File size: 6,369 Bytes
424919d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
import argparse
import json
from time import perf_counter
from datetime import datetime
import torchvision.transforms as transforms
from torchvision.utils import save_image
import torchvision.transforms.functional as TF
from torchvision.transforms import Compose, Resize, CenterCrop
from torchvision.io import decode_jpeg, encode_jpeg
from PIL import Image
import os
import torch
from utils.sanic_utils import *
import typing
import requests
import time # Import the time module
from guided_diffusion.compute_dire_eps import dire_get_first_step_noise, create_argparser
from networks.distill_model import DistilDIREOnlyEPS, DistilDIRE
from guided_diffusion.guided_diffusion.script_util import (
create_model_and_diffusion,
model_and_diffusion_defaults,
dict_parse
)
def download_file(input_path):
"""
Download a file from a given URL and save it locally if input_path is a URL.
If input_path is a local file path and the file exists, skip the download.
:param input_path: The URL of the file to download or a local file path.
:return: The local filepath to the downloaded or existing file.
"""
# Check if input_path is a URL
if input_path.startswith(('http://', 'https://')):
# Extract filename from the URL
# Splits the URL by '/' and get the last part
filename = input_path.split('/')[-1]
# Ensure the filename does not contain query parameters if present in the URL
# Splits the filename by '?' and get the first part
filename = filename.split('?')[0]
# put jpg extension if not present
if '.' not in filename:
filename += ".jpg"
# Define the local path where the file will be saved
local_filepath = os.path.join('.', filename)
# Check if file already exists locally
if os.path.isfile(local_filepath):
print(f"The file already exists locally: {local_filepath}")
return local_filepath
# Start timing the download
start_time = time.time()
# Send a GET request to the URL
response = requests.get(input_path, stream=True)
# Raise an exception if the request was unsuccessful
response.raise_for_status()
# Open the local file in write-binary mode
with open(local_filepath, 'wb') as file:
# Write the content of the response to the local file
for chunk in response.iter_content(chunk_size=8192):
file.write(chunk)
# End timing the download
end_time = time.time()
# Calculate the download duration
download_duration = end_time - start_time
print(
f"Downloaded file saved to {local_filepath} in {download_duration:.2f} seconds.")
else:
# Assume input_path is a local file path
local_filepath = input_path
# Check if the specified local file exists
if not os.path.isfile(local_filepath):
raise FileNotFoundError(f"No such file: '{local_filepath}'")
print(f"Using existing file: {local_filepath}")
return local_filepath
class CustomModel:
"""Wrapper for a DIRE model."""
def __init__(self, net='DIRE', num_frames=15, ckpt='truemedia-global-scaled.pth'):
self.net = net
self.num_frames = num_frames
# self.model = DistilDIREOnlyEPS('cuda').to('cuda')
self.model = DistilDIRE('cuda').to('cuda')
self.trans = transforms.Compose((transforms.Resize(256, antialias=True), transforms.CenterCrop((256, 256)),))
self._load_state_dict(ckpt)
args = create_argparser()
args['timestep_respacing'] = 'ddim20'
adm_model, diffusion = create_model_and_diffusion(**dict_parse(args, model_and_diffusion_defaults().keys()))
adm_model.load_state_dict(torch.load(args['model_path'], map_location="cpu"))
adm_model.cuda()
adm_model.convert_to_fp16()
adm_model.eval()
self.adm_model = adm_model
self.diffusion = diffusion
self.args = args
def _load_state_dict(self, ckpt):
print(f"Loading the model from {ckpt}...")
state_dict = torch.load(ckpt, map_location="cpu")['model']
state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
self.model.load_state_dict(state_dict)
self.model.eval()
self.model.cuda()
print("The model is successfully loaded")
def _forward_dire_img(self, img_path, save_dire=True, thr=0.5):
img = Image.open(img_path).convert("RGB")
img = TF.to_tensor(img)
img = self.trans(img).cuda() * 2 - 1
img = img.unsqueeze(0)
with torch.no_grad():
eps = dire_get_first_step_noise(img, self.adm_model, self.diffusion, self.args, "cuda")
# eps = eps.detach().cpu()
# ext = img_path.split('.')[-1]
# eps_path = img_path.replace(f".{ext}", ".pt")
# torch.save(eps, eps_path)
# eps = torch.load(eps_path, weights_only=True, mmap=True).cuda()
# os.remove(eps_path)
prob = self.model(img, eps)['logit'].sigmoid()
return {"df_probability": prob.median().item(), "prediction": real_or_fake_thres(prob.median().item(), thr)}
def predict(self, inputs: typing.Dict[str, str]) -> typing.Dict[str, str]:
file_path = inputs.get('file_path', None)
video_file = download_file(file_path)
if os.path.isfile(video_file):
try:
if is_image(video_file):
print(f"{self.net} is being run.")
return self._forward_dire_img(video_file)
else:
print(
f"Invalid media file: {video_file}. Please provide a valid video/img file.")
return {"msg": f"Invalid media file: {video_file}. Please provide a valid video/img file."}
except Exception as e:
print(f"An error occurred: {str(e)}")
return {"msg": f"An error occurred: {str(e)}"}
else:
print(f"The file {video_file} does not exist.")
return {"msg": f"The file {video_file} does not exist."}
@classmethod
def fetch(cls) -> None:
cls()
|