766 lines
32 KiB
Python
766 lines
32 KiB
Python
import os
|
|
import tempfile
|
|
import uuid
|
|
import sys
|
|
import shutil
|
|
|
|
# Function to find ComfyUI directories
|
|
def get_comfyui_temp_dir():
|
|
"""Dynamically find the ComfyUI temp directory"""
|
|
# First check using folder_paths if available
|
|
try:
|
|
import folder_paths
|
|
comfy_dir = os.path.dirname(os.path.dirname(os.path.abspath(folder_paths.__file__)))
|
|
temp_dir = os.path.join(comfy_dir, "temp")
|
|
return temp_dir
|
|
except:
|
|
pass
|
|
|
|
# Try to locate based on current script location
|
|
try:
|
|
# This script is likely in a ComfyUI custom nodes directory
|
|
current_dir = os.path.dirname(os.path.abspath(__file__))
|
|
# Go up until we find the ComfyUI directory
|
|
potential_dir = current_dir
|
|
for _ in range(5): # Limit to 5 levels up
|
|
if os.path.exists(os.path.join(potential_dir, "comfy.py")):
|
|
return os.path.join(potential_dir, "temp")
|
|
potential_dir = os.path.dirname(potential_dir)
|
|
except:
|
|
pass
|
|
|
|
# Return None if we can't find it
|
|
return None
|
|
|
|
# Function to clean up any ComfyUI temp directories
|
|
def cleanup_comfyui_temp_directories():
|
|
"""Find and clean up any ComfyUI temp directories"""
|
|
comfyui_temp = get_comfyui_temp_dir()
|
|
if not comfyui_temp:
|
|
print("Could not locate ComfyUI temp directory")
|
|
return
|
|
|
|
comfyui_base = os.path.dirname(comfyui_temp)
|
|
|
|
# Check for the main temp directory
|
|
if os.path.exists(comfyui_temp):
|
|
try:
|
|
shutil.rmtree(comfyui_temp)
|
|
print(f"Removed ComfyUI temp directory: {comfyui_temp}")
|
|
except Exception as e:
|
|
print(f"Could not remove {comfyui_temp}: {str(e)}")
|
|
# If we can't remove it, try to rename it
|
|
try:
|
|
backup_name = f"{comfyui_temp}_backup_{uuid.uuid4().hex[:8]}"
|
|
os.rename(comfyui_temp, backup_name)
|
|
print(f"Renamed {comfyui_temp} to {backup_name}")
|
|
except:
|
|
pass
|
|
|
|
# Find and clean up any backup temp directories
|
|
try:
|
|
all_directories = [d for d in os.listdir(comfyui_base) if os.path.isdir(os.path.join(comfyui_base, d))]
|
|
for dirname in all_directories:
|
|
if dirname.startswith("temp_backup_"):
|
|
backup_path = os.path.join(comfyui_base, dirname)
|
|
try:
|
|
shutil.rmtree(backup_path)
|
|
print(f"Removed backup temp directory: {backup_path}")
|
|
except Exception as e:
|
|
print(f"Could not remove backup dir {backup_path}: {str(e)}")
|
|
except Exception as e:
|
|
print(f"Error cleaning up temp directories: {str(e)}")
|
|
|
|
# Create a module-level function to set up system-wide temp directory
|
|
def init_temp_directories():
|
|
"""Initialize global temporary directory settings"""
|
|
# First clean up any existing temp directories
|
|
cleanup_comfyui_temp_directories()
|
|
|
|
# Generate a unique base directory for this module
|
|
system_temp = tempfile.gettempdir()
|
|
unique_id = str(uuid.uuid4())[:8]
|
|
temp_base_path = os.path.join(system_temp, f"latentsync_{unique_id}")
|
|
os.makedirs(temp_base_path, exist_ok=True)
|
|
|
|
# Override environment variables that control temp directories
|
|
os.environ['TMPDIR'] = temp_base_path
|
|
os.environ['TEMP'] = temp_base_path
|
|
os.environ['TMP'] = temp_base_path
|
|
|
|
# Force Python's tempfile module to use our directory
|
|
tempfile.tempdir = temp_base_path
|
|
|
|
# Final check for ComfyUI temp directory
|
|
comfyui_temp = get_comfyui_temp_dir()
|
|
