canvas_crop.py

import torch

class Canvas_Crop:
    """
    Reduce the canvas of an image by cropping pixels from each side.

    Inputs
    ------
    image : IMAGE
        Torch tensor [B, H, W, C], C=3 (RGB) or C=4 (RGBA), values in [0, 1].
        If RGB is provided, it will be converted to RGBA by appending an opaque alpha channel.

    left, right, up, down : INT
        Number of pixels to remove from the corresponding side.

    Output
    ------
    image : IMAGE
        Torch tensor [B, H - (up+down), W - (left+right), 4] (RGBA).
    """

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "left":  ("INT", {"default": 0, "min": 0, "max": 16384, "step": 1}),
                "right": ("INT", {"default": 0, "min": 0, "max": 16384, "step": 1}),
                "up":    ("INT", {"default": 0, "min": 0, "max": 16384, "step": 1}),
                "down":  ("INT", {"default": 0, "min": 0, "max": 16384, "step": 1}),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = "crop"
    CATEGORY = "image/canvas"

    def _ensure_rgba(self, img: torch.Tensor) -> torch.Tensor:
        # Expect [B, H, W, C]
        if img.dim() != 4:
            raise ValueError(f"Expected image tensor [B, H, W, C], got shape {tuple(img.shape)}")

        c = img.shape[-1]
        if c == 4:
            return img
        elif c == 3:
            # Add opaque alpha if only RGB provided
            alpha = torch.ones((*img.shape[:-1], 1), dtype=img.dtype, device=img.device)
            return torch.cat([img, alpha], dim=-1)
        elif c > 4:
            # If extra channels exist, keep the first 4 (RGBA)
            return img[..., :4]
        else:
            raise ValueError("Expected RGB or RGBA image with 3 or 4 channels.")

    def crop(self, image: torch.Tensor, left: int, right: int, up: int, down: int):
        rgba = self._ensure_rgba(image)  # [B, H, W, 4]
        b, h, w, _ = rgba.shape

        # Sanitize
        left  = max(0, int(left))
        right = max(0, int(right))
        up    = max(0, int(up))
        down  = max(0, int(down))

        # Validate that the requested crop is possible
        if left + right >= w:
            raise ValueError(
                f"Crop too wide: left({left}) + right({right}) >= image width({w})."
            )
        if up + down >= h:
            raise ValueError(
                f"Crop too tall: up({up}) + down({down}) >= image height({h})."
            )

        # Compute crop window
        y0 = up
        y1 = h - down
        x0 = left
        x1 = w - right

        # Perform crop
        out = rgba[:, y0:y1, x0:x1, :]
        return (out,)


# Required mappings for ComfyUI to discover the node
NODE_CLASS_MAPPINGS = {
    "Canvas_Crop": Canvas_Crop
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "Canvas_Crop": "Canvas_Crop (Salia)",
}