Documentation Index
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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. |
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..."}
- 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).
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. |
| 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. |
| 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). |