gaussctrl
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ActiveVisionLab
How to solve this problem when inputting commands for 3D scene editing?
──────────────────────────────────────────────────────── Config ────────────────────────────────────────────────────────
GaussCtrlTrainerConfig(
_target=<class 'gaussctrl.gc_trainer.GaussCtrlTrainer'>,
output_dir=PosixPath('edit/dinosaur'),
method_name='gaussctrl',
experiment_name='experiment0527_2036',
project_name='nerfstudio-project',
timestamp='2025-05-27_130231',
machine=MachineConfig(seed=42, num_devices=1, num_machines=1, machine_rank=0, dist_url='auto', device_type='cuda'),
logging=LoggingConfig(
relative_log_dir=PosixPath('.'),
steps_per_log=10,
max_buffer_size=20,
local_writer=LocalWriterConfig(
_target=<class 'nerfstudio.utils.writer.LocalWriter'>,
enable=True,
stats_to_track=(
<EventName.ITER_TRAIN_TIME: 'Train Iter (time)'>,
<EventName.TRAIN_RAYS_PER_SEC: 'Train Rays / Sec'>,
<EventName.CURR_TEST_PSNR: 'Test PSNR'>,
<EventName.VIS_RAYS_PER_SEC: 'Vis Rays / Sec'>,
<EventName.TEST_RAYS_PER_SEC: 'Test Rays / Sec'>,
<EventName.ETA: 'ETA (time)'>
),
max_log_size=10
),
profiler='basic'
),
viewer=ViewerConfig(
relative_log_filename='viewer_log_filename.txt',
websocket_port=None,
websocket_port_default=7007,
websocket_host='0.0.0.0',
num_rays_per_chunk=32768,
max_num_display_images=512,
quit_on_train_completion=False,
image_format='jpeg',
jpeg_quality=75,
make_share_url=False,
camera_frustum_scale=0.1,
default_composite_depth=True
),
pipeline=GaussCtrlPipelineConfig(
_target=<class 'gaussctrl.gc_pipeline.GaussCtrlPipeline'>,
datamanager=GaussCtrlDataManagerConfig(
_target=gaussctrl.gc_datamanager.GaussCtrlDataManager[gaussctrl.gc_dataset.GCDataset],
data=PosixPath('data/dinosaur'),
masks_on_gpu=False,
images_on_gpu=False,
dataparser=GaussCtrlDataParserConfig(
_target=<class 'gaussctrl.gc_dataparser_ns.GaussCtrlDataParser'>,
data=PosixPath('data/blender/lego'),
scale_factor=1.0,
alpha_color='white',
filename='camera_path_outer240.json',
load_3D_points=True,
train_split_fraction=1.0,
downscale_factor=None,
scene_scale=1.0,
orientation_method='up',
center_method='poses',
auto_scale_poses=True,
eval_mode='fraction',
eval_interval=8,
depth_unit_scale_factor=0.001
),
camera_res_scale_factor=1.0,
eval_num_images_to_sample_from=-1,
eval_num_times_to_repeat_images=-1,
eval_image_indices=(0,),
cache_images='cpu',
cache_images_type='float32',
patch_size=32,
subset_num=4,
sampled_views_every_subset=10,
load_all=False
),
model=GaussCtrlModelConfig(
_target=<class 'gaussctrl.gc_model.GaussCtrlModel'>,
enable_collider=True,
collider_params={'near_plane': 2.0, 'far_plane': 6.0},
loss_coefficients={'rgb_loss_coarse': 1.0, 'rgb_loss_fine': 1.0},
eval_num_rays_per_chunk=4096,
prompt=None,
warmup_length=500,
refine_every=100,
resolution_schedule=250,
background_color='random',
num_downscales=0,
cull_alpha_thresh=0.1,
cull_scale_thresh=0.5,
continue_cull_post_densification=True,
reset_alpha_every=30,
densify_grad_thresh=0.0002,
densify_size_thresh=0.01,
n_split_samples=2,
sh_degree_interval=1000,
cull_screen_size=0.15,
split_screen_size=0.05,
stop_screen_size_at=4000,
random_init=False,
num_random=50000,
random_scale=10.0,
ssim_lambda=0.2,
stop_split_at=15000,
sh_degree=3,
use_scale_regularization=False,
max_gauss_ratio=10.0,
use_lpips=True,
use_l1=True,
patch_size=32,
lpips_loss_mult=1.0
),
render_rate=500,
edit_prompt='a photo of a dinosaur statue in the snow',
reverse_prompt='a photo of a dinosaur statue on the road side',
langsam_obj='',
guidance_scale=5.0,
num_inference_steps=20,
chunk_size=2,
ref_view_num=4,
diffusion_ckpt='CompVis/stable-diffusion-v1-4'
),
optimizers={
'xyz': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.00016,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': ExponentialDecaySchedulerConfig(
_target=<class 'nerfstudio.engine.schedulers.ExponentialDecayScheduler'>,
