app.py

import gradio as gr
import toml
import torch
from PIL import Image
from torch import nn
from torchvision import transforms

import net
from function import *

cfg = toml.load("config.toml")  # static variables

# Setup device
if torch.cuda.is_available() and cfg["use_cuda"]:
    device = torch.device("cuda")
else:
    device = torch.device("cpu")

# Load pretrained models
decoder = net.decoder
vgg = net.vgg

decoder.eval()
vgg.eval()

decoder.load_state_dict(torch.load(cfg["decoder_weight"]))
vgg.load_state_dict(torch.load(cfg["vgg_weight"]))
vgg = nn.Sequential(*list(vgg.children())[:31])

vgg = vgg.to(device)
decoder = decoder.to(device)


def transform(img, size, crop):
    transform_list = []
    if size > 0:
        transform_list.append(transforms.Resize(size))
    if crop:
        transform_list.append(transforms.CenterCrop(size))
    transform_list.append(transforms.ToTensor())
    transform = transforms.Compose(transform_list)
    return transform(img)


@torch.inference_mode()
def style_transfer(content, style, style_type, alpha, keep_resolution):
    """Stylize function"""
    style_type = style_type.lower()

    # Step 1: convert image to PyTorch Tensor
    if keep_resolution:
        style = style.resize(content.size, Image.ANTIALIAS)

    if style_type == "efdm" and not keep_resolution:
        content = transform(content, cfg["content_size"], cfg["crop"])
        style = transform(style, cfg["style_size"], cfg["crop"])
    else:
        content = transform(content, -1, False)
        style = transform(style, -1, False)

    content = content.to(device).unsqueeze(0)
    style = style.to(device).unsqueeze(0)

    # Step 2: extract content feature and style feature
    content_feat = vgg(content)
    style_feat = vgg(style)

    # Step 3: perform style transfer
    transfer = {
        "adain": adaptive_instance_normalization,
        "adamean": adaptive_mean_normalization,
        "adastd": adaptive_std_normalization,
        "efdm": exact_feature_distribution_matching,
        "hm": histogram_matching,
    }[style_type]
    feat = transfer(content_feat, style_feat)

    # Step 4: content-style trade-off
    feat = feat * alpha + content_feat * (1 - alpha)

    # Step 5: decode to image
    output = decoder(feat).cpu().squeeze(0).clamp_(0, 1)
    output = transforms.ToPILImage()(output)

    if torch.cuda.is_available():
        torch.cuda.ipc_collect()
        torch.cuda.empty_cache()

    return output


# Add image examples
example_img_pairs = {
    "examples/content/sailboat.jpg": "examples/style/sketch.png",
    "examples/content/granatum.jpg": "examples/style/flowers_in_a_turquoise_vase.jpg",
    "examples/content/einstein.jpeg": "examples/style/polasticot2.jpeg",
    "examples/content/paris.jpeg": "examples/style/vangogh.jpeg",
    "examples/content/cornell.jpg": "examples/style/asheville.jpg",
}

# Customize interface
title = "Style Transfer with EFDM"
description = """
Gradio demo for neural style transfer using exact feature distribution matching
"""
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2203.07740'>Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization</a></p>"
content_input = gr.inputs.Image(label="Content Image", source="upload", type="pil")
style_input = gr.inputs.Image(label="Style Image", source="upload", type="pil")
style_type = gr.inputs.Radio(
    ["EFDM", "AdaIN", "AdaMean", "AdaStd", "HM"], label="Method"
)
alpha_selector = gr.inputs.Slider(
    minimum=0.0, maximum=1.0, step=0.01, default=1.0, label="Content-Style trade-off"
)
keep_resolution = gr.inputs.Checkbox(
    default=True, label="Keep content image resolution"
)

iface = gr.Interface(
    fn=style_transfer,
    inputs=[content_input, style_input, style_type, alpha_selector, keep_resolution],
    outputs=["image"],
    title=title,
    description=description,
    article=article,
    theme="huggingface",
    examples=[
        [content, style, "EFDM", 1.0, True]
        for content, style in example_img_pairs.items()
    ],
)
iface.launch(debug=False, enable_queue=True)