ComfyUI-LCS

Training-free color control via the Latent Color Subspace, plus sharpness control via a discovered sharpness subspace.

Note: This is an unofficial community implementation. For the official code, see ExplainableML/LCS.

Based on "The Latent Color Subspace" (ICML 2026): color in diffusion model latent patch spaces lives in a 3D subspace (PCA captures 100% color variance), while the remaining 61 dimensions encode structure and detail orthogonally.

This plugin steers colors directly in the 3D LCS during diffusion sampling — no training, no LoRA, no post-processing.

中文版 README

LCS vs Traditional Post-Processing

LCS operates during diffusion sampling, not after — this is the key difference from traditional color grading (Photoshop, filters, etc.).

	Traditional Post-Processing	LCS
When	After VAE decode, in pixel space	During sampling, in latent space
Mechanism	Color filter on the final image	Modifies 3D color subspace mid-generation
Model awareness	None — structure already locked	Model adapts to color shifts in subsequent steps
Result	Colors can look "painted on"	Colors look naturally intended by the model

For example: to get a warm orange sunset, post-processing tints everything orange (muddying shadows and skin tones), while LCS nudges the color subspace early in sampling so clouds, lighting, and reflections are coherently warm.

The core insight: color and structure are orthogonal in the latent patch space — you can steer one without disturbing the other.

Tested Models

Model	Status
FLUX	Tested
FLUX2.klein	Tested
z-image	Tested
z-image-turbo	Tested
Wan (qwen-image)	Tested

LCS calibrates per-VAE, so it should work with any model using a compatible VAE. Feel free to report results with other models.

Features

• Color Steering — Push colors toward any target color
• Batch Multi-Color — Different colors per batch item
• Tone Adjustment — Contrast, brightness, saturation, temperature with one-click presets
• Sharpness Control — Sharpen or blur during generation via a discovered sharpness subspace (PC1 explains ~97% variance)
• Localized Control — Optional mask for region-specific changes
• Latent Color Preview — Visualize color structure without VAE decoding
• Step Observer — Per-step color previews for debugging

Installation

cd ComfyUI/custom_nodes
git clone https://github.com/facok/ComfyUI-LCS.git

Dependencies (usually already present in ComfyUI):

pip install einops safetensors

Quick Start

Basic Color Control

LCS Load Data → LCS Color Intervene → KSampler
                       ↑
                  (pick a color)

1. LCS Load Data — connect your VAE (auto-calibrates on first run)
2. LCS Color Intervene — connect MODEL and LCS_DATA, pick a target color
3. Connect the output MODEL to KSampler

Tone Adjustment

LCS Load Data → LCS Tone Adjust → KSampler

1. LCS Load Data → LCS Tone Adjust
2. Select a preset (e.g., "Cinematic") or adjust sliders manually

Sharpness Control

LCS Load Data ──→ LCS Sharpness Calibrate → LCS Sharpness Intervene → KSampler
                        ↑ lcs_data

1. LCS Sharpness Calibrate — connect VAE (auto-calibrates and caches). Optionally connect lcs_data from LCS Load Data to ensure sharpness edits don't affect color.
2. LCS Sharpness Intervene — connect MODEL and SHARPNESS_DATA, set strength
   • Positive strength → sharper
   • Negative strength → blurrier
   • 0 → no change

Multi-Color Batch

LCS Load Data → LCS Color Batch → KSampler
                      ↓
                  batch_size → EmptyLatentImage

Enter comma-separated hex colors (e.g., #FF0000,#00FF00,#0000FF). Each color applies to one batch item.

Nodes

Calibration

Node	Description
LCS Load Data	Auto-calibrate and cache LCS color data per-VAE. Fingerprints VAE weights for automatic cache management.
LCS Sharpness Calibrate	Discover sharpness subspace via PCA on blur stimuli. Optionally connect lcs_data for color-orthogonal sharpness.

Calibration runs once per VAE and caches automatically. Subsequent runs load instantly.

Intervention

Node	Description
LCS Color Intervene	Steer colors toward a target. Supports Type I (LCS shift), Type II (HSL shift), or interpolated mode.
LCS Color Batch	Different target colors per batch item. Outputs batch_size for EmptyLatentImage.
LCS Tone Adjust	Contrast, brightness, saturation, temperature. Preset dropdown with real-time slider sync.
LCS Sharpness Intervene	Control sharpness during generation. Positive = sharper, negative = blurrier.

