mageflow / 4b-turbo / createImage
The cheapest legit image generation on Civitai's fleet
- loras
- diffusion_model
- api_config
- images
- errors
- workflow_id
- raw_json
Take Microsoft's small 4B MageFlow model, distill it down to 4 steps at CFG 1.0, and you get the node that's basically the value end of the Civitai orchestration catalogue: fast, cheap-per-job text-to-image in the cloud. This is the 4B-turbo createImage variant - prompt in, IMAGE out, nothing runs locally.
It's the node to reach for when you need bulk image generation on the cheap, or a fast draft stage before a fancier local or hosted model does the final pass. MageFlow's own claim to fame is that a 4B model can do this at all, and the turbo variant leans into the speed. Just don't expect the detail ceiling of a 12B or 20B model.
How it works
The standard civitai-comfy-nodes flow: the node submits your prompt to the Civitai Orchestration API, runs MageFlow 4B-turbo on Civitai's Comfy engine, long-polls, and returns a native IMAGE tensor. Turbo = guidance-distilled, so 4 steps at CFG 1.0 are the defaults and you should generally leave them there. The "turbo" in the name is doing all the work.
The inputs that matter
- prompt - required, and it's the only thing you need to fill to get an image.
- diffusion_model (
CIVITAI_AIR) and loras (CIVITAI_LORAS) - optional typed sockets; wire Civitai Model Selector / LoRA Selector nodes to swap models or stack LoRAs on the cloud job. - quantity - up to 12 images per job, which makes this node genuinely useful for batch work.
- steps / cfg_scale - 4 / 1.0 defaults. Distilled model: don't crank CFG.
- sampler / scheduler / seed / width / height - standard, plus a negative prompt slot.
Outputs
images → Preview/Save. errors, workflow_id, and raw_json are diagnostics; raw_json carries the full workflow result.
Installing and authenticating
Pack-wide install:
cd ComfyUI/custom_nodes
git clone https://github.com/civitai/civitai-comfy-nodes.git
pip install -r civitai-comfy-nodes/requirements.txt
or ComfyUI Manager → "Civitai Comfy Nodes" → Install → restart. Dependencies: requests, python-socketio[client].
Auth: Civitai Auth node → CIVITAI_API_TOKEN → stored key/OAuth → browser login. Headless:
export CIVITAI_API_TOKEN=your_token
Where people get burned
- 4 steps is the whole point - and the ceiling. You're trading detail for speed and cost. If your use case needs fidelity, the non-turbo MageFlow create node (20 steps) or a bigger engine is the move.
- CFG 1.0 is not a bug. Setting CFG to 7 "because that's normal" will actively hurt output on a guidance-distilled model.
- Billing is per job, and quantity multiplies it. Yes,
quantityup to 12 is tempting. Yes, you pay per image. Batch responsibly. - Your prompts go to Civitai's servers with hosted moderation. Standard API-node trade.
Inputs (15)
| Name | Type | Default | Description |
|---|---|---|---|
| prompt | STRING | — | |
| steps | INT | 41–150 | — |
| cfg_scale | FLOAT | 1.000–30 | — |
| quantity | INT | 11–12 | — |
| output_formatopt | COMBO | 4 options: , jpeg, png, webP | |
| image_metadataopt | STRING | External metadata that will be stored with the image | |
| negative_promptopt | STRING | — | |
| sampleropt | COMBO | 32 options: , euler, euler_ancestral, euler_cfg_pp, euler_ancestral_cfg_pp, heun, +26 | |
| scheduleropt | COMBO | 8 options: , normal, karras, exponential, sgm_uniform, simple, +2 | |
| seedopt | INT | 00–4294967295 | — |
| lorasopt | CIVITAI_LORAS | — | |
| diffusion_modelopt | CIVITAI_AIR | — | |
| widthopt | INT | 1024512–2048 | — |
| heightopt | INT | 1024512–2048 | — |
| api_configopt | CIVITAI_CONFIG | Optional Civitai Auth connection; defaults to CIVITAI_API_TOKEN or stored OAuth login. |
Outputs (4)
| Name | Type | Description |
|---|---|---|
| images | IMAGE | — |
| errors | STRING | — |
| workflow_id | STRING | — |
| raw_json | STRING | — |