HowToSD-ComfyUI-Pt-Wrapper/docs/reference/node_reference.md

# Node Reference
## Display data
| Node | Description |
| --- | --- |
| [Pt Show Size](pt_show_size.md) | Displays PyTorch Size object as a string. |
| [Pt Show Text](pt_show_text.md) | Displays PyTorch tensor as a string. Note that the tensor is partially printed out when |
## IO
| Node | Description |
| --- | --- |
| [Pt Save Model](pt_save_model.md) | A wrapper class for saving a PyTorch model. |
## PyTorch wrapper - Arithmetic operations
| Node | Description |
| --- | --- |
| [Pt Add](pt_add.md) | Adds two PyTorch tensors. |
| [Pt Div](pt_div.md) | Divides one PyTorch tensor by another element-wise. |
| [Pt Floor Div](pt_floor_divide.md) | Performs element-wise floor division on two PyTorch tensors. |
| [Pt Mul](pt_mul.md) | Multiplies two PyTorch tensors element-wise. |
| [Pt Pow](pt_pow.md) | Raises one PyTorch tensor to the power of another element-wise. |
| [Pt Remainder](pt_remainder.md) | Computes the element-wise remainder of division between two PyTorch tensors. |
| [Pt Sub](pt_sub.md) | Subtracts one PyTorch tensor from another. |
## PyTorch wrapper - Bitwise operations
| Node | Description |
| --- | --- |
| [Pt Bitwise And](pt_bitwise_and.md) | Performs a bitwise AND operation on two PyTorch tensors element-wise. |
| [Pt Bitwise Left Shift](pt_bitwise_left_shift.md) | Performs a bitwise left shift operation on two PyTorch tensors element-wise. |
| [Pt Bitwise Not](pt_bitwise_not.md) | Performs a bitwise NOT operation on a PyTorch tensor element-wise. |
| [Pt Bitwise Or](pt_bitwise_or.md) | Performs a bitwise OR operation on two PyTorch tensors element-wise. |
| [Pt Bitwise Right Shift](pt_bitwise_right_shift.md) | Performs a bitwise right shift operation on two PyTorch tensors element-wise. |
| [Pt Bitwise Xor](pt_bitwise_xor.md) | Performs a bitwise XOR operation on two PyTorch tensors element-wise. |
## PyTorch wrapper - Callable
| Node | Description |
| --- | --- |
| [Ptf GELU](ptf_gelu.md) | The GELU activation function. |
| [Ptf LeakyReLU](ptf_leaky_relu.md) | The LeakyReLU activation function. |
| [Ptf Log Softmax](ptf_log_softmax.md) | The log softmax activation function. |
| [Ptf ReLU](ptf_relu.md) | The ReLU activation function. |
| [Ptf SiLU](ptf_silu.md) | The SiLU activation function. |
| [Ptf Sigmoid](ptf_sigmoid.md) | The sigmoid activation function. |
| [Ptf Softmax](ptf_softmax.md) | The softmax activation function. |
| [Ptf Softplus](ptf_softplus.md) | The Softplus activation function. |
| [Ptf Tanh](ptf_tanh.md) | The tanh activation function. |
## PyTorch wrapper - Comparison operations
| Node | Description |
| --- | --- |
| [Pt Eq](pt_eq.md) | Tests whether two PyTorch tensors are equal element-wise. |
| [Pt Ge](pt_ge.md) | Tests whether elements in the first PyTorch tensor are greater than or equal to the corresponding elements in the second tensor. |
| [Pt Gt](pt_gt.md) | Tests whether elements in the first PyTorch tensor are greater than the corresponding elements in the second tensor. |
| [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 - Data type conversion
| Node | Description |
| --- | --- |
| [Pt To Bfloat16](pt_to_bfloat16.md) | Converts the input tensor's data type to bfloat16. |
| [Pt To Float16](pt_to_float16.md) | Converts the input tensor's data type to float16. |
| [Pt To Float32](pt_to_float32.md) | Converts the input tensor's data type to float32. |
| [Pt To Float64](pt_to_float64.md) | Converts the input tensor's data type to float64. |
| [Pt To Int16](pt_to_int16.md) | Converts the input tensor's data type to int16. |
| [Pt To Int32](pt_to_int32.md) | Converts the input tensor's data type to int32. |
| [Pt To Int64](pt_to_int64.md) | Converts the input tensor's data type to int64. |
| [Pt To Int8](pt_to_int8.md) | Converts the input tensor's data type to int8. |
| [Pt To Uint8](pt_to_uint8.md) | Converts the input tensor's data type to uint8. |
## PyTorch wrapper - Distribution
| Node | Description |
| --- | --- |
| [Ptd Bernoulli](ptd_bernoulli.md) | Instantiates a Bernoulli distribution object. |
