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caching: Remove model awareness from RAM caching

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Model RAM pressure is now implemented via the DynamicVRAM system.
This commit is contained in:
Rattus 2026-03-25 20:18:13 +10:00
parent 2a1f402601
commit 56fe08648c
3 changed files with 4 additions and 17 deletions
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3
comfy/model_patcher.py
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@ -300,9 +300,6 @@ class ModelPatcher:
def model_mmap_residency(self, free=False): def model_mmap_residency(self, free=False):
return comfy.model_management.module_mmap_residency(self.model, free=free) return comfy.model_management.module_mmap_residency(self.model, free=free)
def get_ram_usage(self):
return self.model_size()
def loaded_size(self): def loaded_size(self):
return self.model.model_loaded_weight_memory return self.model.model_loaded_weight_memory

6
comfy/sd.py
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@ -280,9 +280,6 @@ class CLIP:
n.apply_hooks_to_conds = self.apply_hooks_to_conds n.apply_hooks_to_conds = self.apply_hooks_to_conds
return n return n
def get_ram_usage(self):
return self.patcher.get_ram_usage()
def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
return self.patcher.add_patches(patches, strength_patch, strength_model) return self.patcher.add_patches(patches, strength_patch, strength_model)
@ -840,9 +837,6 @@ class VAE:
self.size = comfy.model_management.module_size(self.first_stage_model) self.size = comfy.model_management.module_size(self.first_stage_model)
return self.size return self.size
def get_ram_usage(self):
return self.model_size()
def throw_exception_if_invalid(self): def throw_exception_if_invalid(self):
if self.first_stage_model is None: if self.first_stage_model is None:
raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.") raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.")

12
comfy_execution/caching.py
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@ -494,10 +494,10 @@ class LRUCache(BasicCache):
RAM_CACHE_HYSTERESIS = 1.1 RAM_CACHE_HYSTERESIS = 1.1
#This is kinda in GB but not really. It needs to be non-zero for the below heuristic #Small baseline weight used when a cache entry has no measurable CPU tensors.
#and as long as Multi GB models dwarf this it will approximate OOM scoring OK #Keeps unknown-sized entries in eviction scoring without dominating tensor-backed entries.
RAM_CACHE_DEFAULT_RAM_USAGE = 0.1 RAM_CACHE_DEFAULT_RAM_USAGE = 0.05
#Exponential bias towards evicting older workflows so garbage will be taken out #Exponential bias towards evicting older workflows so garbage will be taken out
#in constantly changing setups. #in constantly changing setups.
@ -545,11 +545,7 @@ class RAMPressureCache(LRUCache):
if isinstance(output, list): if isinstance(output, list):
scan_list_for_ram_usage(output) scan_list_for_ram_usage(output)
elif isinstance(output, torch.Tensor) and output.device.type == 'cpu': elif isinstance(output, torch.Tensor) and output.device.type == 'cpu':
#score Tensors at a 50% discount for RAM usage as they are likely to ram_usage += output.numel() * output.element_size()
#be high value intermediates
ram_usage += (output.numel() * output.element_size()) * 0.5
elif hasattr(output, "get_ram_usage"):
ram_usage += output.get_ram_usage()
scan_list_for_ram_usage(outputs) scan_list_for_ram_usage(outputs)
oom_score *= ram_usage oom_score *= ram_usage