Visualize and manipulate the latent space in ComfyUI
27
stars
56
commits
Python
primary language
Jan 14, 2026
updated
README
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
- Clone this repository into your
ComfyUI/custom_nodes/directory - Restart ComfyUI
Or use the ComfyUI extension manager.
Available Nodes
Preview and Debug
LTPreviewLatent
Visualizes latent tensors for debugging and inspection.
KSampler with additional noise input
LTKSampler
A KSampler variant that accepts an additional input for starting latent space noise.
 |
|---|
| Inputs |
- model: The model used for denoising |
- extra_seed: See for any other noise used by the sampler |
- steps: Number of steps in the denoising process |
- cfg: Classifier-Free Guidance scale |
- sampler_name: Algorithm used for sampling |
- scheduler: Controls how noise is gradually removed |
- positive: Positive conditioning |
- negative: Negative conditioning |
- latent_image: The latent image to denoise |
- latent_noise: Starting noise for the sampler |
- denoise: Amount of denoising to apply |
| Outputs |
- latent: The denoised latent tensor |
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 |
 |
Example:
| "quick brown fox", σ=0.9 μ=0 | "quick brown fox", σ=1.05, μ=0 | "quick brown fox", σ=1, μ=0 | "quick brown fox", σ=1, μ=-0.1 | "quick brown fox", σ=1, μ=0.1 |
|---|---|---|---|---|
 |  |  |  |  |
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 |
 |
Note: Stable Diffusion models are usually trained with Gaussian noise, so the generations from Uniform noise will look unusual.
Example:
| "quick brown fox", -1.67 to 1.67 | "quick brown fox", -1.81 to 1.81 |
|---|---|
 |  |
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=0.1 μ=0 (top) and Random Uniform [-1s, 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) |
|---|---|
 |  |
LTLatentToShape
Extracts the shape of a latent tensor.
 |
|---|
| Inputs |
- input: Input latent tensor |
| Outputs: |
| - Return 7 dimensions of the input latent shape. Non-existing ones are returned as 0 |
LTReshapeLatent
Reshapes a latent tensor to new dimensions.
 |
|---|
| Inputs |
- input: Input latent tensor |
- strict: If True, requires exact size match |
- dim0-dim6: Target dimensions (0 values are ignored) |
| Outputs |
- latent: Reshaped latent tensor |
Example: Reshape one latent to match another one:
Batch helpers
Parameter Randomization
LTNumberRangeUniform and LTRandomRangeGaussian are used to randomize inputs to other nodes when scheduling multiple images.
LTNumberRangeUniform
Generates random float values from a uniform distribution.
 |
|---|
| Inputs |
- min: Minimum value |
- max: Maximum value |
- seed: Random seed - randomize it to get different values for each image |
| Outputs |
- float: Generated number as float |
- int: Generated number as int |
LTNumberRangeGaussian
Generates random values from a Gaussian distribution.
 |
|---|
| Inputs |
- mean: Mean of the normal distribution |
- std: Standard deviation of the normal distribution |
- seed: Random seed - randomize it to get different values for each image |
| Outputs |
- float: Generated number as float |
- int: Generated number as int |
Example:
Let's randomize a bumch of parameters for batch generation:
- For the Gaussian Noise that is used as input:
- the mean value will be itself a random uniform value between -0.2 and 0.2.
- The standard deviation will be itself a random value with mean 1 and std 0.5, clipped to 0.1 at the low end (using a ComfyMath node).
- The number of diffusion steps is random uniform value betwee 5 and 40.
- The cfg is random gussian with mean 8 and std 1, clipped to 0.1 at the low end (using a ComfyMath node).
Using this setup, generate 100 images with different parameters.
 |  |
LTFloat_Steps_0001
LTFloat_Steps_0001
LTFloat_Steps_0002
LTFloat_Steps_0005
LTFloat_Steps_001
LTFloat_Steps_002
LTFloat_Steps_005
LTFloat_Steps_01
LTFloat_Steps_02
LTFloat_Steps_05
LTFloat_Steps_1
These nodes are used to increment/decrement a float value by a fixed amount when scheduling multiple images.
 |
|---|
| Inputs |
- value: float |
| Outputs |
- float: float |
- string: string |
Example:
Generate images with fixed seed Gaussian noise, starting with σ etween 0.8 and 1.2 with 0.001 step increments:
Generate images with fixed seed Gaussian noise, starting with μ between -0.22 and 0.3 with 0.001 step increments:
Result:
| Sweeping Standard Deviation (σ) | Sweeping Mean (μ) |
|---|---|
 |  |
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Visualize and manipulate the latent space in ComfyUI
27
stars
56
commits
Python
primary language
Jan 14, 2026
updated
README
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
- Clone this repository into your
ComfyUI/custom_nodes/directory - Restart ComfyUI
Or use the ComfyUI extension manager.
