Preface:
Learning ComfyUI is a long battle, and ComfyUI layer style is a set of powerful nodes specially designed and produced for image design and integrated with Photoshop functions. The node almost all the functions of PhotoShop migrated to ComfyUI, such as providing imitation Adobe Photoshop layer styles, provide color adjustment functions (brightness, saturation, contrast, etc.), provide Mask auxiliary tools, provide layer compositing tools and workflow-related auxiliary nodes, provide image effects filters and so on. Designed to centralize the work platform so that we can achieve some of the basic functions of PhotoShop in ComfyUI.
catalogs
I. Installation
II. LayerColor: LUT Apply node
III. LayerColor: AutoBrightness node
IV. LayerColor: ColorAdapter node
V. LayerColor: Exposure node
VI. LayerColor: Color of Shadow & HighLight node
VII. LayerColor: Gamma node
VIII. LayerColor: Brightness & Contrast node
IX. LayerColor: RGB \ LayerColor: YUV \ LayerColor: LAB \ LayerColor: HSV Node
I. Installation
Method 1: Installation via ComfyUI Manager (recommended)
Open the Manager interface
Method 2: Installation with git clone command
Enter cmd in the ComfyUI/custom_nodes directory and press Enter to enter the computer terminal.
Enter the following line of code in the terminal to start the download
git clone /chflame163/ComfyUI_LayerStyle.git
II. LayerColor: LUT Apply node
This node focuses on color adjustment of an image using a lookup table. A lookup table is a predefined color mapping table that maps the colors of an input image to a new color space, allowing for color correction or stylization effects.
Input:
image → input image
Parameters:
LUT → Here is a list of .cube files available in the LUT folder, the selected LUT file will be applied to the image
color_space → color space **Please select linear for normal image, log for log color space**.
Output:
image → processed image
Example:
caveat
- LUT format: Ensure that the lookup table format used is compatible with the node, common LUT formats include .cube, .png etc.
- Color space matching: The color space of the input image should match the expected color space of the lookup table for best results.
- Processing performance: Application lookup table processing may require high computational resources, ensuring that system performance is sufficient to support processing requirements.
- LUT Selection: Select the appropriate LUT according to the specific image processing needs to achieve the desired color adjustment effect.
By using the LayerColor: LUT Apply node, you can achieve efficient color adjustment and stylization in the image processing workflow to enhance the visual effect and artistic expression of images.
III. LayerColor: AutoBrightness node
By analyzing the luminance histogram of an image, the node can dynamically adjust the luminance of the image to be more in line with the visually optimal luminance distribution.
Input:
image → input image
mask → input mask
Parameters:
Strength → automatically adjusts the intensity of the brightness **The larger the value, the more toward the center, and the greater the difference from the original image**.
saturation → color saturation **Changes in brightness usually result in changes in color saturation, which can be appropriately compensated here**.
Output:
image → processed image
Example:
caveat
- Input image quality: Ensure that the input image is of good quality, avoiding overexposed or overly dark areas, as these problems may affect the automatic brightness adjustment effect.
- Processing performance: Automatic brightness adjustment processing may require some computing resources, ensure that the system performance is sufficient to support the processing requirements.
- Results Check: While automatic brightness adjustments usually provide good results, manual fine-tuning may still be necessary for some specific scenes to ensure optimal results.
By using the LayerColor: AutoBrightness node, you can achieve efficient automatic brightness optimization in your image processing workflow, improving the visual effect and brightness balance of your images.
IV. LayerColor: ColorAdapter node
This node focuses on making an image conform to a predetermined color scheme or style by adjusting its colors.
Input:
image → input image
color_ref_image → input reference color image
Parameters:
opacity → the opacity of the image after adjusting the color tone.
Output:
image → processed image
Example:
caveat
- Color Scheme Selection: Ensure that the appropriate color scheme for the treatment target is selected to achieve the desired visual effect.
- Input Image Quality: The quality of the input image affects the effect of color adjustment, make sure the image is clear and the color information is complete.
- Processing performance: Color adjustment processing may require some computational resources, ensure that the system performance is sufficient to support the processing requirements.
- Results check: While color adjustments can be applied automatically, manual fine-tuning may still be necessary to ensure optimal results.
By using the LayerColor: ColorAdapter node, you can achieve efficient color adjustments and style conversions in your image processing workflow, improving the visual effect and consistency of your images.
V. LayerColor: Exposure node
This node focuses on adjusting the exposure of an image. By increasing or decreasing the exposure value, the image can be made brighter or darker to achieve the desired visual effect.
Input:
image → input image
Parameters:
Exposure → Exposure Value **Higher values indicate brighter exposures and can be negative **.
Output:
image → processed image
Example:
caveat
- Exposure Value Configuration: Configure the appropriate exposure value according to the specific needs to ensure that the brightness of the adjusted image meets the expectations. A positive exposure value increases the exposure and a negative value decreases the exposure.
- Input Image Quality: The quality of the input image affects the effectiveness of exposure adjustments to ensure that the image is clear and that exposure problems are concentrated in the middle brightness region.
- Processing performance: Exposure adjustment processing may require a certain amount of computational resources to ensure that system performance is sufficient to support processing requirements.
- Result check: After adjusting the exposure, check that the image details and colors remain consistent to prevent over-adjustment that could result in image distortion or loss of detail.
