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Added more PyTorch wrapper nodes.

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docs/reference/node_reference.md
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| [Pt Le](pt_le.md) | Tests whether elements in the first PyTorch tensor are less than or equal to the corresponding elements in the second tensor. |
| [Pt Lt](pt_lt.md) | Tests whether elements in the first PyTorch tensor are less than the corresponding elements in the second tensor. |
| [Pt Ne](pt_ne.md) | Tests whether two PyTorch tensors are not equal element-wise. |
## PyTorch wrapper - Indexing and Slicing Operations
| Node | Description |
| --- | --- |
| [Pt Gather](pt_gather.md) | Generates a tensor based on the index tensor using PyTorch's `gather` function. |
| [Pt Index Select](pt_index_select.md) | Extracts elements from the input tensor along a specified dimension using an index tensor. |
| [Pt Masked Select](pt_masked_select.md) | Extracts elements from the input tensor whose corresponding value in `masked_tens` is `True`. |
| [Pt Scatter](pt_scatter.md) | Generates a new tensor by replacing values at specified positions using an index tensor. |
| [Pt Where](pt_where.md) | Generates a new tensor by selecting values based on a condition tensor. |
## PyTorch wrapper - Logical operations
| Node | Description |
| --- | --- |
@@ -189,6 +197,7 @@
| [Pt Neg](pt_neg.md) | Computes the negation of each element in a PyTorch tensor. |
| [Pt Sin](pt_sin.md) | Computes the sine of a PyTorch tensor element-wise. |
| [Pt Sinh](pt_sinh.md) | Computes the hyperbolic sine of a PyTorch tensor element-wise. |
| [Pt Sqrt](pt_sqrt.md) | Computes the square root of each element in a PyTorch tensor. |
| [Pt Tan](pt_tan.md) | Computes the tangent of a PyTorch tensor element-wise. |
| [Pt Tanh](pt_tanh.md) | Computes the hyperbolic tangent of a PyTorch tensor element-wise. |
## PyTorch wrapper - Matrix operations
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| [Pt Einsum](pt_einsum.md) | Performs Tensor operations specified in the Einstein summation equation. |
| [Pt Mat Mul](pt_matmul.md) | Performs matrix multiplication on two PyTorch tensors. |
| [Pt Mm](pt_mm.md) | Performs 2D matrix multiplication on two PyTorch tensors. |
## PyTorch wrapper - Reduction operation & Summary statistics
| Node | Description |
| --- | --- |
| [Pt Argmax](pt_argmax.md) | Computes the indices of the maximum values of a PyTorch tensor along the specified dimension(s). |
| [Pt Argmin](pt_argmin.md) | Computes the indices of the minimum values of a PyTorch tensor along the specified dimension(s). |
| [Pt Max](pt_max.md) | Computes the maximum values of a PyTorch tensor along the specified dimension(s). |
| [Pt Mean](pt_mean.md) | Computes the mean of a PyTorch tensor along the specified dimension(s). |
| [Pt Median](pt_median.md) | Computes the median of a PyTorch tensor along the specified dimension(s). |
| [Pt Min](pt_min.md) | Computes the minimum values of a PyTorch tensor along the specified dimension(s). |
| [Pt Prod](pt_prod.md) | Computes the product of a PyTorch tensor along the specified dimension(s). |
| [Pt Std](pt_std.md) | Computes the standard deviation of a PyTorch tensor along the specified dimension(s). |
| [Pt Sum](pt_sum.md) | Computes the sum of a PyTorch tensor along the specified dimension(s). |
| [Pt Var](pt_var.md) | Computes the variance of a PyTorch tensor along the specified dimension(s). |
## PyTorch wrapper - Size object support
| Node | Description |
| --- | --- |
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| Node | Description |
| --- | --- |
| [Pt Permute](pt_permute.md) | Permutes the dimensions of a PyTorch tensor according to the specified order. |
| [Pt Reshape](pt_reshape.md) | Reshapes a PyTorch tensor into a specified shape using `torch.reshape()`. |
| [Pt Squeeze](pt_squeeze.md) | Removes a dimension at the specified position in the input tensor if it is of size 1. |
| [Pt Unsqueeze](pt_unsqueeze.md) | Adds a singleton dimension at the specified position in the input tensor. |
| [Pt View](pt_view.md) | Reshapes a PyTorch tensor into a specified shape using `torch.view()`. |
docs/reference/pt_argmax.md
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# Pt Argmax
Computes the indices of the maximum values of a PyTorch tensor along the specified dimension(s).
