }
# content_weight = alpha
# style_weight = beta * 1e6
# optimizer = optim.Adam([target], lr=0.003)
# for ii in range(1, steps+1):
# target_features = get_features(target, vgg)
# content_loss = torch.mean((target_features['conv4_2'] - content_features['conv4_2'])**2)
# style_loss = 0
# for layer in style_weights:
# target_feature = target_features[layer]
# target_gram = gram_matrix(target_feature)
# _, d, h, w = target_feature.shape
# style_gram = style_grams[layer]
# layer_style_loss = style_weights[layer] * torch.mean((target_gram - style_gram)**2)
# style_loss += layer_style_loss / (d * h * w)
# total_loss = content_weight * content_loss + style_weight * style_loss
# optimizer.zero_grad()
# total_loss.backward()
# optimizer.step()
# return im_convert(target)
# Example images
examples = [
["path/to/content1.jpg", "path/to/style1.jpg"],
["path/to/content2.jpg", "path/to/style2.jpg"],
["path/to/content3.jpg", "path/to/style3.jpg"],
]
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Neural Style Transfer")
with gr.Row():
with gr.Column():
content_input = gr.Image(label="Content Image")
style_input = gr.Image(label="Style Image")
with gr.Column():
output_image = gr.Image(label="Output Image")
with gr.Row():
alpha_slider = gr.Slider(minimum=0, maximum=1, value=1, step=0.1, label="Content Weight (α)")
beta_slider = gr.Slider(minimum=0, maximum=1, value=0.1, step=0.1, label="Style Weight (β)")
with gr.Row():
conv1_1_slider = gr.Slider(minimum=0, maximum=1, value=1, step=0.1, label="Conv1_1 Weight")
conv2_1_slider = gr.Slider(minimum=0, maximum=1, value=0.8, step=0.1, label="Conv2_1 Weight")
conv3_1_slider = gr.Slider(minimum=0, maximum=1, value=0.5, step=0.1, label="Conv3_1 Weight")
conv4_1_slider = gr.Slider(minimum=0, maximum=1, value=0.3, step=0.1, label="Conv4_1 Weight")
conv5_1_slider = gr.Slider(minimum=0, maximum=1, value=0.1, step=0.1, label="Conv5_1 Weight")
steps_slider = gr.Slider(minimum=100, maximum=2000, value=1000, step=100, label="Number of Steps")
run_button = gr.Button("Run Style Transfer")
run_button.click(
style_transfer,
inputs=[
content_input,
style_input,
alpha_slider,
beta_slider,
conv1_1_slider,
conv2_1_slider,
conv3_1_slider,
conv4_1_slider,
conv5_1_slider,
steps_slider
],
outputs=output_image
)
gr.Examples(
examples,
inputs=[content_input, style_input]
)
demo.launch() |