ComfyUI Node: Latent Batch Statistics Plot

Latent Batch Statistics Plot:

The Latent Batch Statistics Plot node is designed to analyze and visualize the statistical properties of a batch of latent variables. This node performs a Shapiro-Wilk test on each latent in the batch to assess how closely each latent follows a normal distribution. Additionally, it calculates the mean and standard deviation for each latent. The results are then plotted in a comprehensive figure that includes subplots for the Shapiro-Wilk test p-values, the means, and the standard deviations of the latents. This visualization helps you understand the distribution and variability of the latents in your batch, providing valuable insights for further analysis or adjustments in your AI art generation process.

Latent Batch Statistics Plot Input Parameters:

batch

This parameter represents the batch of latent variables that you want to analyze. The batch should be in the form of a tensor, where each element in the batch is a latent variable. The node will process each latent in the batch individually to compute the necessary statistics. The batch parameter is required for the node to function correctly.

Latent Batch Statistics Plot Output Parameters:

plot_image

This output parameter provides the generated plot as an image. The plot includes three subplots: one for the Shapiro-Wilk test p-values, one for the means of each latent, and one for the standard deviations of each latent. This image helps you visualize the statistical properties of the latents in your batch, making it easier to interpret and analyze the results.

Latent Batch Statistics Plot Usage Tips:

• Ensure that your batch of latents is properly formatted and contains valid latent variables before running the node.
• Use the generated plot to identify any latents that significantly deviate from a normal distribution, as indicated by low p-values in the Shapiro-Wilk test subplot.
• Analyze the means and standard deviations subplots to understand the central tendency and variability of your latents, which can inform adjustments to your AI art generation process.

Latent Batch Statistics Plot Common Errors and Solutions:

ValueError: Latent batches must have the same shape

• Explanation: This error occurs when the latent batches being compared have different shapes, which prevents the node from performing the necessary calculations.
• Solution: Ensure that all latent variables in your batch have the same shape before running the node. You may need to preprocess your latents to ensure uniform dimensions.

TypeError: 'NoneType' object is not subscriptable

• Explanation: This error occurs when the batch parameter is not provided or is incorrectly formatted, leading to a failure in accessing the latent variables.
• Solution: Verify that you have correctly provided a valid batch of latent variables as input to the node. Ensure that the batch is a properly formatted tensor containing the latents you wish to analyze.