if comfyui_temp and os.path.exists(comfyui_temp):
|
|
try:
|
|
shutil.rmtree(comfyui_temp)
|
|
print(f"Removed ComfyUI temp directory: {comfyui_temp}")
|
|
except Exception as e:
|
|
print(f"Could not remove {comfyui_temp}, trying to rename: {str(e)}")
|
|
try:
|
|
backup_name = f"{comfyui_temp}_backup_{unique_id}"
|
|
os.rename(comfyui_temp, backup_name)
|
|
print(f"Renamed {comfyui_temp} to {backup_name}")
|
|
# Try to remove the renamed directory as well
|
|
try:
|
|
shutil.rmtree(backup_name)
|
|
print(f"Removed renamed temp directory: {backup_name}")
|
|
except:
|
|
pass
|
|
except:
|
|
print(f"Failed to rename {comfyui_temp}")
|
|
|
|
print(f"Set up system temp directory: {temp_base_path}")
|
|
return temp_base_path
|
|
|
|
# Function to clean up everything when the module exits
|
|
def module_cleanup():
|
|
"""Clean up all resources when the module is unloaded"""
|
|
global MODULE_TEMP_DIR
|
|
|
|
# Clean up our module temp directory
|
|
if MODULE_TEMP_DIR and os.path.exists(MODULE_TEMP_DIR):
|
|
try:
|
|
shutil.rmtree(MODULE_TEMP_DIR, ignore_errors=True)
|
|
print(f"Cleaned up module temp directory: {MODULE_TEMP_DIR}")
|
|
except:
|
|
pass
|
|
|
|
# Do a final sweep for any ComfyUI temp directories
|
|
cleanup_comfyui_temp_directories()
|
|
|
|
# Call this before anything else
|
|
MODULE_TEMP_DIR = init_temp_directories()
|
|
|
|
# Register the cleanup handler to run when Python exits
|
|
import atexit
|
|
atexit.register(module_cleanup)
|
|
|
|
# Now import regular dependencies
|
|
import math
|
|
import torch
|
|
import random
|
|
import torchaudio
|
|
import folder_paths
|
|
import numpy as np
|
|
import platform
|
|
import subprocess
|
|
import importlib.util
|
|
import importlib.machinery
|
|
import argparse
|
|
from omegaconf import OmegaConf
|
|
from PIL import Image
|
|
from decimal import Decimal, ROUND_UP
|
|
import requests
|
|
|
|
# Modify folder_paths module to use our temp directory
|
|
if hasattr(folder_paths, "get_temp_directory"):
|
|
original_get_temp = folder_paths.get_temp_directory
|
|
folder_paths.get_temp_directory = lambda: MODULE_TEMP_DIR
|
|
else:
|
|
# Add the function if it doesn't exist
|
|
setattr(folder_paths, 'get_temp_directory', lambda: MODULE_TEMP_DIR)
|
|
|
|
def import_inference_script(script_path):
|
|
"""Import a Python file as a module using its file path."""
|
|
if not os.path.exists(script_path):
|
|
raise ImportError(f"Script not found: {script_path}")
|
|
|
|
module_name = "latentsync_inference"
|
|
spec = importlib.util.spec_from_file_location(module_name, script_path)
|
|
if spec is None:
|
|
raise ImportError(f"Failed to create module spec for {script_path}")
|
|
|
|
module = importlib.util.module_from_spec(spec)
|
|
sys.modules[module_name] = module
|
|
|
|
try:
|
|
spec.loader.exec_module(module)
|
|
except Exception as e:
|
|
del sys.modules[module_name]
|
|
raise ImportError(f"Failed to execute module: {str(e)}")
|
|
|
|
return module
|
|
|
|
def check_ffmpeg():
|
|
try:
|
|
if platform.system() == "Windows":
|
|
# Check if ffmpeg exists in PATH
|
|
ffmpeg_path = shutil.which("ffmpeg.exe")
|
|
if ffmpeg_path is None:
|
|
# Look for ffmpeg in common locations
|
|
possible_paths = [
|
|
os.path.join(os.environ.get("ProgramFiles", "C:\\Program Files"), "ffmpeg", "bin"),
|
|
os.path.join(os.environ.get("ProgramFiles(x86)", "C:\\Program Files (x86)"), "ffmpeg", "bin"),
|
|
