Latent Tools for ComfyUI

A collection of nodes for manipulating latent tensors in ComfyUI. These tools provide various operations for working with latent representations in stable diffusion workflows.

Installation

1. Clone this repository into your ComfyUI/custom_nodes/ directory
2. Restart ComfyUI

Or use the ComfyUI extension manager.

Available Nodes

Latent noise generation

LTGaussianLatent

Generates latent tensors filled with random values from a normal (Gaussian) distribution.

| | |------------| | Inputs | | - channels: Number of channels (default: 4) | | - width: Width of the latent space (will be divided by 8) | | - height: Height of the latent space (will be divided by 8) | | - batch_size: Number of samples to generate | | - mean: Mean of the normal distribution | | - std: Standard deviation of the normal distribution | | - seed: Random seed | | Outputs | | - latent: Generated latent tensor | | |

LTUniformLatent

Generates latent tensors with values uniformly distributed between min and max.

| | |------------| | Inputs | | - channels: Number of channels (default: 4) | | - width: Width of the latent space (will be divided by 8) | | - height: Height of the latent space (will be divided by 8) | | - batch_size: Number of samples to generate | | - min: Minimum value | | - max: Maximum value | | - seed: Random seed | | Outputs | | - latent: Generated latent tensor | | |

Latent Operations

LTBlendLatent

Blends two latent tensors using various blending modes.

| | |------------| | Inputs | | - latent1: First latent tensor | | - latent2: Second latent tensor | | - mode: Blending mode | | - ratio: Blend ratio (0.0 to 1.0) Only used for mode=sample or mode=interpolate | | - seed: Random seed Only used for mode=sample | | Outputs | | - latent: Blended latent tensor |

| Blending Modes | Description | |-------------------|-----------------| | interpolate | Linear interpolation between latents | | add | Additive blending | | multiply | Multiplicative blending | | abs_max | Maximum of absolute values | | abs_min | Minimum of absolute values | | max | Element-wise maximum | | min | Element-wise minimum | | sample | Randomly sample from either latent based on ratio |

Example: Inputs: Random Gaussian σ=1 μ=0 (top) and Random Uniform [-1, 1] (bottom) Blend modes: interpolate (top) and sample (bottom)

LTLatentOp

Applies mathematical operations to a latent tensor.

| | |------------| | Inputs | | - latent: Input latent tensor | | - op: Operation to apply | | - arg: Argument to apply (for operations that require an argument) | | Outputs | | - latent: Resulting latent tensor |

| Operation | Description | |---------------|-----------------| | add | Add a value | | mul | Multiply by a value | | pow | Raise to a power | | exp | Exponential | | abs | Absolute value | | clamp_bottom | Clamp minimum value | | clamp_top | Clamp maximum value | | norm | Normalize (zero mean, unit variance) | | mean | Set mean to specified value | | std | Set standard deviation to specified value | | sigmoid | Apply sigmoid function | | nop | No operation |

Example: Inputs: Random Gaussian σ=1 μ=0 Op: abs

LTLatentsConcatenate

Concatenates two latent tensors along a specified dimension.

| | |------------| | Inputs | | - latent1: First latent tensor | | - latent2: Second latent tensor | | - dim: Dimension to concatenate along (supports negative indexing) | | Outputs | | - latent: Concatenated latent tensor |

Example1: 2 images, concatenated along x axis:

Example2: Stable Video Diffusion xt (24 frames total), concatenating

• 10 frames Gaussian noise (σ=1 μ=0)
• 4 frames Gaussian noise (σ=1.2 μ=0)
• 10 frames Gaussian noise (σ=1 μ=0)

| 10f (σ=1 μ=0) + 4f (σ=1.2 μ=0) + 10f (σ=1 μ=0) | 24f (σ=1 μ=0) | |---|---| | | |

LTReshapeLatent

Reshapes a latent tensor to new dimensions.

Inputs:

• input: Input latent tensor
• strict: If True, requires exact size match
• dim0-dim3: Target dimensions (0 = keep original size)

LTLatentToShape

Extracts the shape of a latent tensor.

Outputs:

• Returns up to 7 dimensions of the input latent shape

Preview and Debug

LTPreviewLatent

Visualizes latent tensors for debugging and inspection.

{width=70%}

Parameter Randomization

LTRandomRangeUniform

Generates random values from a uniform distribution.

LTRandomRangeGaussian

Generates random values from a Gaussian distribution.

Usage Examples

1. Generate and Preview Latent

   • Use LTGaussianLatent to create random noise
   • Connect to LTPreviewLatent to visualize

2. Blend Two Latents

   • Generate two different latents
   • Use LTBlendLatent with mode="interpolate"
   • Adjust the ratio parameter to control the blend

3. Apply Operations

   • Load or generate a latent
   • Use LTLatentOp to apply transformations
   • Chain multiple operations for complex effects

License

This project is licensed under the MIT License - see the LICENSE file for details.