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ComfyDeploy: How Comfy Latent Tools works in ComfyUI?

What is Comfy Latent Tools?

A set of tools for manipulating latent tensors in ComfyUI

How to install it in ComfyDeploy?

Head over to the machine page

  1. Click on the "Create a new machine" button
  2. Select the Edit build steps
  3. Add a new step -> Custom Node
  4. Search for Comfy Latent Tools and select it
  5. Close the build step dialig and then click on the "Save" button to rebuild the machine

ComfyLatentTools

A set of custom nodes for ComfyUI, providing a specialized Latent Normalized Lanczos Resize workflow.

Installation

  1. Download or clone this repository into your ComfyUI's custom_nodes folder.
  2. Restart ComfyUI. The node(s) will appear in the image/upscaling category.

Node Overview

MANY MISSING NODES HERE - WILL BE UPDATED

Rescaled PAG ( Perturbed Attention Guidance )

Implementation of Perturbed Attention Guidance that also takes into account the insight of RescaleCFG.

Compared to vanilla PAG this results in much better dynamic range and much less "deep frying" or oversaturation.

This is still a little bit experimental and some amount of experimentation with sampling parameters and PAG parameters is recommended. pag_scale = 1/2 cfg is a good starting point and I recommend a post rescale close to 1.0.

The samplers seem to converge faster with this turned on so a decrease of number of steps might be possible with little loss of quality.

Parameters

Latent Normalized Lanczos Resize (LNLR)

A specialized upscaling node designed to:

  • Perform a Lanczos upscale in image space,
  • Re-encode to latent space,
  • Match the original latent's mean/variance,
  • Optionally add correlated noise or blend with a pure latent-based upscale.

This aims to produce an upscaled latent that stays more faithful to the original diffusion pass, avoiding excessive blur, and optionally adding noise. It can serve as a fast base for subsequent (re-)diffusion or refinement steps, in which case the noise addition can help to introduce additional details and variations at different scales.

Internal Operation Order

  1. Soft Outlier Clamp (optional)
    Uses a “huberize_quantile” method to softly clamp outliers.
  2. Decode
    Converts latent to image.
  3. Lanczos Upscale
    Upscales the image.
  4. Encode
    Converts upscaled image back to latent space.
  5. Weighted Latent Upscale (optional)
    Blends the new latent with a nearest exact upsampled version of the original latent, if the corresponding weight > 0
  6. Moment Matching
    Aligns mean and variance of the upscaled latent with the original.
  7. Add Correlated Gaussian Noise (optional)
    Injects correlated noise for additional variation.

Parameters

  • size_multiplier
    Multiplies original spatial dimensions (width/height).
    (Default: 2.0; Range: 0.1–4.0)

  • soft_clamp_outliers (enable/disable)
    Toggles outlier soft-clamping before decoding.
    (Default: enable)

  • outlier_quantile
    Quantile threshold for outlier soft-clamping.
    (Default: 0.01; Range: 0–1)

  • outlier_clamp_slope
    Slope of the clamp outside the quantile range.
    (Default: 0.1; Range: 0–1)

  • add_latent_noise (enable/disable)
    Adds correlated Gaussian noise to the final latent.
    (Default: disable)

  • latent_noise_std
    Standard deviation for the generated noise.
    (Default: 1.0; Range: 0–10)

  • latent_noise_scale
    Scaling factor applied to the correlated noise before adding.
    (Default: 0.1; Range: 0.01–10)

  • add_latent_upscale_with_weight
    Blends the newly encoded latent with a direct “latent nearest” upscale.
    (Default: 0.0; Range: 0–1)

Additional pages