433 lines
16 KiB
Python
433 lines
16 KiB
Python
import os
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import torch
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import random
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import torchaudio
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import folder_paths
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import numpy as np
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import platform
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import subprocess
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import sys
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import importlib.util
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import importlib.machinery
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import argparse
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from omegaconf import OmegaConf
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from PIL import Image
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import shutil
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def import_inference_script(script_path):
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"""Import a Python file as a module using its file path."""
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if not os.path.exists(script_path):
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raise ImportError(f"Script not found: {script_path}")
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module_name = "latentsync_inference"
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spec = importlib.util.spec_from_file_location(module_name, script_path)
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if spec is None:
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raise ImportError(f"Failed to create module spec for {script_path}")
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module = importlib.util.module_from_spec(spec)
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sys.modules[module_name] = module
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try:
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spec.loader.exec_module(module)
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except Exception as e:
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del sys.modules[module_name]
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raise ImportError(f"Failed to execute module: {str(e)}")
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return module
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def check_ffmpeg():
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try:
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if platform.system() == "Windows":
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# Check if ffmpeg exists in PATH
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ffmpeg_path = shutil.which("ffmpeg.exe")
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if ffmpeg_path is None:
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# Look for ffmpeg in common locations
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possible_paths = [
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os.path.join(os.environ.get("ProgramFiles", "C:\\Program Files"), "ffmpeg", "bin"),
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os.path.join(os.environ.get("ProgramFiles(x86)", "C:\\Program Files (x86)"), "ffmpeg", "bin"),
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os.path.join(os.path.dirname(os.path.abspath(__file__)), "ffmpeg", "bin"),
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]
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for path in possible_paths:
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if os.path.exists(os.path.join(path, "ffmpeg.exe")):
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# Add to PATH
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os.environ["PATH"] = path + os.pathsep + os.environ.get("PATH", "")
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return True
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print("FFmpeg not found. Please install FFmpeg and add it to PATH")
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return False
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return True
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else:
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subprocess.run(["ffmpeg", "-version"], capture_output=True, check=True)
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return True
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except (subprocess.CalledProcessError, FileNotFoundError):
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print("FFmpeg not found. Please install FFmpeg")
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return False
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def check_and_install_dependencies():
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if not check_ffmpeg():
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raise RuntimeError("FFmpeg is required but not found")
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required_packages = [
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'omegaconf',
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'pytorch_lightning',
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'transformers',
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'accelerate',
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'huggingface_hub',
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'einops',
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'diffusers'
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]
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def is_package_installed(package_name):
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return importlib.util.find_spec(package_name) is not None
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def install_package(package):
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python_exe = sys.executable
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try:
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subprocess.check_call([python_exe, '-m', 'pip', 'install', package],
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE)
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print(f"Successfully installed {package}")
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except subprocess.CalledProcessError as e:
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print(f"Error installing {package}: {str(e)}")
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raise RuntimeError(f"Failed to install required package: {package}")
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for package in required_packages:
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if not is_package_installed(package):
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print(f"Installing required package: {package}")
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try:
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install_package(package)
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except Exception as e:
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print(f"Warning: Failed to install {package}: {str(e)}")
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raise
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def normalize_path(path):
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"""Normalize path to handle spaces and special characters"""
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return os.path.normpath(path).replace('\\', '/')
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def get_ext_dir(subpath=None, mkdir=False):
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dir = os.path.dirname(__file__)
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if subpath is not None:
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dir = os.path.join(dir, subpath)
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dir = os.path.abspath(dir)
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if mkdir and not os.path.exists(dir):
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os.makedirs(dir)
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return dir
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def save_and_reload_frames(frames, temp_dir):
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final_frames = []
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for frame in frames:
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# Convert to proper range (0-1)
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frame = frame.float() / max(frame.max(), 1.0)
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# Ensure CHW format
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if frame.shape[0] != 3:
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frame = frame.permute(2, 0, 1)
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final_frames.append(frame)
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stacked = torch.stack(final_frames)
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print(f"Stacked min/max: {stacked.min()}, {stacked.max()}")
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return stacked.to(device='cpu', dtype=torch.float32)
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def setup_models():
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cur_dir = get_ext_dir()
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ckpt_dir = os.path.join(cur_dir, "checkpoints")
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whisper_dir = os.path.join(ckpt_dir, "whisper")
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# Create directories if they don't exist
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os.makedirs(ckpt_dir, exist_ok=True)
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os.makedirs(whisper_dir, exist_ok=True)
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unet_path = os.path.join(ckpt_dir, "latentsync_unet.pt")
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whisper_path = os.path.join(whisper_dir, "tiny.pt")
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if not (os.path.exists(unet_path) and os.path.exists(whisper_path)):
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print("Downloading required model checkpoints... This may take a while.")
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try:
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from huggingface_hub import snapshot_download
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snapshot_download(repo_id="chunyu-li/LatentSync",
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allow_patterns=["latentsync_unet.pt", "whisper/tiny.pt"],
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local_dir=ckpt_dir, local_dir_use_symlinks=False)
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print("Model checkpoints downloaded successfully!")