os.path.join(os.path.dirname(os.path.abspath(__file__)), "ffmpeg", "bin"),
|
|
]
|
|
for path in possible_paths:
|
|
if os.path.exists(os.path.join(path, "ffmpeg.exe")):
|
|
# Add to PATH
|
|
os.environ["PATH"] = path + os.pathsep + os.environ.get("PATH", "")
|
|
return True
|
|
print("FFmpeg not found. Please install FFmpeg and add it to PATH")
|
|
return False
|
|
return True
|
|
else:
|
|
subprocess.run(["ffmpeg"], capture_output=True)
|
|
return True
|
|
except (subprocess.CalledProcessError, FileNotFoundError):
|
|
print("FFmpeg not found. Please install FFmpeg")
|
|
return False
|
|
|
|
def check_and_install_dependencies():
|
|
if not check_ffmpeg():
|
|
raise RuntimeError("FFmpeg is required but not found")
|
|
|
|
required_packages = [
|
|
'omegaconf',
|
|
'transformers',
|
|
'accelerate',
|
|
'huggingface_hub',
|
|
'einops',
|
|
'diffusers',
|
|
'ffmpeg-python'
|
|
]
|
|
|
|
def is_package_installed(package_name):
|
|
return importlib.util.find_spec(package_name) is not None
|
|
|
|
def install_package(package):
|
|
python_exe = sys.executable
|
|
try:
|
|
subprocess.check_call([python_exe, '-m', 'pip', 'install', package],
|
|
stdout=subprocess.PIPE,
|
|
stderr=subprocess.PIPE)
|
|
print(f"Successfully installed {package}")
|
|
except subprocess.CalledProcessError as e:
|
|
print(f"Error installing {package}: {str(e)}")
|
|
raise RuntimeError(f"Failed to install required package: {package}")
|
|
|
|
for package in required_packages:
|
|
if not is_package_installed(package):
|
|
print(f"Installing required package: {package}")
|
|
try:
|
|
install_package(package)
|
|
except Exception as e:
|
|
print(f"Warning: Failed to install {package}: {str(e)}")
|
|
raise
|
|
|
|
def normalize_path(path):
|
|
"""Normalize path to handle spaces and special characters"""
|
|
return os.path.normpath(path).replace('\\', '/')
|
|
|
|
def get_ext_dir(subpath=None, mkdir=False):
|
|
"""Get extension directory path, optionally with a subpath"""
|
|
# Get the directory containing this script
|
|
dir = os.path.dirname(os.path.abspath(__file__))
|
|
|
|
# Special case for temp directories
|
|
if subpath and ("temp" in subpath.lower() or "tmp" in subpath.lower()):
|
|
# Use our global temp directory instead
|
|
global MODULE_TEMP_DIR
|
|
sub_temp = os.path.join(MODULE_TEMP_DIR, subpath)
|
|
if mkdir and not os.path.exists(sub_temp):
|
|
os.makedirs(sub_temp, exist_ok=True)
|
|
return sub_temp
|
|
|
|
if subpath is not None:
|
|
dir = os.path.join(dir, subpath)
|
|
|
|
if mkdir and not os.path.exists(dir):
|
|
os.makedirs(dir, exist_ok=True)
|
|
|
|
return dir
|
|
|
|
def download_model(url, save_path):
|
|
"""Download a model from a URL and save it to the specified path."""
|
|
os.makedirs(os.path.dirname(save_path), exist_ok=True)
|
|
response = requests.get(url, stream=True)
|
|
with open(save_path, "wb") as f:
|
|
for chunk in response.iter_content(chunk_size=8192):
|
|
f.write(chunk)
|
|
|
|
def pre_download_models():
|
|
"""Pre-download all required models."""
|
|
models = {
|
|
"s3fd-e19a316812.pth": "https://www.adrianbulat.com/downloads/python-fan/s3fd-e19a316812.pth",
|
|
# Add other models here
|
|
}
|
|
|
|
cache_dir = os.path.join(MODULE_TEMP_DIR, "model_cache")
|
|
os.makedirs(cache_dir, exist_ok=True)
|
|
|
|
for model_name, url in models.items():
|
|
save_path = os.path.join(cache_dir, model_name)
|
|
if not os.path.exists(save_path):
|
|
print(f"Downloading {model_name}...")
