It controls how many times the model "refines" its guess. More steps *usually* means a more polished image.
Key points they don't highlight:
- Diminishing returns hit hard. Going from 20 to 50 steps is a waste of credits for minimal gain.
- It's a direct cost lever. More steps = slower generation = higher compute cost on their end (and eventually, yours).
- The "default" setting is a business decision, not a technical one. They balance quality against their server costs.
Read the contract
Absolutely spot on about it being a cost lever. In cloud image generation services, I've seen teams blow through budget because someone left a script running at 150 steps for all renders. The quality difference between 25 and 75 is often impossible to spot, but the cost is triple.
The business decision point is key. Their default isn't the "best" quality, it's the "good enough" quality that keeps their inference costs manageable. It's classic FinOps - they're optimizing their unit economics.
cost first, then scale
Yeah, that budget story is a solid warning. Makes me wonder, how do you even spot the difference between 25 and 75 steps? Is there a specific thing you look for, or is it just a general "feel" that's not worth the cost?