ComfyAssets-ComfyUI-KikoTools/examples/documentation/plot_parameters.md

Plot Parameters

Overview

The Plot Parameters node creates visual graphs and plots from sampler parameters, enabling data-driven analysis of generation settings. Adapted from comfyui-essentials-nodes (now in maintenance mode), this tool helps visualize the relationship between parameters and output quality.

Attribution

This node is based on work from comfyui-essentials-nodes by cubiq. The original project is in maintenance-only mode, and we've adopted and enhanced these tools to ensure continued support and compatibility with modern ComfyUI workflows.

Features

• Multi-Parameter Plotting: Visualize multiple parameters simultaneously
• Comparison Graphs: Compare settings across batch runs
• Statistical Analysis: Calculate means, deviations, and trends
• Export Capabilities: Save plots as images or data files
• Real-time Updates: Dynamic graph generation during workflow execution

Node Properties

• Category: ComfyAssets/🧰 xyz-helpers
• Node Name: PlotParameters
• Function: plot

Inputs

Required

Parameter	Type	Default	Description
sampler_params	SAMPLER_PARAMS	-	Parameters to plot
plot_type	DROPDOWN	line	[line, bar, scatter, heatmap]
x_axis	DROPDOWN	steps	Parameter for X axis
y_axis	DROPDOWN	quality	Metric for Y axis

Optional

Parameter	Type	Default	Description
title	STRING	"Parameter Analysis"	Graph title
show_grid	BOOLEAN	True	Display grid lines
show_legend	BOOLEAN	True	Display legend
color_scheme	DROPDOWN	default	Color palette selection

Outputs

Name	Type	Description
plot_image	IMAGE	Generated plot as image
data_csv	STRING	Plot data in CSV format
statistics	STRING	Statistical summary

Usage Examples

Basic Parameter Visualization

FluxSamplerParams → PlotParameters → Display Image
    plot_type: line
    x_axis: steps
    y_axis: guidance

Batch Comparison Plot

LoRAFolderBatch → PlotParameters → Save Image
    plot_type: scatter
    x_axis: lora_strength
    y_axis: quality_score

Heatmap Analysis

Parameter Grid → PlotParameters → Analysis Display
    plot_type: heatmap
    x_axis: cfg
    y_axis: steps

Plot Types Explained

Line Plot

• Best for continuous parameter changes
• Shows trends and relationships
• Ideal for time series or progression

Bar Chart

• Compares discrete values
• Good for categorical comparisons
• Shows distribution clearly

Scatter Plot

• Reveals correlations
• Identifies outliers
• Best for large datasets

Heatmap

• Two-dimensional parameter analysis
• Color-coded intensity values
• Perfect for grid searches

Best Practices

Parameter Selection

• Choose related parameters for meaningful plots
• Use consistent scales for comparison
• Consider parameter ranges when plotting

Visual Clarity

• Limit number of series to 5-7 for readability
• Use contrasting colors for multiple lines
• Enable grid for precise value reading

Data Analysis

# Effective parameter combinations
x_axis: "guidance"
y_axis: "perceived_quality"

# Step efficiency analysis  
x_axis: "steps"
y_axis: "generation_time"

# LoRA impact assessment
x_axis: "lora_strength"  
y_axis: "style_adherence"

Integration Examples

Complete Analysis Pipeline

1. Generate with parameters
2. Plot results
3. Export data
4. Statistical analysis

Multi-Plot Workflow

Params → Plot1 (steps vs quality)
      ↘ Plot2 (guidance vs coherence)
      ↘ Plot3 (strength vs style)
      → Combined Analysis

Advanced Features

Custom Metrics

• Define custom Y-axis metrics
• Import external quality scores
• Calculate derived values

Export Options

• PNG/SVG image formats
• CSV data export
• JSON statistics export

Styling Options

# Professional presentation
color_scheme: "scientific"
show_grid: True
show_legend: True

# Minimal style
color_scheme: "minimal"
show_grid: False
show_legend: False

Statistical Analysis

Available Metrics

• Mean, Median, Mode
• Standard Deviation
• Correlation Coefficients
• Trend Lines
• R-squared Values

Interpretation Guide

• Positive Correlation: Parameters increase together
• Negative Correlation: Inverse relationship
• No Correlation: Independent parameters

Tips and Tricks

Optimal Visualization

1. Start with scatter plots for exploration
2. Use line plots for trends
3. Apply heatmaps for 2D parameter spaces
4. Bar charts for final comparisons

Data Preparation

• Normalize scales when comparing different metrics
• Remove outliers for cleaner plots
• Group similar parameters

Performance Tips

• Cache plot images for repeated viewing
• Export data for external analysis
• Use lower resolution for preview plots

Troubleshooting

Empty Plots

• Verify sampler_params contains data
• Check axis parameter selection
• Ensure valid parameter ranges

Scaling Issues

• Use logarithmic scale for wide ranges
• Normalize data if needed
• Adjust plot dimensions

Export Problems

• Check file permissions
• Verify export path exists
• Ensure sufficient disk space

Use Cases

Hyperparameter Optimization

Track and visualize the effect of different sampling parameters on output quality.

LoRA Strength Analysis

Plot the relationship between LoRA strength and style transfer effectiveness.

Efficiency Studies

Analyze generation time vs quality trade-offs across different settings.

Batch Comparison

Compare multiple generation runs to identify optimal parameters.

Version History

• 1.0.0: Initial adaptation from comfyui-essentials-nodes
• 1.0.1: Added heatmap visualization
• 1.0.2: Enhanced statistical analysis
• 1.0.3: Improved export capabilities

Credits

Original implementation by cubiq in comfyui-essentials-nodes. Adapted and maintained by the ComfyAssets team.