Update app.py

app.py

@@ -108,16 +108,16 @@ isomer_color_weights = torch.from_numpy(np.array([1, 0.5, 1, 0.5])).float().to(d
 
 
 # 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)
@@ -307,7 +307,7 @@ def reconstruct_3d_model(images, prompt):
 def gradio_pipeline(prompt, seed):
     # 生成多视图图像
     # rgb_normal_grid = generate_multi_view_images(prompt, seed)
-    # rgb_normal_grid = np.load("rgb_normal_grid.npy")
+    rgb_normal_grid = np.load("rgb_normal_grid.npy")
     image_preview = Image.fromarray((rgb_normal_grid[0] * 255).astype(np.uint8))
 
     # 3d reconstruction