We rely on peft to supercharge our support doing all things LoRA: https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference.

Currently, it's possible to do multi-LoRA inference seamlessly using peft:
https://huggingface.co/docs/diffusers/main/en/tutorials/using_peft_for_inference#combine-multiple-adapters

However, the limitation of this is the following.

1. It currently supports only one merging method for combining different LoRAs.
2. It doesn't benefit from newer and more advanced merging methods from the community, having the potential to improve generation quality even further.

G.O.A.T @pacman100 is adding new merging methods to peft in huggingface/peft#1364, supporting TIES, DARE_LINEAR, etc.

Inspired by that, I quickly cooked up a Colab Notebook, that:

• Converts diffusers LoRA into PeftModel. Otherwise, we won't be able to do what's described in the next point.
• Loads multiple such PeftModel LoRAs and combines them using add_weighted_adapter() method from peft. The advantage of doing this that add_weighted_adapter() comes supported with all the new merging methods right off the bat.

Check #6892 (comment) for more results.

Limitations

• The notebook shows a PoC where the LoRAs were only targeted for the UNet component of the diffusion pipeline.
• The entire process of getting the PeftModels out seems like a big one.

Questions to discuss

• Are we okay having support for new merging methods like this way?
• Or should we expose a new argument in set_adapters() (merge_method) and add support for them within diffusers natively, possibly leveraging some utilities from peft?

Tagging everyone that might be interested to say their bits from the HF team:
@younesbelkada, @apolinario, @linoytsaban, @BenjaminBossan.

Folks from the community:
@okotaku, @asomoza, @fabiorigano.

Cc: @yiyixuxu for awareness.