ComfyUI Node: BlehLatentScaleBy

BlehLatentScaleBy:

BlehLatentScaleBy is a powerful node designed to upscale latent space representations of images, providing flexibility in both horizontal and vertical scaling methods. This node is particularly useful for AI artists looking to enhance the resolution of their generated images while maintaining control over the scaling process. By allowing different methods for horizontal and vertical upscaling, it offers a high degree of customization, ensuring that the upscaled images meet specific artistic requirements. The node also includes options for antialiasing, which helps in reducing artifacts and preserving image quality during the scaling process. Overall, BlehLatentScaleBy is an essential tool for refining and enhancing latent images, making it a valuable addition to any AI artist's toolkit.

BlehLatentScaleBy Input Parameters:

samples

samples is the latent representation of the image that you want to upscale. This parameter is essential as it contains the data that will be processed by the node.

method_horizontal

method_horizontal specifies the method used for horizontal upscaling. The available options are defined in UPSCALE_METHODS, which typically include methods like "nearest-exact", "bilinear", "area", and "bicubic". This parameter allows you to choose the most suitable method for your specific needs, impacting the quality and characteristics of the upscaled image.

method_vertical

method_vertical specifies the method used for vertical upscaling. It can be set to "same" to use the same method as method_horizontal or any method from UPSCALE_METHODS. This flexibility allows for different scaling techniques in horizontal and vertical directions, providing more control over the final image quality.

scale_width

scale_width is a float value that determines the scaling factor for the width of the image. The default value is 1.5, with a minimum of 0.01 and a maximum of 8.0. This parameter allows you to increase or decrease the width of the image by the specified factor, giving you control over the aspect ratio and resolution.

scale_height

scale_height is a float value that determines the scaling factor for the height of the image. Similar to scale_width, the default value is 1.5, with a minimum of 0.01 and a maximum of 8.0. This parameter allows you to adjust the height of the image, providing flexibility in the overall scaling process.

antialias_size

antialias_size is an integer that specifies the size of the antialiasing filter. The default value is 0. Antialiasing helps in reducing artifacts and smoothing the image during the scaling process. A higher value can result in a smoother image but may also increase processing time.

BlehLatentScaleBy Output Parameters:

LATENT

The output parameter is LATENT, which is the upscaled latent representation of the image. This output retains the enhanced resolution and quality as specified by the input parameters, ready for further processing or final rendering.

BlehLatentScaleBy Usage Tips:

• Experiment with different method_horizontal and method_vertical settings to find the best combination for your specific image.
• Use scale_width and scale_height to maintain the aspect ratio of the image if necessary.
• Adjust antialias_size to reduce artifacts and improve image quality, especially when working with high scaling factors.

BlehLatentScaleBy Common Errors and Solutions:

ValueError: Input tensors a and b must have the same shape.

• Explanation: This error occurs when the input tensors for interpolation do not have the same shape.
• Solution: Ensure that the input tensors have the same dimensions before passing them to the node.

TypeError: Expected tensor

• Explanation: This error occurs when the input is not a tensor.
• Solution: Verify that the input data is in tensor format before using the node.

RuntimeError: Sizes of tensors must match except in dimension 1.

• Explanation: This error occurs when the sizes of the tensors do not match in dimensions other than the first one.
• Solution: Check the dimensions of the input tensors and ensure they match except for the batch dimension.