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Code cleanups.

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main
comfyanonymous 6 months ago
parent c54d3ed5e6
commit 7c6bb84016
2 changed files with 179 additions and 176 deletions
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2
comfy/model_management.py
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@ -562,7 +562,7 @@ def cleanup_models(keep_clone_weights_loaded=False):
to_delete = []
for i in range(len(current_loaded_models)):
#TODO: very fragile function needs improvement
num_refs = sys.getrefcount(current_loaded_models[i].model) - current_loaded_models[i].model.lowvram_patch_counter()
num_refs = sys.getrefcount(current_loaded_models[i].model)
if num_refs <= 2:
if not keep_clone_weights_loaded:
to_delete = [i] + to_delete

353
comfy/model_patcher.py
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@ -91,13 +91,180 @@ def wipe_lowvram_weight(m):
m.weight_function = None
m.bias_function = None
def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
for p in patches:
strength = p[0]
v = p[1]
strength_model = p[2]
offset = p[3]
function = p[4]
if function is None:
function = lambda a: a
old_weight = None
if offset is not None:
old_weight = weight
weight = weight.narrow(offset[0], offset[1], offset[2])
if strength_model != 1.0:
weight *= strength_model
if isinstance(v, list):
v = (calculate_weight(v[1:], v[0].clone(), key, intermediate_dtype=intermediate_dtype), )
if len(v) == 1:
patch_type = "diff"
elif len(v) == 2:
patch_type = v[0]
v = v[1]
if patch_type == "diff":
w1 = v[0]
if strength != 0.0:
if w1.shape != weight.shape:
logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
else:
weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype))
elif patch_type == "lora": #lora/locon
mat1 = comfy.model_management.cast_to_device(v[0], weight.device, intermediate_dtype)
mat2 = comfy.model_management.cast_to_device(v[1], weight.device, intermediate_dtype)
dora_scale = v[4]
if v[2] is not None:
alpha = v[2] / mat2.shape[0]
else:
alpha = 1.0
if v[3] is not None:
#locon mid weights, hopefully the math is fine because I didn't properly test it
mat3 = comfy.model_management.cast_to_device(v[3], weight.device, intermediate_dtype)
final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
try:
lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
if dora_scale is not None:
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
else:
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "lokr":
w1 = v[0]
w2 = v[1]
w1_a = v[3]
w1_b = v[4]
w2_a = v[5]
w2_b = v[6]
t2 = v[7]
dora_scale = v[8]
dim = None
if w1 is None:
dim = w1_b.shape[0]
w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype))
else:
w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype)
if w2 is None:
dim = w2_b.shape[0]
if t2 is None:
w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype))
else:
w2 = torch.einsum('i j k l, j r, i p -> p r k l',
comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype))
else:
w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype)
if len(w2.shape) == 4:
w1 = w1.unsqueeze(2).unsqueeze(2)
if v[2] is not None and dim is not None:
alpha = v[2] / dim
else:
alpha = 1.0
try:
lora_diff = torch.kron(w1, w2).reshape(weight.shape)
if dora_scale is not None:
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
else:
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "loha":
w1a = v[0]
w1b = v[1]
if v[2] is not None:
alpha = v[2] / w1b.shape[0]
else:
alpha = 1.0
w2a = v[3]
w2b = v[4]
dora_scale = v[7]
if v[5] is not None: #cp decomposition
t1 = v[5]
t2 = v[6]
m1 = torch.einsum('i j k l, j r, i p -> p r k l',
comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype))
m2 = torch.einsum('i j k l, j r, i p -> p r k l',
comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype))
else:
m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype))
m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype))
try:
lora_diff = (m1 * m2).reshape(weight.shape)
if dora_scale is not None:
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
else:
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "glora":
if v[4] is not None:
alpha = v[4] / v[0].shape[0]
else:
alpha = 1.0
dora_scale = v[5]
a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype)
a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype)
b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype)
b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype)
try:
lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape)
if dora_scale is not None:
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
else:
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e))
else:
logging.warning("patch type not recognized {} {}".format(patch_type, key))
if old_weight is not None:
weight = old_weight
return weight
class LowVramPatch:
def __init__(self, key, model_patcher):
def __init__(self, key, patches):
self.key = key
self.model_patcher = model_patcher
self.patches = patches
def __call__(self, weight):
return self.model_patcher.calculate_weight(self.model_patcher.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
return calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
class ModelPatcher:
def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
@ -329,7 +496,7 @@ class ModelPatcher:
temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
else:
