ComfyUI Node: 🔧 Debug Tensor Shape

🔧 Debug Tensor Shape+:

The DebugTensorShape+ node is a utility designed to help you understand the structure of tensors within your AI models. Tensors are multi-dimensional arrays that are fundamental to the operations of neural networks, and knowing their shapes can be crucial for debugging and optimizing your models. This node inspects the input tensor and prints out its shape, making it easier to identify any discrepancies or issues in your data pipeline. By providing a clear view of the tensor dimensions, DebugTensorShape+ helps ensure that your data is correctly formatted and compatible with subsequent layers in your model, ultimately aiding in smoother and more efficient model development.

🔧 Debug Tensor Shape+ Input Parameters:

tensor

The tensor parameter is the primary input for the DebugTensorShape+ node. It accepts any tensor-like object, which can be a multi-dimensional array, a list of arrays, or a dictionary containing arrays. The function of this parameter is to provide the node with the data structure whose shape needs to be inspected. There are no specific minimum, maximum, or default values for this parameter, as it is designed to be flexible and accommodate various tensor formats. The impact of this parameter on the node's execution is direct; the node will analyze the provided tensor and print its shape, helping you verify the correctness of your data structure.

🔧 Debug Tensor Shape+ Output Parameters:

None

The DebugTensorShape+ node does not produce any output parameters. Its primary function is to print the shapes of the input tensor to the console, providing a visual confirmation of the tensor's structure. This output is intended for debugging purposes and does not generate any data that can be passed to subsequent nodes.

🔧 Debug Tensor Shape+ Usage Tips:

• Use DebugTensorShape+ to verify the dimensions of your tensors before passing them to other nodes in your model. This can help prevent shape-related errors and ensure compatibility between different layers.
• If you are working with complex data structures, such as nested dictionaries or lists of tensors, DebugTensorShape+ can help you understand the overall structure and identify any inconsistencies.

🔧 Debug Tensor Shape+ Common Errors and Solutions:

Invalid tensor input

• Explanation: The input provided is not a valid tensor or tensor-like object.
• Solution: Ensure that the input is a tensor, list of tensors, or dictionary containing tensors. Verify the data structure before passing it to the node.

AttributeError: 'NoneType' object has no attribute 'shape'

• Explanation: The input tensor contains a None value or an object that does not have a shape attribute.
• Solution: Check the input tensor for any None values or non-tensor objects and remove or replace them with valid tensor objects.