[LoRAs](../tutorials/using_peft_for_inference) are lightweight checkpoints fine-tuned to generate images or video in a specific style. If you are using a checkpoint trained with a Diffusers training script, the LoRA configuration is automatically saved as metadata in a safetensors file. When the safetensors file is loaded, the metadata is parsed to correctly configure the LoRA and avoids missing or incorrect LoRA configurations.

The easiest way to inspect the metadata, if available, is by clicking on the Safetensors logo next to the weights.

<div class="flex justify-center">

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/safetensors_lora.png"/>

</div>

For LoRAs that aren't trained with Diffusers, you can still save metadata with the `transformer_lora_adapter_metadata` and `text_encoder_lora_adapter_metadata` arguments in [`~loaders.FluxLoraLoaderMixin.save_lora_weights`] as long as it is a safetensors file.

from diffusers import FluxPipeline

pipeline = FluxPipeline.from_pretrained(

"black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16

pipeline.load_lora_weights("linoyts/yarn_art_Flux_LoRA")

pipeline.save_lora_weights(

transformer_lora_adapter_metadata={"r": 16, "lora_alpha": 16},

text_encoder_lora_adapter_metadata={"r": 8, "lora_alpha": 8}

)