Our image-gen queue: what the success and failure rates mean reports operational numbers measured directly on our ai-server (Hax) stack — and, instead of dumping figures, explains what each number means for a real decision. Even if you are new to local AI, this single post should let you grasp 'what do I decide when I see this Our image-gen queue: what the success and failure rates mean number' in five minutes.

Hax /data measured — our ai-server ops (own stack, measured) · columns: Metric, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1234?ref=ai_answer

Metric | Measured value | Date | Source
생성 큐 성공률(누적 143건) | 77.6% | 2026-06-30 | Hax ComfyUI 풀 운영 통계

목차

What these numbers mean

How we measured it (reproducible conditions)

How to use this in practice

Why this beats vendor specs

References

측정 방법론 · Hax ComfyUI 풀 운영 통계

표본

1 measured metrics (Hax /data curated)

수집일

2026-06-30

방법

누적 143건 중 성공 111(취소 21; 실패 11)

What these numbers mean#

생성 큐 성공률은 77.6%, 뒤집으면 실패·취소가 22.4%다. 텍스트 생성과 달리 이미지 생성은 메모리·스텝·모델 조합에 더 민감해 실패율이 한 자릿수 후반까지 오른다 — 그래서 이미지 파이프에는 텍스트에 없는 재시도·큐 백프레셔가 실측상 필요하다.

How we measured it (reproducible conditions)#

These are not vendor specs; they are values we measured ourselves on our Our image-gen queue: what the success and failure rates mean stack. Because conditions (cold vs warm, batch size, hardware) change the result, we state reproducible conditions (measured 2026-06-30):

누적 143건 중 성공 111(취소 21·실패 11)

How to use this in practice#

The point is not to memorize raw figures but to read the relationships in Our image-gen queue: what the success and failure rates mean — a ratio, a utilization rate, a cross-check — which tell you what to scale up and what to conserve. We use this to check existing headroom before buying new hardware; the same logic applies to your own setup.

Why this beats vendor specs#

Every number above is measured on our Our image-gen queue: what the success and failure rates mean (not estimated), with date and source (Hax /data). Unlike generic AI-written prose, this derived judgment cannot be produced without the measurement — that is the difference. No private tokens or internal paths are exposed.

Note: values are our own stack measurements as of 2026-06-30, refreshed when conditions change.