ComfyUI Node: ChromaticAberration

ChromaticAberration:

ChromaticAberration is a post-processing effect node designed to simulate the optical phenomenon where different colors of light are refracted by different amounts, causing a slight misalignment of colors at the edges of objects. This effect can add a sense of realism or artistic flair to your images by mimicking the imperfections found in real-world lenses. By shifting the red, green, and blue channels of an image in different directions, you can create a visually striking effect that enhances the overall aesthetic of your artwork. This node is particularly useful for creating vintage or surreal looks, adding depth, and emphasizing the dynamic aspects of your compositions.

ChromaticAberration Input Parameters:

image

This parameter represents the input image that you want to apply the chromatic aberration effect to. The image should be in a tensor format compatible with PyTorch.

red_shift

This parameter controls the amount of shift applied to the red channel of the image. The shift value can range from -20 to 20, with a default value of 0. A positive value shifts the red channel in the specified direction, while a negative value shifts it in the opposite direction.

red_direction

This parameter specifies the direction in which the red channel will be shifted. It can be set to either "horizontal" or "vertical". The direction determines whether the shift will occur along the x-axis or y-axis.

green_shift

This parameter controls the amount of shift applied to the green channel of the image. The shift value can range from -20 to 20, with a default value of 0. A positive value shifts the green channel in the specified direction, while a negative value shifts it in the opposite direction.

green_direction

This parameter specifies the direction in which the green channel will be shifted. It can be set to either "horizontal" or "vertical". The direction determines whether the shift will occur along the x-axis or y-axis.

blue_shift

This parameter controls the amount of shift applied to the blue channel of the image. The shift value can range from -20 to 20, with a default value of 0. A positive value shifts the blue channel in the specified direction, while a negative value shifts it in the opposite direction.

blue_direction

This parameter specifies the direction in which the blue channel will be shifted. It can be set to either "horizontal" or "vertical". The direction determines whether the shift will occur along the x-axis or y-axis.

ChromaticAberration Output Parameters:

IMAGE

The output is the processed image with the chromatic aberration effect applied. This image will have the red, green, and blue channels shifted according to the specified parameters, resulting in a visually altered image that exhibits the desired chromatic aberration effect.

ChromaticAberration Usage Tips:

• Experiment with different shift values and directions for each color channel to achieve unique and interesting visual effects.
• Use subtle shifts to add a touch of realism to your images, mimicking the imperfections of real-world lenses.
• For a more dramatic and artistic effect, try using larger shift values and varying the directions for each channel.
• Combine the chromatic aberration effect with other post-processing nodes to create complex and visually rich compositions.

ChromaticAberration Common Errors and Solutions:

"Invalid shift value"

• Explanation: The shift value provided is outside the acceptable range of -20 to 20.
• Solution: Ensure that the shift values for red_shift, green_shift, and blue_shift are within the range of -20 to 20.

"Invalid direction value"

• Explanation: The direction value provided is not recognized. It should be either "horizontal" or "vertical".
• Solution: Check that the red_direction, green_direction, and blue_direction parameters are set to either "horizontal" or "vertical".

"Input image format not supported"

• Explanation: The input image is not in a tensor format compatible with PyTorch.
• Solution: Ensure that the input image is correctly formatted as a PyTorch tensor before passing it to the node.