# 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 ### Preview and Debug #### LTPreviewLatent Visualizes latent tensors for debugging and inspection. ![alt text](assets/PreviewLatent.png) ### 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 | | ![Gaussian Latent Node](assets/GaussianPlot.png) | **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 | |---|---|---|---|---| ![Random Range Gaussian Example](assets/fox_ddpm_2m-karras_mean_0.0_std_0.9_00001_.png) | ![Random Range Gaussian Example](assets/fox_ddpm_2m-karras_mean_0.0_std_1.05_00001_.png) | ![Random Range Gaussian Example](assets/fox_ddpm_2m-karras_mean_0.0_std_1.0_00001_.png) | ![Random Range Gaussian Example](assets/fox_ddpm_2m-karras_mean_-0.122_std_1.0_00001_.png) | ![Random Range Gaussian Example](assets/fox_ddpm_2m-karras_mean_0.122_std_1.0_00001_.png) | #### 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 | | ![Uniform Latent Node](assets/UniformPlot.png) | > 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 | |---|---| ![Random Range Uniform Example](assets/FoxUniform_-1.67_1.67.png) | ![Random Range Uniform Example](assets/FoxUniform_-1.81_1.81.png) | ### 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) \ ![Blend Latent Node](assets/BlendLatentExample.png) #### 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 ![Latent Op Example](assets/LatentOpExample.png) #### 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: ![Latent Concatenate Example1](assets/LatentsConcatenateExample1.png) 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) ![Latent Concatenate Example2](assets/LatentsConcatenateExample2.png) | 10f (σ=1 μ=0) + 4f (σ=1.2 μ=0) + 10f (σ=1 μ=0) | 24f (σ=1 μ=0) | |---|---| | ![Latent Concatenate Example2](assets/LatentsConcatenateExample2a.gif) | ![Latent Concatenate Example2](assets/LatentsConcatenateExample2b.gif) | #### 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: LTReshapeLatent

## 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. | LTRandomRangeGaussian

| |------------| | **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. | | | |---|---| | ![alt text](assets/NumberRangeExample.png) | random_params

| ### 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. | ![Float Steps](assets/Float_Step_XXX.png) | |------------| | **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: ![alt text](assets/Float_StepExample.png) Generate images with fixed seed Gaussian noise, starting with μ between -0.22 and 0.3 with 0.001 step increments: ![alt text](assets/Float_StepExample2.png) Result: | Sweeping Standard Deviation (σ) | Sweeping Mean (μ) | |---|---| | ![std sweep](assets/std_sweep.gif) | ![mean sweep](assets/mean_sweep.gif) |

LTPreviewLatent

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