temp_weight = weight.to(torch.float32, copy=True)
out_weight = self.calculate_weight(self.patches[key], temp_weight, key)
out_weight = calculate_weight(self.patches[key], temp_weight, key)
out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype)
if inplace_update:
comfy.utils.copy_to_param(self.model, key, out_weight)
@ -360,13 +527,13 @@ class ModelPatcher:
if force_patch_weights:
self.patch_weight_to_device(weight_key)
else:
m.weight_function = LowVramPatch(weight_key, self)
m.weight_function = LowVramPatch(weight_key, self.model_patcher.patches)
patch_counter += 1
if bias_key in self.patches:
if force_patch_weights:
self.patch_weight_to_device(bias_key)
else:
m.bias_function = LowVramPatch(bias_key, self)
m.bias_function = LowVramPatch(bias_key, self.model_patcher.patches)
patch_counter += 1
m.prev_comfy_cast_weights = m.comfy_cast_weights
@ -428,174 +595,6 @@ class ModelPatcher:
self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load)
return self.model
def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32):
for p in patches:
strength = p[0]
v = p[1]
strength_model = p[2]
offset = p[3]
function = p[4]
if function is None:
function = lambda a: a
old_weight = None
if offset is not None:
old_weight = weight
weight = weight.narrow(offset[0], offset[1], offset[2])
if strength_model != 1.0:
weight *= strength_model
if isinstance(v, list):
v = (self.calculate_weight(v[1:], v[0].clone(), key, intermediate_dtype=intermediate_dtype), )
if len(v) == 1:
patch_type = "diff"
elif len(v) == 2:
patch_type = v[0]
v = v[1]
if patch_type == "diff":
w1 = v[0]
if strength != 0.0:
if w1.shape != weight.shape:
logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
else:
weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype))
elif patch_type == "lora": #lora/locon
mat1 = comfy.model_management.cast_to_device(v[0], weight.device, intermediate_dtype)
mat2 = comfy.model_management.cast_to_device(v[1], weight.device, intermediate_dtype)
dora_scale = v[4]
if v[2] is not None:
alpha = v[2] / mat2.shape[0]
else:
alpha = 1.0
if v[3] is not None:
#locon mid weights, hopefully the math is fine because I didn't properly test it
mat3 = comfy.model_management.cast_to_device(v[3], weight.device, intermediate_dtype)
final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
try:
lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
if dora_scale is not None:
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
else:
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "lokr":
w1 = v[0]
w2 = v[1]
w1_a = v[3]
w1_b = v[4]
w2_a = v[5]
w2_b = v[6]
t2 = v[7]
dora_scale = v[8]
dim = None
if w1 is None:
dim = w1_b.shape[0]
w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype))
else:
w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype)
if w2 is None:
dim = w2_b.shape[0]
if t2 is None:
w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype))
else:
w2 = torch.einsum('i j k l, j r, i p -> p r k l',
comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype))
else:
w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype)
if len(w2.shape) == 4:
w1 = w1.unsqueeze(2).unsqueeze(2)
if v[2] is not None and dim is not None:
alpha = v[2] / dim
else:
alpha = 1.0
try:
lora_diff = torch.kron(w1, w2).reshape(weight.shape)
if dora_scale is not None:
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
else:
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "loha":
w1a = v[0]
w1b = v[1]
if v[2] is not None:
alpha = v[2] / w1b.shape[0]
else:
alpha = 1.0
w2a = v[3]
w2b = v[4]
dora_scale = v[7]
if v[5] is not None: #cp decomposition
t1 = v[5]
t2 = v[6]
m1 = torch.einsum('i j k l, j r, i p -> p r k l',
comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype))
m2 = torch.einsum('i j k l, j r, i p -> p r k l',
comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype))
else:
m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype))
m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype),
comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype))
try:
lora_diff = (m1 * m2).reshape(weight.shape)
if dora_scale is not None:
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
else:
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "glora":
if v[4] is not None:
alpha = v[4] / v[0].shape[0]
else:
alpha = 1.0
dora_scale = v[5]
a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype)
a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype)
b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype)
b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype)
try:
lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape)
if dora_scale is not None:
weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
else:
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e))
else:
logging.warning("patch type not recognized {} {}".format(patch_type, key))
if old_weight is not None:
weight = old_weight
return weight
def unpatch_model(self, device_to=None, unpatch_weights=True):
if unpatch_weights:
if self.model.model_lowvram:
@ -695,3 +694,7 @@ class ModelPatcher:
def current_loaded_device(self):
return self.model.device
def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32):
print("WARNING the ModelPatcher.calculate_weight function is deprecated, please use: comfy.model_patcher.calculate_weight instead")
return calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)

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