| [Ptd Beta](ptd_beta.md) | Instantiates a Beta distribution object. |
| [Ptd Binomial](ptd_binomial.md) | Instantiates a Binomial distribution object. |
| [Ptd Categorical](ptd_categorical.md) | Instantiates a Categorical distribution object from the input probabilities or logits. You have to specify one of them and not both. |
| [Ptd Chi2](ptd_chi2.md) | Instantiates a Chi-squared distribution object. |
| [Ptd Exponential](ptd_exponential.md) | Instantiates a Exponential distribution object. |
| [Ptd Gamma](ptd_gamma.md) | Instantiates a Gamma distribution object. |
| [Ptd Normal](ptd_normal.md) | Instantiates a Normal distribution object. |
| [Ptd Poisson](ptd_poisson.md) | Instantiates a Poisson distribution object. |
| [Ptd Student T](ptd_student_t.md) | Instantiates a StudentT distribution object. |
| [Ptd Uniform](ptd_uniform.md) | Instantiates a Uniform distribution object. |
| [Ptdm Cdf](ptdm_cdf.md) | Computes the cumulative distribution function for the input distribution. |
| [Ptdm Cdf Tensor](ptdm_cdf_tensor.md) | Computes the cumulative distribution function for the input distribution. This nodes accepts a tensor so it can be used to compute cdf for multiple values contained in a tensor. |
| [Ptdm Icdf](ptdm_icdf.md) | Computes the inverse of the cumulative distribution function for the input distribution. |
| [Ptdm Icdf Tensor](ptdm_icdf_tensor.md) | Computes the inverse of cumulative distribution function for the input distribution. This nodes accepts a tensor so it can be used to compute cdf for multiple values contained in a tensor. |
| [Ptdm Log Prob](ptdm_log_prob.md) | Computes the log of probability for the input distribution. |
| [Ptdm Log Prob Tensor](ptdm_log_prob_tensor.md) | Computes the log of probability for the input distribution.  This nodes accepts a tensor so it can be used to compute log of probability for multiple values contained in a tensor. |
| [Ptdm Pdf](ptdm_pdf.md) | Computes the probability density for the input distribution. |
| [Ptdm Pdf Tensor](ptdm_pdf_tensor.md) | Computes the probability density for the input distribution.  This nodes accepts a tensor so it can be used to compute pdf for multiple values contained in a tensor. |
| [Ptdm Pmf](ptdm_pmf.md) | Computes the probability for the input distribution. |
| [Ptdm Pmf Tensor](ptdm_pmf_tensor.md) | Computes the probability for the input distribution.  This nodes accepts a tensor so it can be used to compute pmf for multiple values contained in a tensor. |
| [Ptdm Sample](ptdm_sample.md) | Samples from the input distribution. |
## PyTorch wrapper - Image processing
| Node | Description |
| --- | --- |
| [Pt Crop](pt_crop.md) | Crops a PyTorch tensor to the specified size. The input tensor must have a shape of (c, h, w) or (b, c, h, w). |
| [Pt From Image](pt_from_image.md) | Casts an Image tensor as a PyTorch tensor. |
| [Pt From Image Transpose](pt_from_image_transpose.md) | Casts an image tensor to a PyTorch tensor and transposes it from (H, W, C) to (C, H, W). For rank-4 inputs, the batch axis remains unchanged. |
| [Pt Interpolate By Scale Factor](pt_interpolate_by_scale_factor.md) | Resizes a PyTorch tensor using interpolation by scale factor. The input tensor must have a shape of (c, h, w) or (b, c, h, w). |
| [Pt Interpolate To Size](pt_interpolate_to_size.md) | Resizes a PyTorch tensor using interpolation. The input tensor must have a shape of (c, h, w) or (b, c, h, w). |
| [Pt Pad](pt_pad.md) | Pads a PyTorch tensor to the specified size. Padded area will be black. The input tensor must have a shape of (c, h, w) or (b, c, h, w). |
| [Pt To Image](pt_to_image.md) | Casts a PyTorch tensor as an Image tensor. |
| [Pt To Image Transpose](pt_to_image_transpose.md) | Casts a PyTorch tensor as an Image tensor and transposes it from (C, H, W) to (H, W, C). For rank-4 inputs, the batch axis remains unchanged. |
## 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 |
| --- | --- |
| [Pt Logical And](pt_logical_and.md) | Performs a logical AND operation on two PyTorch tensors element-wise. |