|
|
download_model(url, save_path)
|
|
else:
|
|
print(f"{model_name} already exists in cache.")
|
|
|
|
def setup_models():
|
|
"""Setup and pre-download all required models."""
|
|
# Use our global temp directory
|
|
global MODULE_TEMP_DIR
|
|
|
|
# Pre-download additional models
|
|
pre_download_models()
|
|
|
|
# Existing setup logic for LatentSync models
|
|
cur_dir = get_ext_dir()
|
|
ckpt_dir = os.path.join(cur_dir, "checkpoints")
|
|
whisper_dir = os.path.join(ckpt_dir, "whisper")
|
|
os.makedirs(ckpt_dir, exist_ok=True)
|
|
os.makedirs(whisper_dir, exist_ok=True)
|
|
|
|
# Create a temp_downloads directory in our system temp
|
|
temp_downloads = os.path.join(MODULE_TEMP_DIR, "downloads")
|
|
os.makedirs(temp_downloads, exist_ok=True)
|
|
|
|
unet_path = os.path.join(ckpt_dir, "latentsync_unet.pt")
|
|
whisper_path = os.path.join(whisper_dir, "tiny.pt")
|
|
|
|
if not (os.path.exists(unet_path) and os.path.exists(whisper_path)):
|
|
print("Downloading required model checkpoints... This may take a while.")
|
|
try:
|
|
from huggingface_hub import snapshot_download
|
|
snapshot_download(repo_id="ByteDance/LatentSync-1.5",
|
|
allow_patterns=["latentsync_unet.pt", "whisper/tiny.pt"],
|
|
local_dir=ckpt_dir,
|
|
local_dir_use_symlinks=False,
|
|
cache_dir=temp_downloads)
|
|
print("Model checkpoints downloaded successfully!")
|
|
except Exception as e:
|
|
print(f"Error downloading models: {str(e)}")
|
|
print("\nPlease download models manually:")
|
|
print("1. Visit: https://huggingface.co/chunyu-li/LatentSync")
|
|
print("2. Download: latentsync_unet.pt and whisper/tiny.pt")
|
|
print(f"3. Place them in: {ckpt_dir}")
|
|
print(f" with whisper/tiny.pt in: {whisper_dir}")
|
|
raise RuntimeError("Model download failed. See instructions above.")
|
|
|
|
class GeekyLatentSyncNode:
|
|
def __init__(self):
|
|
# Make sure our temp directory is the current one
|
|
global MODULE_TEMP_DIR
|
|
if not os.path.exists(MODULE_TEMP_DIR):
|
|
os.makedirs(MODULE_TEMP_DIR, exist_ok=True)
|
|
|
|
# Ensure ComfyUI temp doesn't exist
|
|
comfyui_temp = "D:\\ComfyUI_windows\\temp"
|
|
if os.path.exists(comfyui_temp):
|
|
backup_name = f"{comfyui_temp}_backup_{uuid.uuid4().hex[:8]}"
|
|
try:
|
|
os.rename(comfyui_temp, backup_name)
|
|
except:
|
|
pass
|
|
|
|
check_and_install_dependencies()
|
|
setup_models()
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"images": ("IMAGE",),
|
|
"audio": ("AUDIO", ),
|
|
"seed": ("INT", {"default": 1247}),
|
|
"lips_expression": ("FLOAT", {"default": 1.5, "min": 1.0, "max": 3.0, "step": 0.1}),
|
|
"inference_steps": ("INT", {"default": 20, "min": 1, "max": 999, "step": 1}),
|
|
},}
|
|
|
|
CATEGORY = "GeekyLatentSync"
|
|
|
|
RETURN_TYPES = ("IMAGE", "AUDIO")
|
|
RETURN_NAMES = ("images", "audio")
|
|
FUNCTION = "inference"
|
|
|
|
def process_batch(self, batch, use_mixed_precision=False):
|
|
with torch.cuda.amp.autocast(enabled=use_mixed_precision):
|
|
processed_batch = batch.float() / 255.0
|
|
if len(processed_batch.shape) == 3:
|
|
processed_batch = processed_batch.unsqueeze(0)
|
|
if processed_batch.shape[0] == 3:
|
|
processed_batch = processed_batch.permute(1, 2, 0)
|
|
if processed_batch.shape[-1] == 4:
|
|
processed_batch = processed_batch[..., :3]
|
|
return processed_batch
|
|
|
|
def inference(self, images, audio, seed, lips_expression=1.5, inference_steps=20):
|
|
# Use our module temp directory
|
|
global MODULE_TEMP_DIR
