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.
How it works
Pinning applies to model nodes. The pin isn't drawn on the node itself — it lives in the hover action bar, the strip of controls that appears above a node when your cursor is over it. Input, output, and notes nodes have no pin, because there is no model call to skip. 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.
With a node selected, P toggles its pin without reaching for the action bar.
Running a workflow that contains pinned nodes
Running the whole workflow doesn't silently reuse the cache — PixelFlow asks first:
Pinned nodes have saved outputs. 1 pinned node has a saved output. Use it as-is, or re-run everything including pinned nodes?
Three answers: Use saved outputs honours the pins, Re-run everything ignores them and re-executes the pinned nodes too, and Cancel aborts the run. So a pin is the default answer to that question rather than an unconditional skip — useful when you want one fresh run without unpinning and re-pinning a stack of nodes.
A pin holds until you release it
Pinning does not track staleness, and nothing invalidates the cache for you. A pinned node keeps serving its saved output until you intervene, in one of two ways:
- Unpin it — it runs again on the next execution.
- Answer "Re-run everything" at the dialog above — a one-off, which leaves the pin in place for later runs.
While a node is pinned its parameters are read-only and its own Run button is replaced by Unpin node, so you cannot edit a pinned node's configuration without unpinning first.
Editing an upstream node does not release the pin. Change the prompt feeding a pinned model and it keeps its old output — the workflow will happily run with the new prompt upstream and the stale image downstream. If you have changed anything that feeds a pinned node and you want that change reflected, unpin it or choose Re-run everything.
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 — the ▶ in a node's hover action bar — pairs well with pinning: it executes only the selected node, reusing whatever its upstream already produced — 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
The Chat History input on PixelFlow's LLM nodes is not yet carrying conversation context between turns. What the connection does today, and what to use instead.