| [Pt Logical Not](pt_logical_not.md) | Performs a logical NOT operation on a PyTorch tensor element-wise. |
| [Pt Logical Or](pt_logical_or.md) | Performs a logical OR operation on two PyTorch tensors element-wise. |
| [Pt Logical Xor](pt_logical_xor.md) | Performs a logical XOR operation on two PyTorch tensors element-wise. |
## PyTorch wrapper - Loss function
| Node | Description |
| --- | --- |
| [Ptn BCE Loss](ptn_bce_loss.md) | A class to compute the binary cross entropy loss. |
| [Ptn BCE With Logits Loss](ptn_bce_with_logits_loss.md) | A class to compute the sigmoid then binary cross entropy loss. |
| [Ptn Cross Entropy Loss](ptn_cross_entropy_loss.md) | A class to compute the cross entropy loss. |
| [Ptn Huber Loss](ptn_huber_loss.md) |  |
| [Ptn KL Div Loss](ptn_kl_div_loss.md) | A class to compute the KL divergence loss. |
| [Ptn L1 Loss](ptn_l1_loss.md) | A class to compute the L1 loss. |
| [Ptn MSE Loss](ptn_mse_loss.md) | A class to compute the squared loss. |
| [Ptn NLL Loss](ptn_nll_loss.md) | A model to compute the negative log likelihood (NLL) loss. |
| [Ptn Smooth L1 Loss](ptn_smooth_l1_loss.md) |  |
## PyTorch wrapper - Math operations
| Node | Description |
| --- | --- |
| [Pt Abs](pt_abs.md) | Computes the absolute value of each element in a PyTorch tensor. |
| [Pt Acos](pt_acos.md) | Computes the arccosine (inverse cosine) of a PyTorch tensor element-wise. |
| [Pt Asin](pt_asin.md) | Computes the arcsine (inverse sine) of a PyTorch tensor element-wise. |
| [Pt Atan](pt_atan.md) | Computes the arc tangent (inverse tangent) of a PyTorch tensor element-wise. |
| [Pt Cos](pt_cos.md) | Computes the cosine of a PyTorch tensor element-wise. |
| [Pt Cosh](pt_cosh.md) | Computes the hyperbolic cosine of a PyTorch tensor element-wise. |
| [Pt Exp](pt_exp.md) | Performs an exponential operation on a PyTorch tensor element-wise. |
| [Pt Log](pt_log.md) | Computes the natural logarithm (log base e) of a PyTorch tensor element-wise. |
| [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
| Node | Description |
| --- | --- |
| [Pt Bmm](pt_bmm.md) | Performs batched matrix multiplication on two 3D PyTorch tensors. |
| [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 - Model
| Node | Description |
| --- | --- |
| [Ptn Avg Pool 2d](ptn_avg_pool2d.md) | An avgpool layer.   |
| [Ptn Batch Norm 2d](ptn_batch_norm2d.md) | A normalization model to normalize over the batch and spatial axes for each channel. |
| [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 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.   |
| [Ptn Linear Model](ptn_linear_model.md) | A linear model consisting of dense layers.   |
| [Ptn Max Pool 2d](ptn_max_pool2d.md) | A maxpool layer.   |
| [Ptn Pre Add Channel Axis](ptn_pre_add_channel_axis.md) | Adds a channel axis after the batch axis if the input is rank 3 (bs, h, w) |
| [Ptn Pre Flatten](ptn_pre_flatten.md) | Flattens the input tensor before processing the tensor in the specified model. |
| [Ptn Resnet Model](ptn_resnet_model.md) | A Resnet model consisting of multiple Resnet layers.   |
## PyTorch wrapper - Optimizer
| Node | Description |
| --- | --- |
| [Pto Adam](pto_adam.md) | Instantiates the Adam optimizer. |
| [Pto AdamW](pto_adamw.md) | Instantiates the AdamW optimizer. |
| [Pto SGD](pto_sgd.md) | Instantiates the SGD optimizer. |
| [Pto Simple](pto_simple.md) |     Instantiates the most basic optimizer to update W using the below formula:   |
## 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 |
| --- | --- |
| [Pt Size](pt_size.md) | Extracts the PyTorch Size object of a PyTorch tensor using the size() method. |
| [Pt Size Create](pt_size_create.md) | Creates a PyTorch Size using values entered in the text field. |
| [Pt Size To Numpy](pt_size_to_numpy.md) | Converts PyTorch Size object to NumPy ndarray. |
| [Pt Size To String](pt_size_to_string.md) | Converts PyTorch Size object to a Python string. |
## PyTorch wrapper - Tensor creation
| Node | Description |
| --- | --- |
| [Pt Arange](pt_arange.md) | Creates a PyTorch tensor using `torch.arange` with the specified start, end, and step values. |
