147 lines
4.9 KiB
Python
147 lines
4.9 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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