Chroma / ERNIE / Qwen / Z-Image LoRA training

Five smaller image-LoRA ecosystems share this page: each has its own ecosystem value and base checkpoint, but the request shape is otherwise the AI Toolkit standard.

`ecosystem`	Base	Buzz / epoch	Best for
`chroma`	`lodestones/Chroma1-HD`	200	Chroma community model fine-tunes
`ernie`	`baidu/ERNIE-Image`	100	ERNIE Image LoRAs
`qwen`	Qwen-Image (versioned)	200	Qwen Image / Qwen-Image-Edit LoRAs
`zimageturbo`	`ostris/Z-Image-De-Turbo` (+ Z-Image-Turbo extras)	100	Z-Image Turbo LoRAs (cheap, fast inference)
`zimagebase`	`Tongyi-MAI/Z-Image`	100	Z-Image base LoRAs

Each ecosystem has its own subsection with a runnable example. The shared schema lives in Common parameters; ecosystem-specific quirks are in each subsection.

Long-running step

Always submit with wait=0. These ecosystems run anywhere from ~10s/epoch (Z-Image Turbo) to ~2min/epoch (Chroma/Qwen). See Results & webhooks.

The request shape

json

{
  "$type": "training",
  "input": {
    "engine":    "ai-toolkit",
    "ecosystem": "chroma"   // chroma | ernie | qwen | zimageturbo | zimagebase
  }
}

Prerequisites

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

Chroma

Trains on the Chroma1-HD base. Uses TextToImageV2Job for sample renders; output LoRA is usable wherever Chroma is supported.

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": "chroma",
      "epochs": 5,
      "resolution": 1024,
      "lr": 0.0001,
      "trainTextEncoder": false,
      "lrScheduler": "cosine",
      "optimizerType": "adamw8bit",
      "networkDim": 16,
      "networkAlpha": 16,
      "trainingData": {
        "type": "zip",
        "sourceUrl": "https://civitai-delivery-worker-prod.5ac0637cfd0766c97916cefa3764fbdf.r2.cloudflarestorage.com/training-images/5418/2382561TrainingData.B6Tr.zip",
        "count": 10
      },
      "samples": {
        "prompts": [
          "woman with red hair, playing chess at the park, dramatic explosion in background",
          "a woman holding a coffee cup, in a beanie, sitting at a cafe",
          "a horse acting as a DJ at a night club, fisheye lens, smoke machine, laser lights"
        ]
      }
    }
  }]
}

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)

{
  "tags": [
    "training"
  ],
  "steps": [
    {
      "$type": "training",
      "priority": "normal",
      "retries": 2,
      "input": {
        "engine": "ai-toolkit",
        "ecosystem": "chroma",
        "epochs": 5,
        "resolution": 1024,
        "lr": 0.0001,
        "trainTextEncoder": false,
        "lrScheduler": "cosine",
        "optimizerType": "adamw8bit",
        "networkDim": 16,
        "networkAlpha": 16,
        "noiseOffset": 0,
        "flipAugmentation": false,
        "shuffleTokens": false,
        "keepTokens": 0,
        "trainingData": {
          "type": "zip",
          "sourceUrl": "https://civitai-delivery-worker-prod.5ac0637cfd0766c97916cefa3764fbdf.r2.cloudflarestorage.com/training-images/5418/2382561TrainingData.B6Tr.zip",
          "count": 10
        },
        "samples": {
          "prompts": [
            "woman with red hair, playing chess at the park, dramatic explosion in background",
            "a woman holding a coffee cup, in a beanie, sitting at a cafe",
            "a horse acting as a DJ at a night club, fisheye lens, smoke machine, laser lights"
          ]
        }
      }
    }
  ]
}

Valid JSON

Chroma defaults: networkDim: 16, optimizerType: adamw8bit, trainTextEncoder: false, lrScheduler: cosine. 200 Buzz / epoch.

ERNIE

Trains on Baidu's ERNIE-Image. Comfy-based ecosystem with built-in diffuser. Uses ComfyImageGenJob for sample renders.

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": "ernie",
      "epochs": 5,
      "lr": 0.0001,
      "trainTextEncoder": false,
      "lrScheduler": "cosine",
      "optimizerType": "adamw8bit",
      "networkDim": 32,
      "networkAlpha": 32,
      "trainingData": {
        "type": "zip",
        "sourceUrl": "urn:air:other:other:civitai-r2:civitai-delivery-worker-prod@training-images/7918795/2435272TrainingData.bJ7P.zip",
        "count": 10
      },
      "samples": {
        "prompts": ["a portrait of TOK", "TOK walking through a comic book city"]
      }
    }
  }]
}

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

ERNIE defaults: networkDim: 32, optimizerType: adamw8bit, trainTextEncoder: false, lrScheduler: cosine. 100 Buzz / epoch.

