Customize your input with more control.
Customize your input with more control.
Hint: Drag and drop files from your computer, images from web pages, paste from clipboard (Ctrl/Cmd+V), or provide a URL.
Customize your input with more control.
The cost of training depends on the number of steps and the reference images. The formula is: 0.002 * steps * reference_multiplier. The reference multiplier for 1, 2, 3 and 4 images is 2.11, 3.44, 5.09, and 6.95 respectively. With 1 reference image and 1000 steps, your request will cost $4.22.
Fine-tune your training parameters and start right now.
Fine-tune FLUX.2 [klein] 9B with LoRA to teach it your custom image-to-image editing behavior. Train once on before/after pairs, then apply consistent, specialized edits forever — no full model retraining needed. A small LoRA (a few MB) captures exactly how you want images transformed, giving you precise control that generic models or prompting alone can’t achieve.
Perfect for:
File naming (strict!)
XXX_start.png/jpg → original image (unedited) XXX_start2.png/jpg → optional extra reference (up to 4 total) XXX_start3.png/jpg XXX_start4.png/jpg XXX_end.png/jpg → desired edited result XXX.txt → optional instruction prompt
Example structure with a single reference image:
editing_dataset.zip ├── 001_start.jpg ├── 001_end.jpg ├── 001.txt # e.g. "apply brand color grading and remove background" ├── 002_start.png ├── 002_end.png ├── 002.txt └── ...
Example structure with 3 reference images:
editing_dataset.zip ├── 001_start.jpg ├── 001_start2.jpg ├── 001_start3.jpg ├── 001_end.jpg ├── 001.txt # e.g. "apply brand color grading and remove background" ├── 002_start.png ├── 002_start2.png ├── 002_start3.png ├── 002_end.png ├── 002.txt └── ...
.txt files are optional but strongly recommended for best results.
If you prefer one caption for everything, just set a Default Caption instead.
That’s it — zip the folder, upload, train a LoRA and get unlimited custom editing intelligence tailored to your exact needs.