|
|
|
|
# Get GPU capabilities and memory
|
|
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
|
BATCH_SIZE = 4
|
|
use_mixed_precision = False
|
|
if torch.cuda.is_available():
|
|
gpu_mem = torch.cuda.get_device_properties(0).total_memory
|
|
# Convert to GB
|
|
gpu_mem_gb = gpu_mem / (1024 ** 3)
|
|
|
|
# Dynamically adjust batch size based on GPU memory
|
|
if gpu_mem_gb > 20: # High-end GPUs
|
|
BATCH_SIZE = 32
|
|
enable_tf32 = True
|
|
use_mixed_precision = True
|
|
elif gpu_mem_gb > 8: # Mid-range GPUs
|
|
BATCH_SIZE = 16
|
|
enable_tf32 = False
|
|
use_mixed_precision = True
|
|
else: # Lower-end GPUs
|
|
BATCH_SIZE = 8
|
|
enable_tf32 = False
|
|
use_mixed_precision = False
|
|
|
|
# Set performance options based on GPU capability
|
|
torch.backends.cudnn.benchmark = True
|
|
if enable_tf32:
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
torch.backends.cudnn.allow_tf32 = True
|
|
|
|
# Clear GPU cache before processing
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.set_per_process_memory_fraction(0.8)
|
|
|
|
# Create a run-specific subdirectory in our temp directory
|
|
run_id = ''.join(random.choice("abcdefghijklmnopqrstuvwxyz") for _ in range(5))
|
|
temp_dir = os.path.join(MODULE_TEMP_DIR, f"run_{run_id}")
|
|
os.makedirs(temp_dir, exist_ok=True)
|
|
|
|
# Ensure ComfyUI temp doesn't exist again (in case something recreated it)
|
|
comfyui_temp = "D:\\ComfyUI_windows\\temp"
|
|
if os.path.exists(comfyui_temp):
|
|
backup_name = f"{comfyui_temp}_backup_{uuid.uuid4().hex[:8]}"
|
|
try:
|
|
os.rename(comfyui_temp, backup_name)
|
|
except:
|
|
pass
|
|
|
|
temp_video_path = None
|
|
output_video_path = None
|
|
audio_path = None
|
|
|
|
try:
|
|
# Create temporary file paths in our system temp directory
|
|
temp_video_path = os.path.join(temp_dir, f"temp_{run_id}.mp4")
|
|
output_video_path = os.path.join(temp_dir, f"latentsync_{run_id}_out.mp4")
|
|
audio_path = os.path.join(temp_dir, f"latentsync_{run_id}_audio.wav")
|
|
|
|
# Get the extension directory
|
|
cur_dir = os.path.dirname(os.path.abspath(__file__))
|
|
|
|
# Process input frames
|
|
if isinstance(images, list):
|
|
frames = torch.stack(images).to(device)
|
|
else:
|
|
frames = images.to(device)
|
|
frames = (frames * 255).byte()
|
|
|
|
# Process audio data to get expected frame count for a single image
|
|
waveform = audio["waveform"].to(device)
|
|
sample_rate = audio["sample_rate"]
|
|
if waveform.dim() == 3:
|
|
waveform = waveform.squeeze(0)
|
|
|
|
# Check if we have a single image (either as a batch of 1 or a single 3D tensor)
|
|
is_single_image = False
|
|
if len(frames.shape) == 3: # Single 3D tensor (H,W,C)
|
|
frames = frames.unsqueeze(0)
|
|
is_single_image = True
|
|
elif frames.shape[0] == 1: # Batch of 1
|
|
is_single_image = True
|
|
|
|
# If it's a single image, duplicate it to match audio duration
|
|
if is_single_image:
|
|
# Calculate audio duration in seconds
|
|
audio_duration = waveform.shape[1] / sample_rate
|
|
|
|
# Calculate how many frames we need at 25fps (standard for this model)
|
|
required_frames = math.ceil(audio_duration * 25)
|
|
|
|
# Duplicate the single frame to match required frame count
|
|
# (minimum 4 frames to avoid tensor stack issues)
|
|
required_frames = max(required_frames, 4)
|
|
single_frame = frames[0]
|
|
duplicated_frames = single_frame.unsqueeze(0).repeat(required_frames, 1, 1, 1)
|
|
frames = duplicated_frames
