try

gradio_app/custom_models/mvimg_prediction.py

@@ -12,10 +12,10 @@ from scripts.utils import session, simple_preprocess
 training_config = "gradio_app/custom_models/image2mvimage.yaml"
 checkpoint_path = "ckpt/img2mvimg/unet_state_dict.pth"
 
+trainer, pipeline = load_pipeline(training_config, checkpoint_path)
+pipeline.enable_model_cpu_offload()
+
 def predict(img_list: List[Image.Image], guidance_scale=2., **kwargs):
-    trainer, pipeline = load_pipeline(training_config, checkpoint_path)
-    pipeline.enable_model_cpu_offload()
-    
     if isinstance(img_list, Image.Image):
         img_list = [img_list]
     img_list = [rgba_to_rgb(i) if i.mode == 'RGBA' else i for i in img_list]

scripts/project_mesh.py

@@ -98,6 +98,7 @@ class Pix2FacesRenderer:
 pix2faces_renderer = None
 
 def get_visible_faces(meshes: Meshes, cameras: CamerasBase, resolution=1024):
+    global pix2faces_renderer
     if pix2faces_renderer is None:
         pix2faces_renderer = Pix2FacesRenderer()
     # pix_to_face = render_pix2faces_py3d(meshes, cameras, H=resolution, W=resolution)['pix_to_face']