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With this guide, you will:
  • Create an image-editing fine-tune
  • Start the fine-tune run
  • Monitor the fine-tune until it completes
  • Deploy the fine-tuned model
  • Run inference with the deployed model
We will use one of the Qwen image-editing models described in Available Fine-Tuning Models:
  • base_model: Qwen/Qwen-Image-Edit
  • script_type: image_editing
Your dataset should follow the schema described there:
  • control_image_column – Input/reference image to edit
  • caption_column – Text prompt describing the desired edit
  • image_column – Target/output image after the edit

​
Prerequisites

  • Repository on Oxen with your training data committed, for example:
    • Namespace: Tutorials
    • Repository: ProductImageEdits
  • Dataset resource inside that repo, for example:
    • main/train_image_edits.parquet
    • Each row contains paths to the control image and edited image, plus a caption.
  • API key with access to the repo:
    • Exported as OXEN_API_KEY
  • Base URL for the Oxen API:
    • Cloud example: https://hub.oxen.ai
    • Exported as OXEN_BASE_URL (optional, defaults shown below)
You can set these in your shell: 
export OXEN_API_KEY="YOUR_API_KEY_HERE"
export OXEN_BASE_URL="https://hub.oxen.ai"
export OXEN_NAMESPACE="Tutorials"
export OXEN_REPO="ProductImageEdits"
For the examples below, we will use:
  • resource: main/train_image_edits.parquet
  • base_model: Qwen/Qwen-Image-Edit
  • script_type: image_editing
Training parameters (you can adjust these to your needs):
  • control_image_column: control_image
  • caption_column: caption
  • image_column: edited_image
  • epochs: 1
  • batch_size: 1
  • learning_rate: 0.0001
  • grad_accum: 1
  • lora_alpha: 16
  • lora_rank: 16
  • seq_length: 1024
  • logging_steps: 10
  • enable_thinking: false
  • neftune_noise_alpha: 0.0
  • save_steps_ratio: 0.25
  • save_strategy: epoch
  • use_lora: true

​
Step 1 – Create an Image Editing Fine-Tune

Endpoint
  • POST /api/repos/{owner}/{repo}/fine_tunes
Example curl request: 
curl --location "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/repos/${OXEN_NAMESPACE:-Tutorials}/${OXEN_REPO:-ProductImageEdits}/fine_tunes" \
  -H "Authorization: Bearer ${OXEN_API_KEY}" \
  -H "Content-Type: application/json" \
  --data '{
    "resource": "main/train_image_edits.parquet",
    "base_model": "Qwen/Qwen-Image-Edit",
    "script_type": "image_editing",
    "training_params": {
      "control_image_column": "control_image",
      "caption_column": "caption",
      "image_column": "edited_image",
      "epochs": 1,
      "batch_size": 1,
      "learning_rate": 0.0001,
      "grad_accum": 1,
      "lora_alpha": 16,
      "lora_rank": 16,
      "seq_length": 1024,
      "logging_steps": 10,
      "enable_thinking": false,
      "neftune_noise_alpha": 0.0,
      "save_steps_ratio": 0.25,
      "save_strategy": "epoch",
      "use_lora": true
    }
  }'
The response will include a fine_tune object. For example: 
{
  "fine_tune": {
    "id": "ft_img_12345",
    "status": "created",
    "resource": "main/train_image_edits.parquet",
    "base_model": "Qwen/Qwen-Image-Edit",
    "script_type": "image_editing",
    "training_params": { ... }
  }
}
Save the id (for example ft_img_12345) for the next steps. If you have jq installed, you can capture it directly: 
FT_ID=$(curl --silent --location "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/repos/${OXEN_NAMESPACE:-Tutorials}/${OXEN_REPO:-ProductImageEdits}/fine_tunes" \
  -H "Authorization: Bearer ${OXEN_API_KEY}" \
  -H "Content-Type: application/json" \
  --data '{
    "resource": "main/train_image_edits.parquet",
    "base_model": "Qwen/Qwen-Image-Edit",
    "script_type": "image_editing",
    "training_params": {
      "control_image_column": "control_image",
      "caption_column": "caption",
      "image_column": "edited_image",
      "epochs": 1,
      "batch_size": 1,
      "learning_rate": 0.0001,
      "grad_accum": 1,
      "lora_alpha": 16,
      "lora_rank": 16,
      "seq_length": 1024,
      "logging_steps": 10,
      "enable_thinking": false,
      "neftune_noise_alpha": 0.0,
      "save_steps_ratio": 0.25,
      "save_strategy": "epoch",
      "use_lora": true
    }
  }' | jq -r '.fine_tune.id')

echo "Created image-edit fine-tune: $FT_ID"