Specify the dimension in the `dim` field using an integer as shown below:
```
0
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| dim | String |
| keepdim | Boolean |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Reduction operation & Summary statistics
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_argmin.md
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# Pt Argmin
Computes the indices of the minimum values of a PyTorch tensor along the specified dimension(s).
Specify the dimension in the `dim` field using an integer as shown below:
```
0
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| dim | String |
| keepdim | Boolean |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Reduction operation & Summary statistics
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_gather.md
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# Pt Gather
Generates a tensor based on the index tensor using PyTorch's `gather` function.
Specify the `dim` field using an integer to indicate the axis along which to gather elements.
Each input tensor element is selected based on the index tensor.
For example, consider the following input tensor:
```
[ [10, 20, 30, 40, 50],
[100, 200, 300, 400, 500]]
```
and the corresponding index tensor:
```
[ [0, 4, 3, 2, 0],
[4, 3, 0, 0, 0]]
```
If `dim = 0`, the function scans row-wise, treating each index value as a row index.
If `dim = 1`, it scans column-wise, interpreting each index value as a column index.
Let's analyze the case where `dim = 1`:
* Start at row 0, column 0.
* The index at this position is `index[0, 0] = 0`.
* PyTorch selects the value at position 0 from the array `[10, 20, 30, 40, 50]`, which is `10`.
* The result at this stage is:
```
[[10, ?, ?, ?, ?]
[?, ?, ?, ?, ?]]
```
* Moving to column 1: `index[0, 1] = 4`.
* The value at index 4 in `[10, 20, 30, 40, 50]` is `50`.
* The updated result is:
```
[[10, 50, ?, ?, ?]
[?, ?, ?, ?, ?]]
```
* Repeating this process for all elements constructs the final gathered tensor.
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| dim | Int |
| index | String |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Indexing and Slicing Operations
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_index_select.md
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# Pt Index Select
Extracts elements from the input tensor along a specified dimension using an index tensor.
`dim` specifies the dimension along which the selection occurs.
* For example, for 2D tensors:
** If `dim=0`, `index` selects rows.
** If `dim=1`, `index` selects columns (for 2D tensors).
`index` is a list of indices that will be converted into a tensor.
Example:
```
Input Tensor:
[[1, 2, 3],
[40, 50, 60],
[700, 800, 900]]
index = "[0, 2]"
dim = 0
Output Tensor:
[[1, 2, 3],
[700, 800, 900]]
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| dim | Int |
| index | String |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Indexing and Slicing Operations
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_masked_select.md
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# Pt Masked Select
Extracts elements from the input tensor whose corresponding value in `masked_tens` is `True`.
Example:
```
Input Tensor:
[[1, 2, 3],
[40, 50, 60],
[700, 800, 900]]
Masked Tensor:
[[True, False, False],
[False, True, False],
[False, False, True]]
Output Tensor:
[1, 50, 900]
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| masked_tens | Tensor |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Indexing and Slicing Operations
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_max.md
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# Pt Max
Computes the maximum values of a PyTorch tensor along the specified dimension(s).
Specify the dimension(s) in the `dim` field using an integer, a list, or a tuple, as shown below:
```
0, [0], or (1, 2)
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| dim | String |
| keepdim | Boolean |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Reduction operation & Summary statistics
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_mean.md
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# Pt Mean
Computes the mean of a PyTorch tensor along the specified dimension(s).
This node supports only floating-point or complex tensors as input.
