Reduce GPU duration and iterations in training and reconstruction functions

demo/gs_train.py

@@ -51,7 +51,7 @@ class ModelParams:
     data_device: str = "cuda"
     eval: bool = False
 
-@spaces.GPU(duration=170)
+@spaces.GPU(duration=20)
 def train(
     data_source_path, iterations, position_lr_init, position_lr_final, position_lr_delay_mult,
     position_lr_max_steps, feature_lr, opacity_lr, scaling_lr, rotation_lr,

demo/mast3r_demo.py

@@ -173,7 +173,7 @@ def save_colmap_scene(scene, save_dir, min_conf_thr=2, clean_depth=False, mask_i
     save_pointcloud_with_normals(imgs, pts3d, masks, sparse_path)
     return save_path
 
-@spaces.GPU(duration=160)
+@spaces.GPU(duration=20)
 def get_reconstructed_scene(snapshot, current_scene_state,
                             min_conf_thr, matching_conf_thr,
                             as_pointcloud, cam_size, shared_intrinsics, clean_depth, filelist, **kw):
@@ -189,9 +189,9 @@ def get_reconstructed_scene(snapshot, current_scene_state,
         filelist = [filelist[0], filelist[0] + '_2']
 
     lr1 = 0.07
-    niter1 = 600
+    niter1 = 100
     lr2 = 0.014
-    niter2 = 300
+    niter2 = 100
     optim_level = 'refine+depth'
     mask_sky, transparent_cams = False, False
     if len(filelist) < 13: