import gradio as gr import numpy as np import tensorflow as tf from tensorflow import keras from huggingface_hub import from_pretrained_keras from tensorflow.keras.applications import vgg19 # result_prefix = "paris_generated" # Weights of the different loss components total_variation_weight = 1e-6 style_weight = 1e-6 content_weight = 2.5e-8 # Build a VGG19 model loaded with pre-trained ImageNet weights model = from_pretrained_keras("keras-io/VGG19") # Get the symbolic outputs of each "key" layer (we gave them unique names). outputs_dict = dict([(layer.name, layer.output) for layer in model.layers]) # Set up a model that returns the activation values for every layer in # VGG19 (as a dict). feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict) # List of layers to use for the style loss. style_layer_names = [ "block1_conv1", "block2_conv1", "block3_conv1", "block4_conv1", "block5_conv1", ] # The layer to use for the content loss. content_layer_name = "block5_conv2" def preprocess_image(image_path): # Util function to open, resize and format pictures into appropriate tensors img = keras.preprocessing.image.load_img( image_path, target_size=(img_nrows, img_ncols) ) img = keras.preprocessing.image.img_to_array(img) img = np.expand_dims(img, axis=0) img = vgg19.preprocess_input(img) return tf.convert_to_tensor(img) def deprocess_image(x): # Util function to convert a tensor into a valid image x = x.reshape((img_nrows, img_ncols, 3)) # Remove zero-center by mean pixel x[:, :, 0] += 103.939 x[:, :, 1] += 116.779 x[:, :, 2] += 123.68 # 'BGR'->'RGB' x = x[:, :, ::-1] x = np.clip(x, 0, 255).astype("uint8") return x # The gram matrix of an image tensor (feature-wise outer product) def gram_matrix(x): x = tf.transpose(x, (2, 0, 1)) features = tf.reshape(x, (tf.shape(x)[0], -1)) gram = tf.matmul(features, tf.transpose(features)) return gram # The "style loss" is designed to maintain # the style of the reference image in the generated image. # It is based on the gram matrices (which capture style) of # feature maps from the style reference image # and from the generated image def style_loss(style, combination): S = gram_matrix(style) C = gram_matrix(combination) channels = 3 size = img_nrows * img_ncols return tf.reduce_sum(tf.square(S - C)) / (4.0 * (channels ** 2) * (size ** 2)) # An auxiliary loss function # designed to maintain the "content" of the # base image in the generated image def content_loss(base, combination): return tf.reduce_sum(tf.square(combination - base)) # The 3rd loss function, total variation loss, # designed to keep the generated image locally coherent def total_variation_loss(x): a = tf.square( x[:, : img_nrows - 1, : img_ncols - 1, :] - x[:, 1:, : img_ncols - 1, :] ) b = tf.square( x[:, : img_nrows - 1, : img_ncols - 1, :] - x[:, : img_nrows - 1, 1:, :] ) return tf.reduce_sum(tf.pow(a + b, 1.25)) def compute_loss(combination_image, base_image, style_reference_image): input_tensor = tf.concat( [base_image, style_reference_image, combination_image], axis=0 ) features = feature_extractor(input_tensor) # Initialize the loss loss = tf.zeros(shape=()) # Add content loss layer_features = features[content_layer_name] base_image_features = layer_features[0, :, :, :] combination_features = layer_features[2, :, :, :] loss = loss + content_weight * content_loss( base_image_features, combination_features ) # Add style loss for layer_name in style_layer_names: layer_features = features[layer_name] style_reference_features = layer_features[1, :, :, :] combination_features = layer_features[2, :, :, :] sl = style_loss(style_reference_features, combination_features) loss += (style_weight / len(style_layer_names)) * sl # Add total variation loss loss += total_variation_weight * total_variation_loss(combination_image) return loss @tf.function def compute_loss_and_grads(combination_image, base_image, style_reference_image): with tf.GradientTape() as tape: loss = compute_loss(combination_image, base_image, style_reference_image) grads = tape.gradient(loss, combination_image) return loss, grads optimizer = keras.optimizers.SGD( keras.optimizers.schedules.ExponentialDecay( initial_learning_rate=100.0, decay_steps=100, decay_rate=0.96 ) ) def get_imgs(base_image_path, style_reference_image_path): # Dimensions of the generated picture. width, height = keras.preprocessing.image.load_img(base_image_path).size global img_nrows global img_ncols img_nrows = 400 img_ncols = int(width * img_nrows / height) base_image = preprocess_image(base_image_path) style_reference_image = preprocess_image(style_reference_image_path) combination_image = tf.Variable(preprocess_image(base_image_path)) iterations = 20 for i in range(1, iterations + 1): loss, grads = compute_loss_and_grads(combination_image, base_image, style_reference_image) optimizer.apply_gradients([(grads, combination_image)]) if i % 5 == 0: print("Iteration %d: loss=%.2f" % (i, loss)) img = deprocess_image(combination_image.numpy()) return img title = "Neural style transfer" description = "Gradio Demo for Neural style transfer. To use it, simply upload a base image and a style image" article = """

Keras Example given by fchollet
Space by @rushic24

""" content = gr.inputs.Image(shape=None, image_mode="RGB", invert_colors=False, source="upload", tool="editor", type="filepath", label=None, optional=False) style = gr.inputs.Image(shape=None, image_mode="RGB", invert_colors=False, source="upload", tool="editor", type="filepath", label=None, optional=False) gr.Interface(get_imgs, inputs=[content, style], outputs=["image"], title=title, description=description, article=article, examples=[["base.jpg", "style.jpg"]], cache_examples=True).launch(enable_queue=True)