src/controlnet_aux/anyline/__init__.py [44:71]:
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        output_type="pil",
    ):
        device = next(iter(self.model.parameters())).device

        if not isinstance(input_image, np.ndarray):
            input_image = np.array(input_image, dtype=np.uint8)
            output_type = output_type or "pil"
        else:
            output_type = output_type or "np"

        original_height, original_width, _ = input_image.shape

        input_image = HWC3(input_image)
        input_image = resize_image(input_image, detect_resolution)

        assert input_image.ndim == 3
        height, width, _ = input_image.shape
        with torch.no_grad():
            image_teed = torch.from_numpy(input_image.copy()).float().to(device)
            image_teed = rearrange(image_teed, "h w c -> 1 c h w")
            edges = self.model(image_teed)
            edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
            edges = [
                cv2.resize(e, (width, height), interpolation=cv2.INTER_LINEAR)
                for e in edges
            ]
            edges = np.stack(edges, axis=2)
            edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
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src/controlnet_aux/teed/__init__.py [41:67]:
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        output_type="pil",
    ):
        device = next(iter(self.model.parameters())).device
        if not isinstance(input_image, np.ndarray):
            input_image = np.array(input_image, dtype=np.uint8)
            output_type = output_type or "pil"
        else:
            output_type = output_type or "np"

        original_height, original_width, _ = input_image.shape

        input_image = HWC3(input_image)
        input_image = resize_image(input_image, detect_resolution)

        assert input_image.ndim == 3
        height, width, _ = input_image.shape
        with torch.no_grad():
            image_teed = torch.from_numpy(input_image.copy()).float().to(device)
            image_teed = rearrange(image_teed, "h w c -> 1 c h w")
            edges = self.model(image_teed)
            edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
            edges = [
                cv2.resize(e, (width, height), interpolation=cv2.INTER_LINEAR)
                for e in edges
            ]
            edges = np.stack(edges, axis=2)
            edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
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