​
Step 2 – Start the Fine-Tune Run

Once you have a fine_tune.id, trigger the run. Endpoint
  • POST /api/repos/{owner}/{repo}/fine_tunes/{fine_tune_id}/actions/run
Example curl request: 
curl --location "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/repos/${OXEN_NAMESPACE:-Tutorials}/${OXEN_REPO:-ProductImageEdits}/fine_tunes/${FT_ID}/actions/run" \
  -H "Authorization: Bearer ${OXEN_API_KEY}" \
  -X POST

​
Step 3 – Monitor Fine-Tune Status

You can poll the fine-tune to see when it completes. Endpoint
  • GET /api/repos/{owner}/{repo}/fine_tunes/{fine_tune_id}
Example curl loop (bash): 
while true; do
  RESP=$(curl --silent "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/repos/${OXEN_NAMESPACE:-Tutorials}/${OXEN_REPO:-ProductImageEdits}/fine_tunes/${FT_ID}" \
    -H "Authorization: Bearer ${OXEN_API_KEY}")

  echo "$RESP" | jq '.'

  STATUS=$(echo "$RESP" | jq -r '.fine_tune.status')
  echo "Status: $STATUS"

  if [ "$STATUS" = "completed" ]; then
    OUTPUT_RESOURCE=$(echo "$RESP" | jq -r '.fine_tune.output_resource')
    echo "Fine-tune completed! Output: $OUTPUT_RESOURCE"
    break
  elif [ "$STATUS" = "errored" ]; then
    ERROR_MSG=$(echo "$RESP" | jq -r '.fine_tune.error')
    echo "Fine-tune failed: $ERROR_MSG"
    exit 1
  elif [ "$STATUS" = "stopped" ]; then
    echo "Fine-tune was stopped"
    break
  fi

  # Wait 30 seconds before checking again
  sleep 30
done

​
Step 4 – Deploy the Fine-Tuned Image Model

Once the fine-tune completes, you can deploy it to a dedicated GPU-backed endpoint via the deploy API. Endpoint
  • POST /api/repos/{owner}/{repo}/fine_tunes/{fine_tune_id}/deploy
Example curl request: 
DEPLOY_RESPONSE=$(curl --silent --location "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/repos/${OXEN_NAMESPACE:-Tutorials}/${OXEN_REPO:-ProductImageEdits}/fine_tunes/${FT_ID}/deploy" \
  -H "Authorization: Bearer ${OXEN_API_KEY}" \
  -X POST)

echo "$DEPLOY_RESPONSE" | jq '.'
The response will include information about the deployment, including the model identifier you can pass to the image editing inference API (for example, a slug such as oxen:your-fine-tuned-image-edit-model). If the response contains a field like model_slug, you can capture it with jq: 
DEPLOYED_MODEL=$(echo "$DEPLOY_RESPONSE" | jq -r '.deployment.model_slug')
echo "Deployed model: $DEPLOYED_MODEL"

​
Step 5 – Run Inference with the Deployed Model

With the deployment live, you can call the image editing inference endpoint using the deployed model identifier. Endpoint
  • POST /api/ai/images/edit
Example curl request (single input image): 
export DEPLOYED_MODEL="${DEPLOYED_MODEL:-oxen:your-fine-tuned-image-edit-model}"

curl -X POST \
  "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/ai/images/edit" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${OXEN_API_KEY}" \
  -d "{
    \"model\": \"${DEPLOYED_MODEL}\",
    \"input_image\": \"https://example.com/image.png\",
    \"prompt\": \"Apply the same style as in my training data\",
    \"num_inference_steps\": 28
  }"
For models that support multiple input images (for example when using a multi-image editing base model), you can pass an array of image URLs: 
curl -X POST \
  "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/ai/images/edit" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${OXEN_API_KEY}" \
  -d "{
    \"model\": \"${DEPLOYED_MODEL}\",
    \"input_image\": [
      \"https://example.com/control_image.png\",
      \"https://example.com/style_reference.png\"
    ],
    \"prompt\": \"Apply the reference style to the control image, matching the fine-tuned behavior\",
    \"num_inference_steps\": 28
  }"
These requests mirror the general image editing examples, but use your fine-tuned image model as the model value instead of a base model. For more background on the image editing inference API, see the Image Editing examples. With these five steps, you have a complete end-to-end image fine-tuning, deployment, and inference workflow using only curl, fully scriptable from the command line.