301 lines
10 KiB
Python
301 lines
10 KiB
Python
import torch, gc, psutil
|
|
import comfy.model_management as mm
|
|
from comfy.model_management import loaded_models, free_memory, get_torch_device
|
|
|
|
try:
|
|
from custom_nodes.ComfyUI_GGUF.nodes import UnetLoaderGGUF
|
|
GGUF_AVAILABLE = True
|
|
except ImportError:
|
|
print("⚠️ ComfyUI_GGUF not available - ControlledUnetLoaderGGUF will not work")
|
|
GGUF_AVAILABLE = False
|
|
UnetLoaderGGUF = None
|
|
|
|
class AnyType(str):
|
|
def __ne__(self, __value: object) -> bool: return False
|
|
any_typ = AnyType("*")
|
|
|
|
|
|
def hard_free_model(model):
|
|
if model is None:
|
|
return
|
|
|
|
try:
|
|
model_type = type(model).__name__
|
|
print(f"Freeing {model_type} model...")
|
|
|
|
# Handle dictionary-style models (common in some workflows)
|
|
if isinstance(model, dict):
|
|
for key, value in list(model.items()):
|
|
if hasattr(value, 'parameters') or hasattr(value, 'model'):
|
|
hard_free_model(value)
|
|
del model[key]
|
|
return
|
|
|
|
# Handle ModelPatcher objects (common in ComfyUI)
|
|
if hasattr(model, 'model') and model.model is not None:
|
|
inner_model = model.model
|
|
if hasattr(inner_model, "parameters"):
|
|
for p in inner_model.parameters():
|
|
if p is not None:
|
|
p.detach_()
|
|
del p
|
|
model.model = None
|
|
|
|
# CLIP-specific cleanup - some CLIP models have additional attributes
|
|
if hasattr(model, "transformer") and model.transformer is not None:
|
|
if hasattr(model.transformer, "parameters"):
|
|
for p in model.transformer.parameters():
|
|
if p is not None:
|
|
p.detach_()
|
|
del p
|
|
model.transformer = None
|
|
|
|
# Handle tokenizer if present (some CLIP implementations)
|
|
if hasattr(model, "tokenizer"):
|
|
model.tokenizer = None
|
|
|
|
# Standard parameter cleanup
|
|
if hasattr(model, "parameters"):
|
|
for p in model.parameters():
|
|
if p is not None:
|
|
p.detach_()
|
|
del p
|
|
|
|
# Clear buffers and tensor attributes
|
|
if hasattr(model, "buffers"):
|
|
for b in model.buffers():
|
|
if b is not None:
|
|
b.detach_()
|
|
del b
|
|
|
|
# Clear any tensor attributes
|
|
for attr_name in list(vars(model).keys()):
|
|
attr = getattr(model, attr_name)
|
|
if isinstance(attr, torch.Tensor):
|
|
attr.detach_()
|
|
delattr(model, attr_name)
|
|
|
|
except Exception as e:
|
|
print(f"❌ Error during model freeing: {e}")
|
|
|
|
def identify_model_type(model_obj):
|
|
"""Identify what type of model this is"""
|
|
if model_obj is None:
|
|
return "Unknown"
|
|
|
|
if isinstance(model_obj, dict):
|
|
model_types = []
|
|
for key, value in model_obj.items():
|
|
if hasattr(value, '__class__'):
|
|
model_types.append(f"{key}:{value.__class__.__name__}")
|
|
return f"DictContainer[{', '.join(model_types)}]"
|
|
|
|
cls_name = model_obj.__class__.__name__.lower()
|
|
|
|
if 'clip' in cls_name:
|
|
return "CLIP"
|
|
