Added LSTM support

docs/reference/node_reference.md

@@ -158,6 +158,8 @@
 | [Ptn GRU](ptn_gru.md) | A gated recurrent unit (GRU) model consisting of one or more of a recurrent layer.   |
 | [Ptn GRU Linear](ptn_gru_linear.md) | A recurrent neural network (GRU) model with a linear head. |
 | [Ptn Instance Norm 2d](ptn_instance_norm2d.md) | A normalization model to normalize elements over spatial axes within each channel for each sample. |
+| [Ptn LSTM](ptn_lstm.md) | A long short-term memory  (LSTM) model consisting of one or more of a recurrent layer.   |
+| [Ptn LSTM Linear](ptn_lstm_linear.md) | A recurrent neural network (LSTM) model with a linear head. |
 | [Ptn Layer Norm](ptn_layer_norm.md) | A normalization model to normalize elements over specified axes. |
 | [Ptn Linear](ptn_linear.md) | A linear model consisting of a single dense layer.   |
 | [Ptn Linear Model](ptn_linear_model.md) | A linear model consisting of dense layers.   |

docs/reference/ptn_lstm.md

@@ -0,0 +1,34 @@
+# Ptn LSTM
+A long short-term memory  (LSTM) model consisting of one or more of a recurrent layer.  
+
+    Args:
+        input_size (int): The number of input features.  
+        hidden_size (int): The number of output features of the hidden layer matrix.  
+        num_layers (int): Number of hidden layers.  
+        bias (bool): Use bias or not.  
+        batch_first (bool): Indicates the shape of the input. Input is assumed to be a rank-3 tensor. If True, the shape is [Batch, Seq, Token]. If False, the shape is [Seq, Batch, Token]. Note that default is set to `True` unlike PyTorch's LSTM default model parameter.
+        dropout (float): Dropout probability to be applied to the output of intermediate layers if non-zero. This will not be applied to the last layer's output.  
+        bidirectional (bool): Processes the input in a backward direction and append the result to the output of the forward direction.  
+        proj_size (int): If set to greater than 0, applies a projection to each output hidden state, reducing its dimensionality to proj_size.
+
+## Input
+| Name | Data type |
+|---|---|
+| input_size | Int |
+| hidden_size | Int |
+| num_layers | Int |
+| bias | Boolean |
+| batch_first | Boolean |
+| dropout | Float |
+| bidirectional | Boolean |
+| proj_size | Int |
+
+## Output
+| Data type |
+|---|
+| Ptmodel |
+
+<HR>
+Category: PyTorch wrapper - Model
+
+ComfyUI Pt Wrapper Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.

docs/reference/ptn_lstm_linear.md

@@ -0,0 +1,38 @@
+# Ptn LSTM Linear
+A recurrent neural network (LSTM) model with a linear head.
+
+    Args:
+        input_size (int): The number of input features.
+        hidden_size (int): The number of output features of the hidden layer matrix.
+        num_layers (int): Number of hidden layers in LSTM.
+        bias (bool): Use bias or not.
+        batch_first (bool): Indicates the shape of the input. Input is assumed to be a rank-3 tensor. If True, the shape is [Batch, Seq, Token]. If False, the shape is [Seq, Batch, Token]. Note that default is set to `True` unlike PyTorch's LSTM default model parameter.
+        dropout (float): Dropout probability to be applied to the output of intermediate layers if non-zero. This will not be applied to the last layer's output.
+        bidirectional (bool): Processes the input in a backward direction and append the result to the output of the forward direction.
+        proj_size (int): If set to greater than 0, applies a projection to each output hidden state, reducing its dimensionality to proj_size.  
+        linear_output_size (int): The number of output features of the linear layer.
+        linar_bias (bool): Use bias or not in linear layer.
+
+## Input
+| Name | Data type |
+|---|---|
+| input_size | Int |
+| hidden_size | Int |
+| num_layers | Int |
+| bias | Boolean |
+| batch_first | Boolean |
+| dropout | Float |
+| bidirectional | Boolean |
+| proj_size | Int |
+| linear_output_size | Int |
+| linear_bias | Boolean |
+
+## Output
+| Data type |
+|---|
+| Ptmodel |
+
+<HR>
+Category: PyTorch wrapper - Model
+
+ComfyUI Pt Wrapper Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.