407 lines
14 KiB
Python
407 lines
14 KiB
Python
# import torch, gc, psutil
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# import comfy.model_management as mm
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# from comfy.model_management import current_loaded_models, LoadedModel
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# class AnyType(str):
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# def __ne__(self, __value: object) -> bool: return False
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# any_typ = AnyType("*")
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# def hard_free(model):
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# """
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# Forcefully free model memory by detaching parameters and clearing caches.
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# """
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# try:
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# # Clear parameters and buffers
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# if hasattr(model, "parameters"):
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# for p in model.parameters():
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# if p is not None:
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# p.detach_()
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# if hasattr(p, 'data'):
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# del p.data
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# del p
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# if hasattr(model, "buffers"):
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# for b in model.buffers():
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# if b is not None:
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# b.detach_()
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# if hasattr(b, 'data'):
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# del b.data
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# del b
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# # Clear any cached attributes
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# for attr_name in list(vars(model).keys()):
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# attr = getattr(model, attr_name)
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# if isinstance(attr, torch.Tensor):
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# attr.detach_()
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# delattr(model, attr_name)
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# except Exception as e:
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# print(f"Hard free error: {e}")
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# try:
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# del model
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# except:
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# pass
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# gc.collect()
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# mm.soft_empty_cache()
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# def purge_specific_from_comfy(model_obj):
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# """
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# Remove a specific model from ComfyUI's current_loaded_models list.
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# Returns the model type if found and removed.
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# """
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# deleted_type = "Unknown"
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# try:
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# # Check if it's a LoadedModel wrapper
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# if isinstance(model_obj, LoadedModel):
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# if model_obj in current_loaded_models:
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# current_loaded_models.remove(model_obj)
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# # Also detach the underlying model
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# if hasattr(model_obj, 'model_unload'):
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# model_obj.model_unload()
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# deleted_type = f"LoadedModel ({type(model_obj.model).__name__})"
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# # Check if it's a raw model that might be wrapped in LoadedModel
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# else:
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# for i, loaded_model in enumerate(current_loaded_models):
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# if loaded_model.model is model_obj:
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# current_loaded_models.pop(i)
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# if hasattr(loaded_model, 'model_unload'):
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# loaded_model.model_unload()
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# deleted_type = f"RawModel ({type(model_obj).__name__})"
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# break
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# # Additional cleanup for common model types
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# if hasattr(model_obj, '__class__'):
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# cls_name = model_obj.__class__.__name__.lower()
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# if 'unet' in cls_name:
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# deleted_type = "UNet"
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# elif 'clip' in cls_name:
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# deleted_type = "CLIP"
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# elif 'vae' in cls_name:
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# deleted_type = "VAE"
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# except Exception as e:
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# print(f"Cache purge error: {e}")
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# return deleted_type
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# class DeleteModelPassthrough:
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# """
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# ComfyUI Custom Node:
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# - Accepts any input + a model (UNet/CLIP/VAE/latent).
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# - Deletes ONLY that model from VRAM, RAM, and ComfyUI cache.
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# - Auto-detects the model type and logs it.
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# - Passes the data unchanged.
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# """
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# @classmethod
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# def INPUT_TYPES(cls):
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# return {"required": {"data": (any_typ,), "model": (any_typ,)}}
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# RETURN_TYPES = (any_typ,)
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# RETURN_NAMES = ("output",)
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# FUNCTION = "run"
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# CATEGORY = "Memory Management"
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# def run(self, data, model):
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# if model is None:
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# print("⚠️ No model provided to delete")
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# return (data,)
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# # Get memory stats before deletion
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# before_vram = torch.cuda.memory_allocated() if torch.cuda.is_available() else 0
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# before_reserved = torch.cuda.memory_reserved() if torch.cuda.is_available() else 0
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# before_ram = psutil.virtual_memory().percent
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# # Remove from ComfyUI management and free memory
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# deleted_type = purge_specific_from_comfy(model)
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# hard_free(model)
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# # Get memory stats after deletion
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# after_vram = torch.cuda.memory_allocated() if torch.cuda.is_available() else 0
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# after_reserved = torch.cuda.memory_reserved() if torch.cuda.is_available() else 0
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# after_ram = psutil.virtual_memory().percent
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# # Log results
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# print(f"🗑️ Deleted model type: {deleted_type}")
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# print(f"System RAM change: {before_ram - after_ram:.2f}%")
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# if torch.cuda.is_available():
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# vram_freed = (before_vram - after_vram) / (1024 * 1024 * 1024)
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# reserved_freed = (before_reserved - after_reserved) / (1024 * 1024 * 1024)
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# print(f"GPU allocated freed: {vram_freed:.3f} GB")
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# print(f"GPU reserved freed: {reserved_freed:.3f} GB")
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# return (data,)
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# # Node mappings
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# NODE_CLASS_MAPPINGS = {"DeleteModelPassthrough": DeleteModelPassthrough}
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# NODE_DISPLAY_NAME_MAPPINGS = {"DeleteModelPassthrough": "Delete Model (Passthrough Any)"}
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# Testing Deepseek's more rigorous version
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import torch, gc, psutil
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import comfy.model_management as mm
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from comfy.model_management import current_loaded_models, LoadedModel
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class AnyType(str):
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def __ne__(self, __value: object) -> bool: return False
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any_typ = AnyType("*")
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def hard_free(model):
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"""
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Forcefully free model memory by detaching parameters and clearing caches.