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except Exception as e:
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print(f"Error downloading models: {str(e)}")
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print("\nPlease download models manually:")
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print("1. Visit: https://huggingface.co/chunyu-li/LatentSync")
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print("2. Download: latentsync_unet.pt and whisper/tiny.pt")
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print(f"3. Place them in: {ckpt_dir}")
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print(f" with whisper/tiny.pt in: {whisper_dir}")
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raise RuntimeError("Model download failed. See instructions above.")
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class LatentSyncNode:
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def __init__(self):
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check_and_install_dependencies()
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setup_models()
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"images": ("IMAGE",),
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"audio": ("AUDIO", ),
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"seed": ("INT", {"default": 1247}),
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},}
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CATEGORY = "LatentSyncNode"
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RETURN_TYPES = ("IMAGE", )
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RETURN_NAMES = ("images", )
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FUNCTION = "inference"
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def inference(self, images, audio, seed):
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cur_dir = get_ext_dir()
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ckpt_dir = os.path.join(cur_dir, "checkpoints")
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output_dir = folder_paths.get_output_directory()
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temp_dir = os.path.join(output_dir, "temp_frames")
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os.makedirs(output_dir, exist_ok=True)
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os.makedirs(temp_dir, exist_ok=True)
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# Create a temporary video file from the input frames
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output_name = ''.join(random.choice("abcdefghijklmnopqrstuvwxyz") for _ in range(5))
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temp_video_path = os.path.join(output_dir, f"temp_{output_name}.mp4")
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output_video_path = os.path.join(output_dir, f"latentsync_{output_name}_out.mp4")
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# Save frames as temporary video
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import torchvision.io as io
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if isinstance(images, list):
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frames = torch.stack(images)
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else:
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frames = images
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print(f"Initial frame count: {frames.shape[0]}")
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frames = (frames * 255).byte()
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if len(frames.shape) == 3:
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frames = frames.unsqueeze(0)
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print(f"Frame count before writing video: {frames.shape[0]}")
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if isinstance(frames, torch.Tensor):
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frames = frames.cpu()
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try:
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io.write_video(temp_video_path, frames, fps=25, video_codec='h264')
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except TypeError:
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# Fallback for newer versions
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import av
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container = av.open(temp_video_path, mode='w')
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stream = container.add_stream('h264', rate=25)
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stream.width = frames.shape[2]
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stream.height = frames.shape[1]
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for frame in frames:
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frame = av.VideoFrame.from_ndarray(frame.numpy(), format='rgb24')
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packet = stream.encode(frame)
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container.mux(packet)
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# Flush stream
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packet = stream.encode(None)
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container.mux(packet)
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container.close()
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video_path = normalize_path(temp_video_path)
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if not os.path.exists(ckpt_dir):
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print("Downloading model checkpoints... This may take a while.")
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from huggingface_hub import snapshot_download
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snapshot_download(repo_id="chunyu-li/LatentSync",
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allow_patterns=["latentsync_unet.pt", "whisper/tiny.pt"],
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local_dir=ckpt_dir, local_dir_use_symlinks=False)
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print("Model checkpoints downloaded successfully!")
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inference_script_path = os.path.join(cur_dir, "scripts", "inference.py")
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unet_config_path = normalize_path(os.path.join(cur_dir, "configs", "unet", "second_stage.yaml"))
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scheduler_config_path = normalize_path(os.path.join(cur_dir, "configs"))
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ckpt_path = normalize_path(os.path.join(ckpt_dir, "latentsync_unet.pt"))
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whisper_ckpt_path = normalize_path(os.path.join(ckpt_dir, "whisper", "tiny.pt"))
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# resample audio to 16k hz and save to wav
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waveform = audio["waveform"]
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sample_rate = audio["sample_rate"]
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if waveform.dim() == 3: # Expected shape: [channels, samples]
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waveform = waveform.squeeze(0)
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if sample_rate != 16000:
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new_sample_rate = 16000
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waveform_16k = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=new_sample_rate)(waveform)
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waveform, sample_rate = waveform_16k, new_sample_rate
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audio_path = normalize_path(os.path.join(output_dir, f"latentsync_{output_name}_audio.wav"))
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torchaudio.save(audio_path, waveform, sample_rate)
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print(f"Using video path: {video_path}")
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print(f"Video file exists: {os.path.exists(video_path)}")
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print(f"Video file size: {os.path.getsize(video_path)} bytes")
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assert os.path.exists(video_path), f"video_path not exists: {video_path}"
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assert os.path.exists(audio_path), f"audio_path not exists: {audio_path}"
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try:
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# Add the package root to Python path
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package_root = os.path.dirname(cur_dir)
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if package_root not in sys.path:
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sys.path.insert(0, package_root)
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# Add the current directory to Python path
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if cur_dir not in sys.path:
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sys.path.insert(0, cur_dir)
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# Import the inference module
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inference_module = import_inference_script(inference_script_path)
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# Create a Namespace object with the arguments
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args = argparse.Namespace(
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unet_config_path=unet_config_path,
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inference_ckpt_path=ckpt_path,
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video_path=video_path,
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audio_path=audio_path,
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video_out_path=output_video_path,
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seed=seed,