|
|
print(f"Duplicated single image to create {required_frames} frames matching audio duration")
|
|
|
|
# Resample audio if needed
|
|
if sample_rate != 16000:
|
|
new_sample_rate = 16000
|
|
resampler = torchaudio.transforms.Resample(
|
|
orig_freq=sample_rate,
|
|
new_freq=new_sample_rate
|
|
).to(device)
|
|
waveform_16k = resampler(waveform)
|
|
waveform, sample_rate = waveform_16k, new_sample_rate
|
|
|
|
# Package resampled audio
|
|
resampled_audio = {
|
|
"waveform": waveform.unsqueeze(0),
|
|
"sample_rate": sample_rate
|
|
}
|
|
|
|
# Move waveform to CPU for saving
|
|
waveform_cpu = waveform.cpu()
|
|
torchaudio.save(audio_path, waveform_cpu, sample_rate)
|
|
|
|
# Move frames to CPU for saving to video
|
|
frames_cpu = frames.cpu()
|
|
try:
|
|
import torchvision.io as io
|
|
io.write_video(temp_video_path, frames_cpu, fps=25, video_codec='h264')
|
|
except TypeError:
|
|
import av
|
|
container = av.open(temp_video_path, mode='w')
|
|
stream = container.add_stream('h264', rate=25)
|
|
stream.width = frames_cpu.shape[2]
|
|
stream.height = frames_cpu.shape[1]
|
|
|
|
for frame in frames_cpu:
|
|
frame = av.VideoFrame.from_ndarray(frame.numpy(), format='rgb24')
|
|
packet = stream.encode(frame)
|
|
container.mux(packet)
|
|
|
|
packet = stream.encode(None)
|
|
container.mux(packet)
|
|
container.close()
|
|
|
|
# Define paths to required files and configs
|
|
inference_script_path = os.path.join(cur_dir, "scripts", "inference.py")
|
|
config_path = os.path.join(cur_dir, "configs", "unet", "stage2.yaml")
|
|
scheduler_config_path = os.path.join(cur_dir, "configs")
|
|
ckpt_path = os.path.join(cur_dir, "checkpoints", "latentsync_unet.pt")
|
|
whisper_ckpt_path = os.path.join(cur_dir, "checkpoints", "whisper", "tiny.pt")
|
|
|
|
# Create config and args
|
|
config = OmegaConf.load(config_path)
|
|
|
|
# Set the correct mask image path
|
|
mask_image_path = os.path.join(cur_dir, "latentsync", "utils", "mask.png")
|
|
# Make sure the mask image exists
|
|
if not os.path.exists(mask_image_path):
|
|
# Try to find it in the utils directory directly
|
|
alt_mask_path = os.path.join(cur_dir, "utils", "mask.png")
|
|
if os.path.exists(alt_mask_path):
|
|
mask_image_path = alt_mask_path
|
|
else:
|
|
print(f"Warning: Could not find mask image at expected locations")
|
|
|
|
# Set mask path in config
|
|
if hasattr(config, "data") and hasattr(config.data, "mask_image_path"):
|
|
config.data.mask_image_path = mask_image_path
|
|
|
|
args = argparse.Namespace(
|
|
unet_config_path=config_path,
|
|
inference_ckpt_path=ckpt_path,
|
|
video_path=temp_video_path,
|
|
audio_path=audio_path,
|
|
video_out_path=output_video_path,
|
|
seed=seed,
|
|
inference_steps=inference_steps,
|
|
guidance_scale=lips_expression, # Using lips_expression for the guidance_scale
|
|
scheduler_config_path=scheduler_config_path,
|
|
whisper_ckpt_path=whisper_ckpt_path,
|
|
device=device,
|
|
batch_size=BATCH_SIZE,
|
|
use_mixed_precision=use_mixed_precision,
|
|
temp_dir=temp_dir,
|
|
mask_image_path=mask_image_path
|
|
)
|
|
|
|
# Set PYTHONPATH to include our directories
|
|
package_root = os.path.dirname(cur_dir)
|
|
if package_root not in sys.path:
|
|
sys.path.insert(0, package_root)
|
|
if cur_dir not in sys.path:
|
|
sys.path.insert(0, cur_dir)
|
|
|
|
# Clean GPU cache before inference
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
|
|
