Flux 1 LoRA training

Train a Flux.1 LoRA on your own image dataset using AI Toolkit. The output LoRA is usable directly in Flux 1 image generation (sdcpp or Comfy paths).

`modelVariant`	Base model	Inference characteristics
`dev` (default)	`black-forest-labs/FLUX.1-dev`	Higher fidelity, ~20–28 sampler steps. Good default for most LoRAs.
`schnell`	`black-forest-labs/FLUX.1-schnell`	Faster inference, 4 sampler steps, no CFG. Use when you specifically want a Schnell-targeted LoRA.

The base checkpoint is fixed by modelVariant — there's no model field to override. To train on a non-BFL Flux.1 finetune, use the SDXL & SD1 or other-image ecosystems instead.

Long-running step

Flux 1 training is the most expensive AI Toolkit ecosystem (200 Buzz/epoch) and runs for ~30s–2min per epoch on a typical 10-image dataset. Always use wait=0 and follow up via polling or a webhook — see Results & webhooks.

The request shape

json

{
  "$type": "training",
  "input": {
    "engine":       "ai-toolkit",
    "ecosystem":    "flux1",
    "modelVariant": "dev"        // dev | schnell
  }
}

Prerequisites

A Civitai orchestration token (Quick start → Prerequisites)
A training-data zip uploaded to a reachable URL (signed R2 URL, Civitai R2 AIR, or any HTTPS URL)
An accurate count of images in the zip

Flux 1 dev (default)

Trains on top of FLUX.1-dev and produces a LoRA usable with any Flux 1 dev workflow.

http

POST https://orchestration.civitai.com/v2/consumer/workflows?wait=0
Authorization: Bearer <your-token>
Content-Type: application/json

{
  "tags": ["training"],
  "steps": [{
    "$type": "training",
    "priority": "normal",
    "retries": 2,
    "input": {
      "engine": "ai-toolkit",
      "ecosystem": "flux1",
      "modelVariant": "dev",
      "epochs": 5,
      "resolution": 1024,
      "lr": 0.0001,
      "trainTextEncoder": false,
      "lrScheduler": "cosine",
      "optimizerType": "adamw8bit",
      "networkDim": 16,
      "networkAlpha": 16,
      "trainingData": {
        "type": "zip",
        "sourceUrl": "urn:air:other:other:civitai-r2:civitai-delivery-worker-prod@training-images/6/2657604TrainingData.EYBd.zip",
        "count": 10
      },
      "samples": {
        "prompts": ["a photo of TOK", "TOK in a garden", "TOK portrait"]
      }
    }
  }]
}

Set your Civitai API token via the Token button in the navbar to enable Try It.

Request body — edit to customize (e.g. swap the image URL or prompt)

Valid JSON

Flux 1 schnell

Trains on top of FLUX.1-schnell. Inference uses 4 steps and cfgScale: 0 — the output LoRA is meant to be used in those conditions.

http

POST https://orchestration.civitai.com/v2/consumer/workflows?wait=0
Authorization: Bearer <your-token>
Content-Type: application/json

{
  "tags": ["training"],
  "steps": [{
    "$type": "training",
    "input": {
      "engine": "ai-toolkit",
      "ecosystem": "flux1",
      "modelVariant": "schnell",
      "epochs": 5,
      "lr": 0.0001,
      "trainTextEncoder": false,
      "networkDim": 16,
      "networkAlpha": 16,
      "trainingData": {
        "type": "zip",
        "sourceUrl": "urn:air:other:other:civitai-r2:civitai-delivery-worker-prod@training-images/6/2657604TrainingData.EYBd.zip",
        "count": 10
      },
      "samples": { "prompts": ["a photo of TOK", "TOK in a garden"] }
    }
  }]
}

Set your Civitai API token via the Token button in the navbar to enable Try It.

Request body — edit to customize (e.g. swap the image URL or prompt)

Valid JSON

Common parameters

Shared by both Flux 1 variants. Defaults shown are after ApplyDefaults.

