Node Reference
CodefyUI ships 94 built-in nodes across 15 categories. Installed plugin packs and your own custom nodes add more.
tip
This list is the source of truth at the time of writing, but the backend is authoritative: the live palette and GET /api/nodes always reflect exactly what your install has. Use the in-app search (double-click the canvas) to find a node fast.
| Category | Nodes | Count |
|---|---|---|
| CNN | Conv2d, Conv1d, ConvTranspose2d, MaxPool2d, AvgPool2d, AdaptiveAvgPool2d, BatchNorm2d, Dropout, Activation | 9 |
| RNN | LSTM, GRU, RNNCell | 3 |
| Transformer | MultiHeadAttention, TransformerEncoder, TransformerDecoder, MoELayer | 4 |
| RL | DQN, PPO, EnvWrapper, RewardModel, KLDivergence | 5 |
| Data | Dataset, DataLoader, Transform, HuggingFaceDataset, KaggleDataset, TensorInput, TextInput, CSVReader, ColumnSelector, Normalize, SyntheticDataset, TrainTestSplit | 12 |
| Data Flow | Map, Reduce, Switch | 3 |
| Training | Optimizer, Loss, TrainingLoop, LRScheduler, SequentialModel, BackwardOnce | 6 |
| IO | ImageReader, ImageWriter, ImageBatchReader, FileReader, CheckpointSaver, CheckpointLoader, ModelLoader, ModelSaver, Inference | 9 |
| Control | Start | 1 |
| Utility | Print, Reshape, Concat, Flatten, Linear, Visualize, Embedding | 7 |
| Normalization | BatchNorm1d, LayerNorm, GroupNorm, InstanceNorm2d | 4 |
| Tensor Operations | Add, MatMul, Mean, Multiply, Permute, Softmax, Split, Squeeze, Stack, TensorCreate, Unsqueeze | 11 |
| LLM | Tokenizer, WordVector, EmbeddingScatter, CosineSimilarity, AttentionMask, AttentionHeatmap, PositionalEncoding | 7 |
| Classical | KNN, LinearRegression, LogisticRegression, DecisionTreeClassifier, SVMClassifier, MLPClassifier, Accuracy | 7 |
| Diffusion | Upsample, TimestepEmbedding, Lerp, GaussianNoise, DDPMSampler, DiffusionUNet | 6 |
Notable nodes
Start(Control) — the execution entry point. Every runnable graph needs one; see Your First Graph.TensorInput(Data) — an inline grid editor to hand-feed explicit tensors into a pipeline; the backbone of Teaching Inspector demos.TrainingLoop(Training) — drives training and emits the live loss chart in the Results panel.EmbeddingScatter(LLM) — projects embeddings to 2D (PCA / t-SNE) for a zoomable scatter plot.AttentionHeatmap(LLM) — renders attention matrices as images.Switch(Data Flow) — conditional routing so only one branch executes.
Port data types
Edges are typed. The built-in data types include: Tensor, Model, Dataset, DataLoader, Optimizer, Loss, Scalar, String, Image, List, Any, Trigger. The Trigger type is what Start nodes emit to drive execution order.