# Check and prevent ComfyUI temp creation again
|
|
if os.path.exists(comfyui_temp):
|
|
try:
|
|
os.rename(comfyui_temp, f"{comfyui_temp}_backup_{uuid.uuid4().hex[:8]}")
|
|
except:
|
|
pass
|
|
|
|
# Import the inference module
|
|
inference_module = import_inference_script(inference_script_path)
|
|
|
|
# Monkey patch any temp directory functions in the inference module
|
|
if hasattr(inference_module, 'get_temp_dir'):
|
|
inference_module.get_temp_dir = lambda *args, **kwargs: temp_dir
|
|
|
|
# Create subdirectories that the inference module might expect
|
|
inference_temp = os.path.join(temp_dir, "temp")
|
|
os.makedirs(inference_temp, exist_ok=True)
|
|
|
|
# Run inference
|
|
inference_module.main(config, args)
|
|
|
|
# Clean GPU cache after inference
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
|
|
# Verify output file exists
|
|
if not os.path.exists(output_video_path):
|
|
raise FileNotFoundError(f"Output video not found at: {output_video_path}")
|
|
|
|
# Read the processed video - ensure it's loaded as CPU tensor
|
|
processed_frames = io.read_video(output_video_path, pts_unit='sec')[0]
|
|
processed_frames = processed_frames.float() / 255.0
|
|
|
|
# Ensure audio is on CPU before returning
|
|
if torch.cuda.is_available():
|
|
if hasattr(resampled_audio["waveform"], 'device') and resampled_audio["waveform"].device.type == 'cuda':
|
|
resampled_audio["waveform"] = resampled_audio["waveform"].cpu()
|
|
if hasattr(processed_frames, 'device') and processed_frames.device.type == 'cuda':
|
|
processed_frames = processed_frames.cpu()
|
|
|
|
return (processed_frames, resampled_audio)
|
|
|
|
except Exception as e:
|
|
print(f"Error during inference: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
raise
|
|
|
|
finally:
|
|
# Clean up temporary files individually
|
|
for path in [temp_video_path, output_video_path, audio_path]:
|
|
if path and os.path.exists(path):
|
|
try:
|
|
os.remove(path)
|
|
print(f"Removed temporary file: {path}")
|
|
except Exception as e:
|
|
print(f"Failed to remove {path}: {str(e)}")
|
|
|
|
# Remove temporary run directory
|
|
if temp_dir and os.path.exists(temp_dir):
|
|
try:
|
|
shutil.rmtree(temp_dir, ignore_errors=True)
|
|
print(f"Removed run temporary directory: {temp_dir}")
|
|
except Exception as e:
|
|
print(f"Failed to remove temp run directory: {str(e)}")
|
|
|
|
# Clean up any ComfyUI temp directories again (in case they were created during execution)
|
|
cleanup_comfyui_temp_directories()
|
|
|
|
# Final GPU cache cleanup
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
|
|
class GeekyVideoLengthAdjuster:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"images": ("IMAGE",),
|
|
"audio": ("AUDIO",),
|
|
"mode": (["normal", "pingpong", "loop_to_audio"], {"default": "normal"}),
|
|
"fps": ("FLOAT", {"default": 25.0, "min": 1.0, "max": 120.0}),
|
|
"silent_padding_sec": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 3.0, "step": 0.1}),
|
|
}
|
|
}
|
|
|
|
CATEGORY = "GeekyLatentSync"
|
|
RETURN_TYPES = ("IMAGE", "AUDIO")
|
|
RETURN_NAMES = ("images", "audio")
|
|
FUNCTION = "adjust"
|
|
|
|
def adjust(self, images, audio, mode, fps=25.0, silent_padding_sec=0.5):
|
|
waveform = audio["waveform"].squeeze(0)
|
|
sample_rate = int(audio["sample_rate"])
|
|
original_frames = [images[i] for i in range(images.shape[0])] if isinstance(images, torch.Tensor) else images.copy()
|
|
|
|
if mode == "normal":
|
|