Specify the dimension(s) in the `dim` field using an integer, a list, or a tuple, as shown below:
```
0, [0], or (1, 2)
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| dim | String |
| keepdim | Boolean |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Reduction operation & Summary statistics
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_median.md
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# Pt Median
Computes the median of a PyTorch tensor along the specified dimension(s).
Specify the dimension(s) in the `dim` field using an integer as shown below:
```
0
```
or
```
1
```
Note that PtMedian calls torch.median(), which returns the lower of the two numbers when the true median falls between them.
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| dim | String |
| keepdim | Boolean |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Reduction operation & Summary statistics
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_min.md
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# Pt Min
Computes the minimum values of a PyTorch tensor along the specified dimension(s).
Specify the dimension(s) in the `dim` field using an integer, a list, or a tuple, as shown below:
```
0, [0], or (1, 2)
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| dim | String |
| keepdim | Boolean |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Reduction operation & Summary statistics
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_prod.md
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# Pt Prod
Computes the product of a PyTorch tensor along the specified dimension(s).
Specify the dimension(s) in the `dim` field using an integer as shown below:
```
0
```
or
```
1
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| dim | String |
| keepdim | Boolean |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Reduction operation & Summary statistics
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_reshape.md
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# Pt Reshape
Reshapes a PyTorch tensor into a specified shape using `torch.reshape()`.
The total number of elements must remain unchanged.
For example, if a tensor has shape (2, 3, 4), and `new_shape="[6, 4]"`,
the output will have shape (6, 4).
Use -1 to automatically infer a dimension, e.g.:
```
[2, -1]
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| new_shape | String |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Transform
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_scatter.md
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# Pt Scatter
Generates a new tensor by replacing values at specified positions using an index tensor.
* Target tensor (tens): Some of whose values are replaced.
* Source tensor (src): Values from this tensor replace a part or whole of the target tensor.
* Index tensor (index): Specifies the positions in the target tensor for replacement.
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| dim | Int |
| index | String |
| src | String |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Indexing and Slicing Operations
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_sqrt.md
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# Pt Sqrt
Computes the square root of each element in a PyTorch tensor.
## Input
| Name | Data type |
|---|---|
| tens_a | Tensor |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Math operations
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_std.md
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# Pt Std
Computes the standard deviation of a PyTorch tensor along the specified dimension(s).
Specify 1 to use Bessel's correction (N-1) for sample standard deviation.
Specify 0 to compute population standard deviation.
Specify the dimension(s) in the `dim` field using an integer, a list, or a tuple, as shown below:
```
0, [0], or (1, 2)
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| correction | Int |
| dim | String |
| keepdim | Boolean |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Reduction operation & Summary statistics
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_sum.md
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# Pt Sum
Computes the sum of a PyTorch tensor along the specified dimension(s).
Specify the dimension(s) in the `dim` field using an integer, a list, or a tuple, as shown below:
```
0, [0], or (1, 2)
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| dim | String |
| keepdim | Boolean |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Reduction operation & Summary statistics
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
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# Pt Var
Computes the variance of a PyTorch tensor along the specified dimension(s).
Specify 1 to use Bessel's correction (N-1) for sample variance.
Specify 0 to compute population variance.
Specify the dimension(s) in the `dim` field using an integer, a list, or a tuple, as shown below:
```
0, [0], or (1, 2)
```
## Input
| Name | Data type |
|---|---|
| tens | Tensor |
| correction | Int |
| dim | String |
| keepdim | Boolean |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Reduction operation & Summary statistics
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
docs/reference/pt_where.md
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# Pt Where
Generates a new tensor by selecting values based on a condition tensor.
* `condition_tens` (torch.Tensor): Boolean tensor that determines which values are selected.
* `true_tens` (torch.Tensor): Values to use where the condition is `True`.
* `false_tens` (torch.Tensor): Values to use where the condition is `False`.
## Input
| Name | Data type |
|---|---|
| condition_tens | Tensor |
| true_tens | Tensor |
| false_tens | Tensor |
## Output
| Data type |
|---|
| Tensor |
<HR>
Category: PyTorch wrapper - Indexing and Slicing Operations
ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.