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ComfyUI
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Fix merging not working when model2 of model merge node was a merge.

This commit is contained in:
comfyanonymous 2023-07-08 22:16:40 -04:00
parent febea8c101
commit a9a4ba7574
2 changed files with 114 additions and 87 deletions
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189
comfy/sd.py
View File

@ -206,7 +206,7 @@ class ModelPatcher:
def __init__(self, model, load_device, offload_device, size=0):
self.size = size
self.model = model
self.patches = []
self.patches = {}
self.backup = {}
self.model_options = {"transformer_options":{}}
self.model_size()
@ -227,7 +227,10 @@ class ModelPatcher:
def clone(self):
n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size)
n.patches = self.patches[:]
n.patches = {}
for k in self.patches:
n.patches[k] = self.patches[k][:]
n.model_options = copy.deepcopy(self.model_options)
n.model_keys = self.model_keys
return n
@ -295,12 +298,28 @@ class ModelPatcher:
return self.model.get_dtype()
def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
p = {}
p = set()
for k in patches:
if k in self.model_keys:
p[k] = patches[k]
self.patches += [(strength_patch, p, strength_model)]
return p.keys()
p.add(k)
current_patches = self.patches.get(k, [])
current_patches.append((strength_patch, patches[k], strength_model))
self.patches[k] = current_patches
return list(p)
def get_key_patches(self, filter_prefix=None):
model_sd = self.model_state_dict()
p = {}
for k in model_sd:
if filter_prefix is not None:
if not k.startswith(filter_prefix):
continue
if k in self.patches:
p[k] = [model_sd[k]] + self.patches[k]
else:
p[k] = (model_sd[k],)
return p
def model_state_dict(self, filter_prefix=None):
sd = self.model.state_dict()
@ -313,85 +332,93 @@ class ModelPatcher:
def patch_model(self):
model_sd = self.model_state_dict()
for p in self.patches:
for k in p[1]:
v = p[1][k]
key = k
if key not in model_sd:
print("could not patch. key doesn't exist in model:", k)
continue
for key in self.patches:
if key not in model_sd:
print("could not patch. key doesn't exist in model:", k)
continue
weight = model_sd[key]
if key not in self.backup:
self.backup[key] = weight.clone()
weight = model_sd[key]
alpha = p[0]
strength_model = p[2]
if key not in self.backup:
self.backup[key] = weight.clone()
if strength_model != 1.0:
weight *= strength_model
if len(v) == 1:
w1 = v[0]
if w1.shape != weight.shape:
print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
else:
weight += alpha * w1.type(weight.dtype).to(weight.device)
elif len(v) == 4: #lora/locon
mat1 = v[0]
mat2 = v[1]
if v[2] is not None:
alpha *= v[2] / mat2.shape[0]
if v[3] is not None:
#locon mid weights, hopefully the math is fine because I didn't properly test it
final_shape = [mat2.shape[1], mat2.shape[0], v[3].shape[2], v[3].shape[3]]
mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1).float(), v[3].transpose(0, 1).flatten(start_dim=1).float()).reshape(final_shape).transpose(0, 1)
weight += (alpha * torch.mm(mat1.flatten(start_dim=1).float(), mat2.flatten(start_dim=1).float())).reshape(weight.shape).type(weight.dtype).to(weight.device)
elif len(v) == 8: #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]
dim = None
if w1 is None:
dim = w1_b.shape[0]
w1 = torch.mm(w1_a.float(), w1_b.float())
if w2 is None:
dim = w2_b.shape[0]
if t2 is None:
w2 = torch.mm(w2_a.float(), w2_b.float())
else:
w2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float(), w2_b.float(), w2_a.float())
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
weight += alpha * torch.kron(w1.float(), w2.float()).reshape(weight.shape).type(weight.dtype).to(weight.device)
else: #loha
w1a = v[0]
w1b = v[1]
if v[2] is not None:
alpha *= v[2] / w1b.shape[0]
w2a = v[3]
w2b = v[4]
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', t1.float(), w1b.float(), w1a.float())
m2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float(), w2b.float(), w2a.float())
else:
m1 = torch.mm(w1a.float(), w1b.float())
m2 = torch.mm(w2a.float(), w2b.float())
weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype).to(weight.device)
weight[:] = self.calculate_weight(self.patches[key], weight.clone(), key)
return self.model
def calculate_weight(self, patches, weight, key):
for p in patches:
alpha = p[0]
v = p[1]
strength_model = p[2]
if strength_model != 1.0:
weight *= strength_model
if isinstance(v, list):
v = (self.calculate_weight(v[1:], v[0].clone(), key), )
if len(v) == 1:
w1 = v[0]
if w1.shape != weight.shape:
print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
else:
weight += alpha * w1.type(weight.dtype).to(weight.device)
elif len(v) == 4: #lora/locon
mat1 = v[0]
mat2 = v[1]
if v[2] is not None:
alpha *= v[2] / mat2.shape[0]
if v[3] is not None:
#locon mid weights, hopefully the math is fine because I didn't properly test it
final_shape = [mat2.shape[1], mat2.shape[0], v[3].shape[2], v[3].shape[3]]
mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1).float(), v[3].transpose(0, 1).flatten(start_dim=1).float()).reshape(final_shape).transpose(0, 1)
weight += (alpha * torch.mm(mat1.flatten(start_dim=1).float(), mat2.flatten(start_dim=1).float())).reshape(weight.shape).type(weight.dtype).to(weight.device)
elif len(v) == 8: #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]
dim = None
if w1 is None:
dim = w1_b.shape[0]
w1 = torch.mm(w1_a.float(), w1_b.float())
if w2 is None:
dim = w2_b.shape[0]
if t2 is None:
w2 = torch.mm(w2_a.float(), w2_b.float())
else:
w2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float(), w2_b.float(), w2_a.float())
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
weight += alpha * torch.kron(w1.float(), w2.float()).reshape(weight.shape).type(weight.dtype).to(weight.device)
else: #loha
w1a = v[0]
w1b = v[1]
if v[2] is not None:
alpha *= v[2] / w1b.shape[0]
w2a = v[3]
w2b = v[4]
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', t1.float(), w1b.float(), w1a.float())
m2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float(), w2b.float(), w2a.float())
else:
m1 = torch.mm(w1a.float(), w1b.float())
m2 = torch.mm(w2a.float(), w2b.float())
weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype).to(weight.device)
return weight
def unpatch_model(self):
model_sd = self.model_state_dict()
keys = list(self.backup.keys())

12
comfy_extras/nodes_model_merging.py
View File

@ -18,9 +18,9 @@ class ModelMergeSimple:
def merge(self, model1, model2, ratio):
m = model1.clone()
sd = model2.model_state_dict("diffusion_model.")
for k in sd:
m.add_patches({k: (sd[k], )}, 1.0 - ratio, ratio)
kp = model2.get_key_patches("diffusion_model.")
for k in kp:
m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
return (m, )
class ModelMergeBlocks:
@ -39,10 +39,10 @@ class ModelMergeBlocks:
def merge(self, model1, model2, **kwargs):
m = model1.clone()
sd = model2.model_state_dict("diffusion_model.")
kp = model2.get_key_patches("diffusion_model.")
default_ratio = next(iter(kwargs.values()))
for k in sd:
for k in kp:
ratio = default_ratio
k_unet = k[len("diffusion_model."):]
@ -52,7 +52,7 @@ class ModelMergeBlocks:
ratio = kwargs[arg]
last_arg_size = len(arg)
m.add_patches({k: (sd[k], )}, 1.0 - ratio, ratio)
m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
return (m, )
class CheckpointSave: