How to use lora while using third model (eg. download from civitai)

[tonera]

When I use the third model and load lora, I get a error:

transformer_flux.py", line 825, in update_lora_params
assert isinstance(block, NunchakuFluxTransformerBlocks)
AssertionError

Here is the code:
`import torch
from diffusers import FluxPipeline

from nunchaku import NunchakuFluxTransformer2dModel
from nunchaku.utils import get_precision
from nunchaku import NunchakuFluxTransformer2dModel, NunchakuT5EncoderModel

from optimum.quanto import freeze, qfloat8, quantize, quantization_map
from torchao.quantization import quantize_, int8_weight_only, int4_weight_only
from tonera.teacache import teacache_forward,set_tea_cache

precision = get_precision() # auto-detect your precision is 'int4' or 'fp4' based on your GPU
base_model = "models/majicflus_v10.safetensors"
NunchakuFluxTransformer2dModel.forward = teacache_forward
transformer = NunchakuFluxTransformer2dModel.from_single_file(
base_model,
torch_dtype=torch.bfloat16,
config='models/FLUX.1-dev/transformer/config.json'
)
set_tea_cache(transformer,30)
quantize_(transformer, int8_weight_only())

LoRA Related Code

transformer.update_lora_params(
"loras/Ghibili-Cartoon-Art.safetensors"
) # Path to your LoRA safetensors, can also be a remote HuggingFace path
transformer.set_lora_strength(1) # Your LoRA strength here

End of LoRA Related Code

text_encoder_2 = NunchakuT5EncoderModel.from_pretrained("weights/nunchaku-t5/awq-int4-flux.1-t5xxl.safetensors")

pipeline = FluxPipeline.from_pretrained(
"models/FLUX.1-dev",
transformer=transformer,
torch_dtype=torch.bfloat16,
text_encoder_2=text_encoder_2
).to("cuda")

image = pipeline(
"GHIBSKY style, cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows", # noqa: E501
num_inference_steps=25,
guidance_scale=3.5,
).images[0]
image.save(f"/home/tonera/website/output/flux.1-dev-ghibsky-int4.png")`