Modal uses evals and inference-time compute scaling to build QArt codes with a ninety-five percent scan rate
QArt codes — QR codes embedded in AI-generated images — reliably failed to scan in real-world use despite impressive demos, making it impossible to consistently produce codes that were both beautiful and scannable.
An initial version of the system produced scannable codes only when using simple texture or style prompts, failing with complex scene descriptions, and falling far short of the original viral demo's promise.
By developing automated evals and applying inference-time compute scaling with eight parallel generations per request, the team achieved a ninety-five percent scan rate SLO while improving aesthetic quality and returning codes to users in under 20s, p95.
Frequently asked questions
What did this team achieve with this AI workflow?
By developing automated evals and applying inference-time compute scaling with eight parallel generations per request, the team achieved a ninety-five percent scan rate SLO while improving aesthetic quality and return…
What tools did this team use?
Stable Diffusion, ControlNet, QReader, YOLO, Weights & Biases' Weave, Modal.
What results were reported?
QR code scan rate SLO: ninety-five percent; End-to-end latency p95: under 20s, p95 (source-reported, not independently verified).
What failed first in this deployment?
An initial version of the system produced scannable codes only when using simple texture or style prompts, failing with complex scene descriptions, and falling far short of the original viral demo's promise.
How is this marketing ops AI workflow structured?
User request initiates batch → Diffusion model generates images → Scan and aesthetic evaluation → Rank and select top four.