Support for Multi-Image Input LoRA Training Pipeline for FLUX.2-Klein (e.g., Style Transfer and Multi-Subject Composition)

### Model/Pipeline/Scheduler description

I would like to request official support in Diffusers for a multi-image input LoRA training pipeline targeting the FLUX.2 Klein model. It seems that existing LoRA training pipelines are designed around single-image conditioning, which limits their applicability for tasks that naturally require multiple reference images. Clear guidance, reference implementations, or examples demonstrating how multi-image conditioning could be handled during training would be highly valuable. Thank you for your continued work on Diffusers, and I would greatly appreciate any insights, recommendations, or future plans related to supporting this capability.

### Open source status

- [ ] The model implementation is available.
- [ ] The model weights are available (Only relevant if addition is not a scheduler).

### Provide useful links for the implementation

_No response_

No files or tests are named. Start by reviewing Diffusers' existing LoRA training pipelines and the FLUX.2 Klein model implementation and weights, then compare their single-image conditioning with the requested multi-image cases. Done means an agreed implementation scope plus official support or documented reference examples for style transfer and multi-subject composition.

Written by the indexing model from the issue text.