Image Editing

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.

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Prerequisites

Create an image editing job

Step 1 — Upload

Step 2 — Configure

Step 3 — Review

​Prerequisites

​Create an image editing job

​Step 1 — Upload

​Step 2 — Configure

​Step 3 — Review

Prerequisites

Create an image editing job

Step 1 — Upload

Step 2 — Configure

Step 3 — Review