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Core implementation of ControlNet and MultiControlNet.
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@ -660,8 +660,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
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else:
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latent_control_input = latent_model_input
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# controlnet inference
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print("control_weight: ", control_weight)
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down_block_res_samples, mid_block_res_sample = self.control_model(
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latent_control_input,
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timestep,
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@ -1005,7 +1003,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
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dtype=torch.float16,
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do_classifier_free_guidance=True,
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):
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if not isinstance(image, torch.Tensor):
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if isinstance(image, PIL.Image.Image):
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image = [image]
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