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import gradio as gr
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import PIL.Image
# Load model from TF-Hub
hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
# Function to convert tensor to image
def tensor_to_image(tensor):
tensor = tensor * 255
tensor = np.array(tensor, dtype=np.uint8)
if np.ndim(tensor) > 3:
assert tensor.shape[0] == 1
tensor = tensor[0]
return PIL.Image.fromarray(tensor)
# Stylize function
def stylize(content_image, style_image):
# Convert to float32 numpy array, add batch dimension, and normalize to range [0, 1]. Example using numpy:
content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.
style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.
# Stylize image
stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
return tensor_to_image(stylized_image)
# Add image examples for users
joker = ["example_joker.jpeg", "example_polasticot1.jpeg"]
paris = ["example_paris.jpeg", "example_vangogh.jpeg"]
einstein = ["example_einstein.jpeg", "example_polasticot2.jpeg"]
# Customize interface
title = "Fast Neural Style Transfer using TF-Hub"
description = "Demo for neural style transfer using the pretrained Arbitrary Image Stylization model from TensorFlow Hub."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1705.06830'>Exploring the structure of a real-time, arbitrary neural artistic stylization network</a></p><p style='text-align: center'>Check out the <a href='https://huggingface.co/spaces/luca-martial/neural-style-transfer'>original space</a> although that one doesn't work</p>"
content_input = gr.inputs.Image(label="Content Image", source="upload")
style_input = gr.inputs.Image(label="Style Image", source="upload")
# Build and launch
iface = gr.Interface(fn=stylize,
inputs=[content_input, style_input],
outputs="image",
title=title,
description=description,
article=article,
examples=[joker, paris, einstein])
iface.launch()
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