apply lint
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@ -126,9 +126,7 @@ def convert_diffusion_stable(
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# UNET
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if single_vae:
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unet_inputs = ["sample", "timestep", "encoder_hidden_states", "class_labels"]
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unet_scale = torch.tensor(4).to(
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device=ctx.training_device, dtype=torch.long
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)
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unet_scale = torch.tensor(4).to(device=ctx.training_device, dtype=torch.long)
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else:
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unet_inputs = ["sample", "timestep", "encoder_hidden_states", "return_dict"]
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unet_scale = torch.tensor(False).to(
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