import onnxruntime as ort from huggingface_hub import hf_hub_download import requests import os import gradio as gr import spaces from typing import Any, List, Callable import cv2 import insightface import time import tempfile import subprocess import gfpgan print("instaling cudnn 9") import subprocess import sys def get_pip_version(package_name): try: result = subprocess.run( [sys.executable, '-m', 'pip', 'show', package_name], capture_output=True, text=True, check=True ) output = result.stdout version_line = next(line for line in output.split('\n') if line.startswith('Version:')) return version_line.split(': ')[1] except subprocess.CalledProcessError as e: print(f"Erro ao executar o comando: {e}") return None package_name = 'nvidia-cudnn-cu12' version = get_pip_version(package_name) print(f"A versão instalada de {package_name} é: {version}") command = "find / -path /proc -prune -o -path /sys -prune -o -name 'libcudnn*' -print" process = subprocess.run(command, shell=True, text=True, capture_output=True) if process.returncode == 0: print("Resultados da busca:\n", process.stdout) else: print("Houve um erro na execução do comando:", process.stderr) source_path = '/usr/local/lib/python3.10/site-packages/nvidia/cublas/lib/libcublasLt.so.12' destination_path = '/usr/local/lib/python3.10/site-packages/nvidia/cudnn/lib/' command = ['mv', source_path, destination_path] subprocess.run(command, check=True) command = ['mv', "/usr/local/lib/python3.10/site-packages/nvidia/cublas/lib/libcublas.so.12", destination_path] subprocess.run(command, check=True) command = ['mv', "/usr/local/lib/python3.10/site-packages/nvidia/cufft/lib/libcufft.so.11", destination_path] subprocess.run(command, check=True) command = ['mv', "/usr/local/lib/python3.10/site-packages/nvidia/cufft/lib/libcufftw.so.11", destination_path] subprocess.run(command, check=True) command = ['mv', "/usr/local/lib/python3.10/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12", destination_path] subprocess.run(command, check=True) command = ['mv', "/usr/local/lib/python3.10/site-packages/nvidia/cuda_cupti/lib/libcupti.so.12", destination_path] subprocess.run(command, check=True) command = ['cp', "/usr/local/lib/python3.10/site-packages/nvidia/curand/lib/libcurand.so.10", destination_path] subprocess.run(command, check=True) command = ['cp', "/usr/local/lib/python3.10/site-packages/nvidia/cusolver/lib/libcusolver.so.11", destination_path] subprocess.run(command, check=True) command = ['cp', "/usr/local/lib/python3.10/site-packages/nvidia/cusolver/lib/libcusolverMg.so.11", destination_path] subprocess.run(command, check=True) command = ['cp', "/usr/local/lib/python3.10/site-packages/nvidia/cusparse/lib/libcusparse.so.12", destination_path] subprocess.run(command, check=True) command = "find / -path /proc -prune -o -path /sys -prune -o -name 'libcu*' -print" process = subprocess.run(command, shell=True, text=True, capture_output=True) if process.returncode == 0: print("Resultados da busca:\n", process.stdout) else: print("Houve um erro na execução do comando:", process.stderr) print("done") print("---------------------") print(ort.get_available_providers()) def conditional_download(download_directory_path, urls): if not os.path.exists(download_directory_path): os.makedirs(download_directory_path) for url in urls: filename = url.split('/')[-1] file_path = os.path.join(download_directory_path, filename) if not os.path.exists(file_path): print(f"Baixando {filename}...") response = requests.get(url, stream=True) if response.status_code == 200: with open(file_path, 'wb') as file: for chunk in response.iter_content(chunk_size=8192): file.write(chunk) print(f"{filename} baixado com sucesso.") else: print(f"Falha ao baixar {filename}. Status code: {response.status_code}") else: print(f"{filename} já existe. Pulando o download.") model_path = hf_hub_download(repo_id="countfloyd/deepfake", filename="inswapper_128.onnx") conditional_download("./", ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth']) FACE_SWAPPER = None FACE_ANALYSER = None FACE_ENHANCER = None @spaces.GPU(duration=300, enable_queue=True) def process_video(source_path: str, target_path: str, enhance = False, progress=gr.Progress(), output_path='result.mp4') -> None: def get_face_analyser(): global FACE_ANALYSER if FACE_ANALYSER is None: FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=["CUDAExecutionProvider"]) FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640)) return FACE_ANALYSER def get_face_enhancer() -> Any: global FACE_ENHANCER if FACE_ENHANCER is None: FACE_ENHANCER = gfpgan.GFPGANer(model_path="./GFPGANv1.4.pth", upscale=2, ) # type: ignore[attr-defined] return FACE_ENHANCER def get_one_face(frame): face = get_face_analyser().get(frame) try: return min(face, key=lambda x: x.bbox[0]) except ValueError: return None def get_face_swapper(): global FACE_SWAPPER if FACE_SWAPPER is None: FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=["CUDAExecutionProvider"]) return FACE_SWAPPER def swap_face(source_face, target_face, temp_frame): return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True) def process_frame(source_face, temp_frame, enhance): target_face = get_one_face(temp_frame) if target_face: temp_frame = swap_face(source_face, target_face, temp_frame) if enhance: temp_frame = enhance_face(temp_frame) return temp_frame def process_image(source_path: str, target_path: str, output_path: str) -> None: source_face = get_one_face(cv2.imread(source_path)) target_frame = cv2.imread(target_path) result = process_frame(source_face, target_frame) cv2.imwrite(output_path, result) def create_temp_directory(): temp_dir = tempfile.mkdtemp() return temp_dir def enhance_face(temp_frame): _, _, temp_frame = get_face_enhancer().enhance( temp_frame, paste_back=True ) return temp_frame def remove_temp_directory(temp_dir): try: for filename in os.listdir(temp_dir): file_path = os.path.join(temp_dir, filename) if os.path.isfile(file_path): os.unlink(file_path) elif os.path.isdir(file_path): os.rmdir(file_path) os.rmdir(temp_dir) except Exception as e: print(f"Erro ao remover a pasta temporária: {e}") def extract_frames(video_path: str): video_capture = cv2.VideoCapture(video_path) if not video_capture.isOpened(): print("Erro ao abrir o vídeo.") return [] frames = [] while True: ret, frame = video_capture.read() if not ret: break frames.append(frame) video_capture.release() return frames def get_video_fps(video_path: str) -> float: video_capture = cv2.VideoCapture(video_path) if not video_capture.isOpened(): raise ValueError("Erro ao abrir o vídeo.") fps = video_capture.get(cv2.CAP_PROP_FPS) video_capture.release() return fps def create_video_from_frames(temp_dir: str, output_video_path: str, fps: float) -> None: temp_frames_pattern = os.path.join(temp_dir, "frame_%04d.jpg") ffmpeg_command = [ 'ffmpeg', '-y', '-framerate', str(fps), '-i', temp_frames_pattern, '-c:v', 'libx264', '-pix_fmt', 'yuv420p', '-preset', 'ultrafast', output_video_path ] subprocess.run(ffmpeg_command, check=True) def extract_audio(video_path: str, audio_path: str) -> None: ffmpeg_command = [ 'ffmpeg', '-y', '-i', video_path, '-q:a', '0', '-map', 'a', '-preset', 'ultrafast', audio_path ] subprocess.run(ffmpeg_command, check=True) def add_audio_to_video(video_path: str, audio_path: str, output_video_path: str) -> None: ffmpeg_command = [ 'ffmpeg', '-y', '-i', video_path, '-i', audio_path, '-c:v', 'copy', '-c:a', 'aac', '-strict', 'experimental', '-preset', 'ultrafast', output_video_path ] subprocess.run(ffmpeg_command, check=True) def delete_file(file_path: str) -> None: try: os.remove(file_path) except Exception as e: print(f"Erro ao remover o arquivo: {e}") def reduce_video(video_path: str, output_video_path: str) -> None: ffmpeg_command = [ 'ffmpeg', '-y', '-i', video_path, '-vf', "scale='if(gte(iw,ih),720,-1)':'if(gte(iw,ih),-1,720)',pad=ceil(iw/2)*2:ceil(ih/2)*2", '-preset', 'ultrafast', '-r','24', output_video_path ] subprocess.run(ffmpeg_command, check=True) temp_dir = create_temp_directory() video_input = temp_dir + "/input.mp4" reduce_video(target_path , video_input) source_face = get_one_face(cv2.imread(source_path)) frames = extract_frames(video_input) for index, frame in progress.tqdm(enumerate(frames), total=len(frames)): processed_frame = process_frame(source_face, frame, enhance) frame_filename = os.path.join(temp_dir, f"frame_{index:04d}.jpg") cv2.imwrite(frame_filename, processed_frame) video_path = temp_dir + "/out.mp4" create_video_from_frames(temp_dir, video_path, get_video_fps(video_input)) audio_path = temp_dir + "/audio.wav" extract_audio(video_input, audio_path) add_audio_to_video(video_path, audio_path, output_path) remove_temp_directory(temp_dir) return output_path app = gr.Interface( fn=process_video, inputs=[gr.Image(type='filepath'), gr.Video(), gr.Checkbox(label="Use Face GAN(increase render time)", value=False)], outputs=[gr.Video()], description="Videos get downsampled to 720p and 24fps. To modify the code or purchase a modification, send an email to fountaiplayer@gmail.com to donate to the owner of the space: https://donate.stripe.com/3csg0D0tadXU4mYcMM" ) app.launch()