Field	Required	Default	Notes
`engine`	✅	—	Always `ai-toolkit`.
`ecosystem`	✅	—	Always `flux1` for this page.
`modelVariant`	✅	—	`dev` or `schnell`. Determines the base checkpoint.
`epochs`		`5`	`1`–`20`. Billed per epoch.
`numberOfRepeats`		auto: `ceil(200 / count)`	`1`–`5000`.
`lr`		`0.0001`	UNet learning rate. Flux 1 is sensitive to high LRs — keep ≤ `0.0005`.
`trainTextEncoder`		`false`	Flux 1 does not benefit much from text-encoder training. Leave off.
`lrScheduler`		`cosine`	`constant`, `constant_with_warmup`, `cosine`, `linear`, `step`.
`optimizerType`		`adamw8bit`	`adamw`, `adamw8bit`, `adam8bit`, `lion`, `lion8bit`, `adafactor`, `adagrad`, `prodigy`, `prodigy8bit`, `automagic`.
`networkDim`		`16`	`1`–`256`. Flux 1's lower default reflects how compactly Flux LoRAs encode style/character vs. SD-family.
`networkAlpha`		matches `networkDim`	`1`–`256`.
`noiseOffset`		`0`	`0`–`1`.
`flipAugmentation`		`false`	Random horizontal flips.
`shuffleTokens` / `keepTokens`		`false` / `0`	Caption-tag shuffling.
`triggerWord`		(none)	Activation token. Recommended for character / style LoRAs.
`trainingData.{type, sourceUrl, count}`	✅	—	Always `type: "zip"`.
`samples.prompts[]`		`[]`	Preview prompts rendered after each epoch using the trained LoRA at strength 1.0.
`samples.negativePrompt`		(none)	—

Reading the result

Same envelope as the other training recipes — see SDXL/SD1 → Reading the result for the full shape. The relevant bit:

json

{
  "output": {
    "moderationStatus": "Approved",
    "epochs": [
      {
        "epochNumber": 1,
        "model": { "id": "blob_...", "url": "https://.../epoch_1.safetensors" },
        "samples": [{ "id": "blob_...", "url": "https://.../sample_0.jpeg" }]
      }
    ]
  }
}

The model blob is your trained LoRA — download it (URLs are signed and expire), or use the blob URL directly with Flux 1 image generation by referencing its AIR in the loras field.

Runtime

Per-epoch wall time on a 10-image dataset, default settings:

Variant	Per-epoch	5-epoch full run
`dev`	~60–120 s	5–15 min
`schnell`	~60–120 s	5–15 min

Always use wait=0.

Cost

total = 200 × epochs   (Buzz)

Configuration	Buzz
`epochs: 5`	1000 + samples
`epochs: 10`	2000 + samples
`epochs: 20` (max)	4000 + samples

Sample-prompt rendering is billed separately at the appropriate Flux 1 generation rate. Run with whatif=true (the Preview cost button on the widgets above) to see the exact pre-flight charge.

Troubleshooting

Symptom	Likely cause	Fix
`400` with "modelVariant required"	Missing `modelVariant` field	Set to `"dev"` or `"schnell"`.
`400` with "epochs out of range"	`epochs` outside `1`–`20`	Cap at 20.
`400` with "trainingData.sourceUrl not reachable"	Signed URL expired	Regenerate. Prefer Civitai R2 AIRs over signed URLs for long-lived references.
Trained LoRA underbaked	Too few epochs for dataset, or `lr` too low	Raise `epochs` to 8–12 for character LoRAs; keep `lr` at `0.0001`–`0.0003`.
Trained LoRA overfits	Too many epochs / too high `networkDim`	Lower `epochs`, drop `networkDim` to 8–12.
Step `failed`, output `moderationStatus: "Rejected"`	Dataset failed content moderation	Replace flagged images.

SDXL & SD1 LoRA training — cheaper, classic SD ecosystems
Flux 2 Klein LoRA training — current Flux generation, including image-edit training
Flux 1 image generation — use a trained LoRA via loras: { "<lora-air>": 1.0 }
Results & webhooks — handling long-running training jobs
SubmitWorkflow / GetWorkflow
Endpoint OpenAPI spec

Flux 1 LoRA training ​

The request shape ​

Prerequisites ​

Flux 1 dev (default) ​

Flux 1 schnell ​

Common parameters ​

Reading the result ​

Runtime ​

Cost ​

Troubleshooting ​

Related ​

Flux 1 LoRA training

The request shape

Prerequisites

Flux 1 dev (default)

Flux 1 schnell

Common parameters

Reading the result

Runtime

Cost

Troubleshooting

Related