ComfyUI Node: ImpactImageInfo

ImpactImageInfo:

The ImpactImageInfo node is designed to extract and provide essential information about an image tensor, specifically its dimensions and batch size. This node is particularly useful for AI artists who need to understand the structure of their image data for further processing or analysis. By utilizing this node, you can easily obtain the batch size, height, width, and number of channels of an image tensor, which are crucial for various image manipulation and generation tasks. The primary function of this node is to simplify the process of retrieving these key attributes, making it easier to manage and manipulate image data within your workflows.

ImpactImageInfo Input Parameters:

value

The value parameter represents the image tensor from which the node will extract information. This tensor should be in the format of a 4-dimensional array, typically used in image processing tasks. The dimensions of the tensor are expected to be in the order of (batch, height, width, channel). This parameter is crucial as it directly impacts the output values, providing the necessary data for the node to process and return the image's dimensions and batch size.

ImpactImageInfo Output Parameters:

batch

The batch output parameter indicates the number of images in the batch. This is the first dimension of the input image tensor and is essential for understanding how many images are being processed simultaneously.

height

The height output parameter represents the height of the images in the batch. This is the second dimension of the input image tensor and is crucial for understanding the vertical size of the images.

width

The width output parameter represents the width of the images in the batch. This is the third dimension of the input image tensor and is crucial for understanding the horizontal size of the images.

channel

The channel output parameter indicates the number of color channels in the images. This is the fourth dimension of the input image tensor and is essential for understanding the color information contained in the images, such as RGB channels.

ImpactImageInfo Usage Tips:

• Ensure that the input image tensor is correctly formatted as a 4-dimensional array with the dimensions in the order of (batch, height, width, channel) to avoid errors and ensure accurate output.
• Use this node to quickly verify the dimensions of your image data before passing it to other nodes or processes that require specific image sizes or batch configurations.

ImpactImageInfo Common Errors and Solutions:

Invalid image tensor format

• Explanation: The input image tensor is not in the expected 4-dimensional format.
• Solution: Verify that your input tensor is correctly formatted as (batch, height, width, channel) and that it contains valid image data.

Missing or None value for input tensor

• Explanation: The input tensor is missing or set to None.
• Solution: Ensure that you provide a valid image tensor as the input to the node. Check your data pipeline to confirm that the tensor is being correctly passed to the node.