ComfyUI Node: Olm Image Adjust

Olm Image Adjust:

OlmImageAdjust is a powerful node designed to enhance and modify images by applying a variety of color and exposure adjustments. This node is particularly beneficial for AI artists who wish to fine-tune the visual aspects of their generated images, allowing for greater control over the final output. By leveraging this node, you can adjust parameters such as exposure, brightness, contrast, gamma, and more, to achieve the desired aesthetic effect. The node is built to handle images in the form of tensors, ensuring compatibility with deep learning frameworks. Its caching mechanism optimizes performance by storing processed images, which can be particularly useful in workflows that require iterative adjustments. Overall, OlmImageAdjust serves as a versatile tool for enhancing image quality and achieving specific visual styles.

Olm Image Adjust Input Parameters:

version

The version parameter specifies the version of the image adjustment process to be used. This can be important for ensuring compatibility with different versions of the node or workflow. There are no specific minimum, maximum, or default values provided for this parameter.

image

The image parameter is the input image that you want to adjust. It must be a torch.Tensor, which is a data structure commonly used in machine learning for handling multi-dimensional arrays. The image should be in a format that the node can process, typically a 3D tensor representing the color channels. If the image is not in the correct format, it will be automatically adjusted by adding an extra dimension.

exposure

The exposure parameter controls the overall exposure level of the image, affecting its brightness. It is a float value where higher values increase the exposure, making the image brighter, while lower values decrease it. The default value is 0.0, with no specified minimum or maximum.

brightness

The brightness parameter adjusts the brightness of the image. It is a float value where values greater than 1.0 increase brightness, and values less than 1.0 decrease it. The default value is 1.0.

contrast

The contrast parameter modifies the contrast of the image, enhancing the difference between light and dark areas. It is a float value, with 1.0 as the default, where values above 1.0 increase contrast and values below 1.0 decrease it.

gamma

The gamma parameter applies gamma correction to the image, which can affect the overall luminance. It is a float value with a default of 1.0, where values greater than 1.0 darken the image and values less than 1.0 lighten it.

shadows

The shadows parameter adjusts the intensity of the shadow areas in the image. It is a float value with a default of 0.0, where positive values lighten shadows and negative values darken them.

midtones

The midtones parameter affects the mid-range tones of the image, allowing for adjustments that do not affect the extremes of light and dark. It is a float value with a default of 1.0.

highlights

The highlights parameter controls the brightness of the lightest areas in the image. It is a float value with a default of 1.0, where values greater than 1.0 increase highlight brightness and values less than 1.0 decrease it.

hue

The hue parameter shifts the colors of the image along the color spectrum. It is a float value with a default of 0.0, where positive or negative values shift the hue in different directions.

saturation

The saturation parameter adjusts the intensity of the colors in the image. It is a float value with a default of 1.0, where values greater than 1.0 increase saturation and values less than 1.0 decrease it.

value

The value parameter affects the overall brightness of the image, similar to brightness but in the context of the HSV color model. It is a float value with a default of 1.0.

vibrance

The vibrance parameter enhances the intensity of less saturated colors, making them more vivid without affecting already saturated colors. It is a float value with a default of 1.0.

extra_pnginfo

The extra_pnginfo parameter is an optional dictionary that can contain additional metadata about the image, such as workflow information. This can be useful for tracking adjustments across different stages of processing.

node_id

The node_id parameter is an optional identifier for the node instance, which can be used for caching and tracking purposes. If not provided, a default value of "x" is used.

Olm Image Adjust Output Parameters:

IMAGE

The IMAGE output parameter is the adjusted image resulting from the application of the specified adjustments. This output is crucial as it represents the final visual product after all modifications have been applied. The image is returned in a format that can be further processed or saved, typically as a tensor that can be converted to other image formats as needed.

Olm Image Adjust Usage Tips:

• To achieve a balanced image, start with small adjustments to exposure, brightness, and contrast, and gradually increase or decrease these values to see their effects.
• Use the vibrance parameter to enhance colors in a subtle way, especially when working with images that have a lot of muted tones.
• Experiment with the gamma setting to correct for lighting conditions that may have affected the original image capture, such as overexposure or underexposure.

Olm Image Adjust Common Errors and Solutions:

Input image must be a torch.Tensor

• Explanation: This error occurs when the input image is not provided as a torch.Tensor, which is the required format for processing.
• Solution: Ensure that the image is converted to a torch.Tensor before passing it to the node. You can use libraries like PyTorch to handle this conversion.

Missing cache key

• Explanation: This error indicates that a required cache key is missing from the request, which is necessary for retrieving or storing cached images.
• Solution: Verify that the cache key is included in the request query. This key is essential for accessing the correct image data in the cache.

Error during preview

• Explanation: This error suggests that an issue occurred while generating a preview of the adjusted image, possibly due to incorrect parameters or data handling.
• Solution: Check the input parameters and ensure they are correctly formatted and within expected ranges. Review any error messages for specific details on what might have gone wrong.