ComfyUI Phase Stretch Transform Node

A ComfyUI custom node that applies the Phase Stretch Transform (PST) to images. PST is a physics-inspired edge detection algorithm that enhances features by simulating wave propagation through a dispersive medium.

Installation

1. Clone or copy this folder into your ComfyUI custom_nodes directory:

   ComfyUI/custom_nodes/Comfyui_PST/
   

2. Install dependencies:

   pip install numpy scipy
   

3. Restart ComfyUI.

The node will appear under image → processing in the node menu.

Node: Phase Stretch Transform

Inputs

Name	Type	Default	Description
image	IMAGE	—	Input image (RGB or grayscale)
lpf_sigma	FLOAT	0.21	Gaussian low-pass filter sigma applied before PST. Higher values smooth the image more, reducing noise sensitivity.
phase_strength	FLOAT	0.48	PST phase strength (S). Controls how strongly phase variations are amplified.
warp_strength	FLOAT	12.14	PST warp strength (W). Controls the frequency-domain warping; higher values emphasise finer edges.
min_thresh	FLOAT	-1.0	Lower phase threshold for binary edge extraction.
max_thresh	FLOAT	0.003	Upper phase threshold for binary edge extraction.
morph_flag	BOOLEAN	True	When enabled, applies morphological closing and opening to clean up the binary edge map. When disabled, outputs a soft (normalised) phase image instead.
output_mode	COMBO	grayscale	grayscale – white edges on black background. rgb_overlay – edges drawn in red over the original image.

Outputs

Name	Type	Description
edge_image	IMAGE	Processed edge map (binary or overlay depending on output_mode).
phase_image	IMAGE	Raw PST phase output normalised to 0–1, useful for further processing or visualisation.

Algorithm

PST processes each image through the following steps:

1. Convert input to grayscale (luminance weights: R×0.2126, G×0.7152, B×0.0722).
2. Compute the 2D FFT and shift to centre zero frequency.
3. Apply a Gaussian low-pass filter in the frequency domain.
4. Apply the PST kernel — a warped phase ramp:

K(r) = \frac{W \cdot r}{1 + W \cdot r} \cdot e^{-j \, S \cdot r \, / \, (1 + r)}

where r is the radial frequency and W, S are the warp and phase strength parameters.

5. Compute the inverse FFT.
6. Extract the phase angle of the complex result.
7. Threshold the phase to produce a binary edge map, then apply morphological clean-up (if morph_flag is enabled).

Parameter Tuning Guide

• More edges / finer detail — increase phase_strength or warp_strength.
• Fewer false edges / less noise — increase lpf_sigma or tighten the threshold range (min_thresh closer to 0, max_thresh closer to 0).
• Soft/continuous edge map — disable morph_flag; use the phase_image output.
• Overlay on original — set output_mode to rgb_overlay.

Example Workflow

Load Image → Phase Stretch Transform → Preview Image
                       ↓
              (edge_image or phase_image)

Screenshots

Requirements

• Python ≥ 3.10
• numpy
• scipy
• torch (provided by ComfyUI)

License

MIT