ComfyUI Node: Eses Image Effect Levels

Eses Image Effect Levels:

The Eses Image Effect Levels node is a powerful tool designed to provide you with precise control over the tonal range of images and masks within the ComfyUI interface. This node functions similarly to levels adjustment tools found in traditional image editing software, allowing you to manipulate the input and output black, mid, and white points of an image. It features an interactive preview and a live histogram, which offers visual feedback on the tonal distribution of the selected channel, making it easier to achieve the desired effect. The node supports multi-channel adjustments, enabling you to apply changes to the combined RGB channels or isolate adjustments to individual Red, Green, or Blue channels. Additionally, it allows for separate level adjustments on input masks. The state of the node, including all level adjustments, is saved with your workflow and persists even after refreshing the browser page, ensuring a seamless user experience. With real-time previews, you can see the effects of your adjustments instantly, making it an invaluable tool for AI artists looking to refine their images with precision.

Eses Image Effect Levels Input Parameters:

preset

The preset parameter allows you to select predefined settings for level adjustments, which can be useful for quickly applying common tonal corrections. This parameter does not have specific minimum or maximum values as it depends on the available presets within the node.

channel

The channel parameter specifies which color channel(s) you want to adjust. Options typically include RGB for combined adjustments or individual channels like Red, Green, and Blue. This parameter impacts which part of the image's tonal range is modified.

auto_levels

The auto_levels parameter, when enabled, automatically adjusts the levels of the image to enhance contrast and brightness based on the image's histogram. This feature is useful for quick corrections without manual input. It is a boolean parameter, typically set to True or False.

auto_color

The auto_color parameter, similar to auto_levels, automatically adjusts the color balance of the image to correct any color casts. This is also a boolean parameter, set to True or False.

auto_sensitivity

The auto_sensitivity parameter determines the sensitivity of the automatic adjustments made by auto_levels and auto_color. Higher sensitivity results in more pronounced adjustments. This parameter typically ranges from 0 to 1, with a default value that balances subtlety and effectiveness.

all_levels_json

The all_levels_json parameter is a JSON object that contains detailed settings for all level adjustments, including input and output points for each channel. This parameter allows for precise customization of the node's behavior.

image

The image parameter is the input image that you want to adjust. It should be provided in a compatible format, such as a tensor or array, and is essential for the node to function.

mask

The mask parameter is an optional input that allows you to apply level adjustments to a specific area of the image. It should be provided in a compatible format and is useful for targeted corrections.

prompt

The prompt parameter is used to provide additional instructions or context for the node's operation. It is optional and can be used to guide the node's behavior in specific scenarios.

extra_pnginfo

The extra_pnginfo parameter allows you to include additional metadata with the output image. This can be useful for tracking adjustments or embedding information within the image file.

unique_id

The unique_id parameter is used to uniquely identify the node instance, which is helpful for managing multiple nodes within a workflow. It ensures that the correct node is referenced during operations.

Eses Image Effect Levels Output Parameters:

adjusted_image_tensor

The adjusted_image_tensor is the primary output of the node, representing the image after the level adjustments have been applied. This output is crucial for further processing or saving the adjusted image.

adjusted_mask_tensor

The adjusted_mask_tensor is the output mask after level adjustments, if a mask was provided. This output is important for maintaining consistency in targeted adjustments and can be used in subsequent processing steps.

image

The image output is the original input image, passed through the node without modifications. This allows for comparison or further use in the workflow.

mask

The mask output is the original input mask, passed through the node without modifications. This is useful for maintaining the original mask for reference or further processing.

Eses Image Effect Levels Usage Tips:

• Use the live histogram to visually assess the tonal distribution of your image and make informed adjustments to the black, mid, and white points.
• Experiment with the auto_levels and auto_color features for quick enhancements, especially when working with a large batch of images.
• Utilize the all_levels_json parameter to save and apply specific level settings across different projects, ensuring consistency in your image adjustments.

Eses Image Effect Levels Common Errors and Solutions:

Error applying levels: <error_message>

• Explanation: This error occurs when there is an issue with the level adjustment process, possibly due to invalid input values or an unsupported image format.
• Solution: Ensure that the input image and mask are in the correct format and that all level adjustment parameters are set within their valid ranges. Check for any typos or incorrect values in the all_levels_json parameter.

Missing data

• Explanation: This error indicates that required data, such as the base image or level settings, is missing from the request.
• Solution: Verify that all necessary input parameters, including base_data_b64 and all_levels_json, are provided and correctly formatted before executing the node.

Missing image data

• Explanation: This error occurs when the node is unable to retrieve the image data needed for processing.
• Solution: Ensure that the base_data_b64 parameter contains valid base64-encoded image data and that the image is properly connected to the node.