FLUX.2 on fal provides two robust pathways for customizing models: Text-to-Image LoRA training, which adapts the model to a new style, character, product, or aesthetic, and Multi Image-to-Image LoRA training, which enables the transformation of one image into another. Users can efficiently create specialized behaviors within FLUX.2 by curating and formatting datasets, which involves collecting 20 to 1,000 images that exhibit the desired style or subject and organizing them in a structured manner with consistent naming conventions and optional caption files. These captions enhance the learning process by providing descriptive content and trigger phrases that the LoRA will learn to generate. Once the dataset is prepared, it is uploaded as a .zip file to the respective FLUX.2 trainer, where users can adjust the training parameters or use default settings. After training, the model's success in learning the desired style or transformation is tested in the FLUX.2 inference playground. The process is similar for Multi Image-to-Image LoRA training, which requires sets of input and output images to teach the model specific transformations, such as staging an apartment from empty to furnished. Users are encouraged to stay connected via social media and community platforms for updates on generative media and new model releases.