thank you linter
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@ -229,9 +229,9 @@ def load_lora(name, lora_on_disk):
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elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3):
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False)
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else:
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print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
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print(f'Lora layer {key_lora} matched a layer with unsupported type: {type(sd_module).__name__}')
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continue
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raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}")
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raise AssertionError(f"Lora layer {key_lora} matched a layer with unsupported type: {type(sd_module).__name__}")
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with torch.no_grad():
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module.weight.copy_(weight)
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@ -243,7 +243,7 @@ def load_lora(name, lora_on_disk):
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elif lora_key == "lora_down.weight":
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lora_module.down = module
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else:
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raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha")
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raise AssertionError(f"Bad Lora layer name: {key_lora} - must end in lora_up.weight, lora_down.weight or alpha")
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if keys_failed_to_match:
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print(f"Failed to match keys when loading Lora {lora_on_disk.filename}: {keys_failed_to_match}")
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