I added support for Kohya's Flux.2 loras to the PR as well.

Reproducer code is:

FLUX.2 Dev

import torch
from diffusers import Flux2Pipeline

pipe = Flux2Pipeline.from_pretrained("black-forest-labs/FLUX.2-dev", torch_dtype=torch.bfloat16)
pipe.load_lora_weights(
    "scenario-labs/musubi-tuner-loras", weight_name="flux2-dev_lora.safetensors",
)

FLUX.2 Klein

import torch
from diffusers import Flux2Pipeline

pipe = Flux2Pipeline.from_pretrained("black-forest-labs/FLUX.2-klein-4b", torch_dtype=torch.bfloat16)
pipe.load_lora_weights(
    "scenario-labs/musubi-tuner-loras", weight_name="flux2-klein-4b_lora.safetensors",
)

Without the fix, I previously got:
No LoRA keys associated to Flux2Transformer2DModel found with the prefix='transformer'. This is safe to ignore if LoRA state dict didn't originally have any Flux2Transformer2DModel related params. You can also try specifying prefix=None to resolve the warning. Otherwise, open an issue if you think it's unexpected: https://github.com/huggingface/diffusers/issues/new

With this PR, on-the-fly conversion now works for Flux.2-dev loras also.

In this implementation, I preserved the logic of _convert_kohya_flux_lora_to_diffusers(), with little differences: first I infer the max number of blocks from the state dict keys, second, I had to fix loras keys for the new modules Flux2FeedForward and Flux2ParallelSelfAttnProcessor.