elif 'unet' in cls_name:
|
|
return "UNet"
|
|
elif 'vae' in cls_name:
|
|
return "VAE"
|
|
elif 'control' in cls_name:
|
|
return "ControlNet"
|
|
elif 'modelpatcher' in cls_name:
|
|
return "ModelPatcher"
|
|
elif 'diffusion' in cls_name:
|
|
return "DiffusionModel"
|
|
else:
|
|
return f"Unknown ({model_obj.__class__.__name__})"
|
|
|
|
|
|
def print_currently_loaded():
|
|
"""Print models currently loaded in ComfyUI's management system"""
|
|
current_models = mm.loaded_models()
|
|
print("📋 Models in loaded_models():")
|
|
if not current_models:
|
|
print(" No models currently managed")
|
|
return 0
|
|
|
|
for i, model in enumerate(current_models):
|
|
try:
|
|
model_type = identify_model_type(model)
|
|
# Remove the problematic formatting entirely
|
|
if hasattr(model, 'model_memory'):
|
|
memory_used = model.model_memory()
|
|
print(f" {i}: {model_type} ({memory_used})")
|
|
else:
|
|
print(f" {i}: {model_type} (unknown memory)")
|
|
except Exception as e:
|
|
print(f" {i}: [Error: {e}]")
|
|
|
|
return len(current_models)
|
|
|
|
|
|
class DeleteModelPassthrough:
|
|
"""
|
|
ComfyUI Custom Node: Properly deletes models using ComfyUI's memory management system
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {"required": {"data": (any_typ,), "model": (any_typ,)}}
|
|
|
|
RETURN_TYPES = (any_typ,)
|
|
RETURN_NAMES = ("output",)
|
|
FUNCTION = "run"
|
|
CATEGORY = "Memory Management"
|
|
|
|
def run(self, data, model):
|
|
if model is None:
|
|
print("⚠️ No model provided to delete")
|
|
return (data,)
|
|
|
|
# Get memory stats before deletion
|
|
before_vram = torch.cuda.memory_allocated() if torch.cuda.is_available() else 0
|
|
before_reserved = torch.cuda.memory_reserved() if torch.cuda.is_available() else 0
|
|
before_ram = psutil.virtual_memory().percent
|
|
|
|
|
|
|
|
model_type = identify_model_type(model)
|
|
# print(f"Target model type: {model_type}")
|
|
|
|
# Print current state
|
|
initial_count = print_currently_loaded()
|
|
|
|
# Try to remove from ComfyUI's management system
|
|
model_removed = False
|
|
current_models = mm.loaded_models()
|
|
|
|
if model in current_models:
|
|
# print("🗑Removing model from ComfyUI management...")
|
|
current_models.remove(model)
|
|
model_removed = True
|
|
else:
|
|
# Check if it's a ModelPatcher or wrapped model
|
|
for managed_model in current_models:
|
|
try:
|
|
if (hasattr(managed_model, 'model') and
|
|
(managed_model.model is model or
|
|
(hasattr(model, 'model') and managed_model.model is model.model))):
|
|
# print("🗑Removing wrapped model from ComfyUI management...")
|
|
current_models.remove(managed_model)
|
|
model_removed = True
|
|
break
|
|
except:
|
|
continue
|
|
|
|
# Free memory using ComfyUI's proper methods
|
|
# print("Freeing memory using ComfyUI's system...")
|
|
mm.free_memory(1e30, mm.get_torch_device(), mm.loaded_models())
