optimum/neuron/pipelines/diffusers/pipeline_controlnet.py [241:263]:
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        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
            logger.info(
                "IP adapter is not supported yet, `ip_adapter_image` and `ip_adapter_image_embeds` will be ignored."
            )

        # 4. Prepare image
        height = self.vae.config.neuron["static_height"] * self.vae_scale_factor
        width = self.vae.config.neuron["static_width"] * self.vae_scale_factor
        if isinstance(controlnet, ControlNetModel):
            image = self.prepare_image(
                image=image,
                width=width,
                height=height,
                batch_size=batch_size * num_images_per_prompt,
                num_images_per_prompt=num_images_per_prompt,
                device=None,
                dtype=None,
                do_classifier_free_guidance=self.do_classifier_free_guidance,
                guess_mode=guess_mode,
            )
            height, width = image.shape[-2:]
        elif isinstance(controlnet, MultiControlNetModel):
            images = []
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optimum/neuron/pipelines/diffusers/pipeline_controlnet_sd_xl.py [314:336]:
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        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
            logger.info(
                "IP adapter is not supported yet, `ip_adapter_image` and `ip_adapter_image_embeds` will be ignored."
            )

        # 4. Prepare image
        height = self.vae.config.neuron["static_height"] * self.vae_scale_factor
        width = self.vae.config.neuron["static_width"] * self.vae_scale_factor
        if isinstance(controlnet, ControlNetModel):
            image = self.prepare_image(
                image=image,
                width=width,
                height=height,
                batch_size=batch_size * num_images_per_prompt,
                num_images_per_prompt=num_images_per_prompt,
                device=None,
                dtype=None,
                do_classifier_free_guidance=self.do_classifier_free_guidance,
                guess_mode=guess_mode,
            )
            height, width = image.shape[-2:]
        elif isinstance(controlnet, MultiControlNetModel):
            images = []
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