diff --git a/diffsynth/models/lora.py b/diffsynth/models/lora.py
index f8e7599..aa21034 100644
--- a/diffsynth/models/lora.py
+++ b/diffsynth/models/lora.py
@@ -77,6 +77,8 @@ class LoRAFromCivitai:
             state_dict_lora = model.__class__.state_dict_converter().from_diffusers(state_dict_lora)
         elif model_resource == "civitai":
             state_dict_lora = model.__class__.state_dict_converter().from_civitai(state_dict_lora)
+        if isinstance(state_dict_lora, tuple):
+            state_dict_lora = state_dict_lora[0]
         if len(state_dict_lora) > 0:
             print(f"    {len(state_dict_lora)} tensors are updated.")
             for name in state_dict_lora:
@@ -96,6 +98,8 @@ class LoRAFromCivitai:
                     converter_fn = model.__class__.state_dict_converter().from_diffusers if model_resource == "diffusers" \
                         else model.__class__.state_dict_converter().from_civitai
                     state_dict_lora_ = converter_fn(state_dict_lora_)
+                    if isinstance(state_dict_lora_, tuple):
+                        state_dict_lora_ = state_dict_lora_[0]
                     if len(state_dict_lora_) == 0:
                         continue
                     for name in state_dict_lora_:
diff --git a/examples/train/README.md b/examples/train/README.md
index 50c2c1a..7848fdc 100644
--- a/examples/train/README.md
+++ b/examples/train/README.md
@@ -156,7 +156,7 @@ After training, use `model_manager.load_lora` to load the LoRA for inference.
 from diffsynth import ModelManager, FluxImagePipeline
 import torch
 
-model_manager = ModelManager(torch_dtype=torch.float16, device="cuda",
+model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda",
                              file_path_list=[
                                  "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
                                  "models/FLUX/FLUX.1-dev/text_encoder_2",
@@ -164,7 +164,7 @@ model_manager = ModelManager(torch_dtype=torch.float16, device="cuda",
                                  "models/FLUX/FLUX.1-dev/flux1-dev.safetensors"
                              ])
 model_manager.load_lora("models/lightning_logs/version_0/checkpoints/epoch=0-step=500.ckpt", lora_alpha=1.0)
-pipe = SDXLImagePipeline.from_model_manager(model_manager)
+pipe = FluxImagePipeline.from_model_manager(model_manager)
 
 torch.manual_seed(0)
 image = pipe(