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Print errors and continue when lora weights are not compatible.

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comfyanonymous 2023-07-20 19:56:22 -04:00
parent 4760c29380
commit 0115018695
1 changed files with 13 additions and 3 deletions
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16
comfy/sd.py
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@ -376,7 +376,10 @@ class ModelPatcher:
mat3 = v[3].float().to(weight.device)
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)
weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
try:
weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
except Exception as e:
print("ERROR", key, e)
elif len(v) == 8: #lokr
w1 = v[0]
w2 = v[1]
@ -407,7 +410,10 @@ class ModelPatcher:
if v[2] is not None and dim is not None:
alpha *= v[2] / dim
weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
try:
weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
except Exception as e:
print("ERROR", key, e)
else: #loha
w1a = v[0]
w1b = v[1]
@ -424,7 +430,11 @@ class ModelPatcher:
m1 = torch.mm(w1a.float().to(weight.device), w1b.float().to(weight.device))
m2 = torch.mm(w2a.float().to(weight.device), w2b.float().to(weight.device))
weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
try:
weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
except Exception as e:
print("ERROR", key, e)
return weight
def unpatch_model(self):