Canvas Basics
Orient yourself in the CodefyUI canvas — the node palette, type-safe ports and edges, config panel, and results panel.
Your First Graph
Build and run a minimal pipeline, and learn why every graph needs a Start node to drive execution.
Running Graphs
How execution works — WebSocket streaming, the results panel, live loss charts, and partial re-execution.
Teaching Inspector
Record per-node outputs, inspect input→output tensor diffs, compare a subgraph segment, capture gradients, and view step traces.
Tabs & Persistence
Multi-tab workspaces, automatic localStorage saving, and importing/exporting graphs as JSON.
Key Bindings
Keyboard and mouse shortcuts for the CodefyUI canvas.
CLI Graph Runner
Execute a saved graph.json directly from the command line with run_graph.py — no server required.
Graph as a Function
Call any saved graph headlessly over HTTP as a named function — declared inputs in, declared outputs out.
Publish (Graphs as Apps)
Freeze a saved graph as a versioned app behind a stable, API-key-protected invoke endpoint, with every run recorded to SQLite.
Version Control Your Graphs
Keep your graph JSON in a git service repo, validate every graph in CI with run_graph.py, and publish from a versioned source.
Project Directories
Turn a service into a self-contained git repo (cdui project) with a logic/layout split, per-project assets and secrets, CI validation, and publish provenance.
Example Projects
Official ready-to-clone example services -- each one a standalone CodefyUI project repository you can run, publish, and fork.
Node Reference
Every built-in node — 95 nodes across 15 categories, from CNN and Transformer layers to RL, LLM, Diffusion, and classical ML.
Examples Gallery
Pre-built example workflows — model architectures, end-to-end training, and LLM demos you can load and run.