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update versions of accelerate and diffusers

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Kohya S
2023-09-24 17:46:57 +09:00
parent 20e929e27e
commit 7e736da30c
3 changed files with 22 additions and 12 deletions
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19
networks/lora_diffusers.py
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@@ -117,7 +117,7 @@ class LoRAModule(torch.nn.Module):
super().__init__()
self.lora_name = lora_name
if org_module.__class__.__name__ == "Conv2d":
if org_module.__class__.__name__ == "Conv2d" or org_module.__class__.__name__ == "LoRACompatibleConv":
in_dim = org_module.in_channels
out_dim = org_module.out_channels
else:
@@ -126,7 +126,7 @@ class LoRAModule(torch.nn.Module):
self.lora_dim = lora_dim
if org_module.__class__.__name__ == "Conv2d":
if org_module.__class__.__name__ == "Conv2d" or org_module.__class__.__name__ == "LoRACompatibleConv":
kernel_size = org_module.kernel_size
stride = org_module.stride
padding = org_module.padding
@@ -166,7 +166,8 @@ class LoRAModule(torch.nn.Module):
self.org_module[0].forward = self.org_forward
# forward with lora
def forward(self, x):
# scale is used LoRACompatibleConv, but we ignore it because we have multiplier
def forward(self, x, scale=1.0):
if not self.enabled:
return self.org_forward(x)
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
@@ -318,8 +319,12 @@ class LoRANetwork(torch.nn.Module):
for name, module in root_module.named_modules():
if module.__class__.__name__ in target_replace_modules:
for child_name, child_module in module.named_modules():
is_linear = child_module.__class__.__name__ == "Linear"
is_conv2d = child_module.__class__.__name__ == "Conv2d"
is_linear = (
child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "LoRACompatibleLinear"
)
is_conv2d = (
child_module.__class__.__name__ == "Conv2d" or child_module.__class__.__name__ == "LoRACompatibleConv"
)
if is_linear or is_conv2d:
lora_name = prefix + "." + name + "." + child_name
@@ -359,7 +364,7 @@ class LoRANetwork(torch.nn.Module):
skipped_te += skipped
print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
if len(skipped_te) > 0:
print(f"skipped {len(skipped_te)} modules because of missing weight.")
print(f"skipped {len(skipped_te)} modules because of missing weight for text encoder.")
# extend U-Net target modules to include Conv2d 3x3
target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE + LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
@@ -368,7 +373,7 @@ class LoRANetwork(torch.nn.Module):
self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules)
print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
if len(skipped_un) > 0:
print(f"skipped {len(skipped_un)} modules because of missing weight.")
print(f"skipped {len(skipped_un)} modules because of missing weight for U-Net.")
# assertion
names = set()