ComfyUI Node: Multi Resize

Multi Resize:

The Zuellni Multi Resize node is designed to efficiently resize images, latents, and masks within your AI art projects. This node allows you to scale these elements up or down based on a specified scale factor, ensuring that the resized outputs maintain their quality and integrity. The node supports various interpolation modes to cater to different resizing needs, making it versatile for a range of applications. Whether you are preparing images for further processing or adjusting the size of latent representations, the Zuellni Multi Resize node provides a robust solution to handle these tasks seamlessly.

Multi Resize Input Parameters:

scale

The scale parameter determines the factor by which the images, latents, and masks will be resized. A value of 1.0 means no resizing, while values greater than 1.0 will upscale the inputs, and values less than 1.0 will downscale them. This parameter is crucial for controlling the final dimensions of the outputs. The default value is 1.0, with no explicit minimum or maximum values provided, but practical limits are typically between 0.1 and 10.0.

mode

The mode parameter specifies the interpolation method used for resizing. Common modes include "nearest", "bilinear", "bicubic", and others, each offering different trade-offs between speed and quality. This parameter impacts the smoothness and accuracy of the resized outputs. Choosing the right mode depends on the specific requirements of your project, such as whether you prioritize speed or visual fidelity.

images

The images parameter is an optional input that accepts a batch of images to be resized. If provided, the images will be permuted, resized using the specified scale and mode, and then center-cropped to ensure dimensions are multiples of 8. This parameter is useful for adjusting the size of image data before further processing or analysis.

latents

The latents parameter is an optional input that accepts latent representations to be resized. Similar to images, the latents will be resized and center-cropped. This parameter is essential for maintaining the consistency of latent dimensions, especially when working with generative models that require specific input sizes.

masks

The masks parameter is an optional input that accepts masks to be resized. The masks will be unsqueezed, resized, and then squeezed back to their original dimensions. This parameter is particularly useful for tasks involving segmentation or masking, where the size of the mask needs to match the size of the corresponding image or latent.

Multi Resize Output Parameters:

IMAGES

The IMAGES output provides the resized images, if the images input was provided. This output is crucial for subsequent image processing steps, ensuring that the images are at the desired scale and ready for further manipulation or analysis.

LATENTS

The LATENTS output provides the resized latent representations, if the latents input was provided. This output is important for maintaining the integrity of latent data, especially when feeding it into models that require specific input dimensions.

MASKS

The MASKS output provides the resized masks, if the masks input was provided. This output ensures that the masks are correctly scaled to match the corresponding images or latents, which is essential for accurate segmentation or masking tasks.

Multi Resize Usage Tips:

• To maintain the quality of your images, choose an interpolation mode like "bicubic" or "lanczos" for smoother results, especially when upscaling.
• When working with generative models, ensure that the resized latents maintain the required dimensions by using the center-crop feature to adjust the final size.
• Use the scale parameter to quickly adjust the size of your inputs without manually calculating the new dimensions, saving time and reducing errors.

Multi Resize Common Errors and Solutions:

"Invalid scale factor"

• Explanation: The scale factor provided is not within a practical range.
• Solution: Ensure that the scale parameter is set to a reasonable value, typically between 0.1 and 10.0.

"Unsupported interpolation mode"

• Explanation: The specified interpolation mode is not recognized.
• Solution: Verify that the mode parameter is set to a supported interpolation method such as "nearest", "bilinear", or "bicubic".

"Input dimensions not compatible"

• Explanation: The input dimensions are not suitable for the resizing operation.
• Solution: Check that the input images, latents, or masks have dimensions that can be resized and center-cropped to multiples of 8.