Our image-infra footprint: checkpoints, LoRAs, samplers, ControlNets
In short: 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,
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.
| 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) |
- 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
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