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Converts image to float values by sampling width and height, aiding AI artists in analyzing visual gradients programmatically.
The GradientToFloat
node is designed to convert an image into a list of float values by sampling the image along its width and height. This node is particularly useful for AI artists who want to extract numerical data from visual gradients, enabling them to analyze or manipulate the image data programmatically. By sampling the image at specified intervals, the node provides a means to quantify the gradient information in a format that can be easily used for further processing or analysis. This can be beneficial for tasks such as texture analysis, pattern recognition, or any application where understanding the gradient distribution within an image is crucial.
The image
parameter expects an image input, which is the source from which the gradient information will be extracted. The image should be in a format that the node can process, typically a tensor with shape [B, H, W, C], where B is the batch size, H is the height, W is the width, and C is the number of channels. This parameter is required for the node to function.
The steps
parameter determines the number of intervals at which the image will be sampled along its width and height. This integer value controls the resolution of the sampling process, with higher values providing more detailed gradient information. The default value is 10, with a minimum of 2 and a maximum of 10000. Adjusting this parameter allows you to balance between computational efficiency and the granularity of the extracted data.
The float_x
output is a list of float values representing the sampled gradient information along the width of the image. These values are obtained by averaging the pixel values across the height for each sampled interval along the width. This output provides a numerical representation of the horizontal gradient distribution in the image.
The float_y
output is a list of float values representing the sampled gradient information along the height of the image. These values are obtained by averaging the pixel values across the width for each sampled interval along the height. This output provides a numerical representation of the vertical gradient distribution in the image.
steps
parameter, but be mindful of the potential increase in computational load.float_x
and float_y
outputs to create custom visualizations or to feed into other nodes for further processing.steps
parameter is set to a value outside the allowed range (2 to 10000).steps
parameter to be within the specified range.© Copyright 2024 RunComfy. All Rights Reserved.