Added GRU support.

docs/reference/node_reference.md

@@ -155,6 +155,8 @@
 | [Ptn Chained Model](ptn_chained_model.md) | Constructs a chained PyTorch model. |
 | [Ptn Conv 2d](ptn_conv2d.md) |     A convolutional model consisting of a single conv2d layer.   |
 | [Ptn Conv Model](ptn_conv_model.md) | A convolutional model consisting of multiple convolutional layers.   |
+| [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 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.   |
@@ -228,7 +230,7 @@
 | [Pt Train Classification Model](pt_train_classification_model.md) | Trains a classification model using a given dataset, optimizer, and number of epochs.   |
 | [Pt Train Classification Model Lr](pt_train_classification_model_lr.md) | Trains a classification model using a given dataset, optimizer, and number of epochs with learning rate decay.   |
 | [Pt Train Model](pt_train_model.md) | Trains a model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay.   |
-| [Pt Train R N N Model](pt_train_rnn_model.md) | Trains an RNN model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay.   |
+| [Pt Train RNN Model](pt_train_rnn_model.md) | Trains an RNN model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay.   |
 | [Pt Train Regression Model](pt_train_regression_model.md) | Trains a regression model using a given dataset, optimizer, and number of epochs.   |
 | [Pto Lr Scheduler Cosine Annealing](pto_lr_scheduler_cosine_annealing.md) | Creates a cosine annealing learning rate scheduler for an optimizer. |
 | [Pto Lr Scheduler Reduce On Plateau](pto_lr_scheduler_reduce_on_plateau.md) | Creates a reduce-on-plateau learning rate scheduler for an optimizer. |

docs/reference/pt_train_rnn_model.md

@@ -1,4 +1,4 @@
-# Pt Train R N N Model
+# Pt Train RNN Model
 Trains an RNN model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay.  
 
    Args:  
@@ -15,7 +15,8 @@ Trains an RNN model using a given dataset, loss function, optimizer, and number
         classification_metrics (bool): If True and if val_loader is set, print out classification metrics.  This is only valid for classification model training.  
         use_valid_token_mean (bool): Computes the mean of the RNN output for non-zero inputs and feeds to the linear layer.
         val_loader (PTDATALOADER): DataLoader for validation dataset (optional).  
-        scheduler (PTLRSCHEDULER): Learning rate scheduler.
+        scheduler (PTLRSCHEDULER): Learning rate scheduler.  
+        h_0 (TENSOR): Optional initial hidden state. For unidirectional models, the shape is [num_layers, batch_size, hidden_size]. For bidirectional models, it is [2 * num_layers, batch_size, hidden_size]. If not provided, a zero tensor is used.
 
 ## Input
 | Name | Data type |
@@ -34,6 +35,7 @@ Trains an RNN model using a given dataset, loss function, optimizer, and number
 | use_valid_token_mean | Boolean |
 | scheduler | Ptlrscheduler |
 | val_loader | Ptdataloader |
+| h_0 | Tensor |
 
 ## Output
 | Data type |

docs/reference/ptn_gru.md

@@ -0,0 +1,32 @@
+# Ptn GRU
+A gated recurrent unit (GRU) 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 GRU 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.
+
+## Input
+| Name | Data type |
+|---|---|
+| input_size | Int |
+| hidden_size | Int |
+| num_layers | Int |
+| bias | Boolean |
+| batch_first | Boolean |
+| dropout | Float |
+| bidirectional | Boolean |
+
+## 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_gru_linear.md

@@ -0,0 +1,36 @@
+# Ptn GRU Linear
+A recurrent neural network (GRU) 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 GRU.
+        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 GRU 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.
+        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 |
+| 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.

docs/reference/ptn_rnn_linear.md

@@ -24,6 +24,8 @@ A recurrent neural network (RNN) model with a linear head.
 | batch_first | Boolean |
 | dropout | Float |
 | bidirectional | Boolean |
+| linear_output_size | Int |
+| linear_bias | Boolean |
 
 ## Output
 | Data type |

docs/training_rnn_for_classification.md

@@ -3,6 +3,7 @@
 You can use **ComfyUI-Pt-Wrapper** to train an RNN for text classification, such as sentiment analysis on a movie review dataset.
 
 You can find the workflow at `examples/workflows/rnn_classification.json`.
+You can also use GRU workflow at `examples/workflows/gru_classification.json`.
 
 ![Workflow](images/rnn_classification.png)
 
@@ -42,3 +43,4 @@ You can also enable `classification_metrics` to print validation accuracy to the
 ## Expected Accuracy
 
 Using this workflow, you should get around **83% validation accuracy** on the Hugging Face version of the IMDB dataset, which is a solid result for an RNN.
+The GRU workflow achieves approximately **87% validation accuracy**.