By using the LayerColor: Exposure node, you can achieve efficient exposure adjustments in your image processing workflow to enhance the brightness and overall visual effect of your images.
VI. LayerColor: Color of Shadow & HighLight node
This node focuses on color adjustment of the shadow and highlight areas of the image. By controlling the color of these two areas individually, more detailed color correction and stylization can be achieved.
Input:
image → input image
mask → mask
Parameters:
shadow_brightness → dark_brightness
shadow_saturation → dark_saturation
shadow_hue → dark_hue
shadow_level_offset → offset for dark fetch **larger value means more areas nearer to bright are included in dark **
shadow_range → dark transition range
highlight_brightness → brightness of the bright section
highlight_saturation → color saturation of highlights
highlight_hue → The hue of the bright part.
highlight_level_offset → offset of the light level value ** smaller value means more areas closer to the shadows are included in the light level**
highlight_range → transition range of the highlight
Output:
image → processed image
Example:
caveat
- Color Parameter Configuration: Configure the color parameters of shadows and highlights according to specific needs to ensure that the adjusted image color meets expectations.
- Input Image Quality: The quality of the input image affects the effectiveness of the color adjustments, make sure that the shadow and highlight areas of the image are well defined and do not have severe color shifts.
- Processing performance: Color adjustment processing may require some computational resources, ensure that the system performance is sufficient to support the processing requirements.
- Result check: After adjusting the shadow and highlight colors, check the overall color balance and visual effect of the image to make sure there is no color distortion caused by over-adjustment.
By using the LayerColor: Color of Shadow & HighLight node, you can achieve efficient shadow and highlight color adjustments in your image processing workflow, improving the color performance and overall visual effect of your images.
VII. LayerColor: Gamma node
This node focuses on performing gamma correction on an image. By adjusting the gamma value, the brightness and contrast of the image can be changed to optimize the overall visual effect of the image.
Input:
image → input image
Parameters:
gamma → Set the gamma value of the processed image.
Output:
image → processed image
Example:
caveat
- Gamma Configuration: Configure the appropriate gamma value according to your specific needs to ensure that the adjusted image brightness and contrast are as expected. A gamma value greater than 1 will decrease the brightness, while a gamma value less than 1 will increase the brightness.
- Input Image Quality: The quality of the input image affects the effectiveness of the gamma correction, ensuring that there is no excessive noise or distortion in the image.
- Processing performance: Gamma correction processing may require a certain amount of computational resources, ensure that the system performance is sufficient to support the processing requirements.
- Result check: After adjusting the gamma value, check the overall visual effect of the image to make sure that there is no over-adjustment resulting in image distortion or loss of detail.
By using the LayerColor: Gamma node, you can achieve efficient gamma correction in your image processing workflow, optimize the brightness and contrast of your images, and enhance the overall visual effect of your images.
VIII. LayerColor: Brightness & Contrast node
This node focuses on making brightness and contrast adjustments to an image. By controlling these two parameters independently, the visualization of the image can be significantly improved.
Input:
image → input image
Parameters:
brightness → brightness of the image
contrast → contrast of the image
saturation → the color saturation of the image
Output:
image → processed image
Example:
caveat
- Brightness and Contrast Configuration: Configure appropriate brightness and contrast values according to specific needs to ensure that the adjusted image results are as expected. Brightness and contrast values are usually between -100 and 100.
- Input Image Quality: The quality of the input image affects the effectiveness of brightness and contrast adjustments, ensuring that there is not excessive noise and distortion in the image.
- Processing Performance: Brightness and contrast adjustment processing may require some computational resources, make sure that the system performance is sufficient to support the processing requirements.
- Results check: After adjusting the brightness and contrast, check the overall visual effect of the image to make sure there is no over-adjustment causing image distortion or loss of detail.
By using the LayerColor: Brightness & Contrast node, you can achieve efficient brightness and contrast adjustments in your image processing workflow, optimizing the visual appearance of your images and making them clearer and more appealing.
IX. LayerColor: RGB \ LayerColor: YUV \ LayerColor: LAB \ LayerColor: HSV Node
Adjusts the RGB, YUV, LAB, and HSV channels of the image separately.
Input:
image → input image
Parameters:
R → R channel **Red channel**
G → G channel **Green channel**
B → B channel **Blue channel**
H → H channel **Hue, Hue channel**
S → S channel **Saturation, color purity channel**
V → V channel **Luminance channel**
L → L channel **Luminance channel**
A → A channel **Component channel from green to red**
B → B channel **Component channel from blue to yellow**
Y → Y channel **Intensity, Luminance channel **
U → U channel **Blue chrominance channel**
V → V channel **Hue, Hue channel**
Output:
image → processed image
Example:
caveat
- Parameter Configuration: Configure appropriate color parameters according to specific needs to ensure that the adjusted image effect meets expectations.
- Input Image Quality: The quality of the input image affects the effect of color adjustment, ensuring that the color information of the image is complete.
- Processing performance: Color adjustment processing may require some computational resources, ensure that the system performance is sufficient to support the processing requirements.
- Result check: After adjusting the colors, check the overall color balance and visual effect of the image to make sure there is no color distortion caused by over-adjustment.
- By using these LayerColor nodes, you can achieve efficient color adjustment and optimization in the image processing workflow to enhance the visual effect and color expression of images.
**To go beyond oneself is to strive diligently. Perseverance is the key to success. **