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Zero
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import torch
import comfy.sample
import comfy.model_management
import comfy.utils
import gc
import logging
import nodes
from typing import Dict, Union
import time
from contextlib import contextmanager
import psutil
class MemoryManager:
"""Manages memory resources for efficient video processing."""
def __init__(self, device=None, log_level: str = "INFO"):
self.logger = logging.getLogger("MemoryManager")
if not self.logger.handlers:
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
self.logger.addHandler(handler)
self.logger.setLevel(getattr(logging, log_level))
self.device = device or comfy.model_management.get_torch_device()
self.logger.info(f"Using device: {self.device}")
# Memory thresholds (percentages)
self.warning_threshold = 85
self.critical_threshold = 95
def is_cuda_device(self) -> bool:
"""Check if the current device is a CUDA device."""
if isinstance(self.device, str):
return self.device.startswith("cuda")
elif isinstance(self.device, torch.device):
return self.device.type == "cuda"
return False
def get_memory_stats(self) -> Dict[str, Union[int, float]]:
"""Get current memory statistics for the device."""
stats = {}
if self.is_cuda_device() and torch.cuda.is_available():
try:
t = torch.cuda.get_device_properties(0)
stats["total"] = t.total_memory
stats["reserved"] = torch.cuda.memory_reserved(0)
stats["allocated"] = torch.cuda.memory_allocated(0)
stats["free"] = stats["total"] - stats["reserved"]
stats["usage_percent"] = (stats["allocated"] / stats["total"]) * 100
except Exception as e:
self.logger.error(f"Error getting CUDA memory stats: {e}")
stats = {"error": str(e)}
else:
# CPU memory stats
vm = psutil.virtual_memory()
stats["total"] = vm.total
stats["available"] = vm.available
stats["used"] = vm.used
stats["free"] = vm.free
stats["usage_percent"] = vm.percent
return stats
def is_memory_critical(self) -> bool:
"""Check if memory usage is at critical levels."""
stats = self.get_memory_stats()
if "error" in stats:
return True # Assume critical if we can't get stats
return stats.get("usage_percent", 0) > self.critical_threshold
@contextmanager
def track_memory(self, label: str = "Operation"):
"""Context manager to track memory usage before and after an operation."""
if self.is_cuda_device() and torch.cuda.is_available():
start_mem = torch.cuda.memory_allocated()
start_time = time.time()
try:
yield
finally:
end_mem = torch.cuda.memory_allocated()
end_time = time.time()
self.logger.info(f"{label} - Memory change: {(end_mem-start_mem)/1024**2:.2f}MB, Time: {end_time-start_time:.2f}s")
else:
start_time = time.time()
try:
yield
finally:
end_time = time.time()
self.logger.info(f"{label} - Time: {end_time-start_time:.2f}s")
def cleanup(self, force: bool = False):
"""Clean up memory resources."""
if self.is_cuda_device() and torch.cuda.is_available():
torch.cuda.empty_cache()
gc.collect()
if force:
# More aggressive cleanup
for obj in gc.get_objects():
try:
if torch.is_tensor(obj) and not obj.is_cuda:
del obj
except:
pass
gc.collect()
if self.is_cuda_device() and torch.cuda.is_available():
torch.cuda.empty_cache()
class WanVideoKsampler:
"""
Video K-sampler node with memory management for processing video latents.
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("MODEL",),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"video_latents": ("LATENT",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"denoise": ("FLOAT", {"default": 1, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "sampling"
def __init__(self):
self.logger = logging.getLogger("WanVideoKsampler")
if not self.logger.handlers:
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
self.logger.addHandler(handler)
self.logger.setLevel(logging.INFO)
# Initialize memory manager
self.memory_manager = None
def sample(
self,
model,
video_latents: Dict[str, torch.Tensor],
positive,
negative,
seed: int,
steps: int,
cfg: float,
sampler_name: str,
scheduler: str,
denoise: float
) -> Dict[str, torch.Tensor]:
"""
Sample video frames with memory management.
Args:
model: Diffusion model
video_latents: Dictionary containing latent tensors
positive: Positive conditioning
negative: Negative conditioning
seed: Random seed
steps: Number of sampling steps
cfg: Classifier-free guidance scale
sampler_name: Name of sampler to use
scheduler: Name of scheduler to use
denoise: Denoising strength
Returns:
Dictionary containing processed latent tensors
"""
start_time = time.time()
device = comfy.model_management.get_torch_device()
# Initialize memory manager if needed
if self.memory_manager is None:
self.memory_manager = MemoryManager(device)
# Log latent size for debugging
if isinstance(video_latents, dict) and 'samples' in video_latents:
latent_samples = video_latents['samples']
total_frames = latent_samples.shape[0]
self.logger.info(f"Processing latent shape: {latent_samples.shape}, total frames: {total_frames}")
else:
self.logger.error("Invalid latent format")
raise ValueError("Expected latent dictionary with 'samples' key")
self.logger.info(f"Processing with {steps} steps, {cfg} CFG, {sampler_name} sampler")
try:
# Process with memory tracking
with self.memory_manager.track_memory("Video processing"):
# Check memory usage before processing
memory_stats = self.memory_manager.get_memory_stats()
if "usage_percent" in memory_stats:
self.logger.info(f"Memory usage before processing: {memory_stats['usage_percent']:.1f}%")
# Apply sampling
result = nodes.common_ksampler(
model, seed, steps, cfg, sampler_name, scheduler,
positive, negative, video_latents, denoise=denoise
)
# Clear memory after processing
self.memory_manager.cleanup()
# Check memory usage after processing
memory_stats = self.memory_manager.get_memory_stats()
if "usage_percent" in memory_stats:
self.logger.info(f"Memory usage after processing: {memory_stats['usage_percent']:.1f}%")
end_time = time.time()
self.logger.info(f"Complete: {total_frames} frames in {end_time - start_time:.2f}s ({(end_time - start_time) / total_frames:.2f}s per frame)")
return result
except Exception as e:
self.logger.error(f"Error during processing: {str(e)}")
# Try to release memory
self.memory_manager.cleanup(force=True)
# Check if it's an out-of-memory error
if "CUDA out of memory" in str(e):
self.logger.error("Out of memory error. Consider reducing frame count or model complexity.")
raise e
# Node registration
NODE_CLASS_MAPPINGS = {
"WanVideoKsampler": WanVideoKsampler,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"WanVideoKsampler": "Wan Video Ksampler",
} |