Update delete_model_passthrough.py
This commit is contained in:
Isi
+370
-136
@@ -148,19 +148,278 @@
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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 > 1: # Freed at least 1000MB
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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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# Testing another one of 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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from comfy.model_management import loaded_models, free_memory, get_torch_device
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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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def hard_free_model(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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Forcefully free model memory using ComfyUI's proper methods
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"""
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if model is None:
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return
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@@ -169,44 +428,41 @@ def hard_free(model):
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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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# Handle dictionary-style models (common in some workflows)
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if isinstance(model, dict):
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print("📦 Handling dictionary-style model container...")
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for key, value in list(model.items()):
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if hasattr(value, 'parameters') or hasattr(value, 'model'):
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print(f" Freeing {key}: {type(value).__name__}")
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hard_free_model(value)
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del model[key]
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return
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# Handle ModelPatcher objects
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if hasattr(model, 'model') and model.model is not None:
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print(" Detaching inner model from ModelPatcher...")
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inner_model = model.model
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if hasattr(inner_model, "parameters"):
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for p in inner_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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# Standard parameter cleanup
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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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# Clear buffers and tensor attributes
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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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@@ -214,37 +470,7 @@ def hard_free(model):
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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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print(f"❌ Error during model freeing: {e}")
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def identify_model_type(model_obj):
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@@ -252,6 +478,13 @@ def identify_model_type(model_obj):
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if model_obj is None:
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return "Unknown"
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if isinstance(model_obj, dict):
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model_types = []
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for key, value in model_obj.items():
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if hasattr(value, '__class__'):
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model_types.append(f"{key}:{value.__class__.__name__}")
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return f"DictContainer[{', '.join(model_types)}]"
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cls_name = model_obj.__class__.__name__.lower()
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if 'clip' in cls_name:
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@@ -263,15 +496,6 @@ def identify_model_type(model_obj):
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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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@@ -279,64 +503,28 @@ def identify_model_type(model_obj):
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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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def print_currently_loaded():
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"""Print models currently loaded in ComfyUI's management system"""
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current_models = mm.loaded_models()
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print("📋 Models in loaded_models():")
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if not current_models:
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print(" No models currently managed")
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return 0
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model_type = identify_model_type(model_obj)
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model_removed = False
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for i, model in enumerate(current_models):
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try:
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model_type = identify_model_type(model)
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memory_used = model.model_memory() / (1024 * 1024) if hasattr(model, 'model_memory') else "unknown"
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print(f" {i}: {model_type} ({memory_used:.1f} MB)")
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except Exception as e:
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print(f" {i}: [Error: {e}]")
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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:
|
||||
print("⚠️ Model not in main tracking system - may use different management")
|
||||
|
||||
# For ControlNet and other specially managed models, we can't remove them from tracking
|
||||
# but we can still free their memory
|
||||
if 'control' in model_type.lower():
|
||||
print("💡 ControlNet models often use custom management systems")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error during purge attempt: {e}")
|
||||
|
||||
# Print final tracking state
|
||||
final_count = print_tracked_models()
|
||||
print(f"📊 Tracking change: {initial_count} → {final_count} models")
|
||||
|
||||
return model_type
|
||||
return len(current_models)
|
||||
|
||||
|
||||
class DeleteModelPassthrough:
|
||||
"""
|
||||
ComfyUI Custom Node: Deletes models from memory regardless of tracking system.
|
||||
ComfyUI Custom Node: Properly deletes models using ComfyUI's memory management system
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
@@ -359,48 +547,94 @@ class DeleteModelPassthrough:
|
||||
before_ram = psutil.virtual_memory().percent
|
||||
|
||||
print("=" * 60)
|
||||
print("🧹 Starting model deletion process")
|
||||
print("🧹 Starting proper model deletion process")
|
||||
print("=" * 60)
|
||||
|
||||
# Try to remove from tracking and free memory
|
||||
deleted_type = purge_specific_from_comfy(model)
|
||||
hard_free(model)
|
||||
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()
|
||||
|
||||
# Log results
|
||||
# print("=" * 60)
|
||||
# print("📊 Memory Free Results:")
|
||||
# print("=" * 60)
|
||||
# print(f"🗑️ Deleted model type: {deleted_type}")
|
||||
# print(f"💾 System RAM change: {before_ram - after_ram:+.2f}%")
|
||||
print("=" * 60)
|
||||
print("📊 Deletion Results:")
|
||||
print("=" * 60)
|
||||
print(f"🗑️ Model type: {model_type}")
|
||||
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(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 based on memory freed
|
||||
if torch.cuda.is_available() and reserved_freed > 1: # Freed at least 1000MB
|
||||
print("✅ SUCCESS: Significant memory freed!")
|
||||
elif torch.cuda.is_available() and reserved_freed > 0:
|
||||
print("⚠️ PARTIAL: Some memory freed, but model may still be referenced")
|
||||
# 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("❌ FAILED: No significant memory freed - model may be already freed or invalid")
|
||||
print("⚠️ PARTIAL: Model may still be referenced somewhere")
|
||||
|
||||
print("=" * 60)
|
||||
|
||||
return (data,)
|
||||
|
||||
|
||||
|
||||
# Node mappings
|
||||
NODE_CLASS_MAPPINGS = {"DeleteModelPassthrough": DeleteModelPassthrough}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {"DeleteModelPassthrough": "Delete Model (Passthrough Any)"}
|
||||
|
||||
Reference in New Issue
Block a user