Node Reference

CodefyUI ships 152 built-in nodes across 16 categories. Installed plugin packs and your own custom nodes add more.

tip

This table reflects the current release, but the backend is authoritative. Check the live node palette or GET /api/nodes for the nodes available in your installation. Double-click the canvas to search for a node.

Category	Nodes	Count
CNN	Conv2d, Conv1d, Conv2dExplicit, ConvTranspose2d, MaxPool2d, AvgPool2d, AdaptiveAvgPool2d, BatchNorm2d, Dropout, Activation	10
RNN	LSTM, GRU, RNNCell	3
Transformer	MultiHeadAttention, TransformerEncoder, TransformerDecoder, MoELayer	4
RL	DQN, PPO, EnvWrapper, RewardModel, KLDivergence, PolicyRollout, PPOClipObjective, GroupRelativeAdvantage, Discount, GridWorldEnv, PreferenceDataset, BradleyTerryLoss, BradleyTerryTrain	13
Data	Dataset, ImageFolderDataset, DataLoader, DatasetBatch, Transform, HuggingFaceDataset, KaggleDataset, TensorInput, TextInput, CSVReader, ColumnSelector, RowSelector, Normalize, SyntheticDataset, SyntheticShapes, SyntheticSegmentation, SyntheticSequence, TrainTestSplit, ResizeTransform, ToTensorTransform, NormalizeTransform, RandomCrop, RandomHorizontalFlip, RandomRotation, ColorJitter, RandAugment, ComposeTransform	27
Data Flow	Map, Reduce, Switch	3
Training	Optimizer, Loss, TrainingLoop, EvaluateModel, LRScheduler, SequentialModel, BackwardOnce	7
IO	ImageReader, ImageWriter, ImageBatchReader, FileReader, CheckpointSaver, CheckpointLoader, ModelLoader, ModelSaver, Inference, GraphInput, GraphOutput, VideoLoad, VideoWrite	13
Control	Start	1
Utility	Print, Reshape, Concat, Flatten, Linear, Visualize, Embedding, PythonScript, ScatterPlot2D, DecisionBoundary	10
Normalization	BatchNorm1d, LayerNorm, GroupNorm, InstanceNorm2d	4
Tensor Operations	Add, MatMul, Mean, Multiply, ScalarMultiply, Permute, Softmax, Argmax, Split, Squeeze, Stack, TensorCreate, Unsqueeze, MaskedFill	14
LLM	LLMChat, Tokenizer, WordVector, TextEmbedding, EmbeddingScatter, CosineSimilarity, AttentionMask, AttentionHeatmap, PositionalEncoding, CausalLMModel, LMCrossEntropyLoss, LMTokenizer, TextCorpusDataset, LMTokenizedDataset, DataMixDataset, PerplexityEvaluate, TextGenerate, DocumentLoader, TextChunker, VectorStore, Retriever, PromptBuilder, HFTextGenerate	23
Classical	KNN, LinearRegression, LogisticRegression, DecisionTreeClassifier, RandomForestClassifier, SVMClassifier, MLPClassifier, Accuracy	8
Diffusion	Upsample, TimestepEmbedding, Lerp, GaussianNoise, DDPMSampler, DiffusionUNet, DiffusionTrainingLoop	7
VLA	VLAModel, VLARollout, VLAActionEval, PushWorldEnv, PushWorldDemos	5