Qwen

Trains on Qwen-Image. The version field selects a specific Qwen-Image release:

`version`	Base resolved to
`latest` (default)	`Qwen/Qwen-Image-Edit-2512`
`2509`	`urn:air:qwen:checkpoint:civitai:1864281@2110043`
`2512`	`Qwen/Qwen-Image-Edit-2512` (same as `latest`)

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": "qwen",
      "version": "latest",
      "epochs": 1,
      "resolution": 1024,
      "lr": 0.00011,
      "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/3315022/2526079TrainingData.o4S8.zip",
        "count": 10
      },
      "samples": {
        "prompts": [
          "woman with red hair, playing chess at the park, dramatic explosion in background",
          "a woman holding a coffee cup, in a beanie, sitting at a cafe"
        ]
      }
    }
  }]
}

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)

{
  "tags": [
    "training"
  ],
  "steps": [
    {
      "$type": "training",
      "priority": "normal",
      "retries": 2,
      "input": {
        "engine": "ai-toolkit",
        "ecosystem": "qwen",
        "version": "latest",
        "epochs": 1,
        "resolution": 1024,
        "lr": 0.00011,
        "trainTextEncoder": false,
        "lrScheduler": "cosine",
        "optimizerType": "adamw8bit",
        "networkDim": 16,
        "networkAlpha": 16,
        "noiseOffset": 0,
        "flipAugmentation": false,
        "trainingData": {
          "type": "zip",
          "sourceUrl": "urn:air:other:other:civitai-r2:civitai-delivery-worker-prod@training-images/3315022/2526079TrainingData.o4S8.zip",
          "count": 10
        },
        "samples": {
          "prompts": [
            "woman with red hair, playing chess at the park, dramatic explosion in background",
            "a woman holding a coffee cup, in a beanie, sitting at a cafe"
          ]
        }
      }
    }
  ]
}

Valid JSON

Qwen defaults: networkDim: 16, optimizerType: adamw8bit, trainTextEncoder: false, lrScheduler: cosine. 200 Buzz / epoch.

Z-Image Turbo

Trains on ostris/Z-Image-De-Turbo and pulls in the original Tongyi-MAI/Z-Image-Turbo as an extras model. Output LoRA is usable in Z-Image generation on the turbo model.

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": "zimageturbo",
      "epochs": 7,
      "resolution": 512,
      "lr": 0.000611,
      "trainTextEncoder": false,
      "lrScheduler": "cosine",
      "optimizerType": "adamw8bit",
      "networkDim": 32,
      "networkAlpha": 32,
      "trainingData": {
        "type": "zip",
        "sourceUrl": "urn:air:other:other:civitai-r2:civitai-delivery-worker-prod@training-images/3315022/2526079TrainingData.o4S8.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

Z-Image Turbo defaults: networkDim: 32, optimizerType: adamw8bit, trainTextEncoder: false. 100 Buzz / epoch.

Z-Image Base

Trains on Tongyi-MAI/Z-Image. The orchestrator overrides optimizerType to automagic and lr to 0.000001 regardless of what you submit — the input fields are accepted but ignored. Use the Z-Image Turbo recipe instead unless you specifically need a base-model LoRA.

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": "zimagebase",
      "epochs": 7,
      "resolution": 512,
      "lr": 0.000611,
      "trainTextEncoder": false,
      "lrScheduler": "cosine",
      "networkDim": 32,
      "networkAlpha": 32,
      "trainingData": {
        "type": "zip",
        "sourceUrl": "urn:air:other:other:civitai-r2:civitai-delivery-worker-prod@training-images/3315022/2526079TrainingData.o4S8.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

Z-Image Base defaults: networkDim: 32, optimizerType: automagic (overridden), lr: 0.000001 (overridden), trainTextEncoder: false. 100 Buzz / epoch.

Common parameters

Defaults shown are the post-ApplyDefaults values; per-ecosystem deviations are noted above.

