> ## Documentation Index
> Fetch the complete documentation index at: https://docs.oxen.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Fine-Tuning an Image Editing Model with API

> This page shows how to fine-tune an **image editing** model on Oxen.ai using **only the HTTP API and `curl`**.

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`](/fine-tuning-api/03_available_fine_tune_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:

```bash theme={null}
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**:

```bash theme={null}
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:

```json theme={null}
{
  "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:

```bash theme={null}
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**:

```bash theme={null}
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):

```bash theme={null}
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**:

```bash theme={null}
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`:

```bash theme={null}
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):

```bash theme={null}
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:

```bash theme={null}
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](/examples/inference/image_editing).

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.