Pinning and Caching
Pin nodes in PixelFlow to cache their output across runs — skip expensive re-execution, iterate faster, and save credits while you refine a workflow.
Pinning caches a node's output. A pinned node is not re-executed on the next run — its previous output is reused and fed to downstream nodes. For expensive or slow models, this is the difference between iterating in seconds and re-paying for the whole pipeline on every tweak.
Video coming soon
A pinned node is skipped on re-run; downstream nodes use its cached output
https://segmind-resources.s3.amazonaws.com/docs/pixelflow/guides/pinning.mp4
How it works
Every node has a pin icon. Click it to toggle:
- Pinned — the icon highlights. On the next run the node is skipped and its most recent successful output is used.
- Unpinned — the node executes as usual.
Only nodes with a successful previous run can serve a cached result.
When pinned nodes re-run anyway
Pinning never serves stale data. A pinned node is automatically unpinned and re-executed when:
- You unpin it — it runs on the next execution.
- Its configuration changes — editing any parameter invalidates the cache.
- An upstream node changes — if anything feeding the pinned node is changed or re-run, the pinned node re-runs to stay consistent.
When to pin
- Iterative development — lock in the parts of the workflow that already work and iterate on the rest.
- Cost control — don't re-pay for a 4K upscale while you fiddle with a caption model downstream.
- Speed — skip slow video or upscaling nodes when their input hasn't changed.
Run node (right-click) pairs well with pinning: it executes only the selected node and its unpinned ancestors — the cheapest possible way to test one step. See the editor.
Build Your First Workflow
A step-by-step PixelFlow tutorial: build a text-to-image workflow from a blank canvas, run it, iterate with pinning, and get it ready to publish as an API.
LLM Chat History
Chain LLM nodes with chat history in PixelFlow to build multi-turn conversations, branching dialogues, and conversational AI pipelines — visually.