ComfyUI

A ComfyUI workflow represents operations as nodes with typed connections. A graph can load a checkpoint, encode a prompt, prepare latent inputs, run a sampler and decode an image, with additional branches for masks, adapters or other conditioning. The graph makes dependencies inspectable and reusable, but behavior still depends on node implementations, model versions and execution settings. Custom nodes extend functionality and add their own dependencies. ComfyUI is a workflow environment rather than a generative architecture: it can orchestrate supported models, and the underlying model and conditioning determine what the system can generate or edit.

Start with a minimal graph and verify each connection, model family and input convention before adding control branches. Record checkpoints, samplers, seeds and node versions, and inspect intermediate outputs when diagnosing failures. Review custom extensions and isolate incompatible dependencies. Evaluate whether conditioning produces the requested visual constraint and whether the saved workflow can be reopened consistently. The deliverable is a documented executable graph plus its required artifacts, allowing another practitioner to reproduce the setup and understand which settings affect quality, control and resource use.

A saved graph may omit external model files or rely on extensions whose behavior changes. Checkpoints, adapters and conditioning nodes must be compatible. A fixed seed does not guarantee identical outputs across every runtime or implementation. Custom nodes can execute code and require review. ComfyUI differs from Diffusers as a programming library and from Stable Diffusion as a model family. Inspect visual outcomes and dependencies directly; graph complexity and many controls do not guarantee faithful editing or production-ready assets.