Available Nodes
Preview and Debug
LTPreviewLatent
Visualizes latent tensors for debugging and inspection.
KSampler with additional noise input
LTKSampler
A KSampler variant that accepts an additional input for starting latent space noise.
|
|---|
| Inputs |
- model: The model used for denoising |
- extra_seed: See for any other noise used by the sampler |
- steps: Number of steps in the denoising process |
- cfg: Classifier-Free Guidance scale |
- sampler_name: Algorithm used for sampling |
- scheduler: Controls how noise is gradually removed |
- positive: Positive conditioning |
- negative: Negative conditioning |
- latent_image: The latent image to denoise |
- latent_noise: Starting noise for the sampler |
- denoise: Amount of denoising to apply |
| Outputs |
- latent: The denoised latent tensor |
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 |
|
Example:
| "quick brown fox", σ=0.9 μ=0 | "quick brown fox", σ=1.05, μ=0 | "quick brown fox", σ=1, μ=0 | "quick brown fox", σ=1, μ=-0.1 | "quick brown fox", σ=1, μ=0.1 |
|---|---|---|---|---|
| | | | |
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 |
|
Note: Stable Diffusion models are usually trained with Gaussian noise, so the generations from Uniform noise will look unusual.
Example:
| "quick brown fox", -1.67 to 1.67 | "quick brown fox", -1.81 to 1.81 |
|---|---|
| |
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=0.1 μ=0 (top) and Random Uniform [-1s, 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) |
|---|---|
| |
LTLatentToShape
Extracts the shape of a latent tensor.
|
|---|
| Inputs |
- input: Input latent tensor |
| Outputs: |
| - Return 7 dimensions of the input latent shape. Non-existing ones are returned as 0 |
LTReshapeLatent
Reshapes a latent tensor to new dimensions.
|
|---|
| Inputs |
- input: Input latent tensor |
- strict: If True, requires exact size match |
- dim0-dim6: Target dimensions (0 values are ignored) |
| Outputs |
- latent: Reshaped latent tensor |
Example: Reshape one latent to match another one:
Batch helpers
Parameter Randomization
LTNumberRangeUniform and LTRandomRangeGaussian are used to randomize inputs to other nodes when scheduling multiple images.
LTNumberRangeUniform
Generates random float values from a uniform distribution.
|
|---|
| Inputs |
- min: Minimum value |
- max: Maximum value |
- seed: Random seed - randomize it to get different values for each image |
| Outputs |
- float: Generated number as float |
- int: Generated number as int |
LTNumberRangeGaussian
Generates random values from a Gaussian distribution.
|
|---|
| Inputs |
- mean: Mean of the normal distribution |
- std: Standard deviation of the normal distribution |
- seed: Random seed - randomize it to get different values for each image |
| Outputs |
- float: Generated number as float |
- int: Generated number as int |
Example:
Let's randomize a bumch of parameters for batch generation:
- For the Gaussian Noise that is used as input:
- the mean value will be itself a random uniform value between -0.2 and 0.2.
- The standard deviation will be itself a random value with mean 1 and std 0.5, clipped to 0.1 at the low end (using a ComfyMath node).
- The number of diffusion steps is random uniform value betwee 5 and 40.
- The cfg is random gussian with mean 8 and std 1, clipped to 0.1 at the low end (using a ComfyMath node).
Using this setup, generate 100 images with different parameters.
| |
LTFloat_Steps_0001
LTFloat_Steps_0001
LTFloat_Steps_0002
LTFloat_Steps_0005
LTFloat_Steps_001
LTFloat_Steps_002
LTFloat_Steps_005
LTFloat_Steps_01
LTFloat_Steps_02
LTFloat_Steps_05
LTFloat_Steps_1
These nodes are used to increment/decrement a float value by a fixed amount when scheduling multiple images.
|
|---|
| Inputs |
- value: float |
| Outputs |
- float: float |
- string: string |
Example:
Generate images with fixed seed Gaussian noise, starting with σ etween 0.8 and 1.2 with 0.001 step increments:
Generate images with fixed seed Gaussian noise, starting with μ between -0.22 and 0.3 with 0.001 step increments:
Result:
| Sweeping Standard Deviation (σ) | Sweeping Mean (μ) |
|---|---|
| |
See what people are saying
Similar repos
Languages
Python
97.7%
JavaScript
2.3%