Comfy-Org/ComfyUI · error · ValueError

Invalid image dimensions: {w}x{h}

Error message

Invalid image dimensions: {w}x{h}

What it means

ValueError from validate_image_aspect_ratio when either dimension of the image is <= 0. After get_image_dimensions returns (w, h), a non-positive width or height means the tensor is degenerate (empty batch slice, zero-size dim) and the ratio computation w/h would divide by zero or be meaningless, so it refuses to compute an aspect ratio.

Source

Thrown at comfy_api_nodes/util/validation_utils.py:46

    if max_width is not None and width > max_width:
        raise ValueError(f"Image width must be at most {max_width}px, got {width}px")
    if min_height is not None and height < min_height:
        raise ValueError(f"Image height must be at least {min_height}px, got {height}px")
    if max_height is not None and height > max_height:
        raise ValueError(f"Image height must be at most {max_height}px, got {height}px")


def validate_image_aspect_ratio(
    image: torch.Tensor,
    min_ratio: tuple[float, float] | None = None,  # e.g. (1, 4)
    max_ratio: tuple[float, float] | None = None,  # e.g. (4, 1)
    *,
    strict: bool = True,  # True -> (min, max); False -> [min, max]
) -> float:
    """Validates that image aspect ratio is within min and max. If a bound is None, that side is not checked."""
    w, h = get_image_dimensions(image)
    if w <= 0 or h <= 0:
        raise ValueError(f"Invalid image dimensions: {w}x{h}")
    ar = w / h
    _assert_ratio_bounds(ar, min_ratio=min_ratio, max_ratio=max_ratio, strict=strict)
    return ar


def validate_images_aspect_ratio_closeness(
    first_image: torch.Tensor,
    second_image: torch.Tensor,
    min_rel: float,  # e.g. 0.8
    max_rel: float,  # e.g. 1.25
    *,
    strict: bool = False,  # True -> (min, max); False -> [min, max]
) -> float:
    """
    Validates that the two images' aspect ratios are 'close'.
    The closeness factor is C = max(ar1, ar2) / min(ar1, ar2)  (C >= 1).
    We require C <= limit, where limit = max(max_rel, 1.0 / min_rel).

View on GitHub (pinned to 1c6d8d45b3)

Solutions

1. Check tensor.shape for zero dims before calling the validator.
2. Fix the upstream crop/slice logic that produced a zero-sized dimension.
3. Guard custom nodes: skip or error clearly when any dim is 0 instead of passing the tensor on.

Example fix

# before
validate_image_aspect_ratio(image, min_ratio=(1, 4))
# after
assert image.shape[-2] > 0 and image.shape[-3] > 0, "empty image"
validate_image_aspect_ratio(image, min_ratio=(1, 4))

def has_positive_dims(image: torch.Tensor) -> bool:
    h = image.shape[1] if image.dim() == 4 else image.shape[0]
    w = image.shape[2] if image.dim() == 4 else image.shape[1]
    return h > 0 and w > 0

def is_usable_image(t: torch.Tensor) -> bool:
    return isinstance(t, torch.Tensor) and t.dim() in (3, 4) and min(t.shape[-2], t.shape[-3]) > 0

When it happens

Trigger: Passing a tensor with a 0-sized dimension (e.g., image[:, :, :0, :] after a bad crop, or an empty batch indexed as image[i] on a 0-batch tensor) to validate_image_aspect_ratio.

Common situations: Bad crop/slice parameters producing zero-size dimensions; empty tensors from a failed upstream generation; off-by-one slicing bugs in custom nodes.

Related errors

• Invalid image tensor shape.
• Invalid image dimensions
• JoyImage reference inputs must contain one image each
• The maximum number of reference images is 10.
• sync.so rejects images above 4K (4096x2160); got {width}x{he

AI-assisted analysis of Comfy-Org/ComfyUI@1c6d8d45b3 (2026-08-14). Data as JSON: /api/errors/1949289aee438aa0. Report an issue: GitHub.