def get_cross_attention_kwargs_without_grounded(self, hidden_size, repeat_batch, max_objs, device):
        """
        Prepare the cross-attention kwargs without information about the grounded input (boxes, mask, image embedding,
        phrases embedding) (All are zero tensor).
        """
        boxes = torch.zeros(max_objs, 4, device=device, dtype=self.text_encoder.dtype)
        masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
        phrases_masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
        image_masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
        phrases_embeddings = torch.zeros(max_objs, hidden_size, device=device, dtype=self.text_encoder.dtype)
        image_embeddings = torch.zeros(max_objs, hidden_size, device=device, dtype=self.text_encoder.dtype)

        out = {
            "boxes": boxes.unsqueeze(0).repeat(repeat_batch, 1, 1),
            "masks": masks.unsqueeze(0).repeat(repeat_batch, 1),
            "phrases_masks": phrases_masks.unsqueeze(0).repeat(repeat_batch, 1),
            "image_masks": image_masks.unsqueeze(0).repeat(repeat_batch, 1),
            "phrases_embeddings": phrases_embeddings.unsqueeze(0).repeat(repeat_batch, 1, 1),
            "image_embeddings": image_embeddings.unsqueeze(0).repeat(repeat_batch, 1, 1),
        }

        return out

        Prepare the cross-attention kwargs without information about the grounded input (boxes, mask, image embedding,
        phrases embedding) (All are zero tensor).
        
src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py