app.py

import io
import os
import shutil
import requests
import time
import numpy as np
from PIL import Image, ImageOps
from math import nan
import math
import pickle
import warnings
warnings.filterwarnings("ignore")

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

from torch.utils.data import Dataset, ConcatDataset, DataLoader
from torchvision.datasets import ImageFolder
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from torch.cuda.amp import autocast, GradScaler
import jax
import jax.numpy as jnp

import transformers
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from vqgan_jax.modeling_flax_vqgan import VQModel

import gradio as gr

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


class Model_Z1(nn.Module):
    def __init__(self):
        super(Model_Z1, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=256, out_channels=2048, kernel_size=3, padding=1)
        self.batchnorm = nn.BatchNorm2d(2048)
        self.conv2 = nn.Conv2d(in_channels=2048, out_channels=256, kernel_size=3, padding=1)
        self.batchnorm2 = nn.BatchNorm2d(256)
        self.conv3 = nn.Conv2d(in_channels=256, out_channels=1024, kernel_size=3, padding=1)
        self.batchnorm3 = nn.BatchNorm2d(1024)
        self.conv4 = nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=3, padding=1)
        self.batchnorm4 = nn.BatchNorm2d(256)
        self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1)
        self.batchnorm5 = nn.BatchNorm2d(512)
        self.conv6 = nn.Conv2d(in_channels=512, out_channels=256, kernel_size=3, padding=1)
        self.elu = nn.ELU()

    def forward(self, x):
        res = x
        x = self.elu(self.conv1(x))
        x = self.batchnorm(x)
        x = self.elu(self.conv2(x)) + res
        x = self.batchnorm2(x)
        x = self.elu(self.conv3(x))
        x = self.batchnorm3(x)
        x = self.elu(self.conv4(x)) + res
        x = self.batchnorm4(x)
        x = self.elu(self.conv5(x))
        x = self.batchnorm5(x)
        out = self.elu(self.conv6(x)) + res
        return out

class Model_Z(nn.Module):
    def __init__(self):
        super(Model_Z, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=256, out_channels=2048, kernel_size=3, padding=1)
        self.batchnorm = nn.BatchNorm2d(2048)
        self.conv2 = nn.Conv2d(in_channels=2048, out_channels=256, kernel_size=3, padding=1)
        self.batchnorm2 = nn.BatchNorm2d(256)
        self.conv3 = nn.Conv2d(in_channels=256, out_channels=1024, kernel_size=3, padding=1)
        self.batchnorm3 = nn.BatchNorm2d(1024)
        self.conv4 = nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=3, padding=1)
        self.batchnorm4 = nn.BatchNorm2d(256)
        self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1)
        self.batchnorm5 = nn.BatchNorm2d(512)
        self.conv6 = nn.Conv2d(in_channels=512, out_channels=256, kernel_size=3, padding=1)
        self.batchnorm6 = nn.BatchNorm2d(256)
        self.conv7 = nn.Conv2d(in_channels=256, out_channels=448, kernel_size=3, padding=1)
        self.batchnorm7 = nn.BatchNorm2d(448)
        self.conv8 = nn.Conv2d(in_channels=448, out_channels=384, kernel_size=3, padding=1)
        self.batchnorm8 = nn.BatchNorm2d(384)
        self.conv9 = nn.Conv2d(in_channels=384, out_channels=320, kernel_size=3, padding=1)
        self.batchnorm9 = nn.BatchNorm2d(320)
        self.conv10 = nn.Conv2d(in_channels=320, out_channels=256, kernel_size=3, padding=1)
        self.elu = nn.ELU()

    def forward(self, x):
        res = x
        x = self.elu(self.conv1(x))
        x = self.batchnorm(x)
        x = self.elu(self.conv2(x)) + res
        x = self.batchnorm2(x)
        x = self.elu(self.conv3(x))
        x = self.batchnorm3(x)
        x = self.elu(self.conv4(x)) + res
        x = self.batchnorm4(x)
        x = self.elu(self.conv5(x))
        x = self.batchnorm5(x)
        x = self.elu(self.conv6(x)) + res
        x = self.batchnorm6(x)
        x = self.elu(self.conv7(x))
        x = self.batchnorm7(x)
        x = self.elu(self.conv8(x))
        x = self.batchnorm8(x)
        x = self.elu(self.conv9(x))
        x = self.batchnorm9(x)
        out = self.elu(self.conv10(x)) + res
        return out


def tensor_jax(x):
    if x.dim() == 3:
        x = x. unsqueeze(0)

    x_np = x.detach().permute(0, 2, 3, 1).cpu().numpy()  # Convert from (N, C, H, W) to (N, H, W, C) and move to CPU
    x_jax = jnp.array(x_np)
    return x_jax

def jax_to_tensor(x):
    x_tensor = torch.tensor(np.array(x),requires_grad=True).permute(0, 3, 1, 2).to(device)  # Convert from (N, H, W, C) to (N, C, H, W)
    return x_tensor