lr_pre_warmup=1e-08,
lr_final=1.6e-06,
warmup_steps=0,
max_steps=30000,
ramp='cosine'
)
},
'features_dc': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.0025,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
},
'features_rest': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.000125,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
},
'opacity': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.05,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
},
'scaling': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.005,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
},
'rotation': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.001,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
},
'camera_opt': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.001,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': ExponentialDecaySchedulerConfig(
_target=<class 'nerfstudio.engine.schedulers.ExponentialDecayScheduler'>,
lr_pre_warmup=1e-08,
lr_final=5e-05,
warmup_steps=0,
max_steps=30000,
ramp='cosine'
)
}
},
vis='viewer',
data=None,
prompt=None,
relative_model_dir=PosixPath('nerfstudio_models'),
load_scheduler=True,
steps_per_save=250,
steps_per_eval_batch=0,
steps_per_eval_image=100,
steps_per_eval_all_images=1000,
max_num_iterations=1000,
mixed_precision=False,
use_grad_scaler=False,
save_only_latest_checkpoint=True,
load_dir=None,
load_step=None,
load_config=None,
load_checkpoint=PosixPath('output/dinosaur/experiment0527_1458/splatfacto/2025-05-27_071221/nerfstudio_models/step-0
00029999.ckpt'),
log_gradients=False,
gradient_accumulation_steps={'camera_opt': 100}
)
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
[13:02:32] Saving config to: experiment_config.py:136
edit/dinosaur/experiment0527_2036/gaussctrl/2025-05-27_130231/config.yml
/root/anaconda3/envs/gaussctrl/lib/python3.8/site-packages/nerfstudio/engine/trainer.py:131: FutureWarning: torch.cuda.amp.GradScaler(args...) is deprecated. Please use torch.amp.GradScaler('cuda', args...) instead.
self.grad_scaler = GradScaler(enabled=self.use_grad_scaler)
Saving checkpoints to: trainer.py:136
edit/dinosaur/experiment0527_2036/gaussctrl/2025-05-27_130231/nerfstudio_models
Auto image downscale factor of 1 gc_dataparser_ns.py:498
/root/anaconda3/envs/gaussctrl/lib/python3.8/site-packages/nerfstudio/cameras/camera_utils.py:460: UserWarning: Using torch.cross without specifying the dim arg is deprecated.
Please either pass the dim explicitly or simply use torch.linalg.cross.
The default value of dim will change to agree with that of linalg.cross in a future release. (Triggered internally at ../aten/src/ATen/native/Cross.cpp:62.)
v = torch.cross(a, b)
[13:02:33] Caching / undistorting train images gc_datamanager.py:115
Caching / undistorting eval images gc_datamanager.py:141
/root/anaconda3/envs/gaussctrl/lib/python3.8/site-packages/torchmetrics/functional/image/lpips.py:325: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
self.load_state_dict(torch.load(model_path, map_location="cpu"), strict=False)
Warning: LangSAM module is not available. Object-specific editing disabled.
Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████| 7/7 [00:01<00:00, 6.82it/s]
/root/anaconda3/envs/gaussctrl/lib/python3.8/site-packages/nerfstudio/engine/trainer.py:414: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
loaded_state = torch.load(load_checkpoint, map_location="cpu")
Done loading Nerfstudio checkpoint from
output/dinosaur/experiment0527_1458/splatfacto/2025-05-27_071221/nerfstudio_models/step-000029999.ckpt
Rendering view 0
/root/anaconda3/envs/gaussctrl/lib/python3.8/site-packages/torch/utils/cpp_extension.py:1965: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].
warnings.warn(
Traceback (most recent call last):
File "/root/anaconda3/envs/gaussctrl/bin/ns-train", line 8, in