Notable nodes

• Start (Control) — defines the execution entry point. Every runnable graph requires one; see Your First Graph.
• TensorInput (Data) — provides an inline grid for entering explicit tensor values. Teaching Inspector examples use it as their input node.
• The transform chain (Data) — nine nodes compose a transforms.Compose: ResizeTransform resizes to a square; ToTensorTransform converts a PIL image to a [0, 1] tensor; NormalizeTransform applies per-channel normalisation and provides presets; RandomCrop, RandomHorizontalFlip, RandomRotation, ColorJitter, and RandAugment apply augmentations; and ComposeTransform joins two separately constructed chains. See Data and Augmentation for parameters and ordering rules.
• ImageFolderDataset (Data) — loads images from one directory per class, using the directory structure expected by torchvision's ImageFolder.
• TrainingLoop (Training) — runs training and sends the live loss chart to the Results panel. Its Advanced section contains the memory controls precision and accumulate_steps; see Training Memory.
• SequentialModel (Training) — represents a layer stack in one node. Double-click it to open the Model Architecture Editor. The editor provides a searchable layer palette grouped by category and a canvas for connecting layers. An architecture must have exactly one Input node and one Output node, and merge layers have a port-list editor. The controls include Snap: ON/OFF, top-to-bottom Auto Layout, JSON Import and Export, and Apply. Validation rejects cycles.
• EmbeddingScatter (LLM) — projects embeddings to 2D with PCA or t-SNE and displays a zoomable scatter plot.
• AttentionHeatmap (LLM) — forwards attention weights, optionally selecting one head, and forwards optional token labels. The node card displays the weights as a heatmap, and a full-size viewer is available.
• Pack-backed backends (LLM) — WordVector's glove-50d and sentence-encoder options, and all of TextEmbedding, load models installed by the Package Center. Options without an installed model are greyed out. If a missing option is the graph's saved value, it remains selectable and displays a warning. Graph execution does not download these models. See Optional Packs for pack sizes, file locations, and encoder guidance.
• The language-model chain (LLM) — seven nodes form a training path for a GPT-style decoder: TextCorpusDataset reads text rows from the Hugging Face Hub or a local .txt; LMTokenizedDataset creates fixed-length next-token blocks; DataLoader supplies batches to TrainingLoop; CausalLMModel provides the model; LMCrossEntropyLoss provides the loss; and LMTokenizer supplies the shared tokenizer. PerplexityEvaluate scores a held-out split, and TextGenerate samples from the trained weights. The default CausalLMModel has 203,668,480 parameters; reduce d_model and n_layers for laptop-scale training. See the Train a Causal LM on TinyStories example in Examples Gallery.
• The RAG chain (LLM) — seven nodes use supplied documents as answer context: DocumentLoader reads each .md and .txt in a directory as {text, source}; TextChunker creates embeddable chunks with source names and character offsets; TextEmbedding creates vectors; VectorStore stores one [N, D] matrix with the chunk text and searches it with one matrix multiplication; Retriever returns the nearest top_k chunks and their scores; PromptBuilder inserts the chunks and question into a context-only template; and HFTextGenerate runs Qwen2.5-0.5B-Instruct locally. LLMChat can replace HFTextGenerate to use Ollama or a hosted provider. TextEmbedding requires the sentence-embeddings pack, and HFTextGenerate requires the rag pack. See RAG, fully local and RAG with a chat API in Examples Gallery, and see Optional Packs for the downloads.
• The VLA chain (VLA) — the VLA category contains five specialized nodes. A training and evaluation graph connects PushWorldEnv, PushWorldDemos, VLAModel, and TrainingLoop, then uses VLAActionEval and VLARollout. PushWorldEnv implements a language-conditioned 2D push task in torch; distractor pucks make the instruction necessary to identify the goal. PushWorldDemos creates scripted-expert behaviour-cloning samples and a held-out split. VLAModel combines a vision stem, byte-level instruction embedding, transformer trunk, and chunked action expert. VLAActionEval measures held-out open-loop MSE against the expert, while VLARollout measures closed-loop success with receding-horizon execution. The README for Train a VLA on PushWorld recommends a CUDA GPU and reports a run time of about 56 minutes on an RTX 4080.
• The RL nodes (RL) — GridWorldEnv provides a gridworld without a Gymnasium dependency. PolicyRollout runs a policy for N episodes and records states, actions, rewards, and logits. Discount computes returns, GroupRelativeAdvantage computes GRPO's group-mean baseline, and PPOClipObjective computes PPO's clipped surrogate and returns a mask showing which samples used the clipped branch. RewardModel produces one scalar score per sequence. PreferenceDataset creates training and held-out preference pairs; BradleyTerryLoss computes the preference loss; and BradleyTerryTrain fits a reward head and reports training and held-out accuracy to expose shortcut learning. KLDivergence computes the policy-to-reference regularisation term. DQN, PPO, and EnvWrapper integrate with Gymnasium. See RLHF building blocks: reward + KL in Examples Gallery.
• The diffusion chain (Diffusion) — GaussianNoise creates seeded noise and can match an upstream tensor's shape. DiffusionUNet provides a toy U-Net that maps (x, t) to predicted noise, and DiffusionTrainingLoop trains it. DDPMSampler performs the reverse process in one node so the graph remains acyclic; its verbose trace records trajectory snapshots. TimestepEmbedding, Upsample, and Lerp support an explicit construction of the same operations. The Diffusion examples are Forward Process (GaussianNoise and Lerp) and Toy Sampling and Mini U-Net (GaussianNoise → DiffusionUNet → DDPMSampler). See Examples Gallery.
• ModelSaver / ModelLoader (IO) — write and read model files. Their default state_dict mode stores tensors and imposes no class-loading requirements. Their full_model mode stores the pickled module and loads it with a restricted unpickler that permits torch and CodefyUI layer classes and the two torch activation functions stored by CodefyUI's transformer layers. See Saving and Loading Models for selection and loading requirements.
• Switch (Data Flow) — uses conditional routing so that 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, Transform, Any, Trigger. The Trigger type is what Start nodes emit to drive execution order, and Transform is what the transform-chain nodes pass to each other.