# Define the transform
transform = T.Compose([
    T.Resize((256, 256)),
    T.ToTensor()
])

def gen_sources(img):
    model_name = "dalle-mini/vqgan_imagenet_f16_16384"
    model_vaq = VQModel.from_pretrained(model_name)

    model_z1 = Model_Z1()
    model_z1 = model_z1.to(device)
    model_z1.load_state_dict(torch.load("./model_z1.pth",map_location=device))

    model_z2 = Model_Z()
    model_z2 = model_z2.to(device)
    model_z2.load_state_dict(torch.load("./model_z2.pth",map_location=device))

    model_zdf = Model_Z()
    model_zdf = model_zdf.to(device)
    model_zdf.load_state_dict(torch.load("./model_zdf.pth",map_location=device))

    criterion = nn.MSELoss()
    model_z1.eval()
    model_z2.eval()
    model_zdf.eval()

    with torch.no_grad():
        img = img.convert('RGB')
        df_img = transform(img)
        df_img = df_img.unsqueeze(0)  # Change shape to (1, 3, 256, 256)
        df_img = df_img.to(device)
        #convert images: tensor --> jax_array
        df_img_jax = tensor_jax(df_img)
        #calculate quantized_code(z) for all images
        z_df,_ = model_vaq.encode(df_img_jax)
        #convert quantized_code(z): jax_array --> tensor
        z_df_tensor = jax_to_tensor(z_df)
        ##----------------------------------------------------------------------
        ##----------------------model_z1-----------------------
        outputs_z1 = model_z1(z_df_tensor)
        #generate img1
        z1_rec_jax = tensor_jax(outputs_z1)
        rec_img1 = model_vaq.decode(z1_rec_jax)
        ##----------------------------------------------------------------------
        ##----------------------model_z2-----------------------
        outputs_z2 = model_z2(z_df_tensor)
        #generate img2
        z2_rec_jax = tensor_jax(outputs_z2)
        rec_img2 = model_vaq.decode(z2_rec_jax)
        ##----------------------------------------------------------------------
        ##----------------------model_zdf-----------------------
        z_rec = outputs_z1 + outputs_z2
        outputs_zdf = model_zdf(z_rec)
        lossdf = criterion(outputs_zdf, z_df_tensor)
        #calculate dfimg reconstruction loss
        zdf_rec_jax = tensor_jax(outputs_zdf)
        rec_df = model_vaq.decode(zdf_rec_jax)
        rec_df_tensor = jax_to_tensor(rec_df)
        dfimgloss = criterion(rec_df_tensor, df_img)
        # Convert tensor back to a PIL image
        rec_img1 = jax_to_tensor(rec_img1)
        rec_img1 = rec_img1.squeeze(0)
        rec_img2 = jax_to_tensor(rec_img2)
        rec_img2 = rec_img2.squeeze(0)
        rec_df = jax_to_tensor(rec_df)
        rec_df = rec_df.squeeze(0)
        rec_img1_pil = T.ToPILImage()(rec_img1)
        rec_img2_pil = T.ToPILImage()(rec_img2)
        rec_df_pil = T.ToPILImage()(rec_df)

        return (rec_img1_pil, rec_img2_pil, round(dfimgloss.item(),3))

# Create the Gradio interface
interface = gr.Interface(
    fn=gen_sources,
    inputs=gr.Image(type="pil", label="Input Image"),
    outputs=[
        gr.Image(type="pil", label="Source Image 1"),
        gr.Image(type="pil", label="Source Image 2"),
        #gr.Image(type="pil", label="Deepfake Image"),
        gr.Number(label="Reconstruction Loss")
    ],
    examples = ["./df1.jpg","./df2.jpg","./df3.jpg","./df4.jpg"],
    theme = gr.themes.Soft(),
    title="Uncovering Deepfake Image",
    description="Upload an image.",
)

interface.launch()