Define the production output
Start with image dimensions, visual style, review criteria, licensing constraints and delivery volume. The workflow should serve an output standard, not a collection of nodes.
Reproducibility
Store the workflow graph, prompt template, seed policy and exact model identifiers together. Track third-party model licenses and permitted business use.
Capacity
GPU memory depends on model, resolution, batch size and extensions. Benchmark one representative workflow before renting long-term capacity.
Human review
Add checks for brand consistency, unsafe content, artifacts, text accuracy and provenance before publication.
From need to working outcome
Write the output specification
Define dimensions, style, volume, licensing and acceptance criteria before selecting models.
Install and isolate ComfyUI
Use an isolated environment, record versions and keep the service behind authenticated access.
Build a minimal workflow graph
Start with the fewest nodes needed to produce the target output, then add controls one at a time.
Benchmark GPU memory and latency
Run representative dimensions and batch sizes while recording peak GPU memory and generation time.
Add review and version control
Save the graph and model identifiers with each approved output template and define a human review checklist.
AI tools for this workflow
What to know before you start
How much GPU memory does ComfyUI need?
There is no single requirement. Model family, resolution, batch size and extensions change memory use, so benchmark the exact graph.
Can the workflow be used commercially?
The graph itself does not grant model or asset rights. Review every model, LoRA, image and font license used in the pipeline.
Why store the seed and model identifier?
They improve reproducibility and make it easier to audit why an output changed after an update.