Does Flux.2 support LORA training for single-image editing?

I used Kontext to train a LoRA model for single-image editing with Flux.2 using similar CSV files. There were no errors during the training process, but I'm unsure if the training was actually effective.
data_file_keys,extra_inputs,Will these two fields be generated?

The specific image preprocessing script is as follows:
nohup accelerate launch examples/flux2/model_training/train.py
--dataset_base_path xxx
--dataset_metadata_path xxxx/metadata.csv
--data_file_keys "image,flux2_images"
--max_pixels 1048576
--dataset_repeat 1
--model_id_with_origin_paths "black-forest-labs/FLUX.2-dev:text_encoder/*.safetensors,black-forest-labs/FLUX.2-dev:vae/diffusion_pytorch_model.safetensors"
--learning_rate 1e-4
--num_epochs 5
--remove_prefix_in_ckpt "pipe.dit."
--output_path "./models/train/FLUX.2-dev-LoRA-splited-cache-121001"
--lora_base_model "dit"
--lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_qkv_mlp_proj,to_out.0,to_add_out,linear_in,linear_out,single_transformer_blocks.0.attn.to_out,single_transformer_blocks.1.attn.to_out,single_transformer_blocks.2.attn.to_out,single_transformer_blocks.3.attn.to_out,single_transformer_blocks.4.attn.to_out,single_transformer_blocks.5.attn.to_out,single_transformer_blocks.6.attn.to_out,single_transformer_blocks.7.attn.to_out,single_transformer_blocks.8.attn.to_out,single_transformer_blocks.9.attn.to_out,single_transformer_blocks.10.attn.to_out,single_transformer_blocks.11.attn.to_out,single_transformer_blocks.12.attn.to_out,single_transformer_blocks.13.attn.to_out,single_transformer_blocks.14.attn.to_out,single_transformer_blocks.15.attn.to_out,single_transformer_blocks.16.attn.to_out,single_transformer_blocks.17.attn.to_out,single_transformer_blocks.18.attn.to_out,single_transformer_blocks.19.attn.to_out,single_transformer_blocks.20.attn.to_out,single_transformer_blocks.21.attn.to_out,single_transformer_blocks.22.attn.to_out,single_transformer_blocks.23.attn.to_out,single_transformer_blocks.24.attn.to_out,single_transformer_blocks.25.attn.to_out,single_transformer_blocks.26.attn.to_out,single_transformer_blocks.27.attn.to_out,single_transformer_blocks.28.attn.to_out,single_transformer_blocks.29.attn.to_out,single_transformer_blocks.30.attn.to_out,single_transformer_blocks.31.attn.to_out,single_transformer_blocks.32.attn.to_out,single_transformer_blocks.33.attn.to_out,single_transformer_blocks.34.attn.to_out,single_transformer_blocks.35.attn.to_out,single_transformer_blocks.36.attn.to_out,single_transformer_blocks.37.attn.to_out,single_transformer_blocks.38.attn.to_out,single_transformer_blocks.39.attn.to_out,single_transformer_blocks.40.attn.to_out,single_transformer_blocks.41.attn.to_out,single_transformer_blocks.42.attn.to_out,single_transformer_blocks.43.attn.to_out,single_transformer_blocks.44.attn.to_out,single_transformer_blocks.45.attn.to_out,single_transformer_blocks.46.attn.to_out,single_transformer_blocks.47.attn.to_out"
--lora_rank 32
--use_gradient_checkpointing
--dataset_num_workers 8
--extra_inputs "flux2_images"
--task "sft:data_process" \

train_flux2-text-encoder_121001.log 2>&1 &

Start with examples/flux2/model_training/train.py and the supplied metadata.csv schema; inspect how data_file_keys and extra_inputs are consumed for Flux.2 single-image editing. Reproduce the command to determine whether this training path is supported, and define done as a clear answer backed by the relevant behavior or tests.