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scheduler_config_path=scheduler_config_path,
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whisper_ckpt_path=whisper_ckpt_path
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)
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# Load the config
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config = OmegaConf.load(unet_config_path)
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# Call main with both config and args
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inference_module.main(config, args)
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# Load the processed video back as frames
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processed_frames = io.read_video(output_video_path, pts_unit='sec')[0] # [T, H, W, C]
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print(f"Frame count after reading video: {processed_frames.shape[0]}")
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# Process frames following wav2lip.py pattern
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out_tensor_list = []
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for frame in processed_frames:
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# Convert to numpy and ensure correct format
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frame = frame.numpy()
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# Convert frame to float32 and normalize
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frame = frame.astype(np.float32) / 255.0
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# Convert back to tensor
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frame = torch.from_numpy(frame)
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# Ensure we have 3 channels
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if len(frame.shape) == 2: # If grayscale
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frame = frame.unsqueeze(2).repeat(1, 1, 3)
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elif frame.shape[2] == 4: # If RGBA
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frame = frame[:, :, :3]
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# Change to [C, H, W] format
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frame = frame.permute(2, 0, 1)
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out_tensor_list.append(frame)
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processed_frames = io.read_video(output_video_path, pts_unit='sec')[0] # [T, H, W, C]
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processed_frames = processed_frames.float() / 255.0
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print(f"Frame count after normalization: {processed_frames.shape[0]}")
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# Fix dimensions for VideoCombine compatibility
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if len(processed_frames.shape) == 3:
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processed_frames = processed_frames.unsqueeze(0)
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if processed_frames.shape[0] == 1 and len(processed_frames.shape) == 4:
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processed_frames = processed_frames.squeeze(0)
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if processed_frames.shape[0] == 3: # If in CHW format
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processed_frames = processed_frames.permute(1, 2, 0) # Convert to HWC
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if processed_frames.shape[-1] == 4: # If RGBA
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processed_frames = processed_frames[..., :3]
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print(f"Final frame count: {processed_frames.shape[0]}")
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print(f"Final shape: {processed_frames.shape}")
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# Clean up
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if os.path.exists(temp_video_path):
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os.remove(temp_video_path)
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if os.path.exists(output_video_path):
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os.remove(output_video_path)
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shutil.rmtree(temp_dir, ignore_errors=True)
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except Exception as e:
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# Clean up on error
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if os.path.exists(temp_video_path):
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os.remove(temp_video_path)
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if os.path.exists(output_video_path):
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os.remove(output_video_path)
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shutil.rmtree(temp_dir, ignore_errors=True)
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print(f"Error during inference: {str(e)}")
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import traceback
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traceback.print_exc()
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raise
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return (processed_frames,)
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class VideoLengthAdjuster:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"images": ("IMAGE",),
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"audio": ("AUDIO", ),
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"mode": (["normal", "pingpong", "loop_to_audio"], {"default": "normal"}),
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},}
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CATEGORY = "LatentSyncNode"
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RETURN_TYPES = ("IMAGE", "AUDIO") # Add AUDIO back
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RETURN_NAMES = ("images", "audio") # Add audio back
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FUNCTION = "adjust"
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def adjust(self, images, audio, mode):
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if isinstance(images, list):
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frames = len(images)
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else:
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frames = images.shape[0]
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images = [images[i] for i in range(frames)]
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# Handle images as before
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video_duration = frames / 25
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waveform = audio["waveform"]
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sample_rate = audio["sample_rate"]
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if waveform.dim() == 3:
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waveform = waveform.squeeze(0)
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# Add 1 second of silence to audio
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silence_samples = sample_rate # 1 second worth of samples
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silence = torch.zeros((waveform.shape[0], silence_samples), dtype=waveform.dtype)
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padded_waveform = torch.cat([waveform, silence], dim=1)
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# Create modified audio dict
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padded_audio = {
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"waveform": padded_waveform,
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"sample_rate": sample_rate
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}
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audio_samples = padded_waveform.shape[1]
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audio_duration = audio_samples / sample_rate
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print(f"Video Length Adjuster Info:")
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print(f"Video frames: {frames}")
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print(f"Video duration: {video_duration:.2f} seconds")
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print(f"Audio samples: {audio_samples}")
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print(f"Audio sample rate: {sample_rate}")
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print(f"Audio duration (with padding): {audio_duration:.2f} seconds")
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if mode == "pingpong":
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backward_frames = images[-2:0:-1]
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images = images + backward_frames
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if mode in ["pingpong", "loop_to_audio"] and audio_duration > video_duration:
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frames_needed = int(audio_duration * 25)
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complete_sequence = images
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while len(images) < frames_needed:
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images.extend(complete_sequence[:frames_needed-len(images)])
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return (torch.stack(images), padded_audio)
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NODE_CLASS_MAPPINGS = {
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"D_LatentSyncNode": LatentSyncNode,
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"D_VideoLengthAdjuster": VideoLengthAdjuster,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"D_LatentSyncNode": "LatentSync Node",
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"D_VideoLengthAdjuster": "Video Length Adjuster",
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}
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