Latent Utils for ComfyUI

*Example output showing original (left) and enhanced image (right)*

*high_freq_mult: 2 is an extreme value to show the difference (demo only).*

🌟 Overview

This custom ComfyUI node performs selective latent denoising and detail enhancement using Fourier Transform (FFT) techniques. It intelligently separates image frequencies to:

Preserve and enhance important high-frequency details

Suppress background noise and artifacts

Output a visual preview of the processing mask

Ideal for refining AI-generated images while maintaining sharp features and eliminating graininess.

Supports models:

Wan vae (Qwen, etc)

Flux vae (ZImageTurbo, Flux1.dev, etc)

Other models not tested

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⚙️ Installation

Navigate to your ComfyUI custom nodes directory:

cd ComfyUI/custom_nodes

Clone this repository:

git clone https://github.com/lrzjason/comfyui-latent-frequency-enhancer.git

Restart ComfyUI

Find the nodes under:

latent → enhancement → Latent Frequency Enhancer (lrzjason)

sampling → HFEPostProcessor (lrzjason)

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🔬 How It Works

Frequency Separation

Uses FFT to split latent into:

Low frequencies: Base composition and smooth areas

High frequencies: Details, textures, and noise

Smart Mask Generation

Creates a dynamic mask using:

Sigmoid soft-gating function

Adjustable noise threshold

Pre-blur for noise coherence

Selective Enhancement

Boosts important high-frequency details

Suppresses noise below threshold

Smoothly blends components using frequency-aware masking

Visual Feedback

Outputs a mask preview showing:

White areas: Preserved/enhanced details

Black areas: Suppressed noise

Gray transitions: Smooth blending zones

🚀 HFEPostProcessor: In-Process Enhancement

The HFEPostProcessor node applies high-frequency enhancement during the sampling process rather than after it. This approach:

Applies enhancement at specific sampling steps (after basic sampling)

Integrates frequency enhancement directly into the generation pipeline

Allows for more refined control by specifying which steps to enhance

Works as a sampler replacement that combines sampling with enhancement

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🎚️ Parameters Explained

Latent Frequency Enhancer Node

| Parameter | Default | Range | Description |

|———–|———|——-|————-|

| Detail Strength (HF Mult) | 1.15 | 1.0-2.0 | Multiplier for high-frequency details (values >1 enhance details) |

| Frequency Split Sigma | 2.0 | 0.1-20.0 | Controls frequency separation sharpness (higher = more low frequencies preserved) |

| Noise Threshold | 0.05 | 0.0-1.0 | Minimum magnitude to preserve details (higher = more aggressive denoising) |

| Mask Hardness | 2.0 | 1.0-100.0 | Transition sharpness in noise suppression (higher = sharper cutoff) |

| Noise Grouping (Pre-Blur) | 0.5 | 0.0-1.0 | Pre-blur strength for noise coherence (0.0 disables) |

HFEPostProcessor Node

| Parameter | Default | Range | Description |

|———–|———|——-|————-|

| Model | – | – | The diffusion model to use for sampling |

| Steps | 8 | 1-10000 | Total number of sampled steps |

| HFE Steps | 2 | 1-100 | Number of steps to apply high-frequency enhancement |

| Latent Image | – | – | The latent image to enhance |

| Noise Seed | 0 | 0-18446744073709551615 | Random seed for noise generation |

| CFG Scale | 1.0 | 0.0-100.0 | Classifier-free guidance scale |

| Sampler Name | – | Various | Name of the sampler to use |

| Scheduler | – | Various | Scheduler to use for sampling |

| Positive | – | – | Positive conditioning |

| Negative | – | – | Negative conditioning |

| Detail Strength (HF Mult) | 1.05 | 1.0-2.0 | Multiplier for high-frequency details during enhancement |

| Frequency Split Sigma | 5.0 | 0.01-20.0 | Controls frequency separation during enhancement |

| Noise Threshold | 0.05 | 0.0-1.0 | Minimum magnitude to preserve details during enhancement |

| Mask Hardness | 2.0 | 0.01-100.0 | Transition sharpness in noise suppression during enhancement |

| Noise Grouping (Pre-Blur) | 0.5 | 0.0-1.0 | Pre-blur strength for noise coherence during enhancement |

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🖼️ Output Preview

The Latent Frequency Enhancer node outputs two items:

Enhanced Latent (enhanced_latent)

The processed latent ready for decoding

Mask Preview (mask_preview)

Visual representation of the processing mask

The HFEPostProcessor node outputs one item:

Enhanced Latent (LATENT)

The final processed latent after both sampling and enhancement

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💡 Pro Tips

For Latent Frequency Enhancer Node:

Start with defaults for most images, then adjust:

Increase Detail Strength for sharper outputs

Raise Noise Threshold for noisy generations

Lower Frequency Split Sigma for cartoon/anime styles

Mask interpretation:

If mask shows important details as black → Lower noise threshold

If noise remains visible → Increase mask hardness

For soft-focus effects → Increase pre-blur sigma

Combine with other nodes:

Use after KSampler but before VAEDecode

Chain with ControlNet for detail preservation

Follow with Color Correct nodes for final polish

For HFEPostProcessor Node:

Integration with sampling:

The Steps parameter should match your basic sampling steps

Use HFE Steps to specify how many steps to apply high-frequency enhancement

Start with fewer HFE steps (2-4) and adjust as needed

Parameter adjustments:

Use lower Detail Strength values (1.05-1.15) compared to post-process mode

Higher Frequency Split Sigma (5.0+) may work better during sampling

The enhancement happens mid-generation, so parameters may differ from post-processing

Workflow integration:

Replace standard samplers with HFEPostProcessor to combine sampling and enhancement

The node handles both sampling and enhancement in one step

Use when you want the enhancement to influence the remaining sampling steps

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📜 Technical Notes

For Latent Frequency Enhancer Node:

WAN Format Compatible: Automatically handles WAN-style latent tensors

Memory Efficient: Processes entirely on GPU when available

Deterministic: Uses stable FFT operations with no random elements

For HFEPostProcessor Node:

Integrated Sampling: Combines sampling and enhancement in one node

Step Control: Allows specifying when during sampling enhancement begins

Compatible: Works with all standard ComfyUI samplers and schedulers

Efficient: Applies enhancement only at specified steps for optimal performance

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🙏 Credits

FFT implementation inspired by academic frequency-domain processing papers

Sigmoid gating adapted from noise2noise research

ComfyUI node template by ComfyOrg

Note: This is a research-grade implementation. Results may vary based on model and generation parameters. Always validate outputs visually.

[](LICENSE)

*For research and personal use only. Not for commercial deployment without permission.*

Contact

Twitter: @Lrzjason

Email: lrzjason@gmail.com

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Wechatid: fkdeai

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