# Add silent padding to the audio and then trim video to match
|
|
audio_duration = waveform.shape[1] / sample_rate
|
|
|
|
# Add silent padding to the audio
|
|
silence_samples = math.ceil(silent_padding_sec * sample_rate)
|
|
silence = torch.zeros((waveform.shape[0], silence_samples), dtype=waveform.dtype)
|
|
padded_audio = torch.cat([waveform, silence], dim=1)
|
|
|
|
# Calculate required frames based on the padded audio
|
|
padded_audio_duration = (waveform.shape[1] + silence_samples) / sample_rate
|
|
required_frames = int(padded_audio_duration * fps)
|
|
|
|
if len(original_frames) > required_frames:
|
|
# Trim video frames to match padded audio duration
|
|
adjusted_frames = original_frames[:required_frames]
|
|
else:
|
|
# If video is shorter than padded audio, keep all video frames
|
|
# and trim the audio accordingly
|
|
adjusted_frames = original_frames
|
|
required_samples = int(len(original_frames) / fps * sample_rate)
|
|
padded_audio = padded_audio[:, :required_samples]
|
|
|
|
return (
|
|
torch.stack(adjusted_frames),
|
|
{"waveform": padded_audio.unsqueeze(0), "sample_rate": sample_rate}
|
|
)
|
|
|
|
elif mode == "pingpong":
|
|
video_duration = len(original_frames) / fps
|
|
audio_duration = waveform.shape[1] / sample_rate
|
|
if audio_duration <= video_duration:
|
|
required_samples = int(video_duration * sample_rate)
|
|
silence = torch.zeros((waveform.shape[0], required_samples - waveform.shape[1]), dtype=waveform.dtype)
|
|
adjusted_audio = torch.cat([waveform, silence], dim=1)
|
|
|
|
return (
|
|
torch.stack(original_frames),
|
|
{"waveform": adjusted_audio.unsqueeze(0), "sample_rate": sample_rate}
|
|
)
|
|
|
|
else:
|
|
silence_samples = math.ceil(silent_padding_sec * sample_rate)
|
|
silence = torch.zeros((waveform.shape[0], silence_samples), dtype=waveform.dtype)
|
|
padded_audio = torch.cat([waveform, silence], dim=1)
|
|
total_duration = (waveform.shape[1] + silence_samples) / sample_rate
|
|
target_frames = math.ceil(total_duration * fps)
|
|
reversed_frames = original_frames[::-1][1:-1] # Remove endpoints
|
|
frames = original_frames + reversed_frames
|
|
while len(frames) < target_frames:
|
|
frames += frames[:target_frames - len(frames)]
|
|
return (
|
|
torch.stack(frames[:target_frames]),
|
|
{"waveform": padded_audio.unsqueeze(0), "sample_rate": sample_rate}
|
|
)
|
|
|
|
elif mode == "loop_to_audio":
|
|
# Add silent padding then simple loop
|
|
silence_samples = math.ceil(silent_padding_sec * sample_rate)
|
|
silence = torch.zeros((waveform.shape[0], silence_samples), dtype=waveform.dtype)
|
|
padded_audio = torch.cat([waveform, silence], dim=1)
|
|
total_duration = (waveform.shape[1] + silence_samples) / sample_rate
|
|
target_frames = math.ceil(total_duration * fps)
|
|
|
|
frames = original_frames.copy()
|
|
while len(frames) < target_frames:
|
|
frames += original_frames[:target_frames - len(frames)]
|
|
|
|
return (
|
|
torch.stack(frames[:target_frames]),
|
|
{"waveform": padded_audio.unsqueeze(0), "sample_rate": sample_rate}
|
|
)
|
|
|
|
# Node Mappings for ComfyUI
|
|
NODE_CLASS_MAPPINGS = {
|
|
"GeekyLatentSyncNode": GeekyLatentSyncNode,
|
|
"GeekyVideoLengthAdjuster": GeekyVideoLengthAdjuster,
|
|
}
|
|
|
|
# Display Names for ComfyUI
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"GeekyLatentSyncNode": "Geeky LatentSync 1.5",
|
|
"GeekyVideoLengthAdjuster": "Geeky Video Length Adjuster",
|
|
}
|