|
|
|
|
# Additional forceful cleanup
|
|
# print("Forceful cleanup...")
|
|
hard_free_model(model)
|
|
|
|
# ComfyUI's cache cleanup
|
|
mm.soft_empty_cache(force=True)
|
|
|
|
# Standard Python cleanup
|
|
gc.collect()
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
|
|
# Get memory stats after deletion
|
|
after_vram = torch.cuda.memory_allocated() if torch.cuda.is_available() else 0
|
|
after_reserved = torch.cuda.memory_reserved() if torch.cuda.is_available() else 0
|
|
after_ram = psutil.virtual_memory().percent
|
|
|
|
# Print final state
|
|
final_count = print_currently_loaded()
|
|
|
|
print(f"Managed models: {initial_count} → {final_count}")
|
|
print(f"System RAM change: {before_ram - after_ram:+.2f}%")
|
|
|
|
if torch.cuda.is_available():
|
|
vram_freed = (before_vram - after_vram) / (1024 * 1024 * 1024)
|
|
reserved_freed = (before_reserved - after_reserved) / (1024 * 1024 * 1024)
|
|
print(f"GPU allocated freed: {vram_freed:.3f} GB")
|
|
print(f"GPU reserved freed: {reserved_freed:.3f} GB")
|
|
# print(f"Final allocated: {after_vram / (1024 * 1024 * 1024):.3f} GB")
|
|
# print(f"Final reserved: {after_reserved / (1024 * 1024 * 1024):.3f} GB")
|
|
|
|
print("=" * 60)
|
|
|
|
# Success determination
|
|
if model_removed:
|
|
print("SUCCESS: Model removed from management system!")
|
|
elif torch.cuda.is_available() and reserved_freed > 0.1:
|
|
print("SUCCESS: Significant memory freed (model may use custom management)")
|
|
else:
|
|
print("⚠PARTIAL: Model may still be referenced somewhere")
|
|
|
|
print("=" * 60)
|
|
|
|
return (data,)
|
|
|
|
# class ControlledUnetLoaderGGUF:
|
|
# @classmethod
|
|
# def INPUT_TYPES(s):
|
|
# # Get the original input types and add trigger
|
|
# original_types = UnetLoaderGGUF.INPUT_TYPES()
|
|
# original_types["required"]["trigger"] = (any_typ, {"default": None})
|
|
# return original_types
|
|
|
|
# RETURN_TYPES = ("MODEL",)
|
|
# FUNCTION = "load_unet"
|
|
# CATEGORY = "Memory Management"
|
|
# TITLE = "Controlled UNet Loader (GGUF)"
|
|
|
|
# def load_unet(self, trigger, *args, **kwargs):
|
|
# if trigger is None:
|
|
# print("⏸UNet loading paused - no trigger received")
|
|
# return (None,)
|
|
|
|
# # Simply call the original class method
|
|
# return UnetLoaderGGUF.load_unet(self, *args, **kwargs)
|
|
|
|
class ControlledUnetLoaderGGUF:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
if not GGUF_AVAILABLE:
|
|
return {"required": {"trigger": (any_typ, {"default": None})}}
|
|
|
|
# Get the original input types and add trigger
|
|
original_types = UnetLoaderGGUF.INPUT_TYPES()
|
|
if "required" in original_types:
|
|
original_types["required"]["trigger"] = (any_typ, {"default": None})
|
|
else:
|
|
original_types["required"] = {"trigger": (any_typ, {"default": None})}
|
|
return original_types
|
|
|
|
RETURN_TYPES = ("MODEL",)
|
|
FUNCTION = "load_unet"
|
|
CATEGORY = "Memory Management"
|
|
TITLE = "Controlled UNet Loader (GGUF)"
|
|
|
|
def load_unet(self, trigger, *args, **kwargs):
|
|
if not GGUF_AVAILABLE:
|
|
print("❌ ComfyUI_GGUF not installed - cannot load UNet")
|
|
return (None,)
|
|
|
|
if trigger is None:
|
|
print("⏸UNet loading paused - no trigger received")
|
|
return (None,)
|
|
|
|
print(f"Loading UNet...")
|
|
# Simply call the original class method
|
|
return UnetLoaderGGUF.load_unet(self, *args, **kwargs)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"DeleteModelPassthrough": DeleteModelPassthrough,
|
|
"ControlledUnetLoaderGGUF": ControlledUnetLoaderGGUF
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"DeleteModelPassthrough": "Delete Model (Passthrough Any)",
|
|
"ControlledUnetLoaderGGUF": "Controlled UNet Loader (GGUF)"
|
|
}
|