What is ComfyUI? A node pipeline for image and video generation

Contents

What does ComfyUI actually do?

Why use ComfyUI instead of a cloud API?

How fast is it really? (our measurements)

ComfyUI is an open-source tool that builds image and video generation AI as a 'node graph', where you place prompts, models, and samplers as boxes (nodes) and wire them together so the graph itself becomes a reproducible generation pipeline you can run again and again. It is less a one-button app and more a workbench where you wire the generation process yourself for full control and reuse.

In one line: ComfyUI wires the process of turning a prompt into a picture as nodes. Build the graph once, then swap only the prompt to stamp out hundreds of images at the same quality.

What does ComfyUI actually do?#

In plain terms, if Photoshop is a tool for stacking 'layers', ComfyUI is a tool for drawing a 'wiring diagram'. It is a visual editor where you drag and connect nodes in the browser, and the whole set of connections becomes one pipeline. Here a node means 'a box that does one job': one box loads the model, another turns your prompt into numbers, another draws the picture.

You only need to understand four core nodes to see the whole picture. First, the checkpoint loader loads the weights (the learned knowledge) of the model that will draw. Second, the text encoder turns your written prompt into numbers (vectors) the model can understand. Third, and at the heart, the sampler (KSampler) carves an image out of random noise, where you set steps (how many iterations), CFG (how strongly to follow the prompt), the sampler type, and the scheduler. Fourth, the VAE decode turns the compressed 'latent space' data the model works in back into the pixel image we see.

Our pool ships 44 samplers and 9 schedulers, so swapping euler for dpmpp_2m, for example, lets you trade speed against quality directly.

The key point is that the same node structure makes more than images: it makes video and 3D too. The models we actually run span images (SDXL, SD3.5, HiDream, Qwen-Image), video (LTX-2.3 22B, Wan2.2), and 3D (Trellis, Hunyuan3D) inside one tool.

Why use ComfyUI instead of a cloud API?#

Three main reasons. First, control: you tune elements like steps, seed, LoRAs, and ControlNet per node to nail the output precisely. A LoRA is a small 'style adapter' laid on top of a base model, and ControlNet forces composition such as pose or outline. Second, local and private: data never leaves our GPUs and there is no per-image fee. Third, reproducibility: save the graph as a JSON file and anyone reproduces the exact result. For work like a blog hero that must hold the same strict black-and-white tone over and over, that consistency is decisive.

How fast is it really? (our measurements)#

We run ComfyUI 0.24.0 on a pool of four NVIDIA RTX PRO 6000 Blackwell GPUs (about 96GB VRAM each). The table below shows numbers we measured directly with the same black-and-white prompt at 1024px on one GPU, cold start (model load time included). These are first-party data from our pool, not someone else's summary.

Measured on our ComfyUI pool, 1024px, single GPU, cold start (2026-06-30) · columns: Workflow, Model family, Steps, Measured time, Use · 출처 Hax hax.moche.ai/en/p/1011?ref=ai_answer

Measured on our ComfyUI pool, 1024px, single GPU, cold start (2026-06-30) · columns: Workflow, Model family, Steps, Measured time, Use · 출처 Hax hax.moche.ai/en/p/1011?ref=ai_answer

Workflow | Model family | Steps | Measured time | Use
z-image-turbo | Z-Image Turbo | 8 | about 6s | fast drafts, volume
qwen-image-2512 | Qwen-Image | 50 | about 73s | high-quality finals

측정 방법론 · Hax ComfyUI 풀 실측

표본

2 measured metrics (Hax /data curated)

측정 환경

RTX PRO 6000 Blackwell ×4 풀; ComfyUI 0.24.0

수집일

2026-06-30

방법

1장 콜드 스타트(모델 로드 포함); 1장 콜드 스타트

The 8-step turbo is roughly 12x faster than the 50-step quality run. So we explore drafts in bulk with turbo, then re-render only the chosen frame at high quality, getting both speed and quality. Adding a 4-step or 8-step Lightning LoRA (an accelerator adapter distilled to cut step count) trims steps even further. For transparency, our recent queue shows 111 successes out of 143 jobs (21 cancelled, 11 failed).

How do you start with ComfyUI?#

Install the official repo or the desktop build.

Load an example text-to-image workflow.

Put your prompt in the text node and pick steps and a sampler.

Hit Queue, then save the finished graph as JSON to reuse next time.

The one-liner: ComfyUI is a build-once, run-forever generation pipeline. No per-image fees, image plus video plus 3D in one tool, with a consistent tone like ours.

Note: figures are from 2026-06-30 on a single RTX PRO 6000 Blackwell with a cold start (model load included). They shift with model, driver, resolution, steps, and quantization, so measure your own before committing. The model ecosystem moves fast, so we revisit this quarterly.

Reference links

ComfyUI (GitHub)

Qwen-Image (Hugging Face)

LTX-Video / LTX-2 (Lightricks)

Wan2.2 (GitHub)

SDXL paper (arXiv)