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Special handling for different model types.
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"""
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if model is None:
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return
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try:
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model_type = type(model).__name__
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print(f"🔧 Freeing {model_type} model...")
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# Handle ModelPatcher objects (common for CLIP, UNet, VAE)
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if model_type == 'ModelPatcher' and hasattr(model, 'model'):
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print(" Detaching ModelPatcher parameters...")
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real_model = model.model
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if hasattr(real_model, "parameters"):
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for p in real_model.parameters():
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if p is not None:
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p.detach_()
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del p
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model.model = None
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# Handle ControlNet models (they have different structure)
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elif 'Control' in model_type or 'control' in model_type.lower():
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print(" Detaching ControlNet parameters...")
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if hasattr(model, "parameters"):
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for p in model.parameters():
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if p is not None:
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p.detach_()
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del p
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if hasattr(model, "weights"):
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model.weights = None
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if hasattr(model, "model"):
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model.model = None
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# Standard cleanup for any model
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if hasattr(model, "parameters"):
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for p in model.parameters():
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if p is not None:
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p.detach_()
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del p
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if hasattr(model, "buffers"):
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for b in model.buffers():
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if b is not None:
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b.detach_()
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del b
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# Clear any cached tensor attributes
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for attr_name in list(vars(model).keys()):
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attr = getattr(model, attr_name)
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if isinstance(attr, torch.Tensor):
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attr.detach_()
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delattr(model, attr_name)
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except Exception as e:
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print(f"❌ Hard free error: {e}")
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try:
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del model
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except:
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pass
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gc.collect()
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mm.soft_empty_cache()
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def print_tracked_models():
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"""Print all models currently tracked by ComfyUI's main system"""
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print("📋 Models in current_loaded_models:")
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if not current_loaded_models:
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print(" No models tracked in main system")
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return 0
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count = 0
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for i, loaded_model in enumerate(current_loaded_models):
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try:
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if hasattr(loaded_model, 'model'):
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model_obj = loaded_model.model
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model_type = type(model_obj).__name__
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model_id = id(model_obj)
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print(f" {i}: {model_type} (id: {model_id})")
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count += 1
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except Exception as e:
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print(f" {i}: [Error: {e}]")
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return count
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def identify_model_type(model_obj):
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"""Identify what type of model this is"""
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if model_obj is None:
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return "Unknown"
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cls_name = model_obj.__class__.__name__.lower()
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if 'clip' in cls_name:
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return "CLIP"
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elif 'unet' in cls_name:
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return "UNet"
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elif 'vae' in cls_name:
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return "VAE"
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elif 'control' in cls_name:
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return "ControlNet"
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elif 'modelpatcher' in cls_name:
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# Check what's inside the ModelPatcher
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if hasattr(model_obj, 'model') and model_obj.model is not None:
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inner_type = model_obj.model.__class__.__name__.lower()
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if 'clip' in inner_type:
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return "CLIP (ModelPatcher)"
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elif 'unet' in inner_type:
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return "UNet (ModelPatcher)"
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elif 'vae' in inner_type:
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return "VAE (ModelPatcher)"
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return "ModelPatcher"
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elif 'diffusion' in cls_name:
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return "DiffusionModel"
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else:
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return f"Unknown ({model_obj.__class__.__name__})"
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def purge_specific_from_comfy(model_obj):
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"""
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Try to remove model from ComfyUI's tracking systems.
