Our image-infra footprint: checkpoints, LoRAs, samplers, ControlNets 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-infra footprint: checkpoints, LoRAs, samplers, ControlNets 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/1233?ref=ai_answer

Hax /data measured — our ai-server ops (own stack, measured) · columns: Metric, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1233?ref=ai_answer

Metric | Measured value | Date | Source
설치된 체크포인트 수 | 32개 | 2026-07-04 | bench_harness.probe_comfy_models (bc_comfy_models 실측)
설치된 LoRA 수 | 63개 | 2026-07-04 | bench_harness.probe_comfy_models (bc_comfy_models 실측)
설치된 샘플러 수 | 44종 | 2026-07-04 | bench_harness.probe_comfy_models (bc_comfy_models 실측)
설치된 ControlNet 수 | 15개 | 2026-07-04 | bench_harness.probe_comfy_models (bc_comfy_models 실측)

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 · bench_harness.probe_comfy_models (bc_comfy_models 실측)

표본

4 measured metrics (Hax /data curated)

수집일

2026-07-04

방법

bench_harness.probe_comfy_models (bc_comfy_models 실측)

What these numbers mean#

설치 자산은 체크포인트 32·LoRA 63·샘플러 44·ControlNet 15로 총 154개, 그중 LoRA가 63개로 가장 많다. LoRA가 최다라는 건 새 베이스 모델을 늘리기보다 기존 모델에 스타일을 얹는 방향으로 인프라를 키워왔다는 뜻이다 — 적은 VRAM으로 표현 다양성을 확보하는 실전 선택이다.

How we measured it (reproducible conditions)#

These are not vendor specs; they are values we measured ourselves on our Our image-infra footprint: checkpoints, LoRAs, samplers, ControlNets stack. Because conditions (cold vs warm, batch size, hardware) change the result, we state reproducible conditions (measured 2026-07-04):

bench_harness.probe_comfy_models (bc_comfy_models 실측)

How to use this in practice#

The point is not to memorize raw figures but to read the relationships in Our image-infra footprint: checkpoints, LoRAs, samplers, ControlNets — 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-infra footprint: checkpoints, LoRAs, samplers, ControlNets (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 2026-07-04, refreshed when conditions change.

Related reading: 우리 comfy-pool 이미지 생성 실측: z-image vs qwen-image, 우리 GPU 풀 실측: RTX PRO 6000 ×4의 VRAM 사용률과 헤드룸

References#

Hax data