try fix

gradio_app/custom_models/mvimg_prediction.py

@@ -14,7 +14,7 @@ checkpoint_path = "ckpt/img2mvimg/unet_state_dict.pth"
 
 def predict(img_list: List[Image.Image], guidance_scale=2., **kwargs):
     trainer, pipeline = load_pipeline(training_config, checkpoint_path)
-    pipeline.enable_model_cpu_offload()
+    # pipeline.enable_model_cpu_offload()
     
     if isinstance(img_list, Image.Image):
         img_list = [img_list]

gradio_app/custom_models/normal_prediction.py

@@ -10,7 +10,7 @@ checkpoint_path = "ckpt/image2normal/unet_state_dict.pth"
 
 def predict_normals(image: List[Image.Image], guidance_scale=2., do_rotate=True, num_inference_steps=30, **kwargs):
     trainer, pipeline = load_pipeline(training_config, checkpoint_path)
-    pipeline.enable_model_cpu_offload()
+    # pipeline.enable_model_cpu_offload()
     
     img_list = image if isinstance(image, list) else [image]
     img_list = [rgba_to_rgb(i) if i.mode == 'RGBA' else i for i in img_list]

gradio_app/custom_models/utils.py

@@ -71,5 +71,5 @@ def load_pipeline(config_path, ckpt_path, pipeline_filter=lambda x: True, weight
             pipeline = trainer.construct_pipeline(shared_modules, configurable_unet.unet)
             pipeline.set_progress_bar_config(disable=False)
             trainer_out = trainer
-    pipeline = pipeline.to(device)
+    pipeline = pipeline.to(device, dtype=weight_dtype)
     return trainer_out, pipeline