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Speed up lora loading a bit.

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comfyanonymous
2023-07-15 13:24:05 -04:00
parent 50b1180dde
commit 490771b7f4
3 changed files with 35 additions and 25 deletions
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33
comfy/sd.py
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@@ -340,7 +340,7 @@ class ModelPatcher:
weight = model_sd[key]
if key not in self.backup:
self.backup[key] = weight.clone()
self.backup[key] = weight.to(self.offload_device, copy=True)
temp_weight = weight.to(torch.float32, copy=True)
weight[:] = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
@@ -367,15 +367,16 @@ class ModelPatcher:
else:
weight += alpha * w1.type(weight.dtype).to(weight.device)
elif len(v) == 4: #lora/locon
mat1 = v[0]
mat2 = v[1]
mat1 = v[0].float().to(weight.device)
mat2 = v[1].float().to(weight.device)
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)
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)
elif len(v) == 8: #lokr
w1 = v[0]
w2 = v[1]
@@ -389,20 +390,24 @@ class ModelPatcher:
if w1 is None:
dim = w1_b.shape[0]
w1 = torch.mm(w1_a.float(), w1_b.float())
else:
w1 = w1.float().to(weight.device)
if w2 is None:
dim = w2_b.shape[0]
if t2 is None:
w2 = torch.mm(w2_a.float(), w2_b.float())
w2 = torch.mm(w2_a.float().to(weight.device), w2_b.float().to(weight.device))
else:
w2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float(), w2_b.float(), w2_a.float())
w2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float().to(weight.device), w2_b.float().to(weight.device), w2_a.float().to(weight.device))
else:
w2 = w2.float().to(weight.device)
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)
weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
else: #loha
w1a = v[0]
w1b = v[1]
@@ -413,13 +418,13 @@ class ModelPatcher:
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())
m1 = torch.einsum('i j k l, j r, i p -> p r k l', t1.float().to(weight.device), w1b.float().to(weight.device), w1a.float().to(weight.device))
m2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float().to(weight.device), w2b.float().to(weight.device), w2a.float().to(weight.device))
else:
m1 = torch.mm(w1a.float(), w1b.float())
m2 = torch.mm(w2a.float(), w2b.float())
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).to(weight.device)
weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
return weight
def unpatch_model(self):