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Merge branch 'comfyanonymous:master' into feat/is_change_object_storage

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Dr.Lt.Data 2023-07-23 12:07:19 +09:00 committed by GitHub
commit c1245a9106
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4 changed files with 16 additions and 6 deletions
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8
comfy/model_management.py
View File

@ -281,19 +281,23 @@ def load_model_gpu(model):
vram_set_state = VRAMState.LOW_VRAM
real_model = model.model
patch_model_to = None
if vram_set_state == VRAMState.DISABLED:
pass
elif vram_set_state == VRAMState.NORMAL_VRAM or vram_set_state == VRAMState.HIGH_VRAM or vram_set_state == VRAMState.SHARED:
model_accelerated = False
real_model.to(torch_dev)
patch_model_to = torch_dev
try:
real_model = model.patch_model()
real_model = model.patch_model(device_to=patch_model_to)
except Exception as e:
model.unpatch_model()
unload_model()
raise e
if patch_model_to is not None:
real_model.to(torch_dev)
if vram_set_state == VRAMState.NO_VRAM:
device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"})
accelerate.dispatch_model(real_model, device_map=device_map, main_device=torch_dev)

5
comfy/samplers.py
View File

@ -248,7 +248,10 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
c['transformer_options'] = transformer_options
output = model_function(input_x, timestep_, **c).chunk(batch_chunks)
if 'model_function_wrapper' in model_options:
output = model_options['model_function_wrapper'](model_function, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
else:
output = model_function(input_x, timestep_, **c).chunk(batch_chunks)
del input_x
model_management.throw_exception_if_processing_interrupted()

7
comfy/sd.py
View File

@ -338,7 +338,7 @@ class ModelPatcher:
sd.pop(k)
return sd
def patch_model(self):
def patch_model(self, device_to=None):
model_sd = self.model_state_dict()
for key in self.patches:
if key not in model_sd:
@ -350,7 +350,10 @@ class ModelPatcher:
if key not in self.backup:
self.backup[key] = weight.to(self.offload_device)
temp_weight = weight.to(torch.float32, copy=True)
if device_to is not None:
temp_weight = weight.float().to(device_to, copy=True)
else:
temp_weight = weight.to(torch.float32, copy=True)
out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
set_attr(self.model, key, out_weight)
del temp_weight

2
cuda_malloc.py
View File

@ -37,7 +37,7 @@ def get_gpu_names():
return set()
def cuda_malloc_supported():
blacklist = {"GeForce GTX 960M", "GeForce GTX 950M", "GeForce 945M", "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745"}
blacklist = {"GeForce GTX 960", "GeForce GTX 950", "GeForce 945M", "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745"}
try:
names = get_gpu_names()
except: