ComfyUI Node: Fit Image And Resize (FS)

Fit Image And Resize (FS):

The FS: Fit Image And Resize node is designed to help you resize images to fit within a specified maximum size while maintaining their aspect ratio. This node is particularly useful for preparing images for further processing or analysis, ensuring that they are scaled appropriately without distortion. It leverages various resampling methods to achieve high-quality resizing and can optionally upscale images if needed. Additionally, it can handle batch processing and add noise to the images, making it a versatile tool for AI artists looking to preprocess their images efficiently.

Fit Image And Resize (FS) Input Parameters:

image

This parameter accepts the image you want to resize. The image should be in a format that the node can process, typically a tensor representation of the image.

vae

This parameter requires a Variational Autoencoder (VAE) model, which is used to encode the image into a latent space. The VAE helps in resizing the image while preserving its essential features.

max_size

This integer parameter sets the maximum size (in pixels) for the longest dimension of the image. The default value is 768, and it can be adjusted in steps of 8. This parameter ensures that the resized image fits within the specified dimensions without exceeding them.

resampling

This parameter allows you to choose the resampling method used for resizing the image. The available options are lanczos, nearest, bilinear, and bicubic. The default method is bicubic, which provides a good balance between quality and performance.

upscale

This boolean parameter determines whether the image should be upscaled if its dimensions are smaller than the specified max_size. The options are false and true, with the default being false.

batch_size

This integer parameter specifies the number of images to process in a batch. The default value is 1, with a minimum of 1 and a maximum of 64. This is useful for processing multiple images simultaneously.

add_noise

This float parameter allows you to add a specified amount of noise to the image. The value ranges from 0 to 1, with a default of 0. This can be useful for data augmentation or testing the robustness of your models.

Fit Image And Resize (FS) Output Parameters:

Latent

This output provides the latent representation of the resized image, encoded by the VAE. It is useful for further processing in latent space.

Image

This output is the resized image in its tensor format. It can be used directly for visualization or further image processing tasks.

Fit Width

This integer output indicates the width of the resized image. It helps you understand the new dimensions of the image after resizing.

Fit Height

This integer output indicates the height of the resized image. It helps you understand the new dimensions of the image after resizing.

Aspect Ratio

This float output provides the aspect ratio of the resized image. It is useful for maintaining the visual proportions of the image in subsequent processing steps.

Fit Image And Resize (FS) Usage Tips:

• To achieve the best quality resizing, use the bicubic resampling method, especially for images with fine details.
• If you need to process multiple images at once, adjust the batch_size parameter to match the number of images you have.
• Use the add_noise parameter to introduce variability in your dataset, which can be beneficial for training more robust models.
• Ensure that the max_size parameter is set according to the requirements of your subsequent processing steps to avoid unnecessary resizing later.

Fit Image And Resize (FS) Common Errors and Solutions:

Invalid image file: <image_path>

• Explanation: This error occurs when the specified image file cannot be found or is not in a valid format.
• Solution: Verify that the image path is correct and that the file exists. Ensure the image is in a supported format.

VAE model not provided

• Explanation: This error occurs when the VAE model is not supplied to the node.
• Solution: Ensure that you provide a valid VAE model as input to the node.

Unsupported resampling method

• Explanation: This error occurs when an invalid resampling method is specified.
• Solution: Choose a valid resampling method from the available options: lanczos, nearest, bilinear, or bicubic.

Batch size out of range

• Explanation: This error occurs when the batch size is set outside the allowed range (1 to 64).
• Solution: Adjust the batch size to be within the range of 1 to 64.

Add noise value out of range

• Explanation: This error occurs when the add_noise parameter is set outside the range of 0 to 1.
• Solution: Ensure that the add_noise value is between 0 and 1.