Update app.py

app.py

@@ -108,16 +108,16 @@ flux_pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_tha
 
 
 # lrm
-config = OmegaConf.load("./models/lrm/config/PRM_inference.yaml")
-model_config = config.model_config
-infer_config = config.infer_config
-model = instantiate_from_config(model_config)
-model_ckpt_path = hf_hub_download(repo_id="LTT/PRM", filename="final_ckpt.ckpt", repo_type="model")
-state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
-state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.')}
-model.load_state_dict(state_dict, strict=True)
-model = model.to(device_1)
-torch.cuda.empty_cache()
+# config = OmegaConf.load("./models/lrm/config/PRM_inference.yaml")
+# model_config = config.model_config
+# infer_config = config.infer_config
+# model = instantiate_from_config(model_config)
+# model_ckpt_path = hf_hub_download(repo_id="LTT/PRM", filename="final_ckpt.ckpt", repo_type="model")
+# state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
+# state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.')}
+# model.load_state_dict(state_dict, strict=True)
+# model = model.to(device_1)
+# torch.cuda.empty_cache()
 @spaces.GPU
 def lrm_reconstructions(image, input_cameras, save_path=None, name="temp", export_texmap=False, if_save_video=False):
     images = image.unsqueeze(0).to(device_1)