| [Pt Bool Create](pt_bool_create.md) | Creates a PyTorch tensor of dtype bool from True or False values entered as a list in the text field. |
| [Pt Float Create](pt_float_create.md) | Creates a PyTorch tensor with 32-bit floating point precision  |
| [Pt From Latent](pt_from_latent.md) | Casts a latent tensor as a PyTorch tensor. |
| [Pt From Numpy](pt_from_numpy.md) | Converts a NumPy ndarray to a PyTorch tensor while preserving its data type. |
| [Pt Full](pt_full.md) | Creates a PyTorch tensor filled with a specified value using the size entered in the text field. |
| [Pt Int Create](pt_int_create.md) | Creates a PyTorch tensor with 32-bit integer  |
| [Pt Linspace](pt_linspace.md) | Creates a PyTorch tensor using `torch.linspace` with the specified start, end, and steps values. |
| [Pt Ones](pt_ones.md) | Creates a PyTorch tensor of ones using the size entered in the text field. |
| [Pt Rand](pt_rand.md) | Creates a PyTorch tensor with values sampled from a uniform distribution  |
| [Pt Rand Int](pt_rand_int.md) | Creates a PyTorch tensor filled with random integers within a specified range using the size entered in the text field. |
| [Pt Randn](pt_randn.md) | Creates a PyTorch tensor with values sampled from a standard normal distribution (mean=0, std=1)  |
| [Pt Zeros](pt_zeros.md) | Creates a PyTorch tensor of zeros using the size entered in the text field. |
## PyTorch wrapper - Tensor data conversion
| Node | Description |
| --- | --- |
| [Pt To Latent](pt_to_latent.md) | Casts a PyTorch tensor as a latent tensor. |
| [Pt To Numpy](pt_to_numpy.md) | Converts PyTorch tensor to NumPy ndarray. |
| [Pt To Rgb Tensors](pt_to_rgb_tensors.md) | Splits a PyTorch tensor into R, G, and B tensors. |
## PyTorch wrapper - Training
| Node | Description |
| --- | --- |
| [Pt Apply Function](pt_apply_function.md) | Applies a function to the input tensor. |
| [Pt Compute Loss](pt_compute_loss.md) | Computes loss for the input with the target using the specified loss function. |
| [Pt Data Loader](pt_data_loader.md) | Loads data from a dataset node and creates a PyTorch DataLoader.   |
| [Pt Data Loader From Tensors](pt_data_loader_from_tensors.md) | Creates a Torchvision Dataloader from a pair of tensors. |
| [Pt Evaluate Classification Model](pt_evaluate_classification_model.md) | Performs inference on test data and computes evaluation metrics. |
| [Pt Load Model](pt_load_model.md) | A wrapper class for saving a PyTorch model. |
| [Pt Predict Classification Model](pt_predict_classification_model.md) | Performs inference on input data. |
| [Pt Predict Regression Model](pt_predict_regression_model.md) | Performs inference on input data. |
| [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 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. |
| [Pto Lr Scheduler Step](pto_lr_scheduler_step.md) | Creates a StepLR learning rate scheduler for an optimizer. |
| [Ptv Dataset](ptv_dataset.md) | A Torchvision Dataset class wrapper. |
| [Ptv Dataset Len](ptv_dataset_len.md) | A wrapper class that calls Python len on a dataset. |
| [Ptv Dataset Loader](ptv_dataset_loader.md) | A node to combine the dataset and data loader into a single node. |
| [Ptv Image Folder Dataset](ptv_image_folder_dataset.md) | A Torchvision ImageFolder Dataset class wrapper. |
| [Ptv Transforms Data Augment](ptv_transforms_data_augment.md) | Applies data augmentation transformations to dataset elements. |
| [Ptv Transforms Resize](ptv_transforms_resize.md) | Resizes and transforms elements of dataset to PyTorch tensors. |
| [Ptv Transforms To Tensor](ptv_transforms_to_tensor.md) | Transforms elements of dataset to PyTorch tensors. |
## PyTorch wrapper - Transform
| Node | Description |
| --- | --- |
| [Pt Concat](pt_concat.md) | Concatenates two PyTorch tensors. |
| [Pt Flatten](pt_flatten.md) | Flattens a PyTorch tensor into a 1D tensor. |
| [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 Stack](pt_stack.md) | Creates a new axis and stacks two PyTorch tensors along the new axis. |
| [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()`. |