age = np.concatenate([image, image, image], axis=2)
    detected_map = torch.from_numpy(image).float() / 255.0
    hint = detected_map.permute(2, 0, 1)
    return hint

depth_estimator = pipeline("depth-estimation")
hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda") Load the prior pipeline and the KandinskyV22ControlnetImg2ImgPipeline:   Copied prior_pipeline = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
    "kandinsky-