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Returns the identified model type.
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"""
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if model_obj is None:
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return "None"
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model_type = identify_model_type(model_obj)
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model_removed = False
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print(f"🔍 Target model type: {model_type}")
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print(f"🔍 Model object type: {type(model_obj).__name__}")
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print(f"🔍 Model ID: {id(model_obj)}")
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# Print current tracking state
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initial_count = print_tracked_models()
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try:
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# Try to remove from main tracking system (for CLIP/UNet/VAE)
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if current_loaded_models:
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for i, loaded_model in enumerate(current_loaded_models):
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try:
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# Check if this LoadedModel contains our target model
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if (hasattr(loaded_model, 'model') and
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(loaded_model.model is model_obj or
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(hasattr(model_obj, 'model') and loaded_model.model is model_obj.model))):
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print(f"🗑️ Removing from tracked models: {type(loaded_model.model).__name__}")
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current_loaded_models.pop(i)
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if hasattr(loaded_model, 'model_unload'):
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loaded_model.model_unload()
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model_removed = True
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break
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except:
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continue
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if not model_removed:
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print("⚠️ Model not in main tracking system - may use different management")
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# For ControlNet and other specially managed models, we can't remove them from tracking
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# but we can still free their memory
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if 'control' in model_type.lower():
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print("💡 ControlNet models often use custom management systems")
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except Exception as e:
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print(f"❌ Error during purge attempt: {e}")
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# Print final tracking state
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final_count = print_tracked_models()
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print(f"📊 Tracking change: {initial_count} → {final_count} models")
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return model_type
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class DeleteModelPassthrough:
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"""
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ComfyUI Custom Node: Deletes models from memory regardless of tracking system.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {"data": (any_typ,), "model": (any_typ,)}}
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RETURN_TYPES = (any_typ,)
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RETURN_NAMES = ("output",)
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FUNCTION = "run"
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CATEGORY = "Memory Management"
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def run(self, data, model):
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if model is None:
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print("⚠️ No model provided to delete")
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return (data,)
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# Get memory stats before deletion
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before_vram = torch.cuda.memory_allocated() if torch.cuda.is_available() else 0
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before_reserved = torch.cuda.memory_reserved() if torch.cuda.is_available() else 0
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before_ram = psutil.virtual_memory().percent
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print("=" * 60)
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print("🧹 Starting model deletion process")
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print("=" * 60)
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# Try to remove from tracking and free memory
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deleted_type = purge_specific_from_comfy(model)
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hard_free(model)
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# Get memory stats after deletion
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after_vram = torch.cuda.memory_allocated() if torch.cuda.is_available() else 0
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after_reserved = torch.cuda.memory_reserved() if torch.cuda.is_available() else 0
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after_ram = psutil.virtual_memory().percent
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# Log results
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print("=" * 60)
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print("📊 Memory Free Results:")
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print("=" * 60)
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print(f"🗑️ Deleted model type: {deleted_type}")
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print(f"💾 System RAM change: {before_ram - after_ram:+.2f}%")
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if torch.cuda.is_available():
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vram_freed = (before_vram - after_vram) / (1024 * 1024 * 1024)
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reserved_freed = (before_reserved - after_reserved) / (1024 * 1024 * 1024)
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print(f"🎮 GPU allocated freed: {vram_freed:.3f} GB")
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print(f"🎮 GPU reserved freed: {reserved_freed:.3f} GB")
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print(f"🎮 Final allocated: {after_vram / (1024 * 1024 * 1024):.3f} GB")
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print(f"🎮 Final reserved: {after_reserved / (1024 * 1024 * 1024):.3f} GB")
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print("=" * 60)
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# Success determination based on memory freed
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if torch.cuda.is_available() and reserved_freed > 0.1: # Freed at least 100MB
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print("✅ SUCCESS: Significant memory freed!")
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elif torch.cuda.is_available() and reserved_freed > 0:
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print("⚠️ PARTIAL: Some memory freed, but model may still be referenced")
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else:
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print("❌ FAILED: No significant memory freed - model may be already freed or invalid")
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print("=" * 60)
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return (data,)
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# Node mappings
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NODE_CLASS_MAPPINGS = {"DeleteModelPassthrough": DeleteModelPassthrough}
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NODE_DISPLAY_NAME_MAPPINGS = {"DeleteModelPassthrough": "Delete Model (Passthrough Any)"}
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