ComfyUI Node: Batch Average Un-Jittered

Batch Average Un-Jittered:

The BatchAverageUnJittered node is designed to process a batch of images by averaging them in a way that reduces jittering effects. This node is particularly useful for AI artists who want to create smoother and more consistent image outputs from a set of jittered images. By applying either a mean or median operation across groups of images, this node helps in stabilizing the visual output, making it more aesthetically pleasing and less noisy. The primary goal of this node is to enhance the quality of image batches by mitigating the jittering artifacts that can occur during image processing.

Batch Average Un-Jittered Input Parameters:

images

This parameter expects a batch of images as input. The images should be provided in a format that the node can process, typically as a tensor. The function of this parameter is to supply the raw image data that will be averaged to reduce jittering effects. The quality and consistency of the input images directly impact the effectiveness of the jitter reduction.

operation

This parameter determines the type of averaging operation to be applied to the images. It accepts two options: "mean" and "median". The "mean" operation calculates the average pixel values across the images, resulting in a smooth blend. The "median" operation, on the other hand, selects the median pixel value, which can be more robust to outliers and noise. Choosing the appropriate operation depends on the desired outcome and the nature of the input images.

Batch Average Un-Jittered Output Parameters:

IMAGE

The output of this node is a single image or a batch of images that have been processed to reduce jittering effects. The resulting image(s) will have smoother transitions and fewer artifacts, making them more visually appealing. This output is crucial for AI artists looking to enhance the quality of their image batches by mitigating the jittering artifacts.

Batch Average Un-Jittered Usage Tips:

• For smoother and more blended results, use the "mean" operation.
• If your images contain significant noise or outliers, the "median" operation might provide better results by being more robust to such variations.
• Ensure that your input images are of consistent size and format to avoid processing errors.

Batch Average Un-Jittered Common Errors and Solutions:

RuntimeError: Expected 4-dimensional input for 4-dimensional weight [9, 3, 3, 3], but got 3-dimensional input of size [3, 3, 3] instead

• Explanation: This error occurs when the input images are not in the expected 4-dimensional format (batch size, channels, height, width).
• Solution: Ensure that your input images are correctly formatted as a 4-dimensional tensor before passing them to the node.

ValueError: The number of images in the batch is not a multiple of 9

• Explanation: The node processes images in groups of 9, and this error occurs if the total number of images is not divisible by 9. - Solution: Adjust the number of images in your batch to be a multiple of 9 to avoid this error.

TypeError: Unsupported operation type

• Explanation: This error occurs if an invalid operation type is provided.
• Solution: Ensure that the operation parameter is set to either "mean" or "median".