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

# Image Editing

> Fine-tune an image editing model on your own image pairs to learn specific styles, objects, or editing tasks.

Image Editing fine-tuning trains a LoRA adapter on paired images so the model learns to apply specific edits, styles, or object transformations.

## Prerequisites

* An EigenAI account with available credits.
* A training dataset as a `.zip` file containing paired source and target images.

## Create an image editing job

Click **Fine-Tune a Model** on the Fine-tuning page, then select the **Image Editing** tab to open the 3-step wizard.

### Step 1 — Upload

Provide your dataset name and upload your image pairs.

| Field                   | Description                                                                      |
| ----------------------- | -------------------------------------------------------------------------------- |
| Dataset Name            | A name to identify this dataset. Required.                                       |
| Use an existing dataset | Pick a previously uploaded image editing dataset instead of uploading a new zip. |
| Dataset File (.zip)     | A `.zip` file up to 10 GB containing your image pairs.                           |

**Zip file structure**

Your zip must follow this layout:

```
your_dataset.zip
├── edit_images/          # Folders with input (source) images
│   ├── image_0001/       # Folder name = target image name
│   │   ├── 001.png       # One or more reference images
│   │   ├── 002.png
│   │   └── 003.png
│   └── image_0002/
│       ├── 001.png
│       └── 002.png
├── images/               # Target images (the desired edited results)
│   ├── image_0001.png    # Filename matches folder name in edit_images/
│   └── image_0002.png
└── prompts.json          # {"image_0001": "prompt...", "image_0002": "prompt..."}
```

**Key requirements**

* Folder names in `edit_images/` must match the target image filenames in `images/` (without extension).
* Each folder can contain one or more reference images.
* Supported formats: PNG, JPG, JPEG, WebP.
* `prompts.json` keys must match image names (without extension).

### Step 2 — Configure

Configure the base model and training hyperparameters.

**Base model**

| Model                | Description                                             |
| -------------------- | ------------------------------------------------------- |
| Qwen-Image-Edit      | Standard image editing model with balanced performance. |
| Qwen-Image-Edit-2509 | Updated model with improved editing capabilities.       |

**Training parameters**

| Parameter       | Options                                                       | Default  | Description                                                                                   |
| --------------- | ------------------------------------------------------------- | -------- | --------------------------------------------------------------------------------------------- |
| LoRA Rank       | 8, 16, 32, 64, 128                                            | 32       | Controls the capacity of the LoRA adapter. Higher rank = more expressive but slower to train. |
| Training Epochs | 1, 2, 3, 4, 5                                                 | 3        | How many times the model trains over the full dataset.                                        |
| Learning Rate   | Conservative (5×10⁻⁵), Balanced (1×10⁻⁴), Aggressive (2×10⁻⁴) | Balanced | Controls how quickly the model adapts.                                                        |

**Advanced options**

| Field       | Description                                                     |
| ----------- | --------------------------------------------------------------- |
| Output Name | Custom name for the trained LoRA. Auto-generated if left blank. |

### Step 3 — Review

Review the training summary before launching.

| Field          | Description                                       |
| -------------- | ------------------------------------------------- |
| Dataset        | The dataset name you provided.                    |
| Model          | The selected base model.                          |
| LoRA Rank      | The selected rank value.                          |
| Epochs         | Number of training epochs.                        |
| Learning Rate  | The selected learning rate.                       |
| Accelerator    | Hardware used for training (H200 × 8).            |
| Estimated cost | Credit cost for this training run (Beta pricing). |

Click **Confirm & Create** to submit the job.