ComfyUI Node: NNT Train Model

Authored by inventorado

Created

Updated

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Category

NNT Neural Network Toolkit/Models

Inputs

MODEL MODEL
training_data TENSOR
target_data TENSOR
experiment_name STRING
batch_size INT
epochs INT
optimizer
  • Adadelta
  • Adagrad
  • Adam
  • AdamW
  • SparseAdam
  • Adamax
  • ASGD
  • NAdam
  • RAdam
  • RMSprop
  • Rprop
  • SGD
learning_rate FLOAT
weight_decay FLOAT
momentum FLOAT
loss_function
  • L1Loss
  • MSELoss
  • CrossEntropyLoss
  • CTCLoss
  • NLLLoss
  • PoissonNLLLoss
  • GaussianNLLLoss
  • KLDivLoss
  • BCELoss
  • BCEWithLogitsLoss
  • MarginRankingLoss
  • HingeEmbeddingLoss
  • MultiLabelMarginLoss
  • HuberLoss
  • SmoothL1Loss
  • SoftMarginLoss
  • MultiLabelSoftMarginLoss
  • CosineEmbeddingLoss
  • MultiMarginLoss
  • TripletMarginLoss
  • TripletMarginWithDistanceLoss
reduction
  • mean
  • sum
  • none
weight_enabled
  • True
  • False
class_weights STRING
margin FLOAT
use_lr_scheduler
  • True
  • False
scheduler_type
  • StepLR
  • ReduceLROnPlateau
  • CosineAnnealingLR
scheduler_step_size INT
scheduler_gamma FLOAT
min_lr FLOAT
use_early_stopping
  • True
  • False
patience INT
min_delta FLOAT
reshape_input
  • True
  • False
input_reshape_dim STRING
flatten_input
  • True
  • False
hyperparameters DICT

Outputs

MODEL

STRING

DICT

Extension: ComfyUI Neural Network Toolkit NNT

Neural Network Toolkit (NNT) for ComfyUI is an extensive set of custom ComfyUI nodes for designing, training, and fine-tuning neural networks. This toolkit allows defining models, layers, training workflows, transformers, and tensor operations in a visual manner using nodes.

Authored by inventorado

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