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Don't unload model weights for non weight patches.

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comfyanonymous
2024-03-20 01:29:26 -04:00
parent 150a3e946f
commit c18a203a8a
2 changed files with 76 additions and 28 deletions
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44
comfy/model_management.py
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@@ -273,6 +273,7 @@ class LoadedModel:
def __init__(self, model):
self.model = model
self.device = model.load_device
self.weights_loaded = False
def model_memory(self):
return self.model.model_size()
@@ -289,11 +290,13 @@ class LoadedModel:
self.model.model_patches_to(self.device)
self.model.model_patches_to(self.model.model_dtype())
load_weights = not self.weights_loaded
try:
if lowvram_model_memory > 0:
if lowvram_model_memory > 0 and load_weights:
self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory)
else:
self.real_model = self.model.patch_model(device_to=patch_model_to)
self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights)
except Exception as e:
self.model.unpatch_model(self.model.offload_device)
self.model_unload()
@@ -302,11 +305,13 @@ class LoadedModel:
if is_intel_xpu() and not args.disable_ipex_optimize:
self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True)
self.weights_loaded = True
return self.real_model
def model_unload(self):
self.model.unpatch_model(self.model.offload_device)
def model_unload(self, unpatch_weights=True):
self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights)
self.model.model_patches_to(self.model.offload_device)
self.weights_loaded = self.weights_loaded and not unpatch_weights
def __eq__(self, other):
return self.model is other.model
@@ -314,15 +319,35 @@ class LoadedModel:
def minimum_inference_memory():
return (1024 * 1024 * 1024)
def unload_model_clones(model):
def unload_model_clones(loaded_model, unload_weights_only=True):
model = loaded_model.model
to_unload = []
for i in range(len(current_loaded_models)):
if model.is_clone(current_loaded_models[i].model):
to_unload = [i] + to_unload
if len(to_unload) == 0:
return
same_weights = 0
for i in to_unload:
logging.debug("unload clone {}".format(i))
current_loaded_models.pop(i).model_unload()
if model.clone_has_same_weights(current_loaded_models[i].model):
same_weights += 1
if same_weights == len(to_unload):
unload_weight = False
else:
unload_weight = True
if unload_weights_only and unload_weight == False:
return
for i in to_unload:
logging.debug("unload clone {} {}".format(i, unload_weight))
current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight)
loaded_model.weights_loaded = not unload_weight
def free_memory(memory_required, device, keep_loaded=[]):
unloaded_model = False
@@ -377,13 +402,16 @@ def load_models_gpu(models, memory_required=0):
total_memory_required = {}
for loaded_model in models_to_load:
unload_model_clones(loaded_model.model)
unload_model_clones(loaded_model, unload_weights_only=True) #unload clones where the weights are different
total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
for device in total_memory_required:
if device != torch.device("cpu"):
free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded)
for loaded_model in models_to_load:
unload_model_clones(loaded_model, unload_weights_only=False) #unload the rest of the clones where the weights can stay loaded
for loaded_model in models_to_load:
model = loaded_model.model
torch_dev = model.load_device