Observation

Node	Description
LCS Preview Colors	Decode latent colors to RGB preview without VAE decoding.
LCS Step Observer	Save per-step color preview PNGs to ComfyUI temp directory.

Intervention Modes

Mode	Description	Best For
interpolated (default)	Blends Type I and Type II using sigma	General use
type_i	Direct translation in 3D LCS space	Strong global color shifts
type_ii	Per-patch HSL interpolation via bicone geometry	Precise local color control

Key Parameters

Color Intervention

• strength (0.0–2.0): Intervention intensity. 1.0 = full, 0.0 = none.
• start_step / end_step: Step range for intervention. Paper optimal: steps 8–10 of 50.
• mask: Optional. Downsampled to patch grid for localized control.

Sharpness Intervention

• strength (-5.0–5.0): Positive = sharper, negative = blurrier, 0 = no change.
• start_step / end_step: Step range (default 5–15).
• mask: Optional. Localized sharpness control.

Tip for distilled models: Step-distilled models (e.g., z-image-turbo) use far fewer steps, so intervention should start earlier — even from step 0.

Tone Presets

Select a preset — sliders update in real-time. Tweak after selecting for fine-tuning. Select Custom to set values manually.

Preset	Contrast	Brightness	Saturation	Temperature
Base	1.0	0.0	1.0	0.0
Cinematic	1.20	-0.05	0.90	0.05
HDR	1.40	0.0	1.20	0.0
Vivid	1.10	0.0	1.50	0.0
Dramatic	1.50	-0.10	0.85	0.0
Low Key	1.30	-0.20	0.80	0.0
High Key	0.80	0.20	0.90	0.0
Warm	1.05	0.03	1.10	0.30
Cool	1.05	0.0	1.05	-0.30
Desaturated	1.0	0.0	0.40	0.0

How It Works

Color (LCS)

1. Project — Convert denoised prediction to 64D patch space, project onto 3D LCS basis
2. Decompose — Separate 3D color coordinates from the 61D structural residual
3. Normalize — Transform to reference timestep (t=50) using learned alpha/beta statistics
4. Manipulate — Shift colors, adjust tone, or apply other transformations in 3D LCS
5. Reconstruct — Denormalize, add back the preserved 61D residual, convert to latent space

The 61D residual (structure, texture, detail) is never modified — only the 3D color subspace is touched.

Sharpness

Sharpness lives in a separate subspace orthogonal to color:

1. Calibrate — Generate grayscale noise images at multiple blur levels, VAE-encode, PCA on color-removed patch vectors. PC1 captures ~97% of sharpness variance.
2. Intervene — Add strength * pc1_direction to each patch. Since pc1_direction is orthogonal to color (calibrated with LCS removal) and DC-free (per-vector zero-mean before PCA), this modifies only spatial frequency content without affecting color or brightness.

File Structure

ComfyUI-LCS/
├── __init__.py           # Entry point (V3 + V2 compat)
├── requirements.txt
├── core/
│   ├── calibration.py    # PCA calibration pipeline (color)
│   ├── color_space.py    # Bicone LCS ↔ HSL mapping
│   ├── defaults.py       # Alpha/beta tables from paper
│   ├── lcs_data.py       # LCSData dataclass
│   ├── patchify.py       # Patch ↔ latent conversion
│   ├── sampling.py       # Shared constants & step utilities
│   ├── sharpness.py      # Sharpness subspace calibration
│   └── timestep.py       # Sigma/timestep utilities
├── nodes/
│   ├── calibrate.py      # LCSLoadData (auto-calibrate + cache)
│   ├── intervene.py      # LCSColorIntervene, LCSColorBatch, LCSToneAdjust
│   ├── observe.py        # LCSPreviewColors, LCSStepObserver
│   └── sharpen.py        # LCSSharpnessCalibrate, LCSSharpnessIntervene
├── data/                 # Cached calibration files
└── web/js/
    └── tone_preset.js    # Frontend preset sync

Citation

Official repository: ExplainableML/LCS

@article{pach2026latentcolorsubspace,
  title={The Latent Color Subspace: Emergent Order in High-Dimensional Chaos},
  author={Mateusz Pach and Jessica Bader and Quentin Bouniot and Serge Belongie and Zeynep Akata},
  journal={arxiv},
  year={2026}
}

Acknowledgments

Thanks to Mateusz Pach, Jessica Bader, Quentin Bouniot, Serge Belongie, and Zeynep Akata for their research making training-free color control possible.

License

MIT