import torch

class BatchImages_Tiled:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image1": ("IMAGE",),
            },
            "optional": {
                "image2": ("IMAGE",),
                "image3": ("IMAGE",),
                "image4": ("IMAGE",),
                "image5": ("IMAGE",),
                "image6": ("IMAGE",),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "batch"
    CATEGORY = "SplitImage"

    def batch(self, image1, **kwargs):
        images = [image1]
        for i in range(2, 7):
            img = kwargs.get(f"image{i}", None)
            if img is not None:
                images.append(img)
        
        # Ensure all images have the same dimensions (W, H, C)
        # We take the first image as reference
        ref_h, ref_w, ref_c = image1.shape[1:]
        
        valid_images = []
        for i, img in enumerate(images):
            if img.shape[1:] != (ref_h, ref_w, ref_c):
                print(f"BatchImages_Tiled: Warning, image{i+1} has different dimensions {img.shape[1:]} than image1 {ref_h, ref_w, ref_c}. Skipping.")
                continue
            valid_images.append(img)
            
        return (torch.cat(valid_images, dim=0),)