how to load lora weight with fp8 transfomer model?

Hi, I want to run fluxcontrolpipeline with transformer_fp8 reference the code :
https://huggingface.co/docs/diffusers/api/pipelines/flux#quantization

```
import torch
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, FluxTransformer2DModel, FluxControlPipeline
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel

quant_config = BitsAndBytesConfig(load_in_8bit=True)
text_encoder_8bit = T5EncoderModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="text_encoder_2",
quantization_config=quant_config,
torch_dtype=torch.float16,
)

quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = FluxTransformer2DModel.from_pretrained(
"black-forest-labs/FLUX.1-dev",
subfolder="transformer",
quantization_config=quant_config,
torch_dtype=torch.float16,
)

pipeline = FluxControlPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
text_encoder_2=text_encoder_8bit,
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
)

prompt = "a tiny astronaut hatching from an egg on the moon"
image = pipeline(prompt, guidance_scale=3.5, height=768, width=1360, num_inference_steps=50).images[0]
image.save("flux.png")
```

but when I load lora after build a pipeline

```
pipeline = FluxControlPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
text_encoder_2=text_encoder_8bit,
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
)

pipe.load_lora_weights("black-forest-labs/FLUX.1-Depth-dev-lora")
```
There a error:
not support fp8 weight , how to fix it??

Die gemeldeten Einstiegspunkte sind FluxControlPipeline.from_pretrained und load_lora_weights, wobei das in der Issue gezeigte Setup von quantized FluxTransformer2DModel verwendet wird. Reproduziere zuerst den fp8-weight-Fehler anhand des verlinkten Hugging Face-Quantisierungsbeispiels und der FLUX.1-Depth-dev-lora-Referenz; als abgeschlossen gilt dies, wenn der unterstützte Ladepfad identifiziert oder die Einschränkung dokumentiert ist.

Vom Indexierungsmodell aus dem Issue-Text verfasst.