High-Frequency Detail Sampling based on Res Sampling

This is a ComfyUI custom node that enhances fine detail preservation in diffusion model outputs using spectral high-frequency emphasis (HFE).

Installation

Clone or copy this folder into your ComfyUI custom_nodes directory:

ComfyUI/
  custom_nodes/
    RES4SHO/
      __init__.py
      sampling.py

Restart ComfyUI. The new samplers and schedulers will appear in the dropdown menus of any KSampler node.

Samplers

All samplers are exponential integrators with phi-function coefficients. The HFE enhancement extracts high-frequency detail from inter-stage correction deltas via a 3x3 spatial high-pass filter and re-injects it with configurable strength.

Fixed-Strength Presets

Each stage count offers 8 strength levels (s1 = no emphasis, s8 = maximum potential sharpness):

Sampler	Stages	Model Evals/Step
hfe_s1 .. hfe_s8	2	2
hfe3_s1 .. hfe3_s8	3	3
hfe4_s1 .. hfe4_s8	4	4
hfe5_s1 .. hfe5_s8	5	5

Higher stage counts provide better ODE integration accuracy at the cost of more model evaluations per step.

Adaptive (Auto) Samplers

Per-step adaptive eta based on sigma envelope and content gating:

Sampler	Stages	Description
hfe_auto	2	Variable c2, eta, and kernel per step
hfe3_auto	3	Per-step eta with 3-stage integrator
hfe4_auto	4	Per-step eta with 4-stage integrator
hfe5_auto	5	Per-step eta with 5-stage integrator

How auto adapts:

• Sigma envelope (smoothstep): suppresses emphasis at high noise (early steps), full strength in the detail-forming range
• Content gate: reduces emphasis when the model correction is already HF-rich; increases it when the correction is smooth and needs boosting

Experimental Samplers (hfx_*)

Alternative HF extraction methods, all using a 2-stage base:

Sampler	Method
hfx_lap	Laplacian pyramid multi-scale (3 bands)
hfx_mom	Correction momentum (EMA across steps)
hfx_fft	FFT spectral high-pass with smooth cutoff
hfx_sde	Stochastic HF noise injection
hfx_spatial	Spatially-adaptive per-pixel gating

Hybrids (combine two techniques):

• hfx_lap_mom -- Laplacian pyramid + momentum
• hfx_lap_spatial -- Laplacian pyramid + spatial gating
• hfx_fft_spatial -- FFT spectral + spatial gating

Band profile variants:

• hfx_lap_fine -- fine-detail emphasis (edges, texture)
• hfx_lap_broad -- even emphasis across frequency bands

Each experimental mode also has 4 graduated strength presets (_s1 .. _s4), e.g. hfx_lap_s1, hfx_mom_s3, etc.

Schedulers

Arctangent S-curve schedulers that concentrate step density in the detail-forming sigma range:

Scheduler	Description
atan_gentle	Mild mid-sigma concentration
atan_focused	Moderate detail-range concentration
atan_steep	Aggressive detail-range concentration
karras_tan	Karras-Tangent hybrid (experimental)
logistic	Logistic sigmoid S-curve (experimental)

An ASCII sigma chart is printed to the console when a scheduler is used.

Recommended Combinations

Getting Started

Goal	Sampler	Scheduler	Notes
General use	hfe_auto	atan_focused	Best all-rounder -- adaptive emphasis handles most content
Subtle enhancement	hfe_s3	atan_gentle	Light touch, minimal risk of artifacts
Strong detail	hfe_s6	atan_steep	Noticeably sharper textures and edges
Maximum sharpness	hfe_s7 or hfe_s8	atan_steep	Aggressive -- inspect for over-sharpening

By Content Type

Content	Sampler	Scheduler	Why
Portraits / faces	hfe_auto	atan_focused	Auto gate protects smooth skin while sharpening eyes, hair, pores
Landscapes / nature	hfe_s5	atan_gentle	Fixed mid-strength avoids over-enhancing skies and gradients
Architecture / hard surfaces	hfe_s7	atan_steep	Strong emphasis on edges and geometric detail
Text / UI renders	hfx_lap_fine	atan_steep	Fine-band Laplacian targets glyph edges specifically
Fabric / organic texture	hfx_lap_broad	atan_focused	Even multi-scale emphasis across weave and folds
Illustrations / anime	hfe_s4	atan_gentle	Light emphasis preserves flat shading without adding unwanted texture

High-Accuracy Integrators

More model evaluations per step for better ODE integration -- useful at low step counts or with difficult models:

Sampler	Scheduler	Use Case
hfe3_auto	atan_focused	Good balance of accuracy and speed (3 evals/step)
hfe4_auto	atan_focused	High accuracy for complex prompts (4 evals/step)
hfe5_auto	atan_gentle	Maximum integration accuracy (5 evals/step)
hfe4_s5	atan_steep	Fixed-strength detail + 4-stage accuracy
hfe5_s6	karras_tan	High emphasis + high accuracy + Karras hybrid spacing

Experimental Combinations

Sampler	Scheduler	Character
hfx_lap	atan_focused	Multi-scale detail -- good default experimental choice
hfx_fft	atan_steep	Frequency-domain sharpening -- clean spectral separation
hfx_spatial	atan_focused	Sharpens high-variance regions, leaves smooth areas alone
hfx_mom	atan_gentle	Accumulates detail across steps -- builds up gradually
hfx_sde	atan_gentle	Stochastic texture injection -- adds micro-variation
hfx_lap_mom	atan_focused	Multi-scale + momentum -- rich progressive detail
hfx_lap_spatial	atan_steep	Multi-scale + spatial gating -- targeted sharpening
hfx_fft_spatial	atan_focused	Spectral + spatial -- precise frequency-aware gating

Scheduler Pairings

Scheduler	Best With	Character
atan_gentle	Low-strength samplers (s1-s4), stochastic modes	Mild concentration, safe for all content
atan_focused	Auto samplers, mid-strength presets (s4-s6)	Balanced step density in detail range
atan_steep	High-strength samplers (s6-s8), architectural content	Aggressive detail-range concentration
karras_tan	High-stage integrators (hfe4_*, hfe5_*)	Karras optimal spacing + tangent warp
logistic	Any -- alternative S-curve shape	Sharper transition through detail range, flatter extremes

How It Works

Base integrator: Multi-stage singlestep exponential integrator (res_Ns) with phi-function coefficients, giving exact treatment of exponential decay and higher-order corrections from intermediate evaluations.

HFE enhancement: The inter-stage correction delta captures what the model reveals at lower noise -- texture, edges, micro-structure. A spatial high-pass (residual after box blur in latent space) extracts the fine detail component, which is re-injected with extra weight eta. This compounds across every step.

Cost: One 3x3 avg_pool per step for all variants (negligible vs. model evaluation). Auto samplers add a few scalar ops on top.

License

MIT