Field	Required	Default	Notes
`engine`	✅	—	Always `ai-toolkit`.
`ecosystem`	✅	—	One of: `chroma`, `ernie`, `qwen`, `zimageturbo`, `zimagebase`.
`version`	(qwen only)	`latest`	`latest`, `2509`, `2512`. Selects the Qwen-Image base release.
`epochs`		`5`	`1`–`20`. Billed per epoch.
`numberOfRepeats`		varies (see ecosystem)	`1`–`5000`. ERNIE / Z-Image auto-derive `ceil(200 / count)`; Chroma / Qwen don't auto-set.
`lr`		`0.0001`	UNet learning rate.
`trainTextEncoder`		`false`	All five ecosystems leave the text encoder frozen.
`lrScheduler`		`cosine`	`constant`, `constant_with_warmup`, `cosine`, `linear`, `step`.
`optimizerType`		`adamw8bit` (`automagic` for Z-Image Base)	Full enum on the SDXL/SD1 page.
`networkDim`		`32` (`16` for Chroma / Qwen)	`1`–`256`.
`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 on Chroma, Z-Image.
`trainingData.{type, sourceUrl, count}`	✅	—	`type: "zip"`.
`samples.prompts[]`		`[]`	Per-epoch preview prompts rendered with the trained LoRA.
`samples.negativePrompt`		(none)	—

Reading the result

Same envelope as the other training recipes — see SDXL/SD1 → Reading the result. Each epoch yields a .safetensors LoRA blob plus any sample images.

The trained LoRA is usable in the corresponding generation recipe — Chroma LoRAs in any Chroma workflow, ERNIE LoRAs in ERNIE image generation, Qwen LoRAs in Qwen image generation, Z-Image LoRAs in Z-Image generation.

Runtime

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

Ecosystem	Per-epoch	Typical full run
`chroma`	~60–120 s	5–15 min for 5 epochs
`ernie`	~30–60 s	3–8 min for 5 epochs
`qwen`	~60–120 s	5–15 min for 5 epochs
`zimageturbo`	~10–25 s	1–4 min for 7 epochs
`zimagebase`	~10–25 s	1–4 min for 7 epochs

Always use wait=0.

Cost

total = costPerEpoch × epochs

Ecosystem	Buzz / epoch	`epochs: 5`	`epochs: 10`
`chroma`	200	1000	2000
`ernie`	100	500	1000
`qwen`	200	1000	2000
`zimageturbo`	100	500	1000
`zimagebase`	100	500	1000

Sample-prompt rendering is billed separately at each ecosystem's image-generation rate. Use whatif=true (the Preview cost button on the widgets above) to confirm exact charges before submitting.

Troubleshooting

Symptom	Likely cause	Fix
`400` with "ecosystem unknown"	Typo, or not one of `chroma` / `ernie` / `qwen` / `zimageturbo` / `zimagebase`	Check spelling.
`400` with "version not allowed" (Qwen only)	`version` not one of `latest` / `2509` / `2512`	Use one of the listed values.
Z-Image Base: `optimizerType` you set seems ignored	Intentional — `ApplyDefaults` overrides to `automagic`	Use Z-Image Turbo if you need full optimizer control.
Trained LoRA underbaked	Too few epochs / too low `lr`	Raise `epochs` to 8–15 (these ecosystems often need more epochs than SDXL); keep `lr` ≤ `5e-4`.
Trained LoRA overcooked	Too many epochs or `networkDim` too high	Drop `networkDim` to 16, lower `epochs`.
Step `failed`, `moderationStatus: "Rejected"`	Dataset failed content moderation	Replace flagged images.

SDXL & SD1 LoRA training — classic Stable Diffusion ecosystems
Flux 1 LoRA training / Flux 2 Klein LoRA training — Flux family
Wan video LoRA training / LTX2 video LoRA training — video LoRAs
Generation recipes for these ecosystems: Z-Image, Qwen, ERNIE
Results & webhooks
SubmitWorkflow / GetWorkflow
Endpoint OpenAPI spec

Chroma / ERNIE / Qwen / Z-Image LoRA training ​

The request shape ​

Prerequisites ​

Chroma ​

ERNIE ​

Qwen ​

Z-Image Turbo ​

Z-Image Base ​

Common parameters ​

Reading the result ​

Runtime ​

Cost ​

Troubleshooting ​

Related ​

Chroma / ERNIE / Qwen / Z-Image LoRA training

The request shape

Prerequisites

Chroma

ERNIE

Qwen

Z-Image Turbo

Z-Image Base

Common parameters

Reading the result

Runtime

Cost

Troubleshooting

Related