ActiveVisionLab
OOM error during ns-train
Running ns-train is causing an OOM error, even when using a downscale factor of 8. Is there a limit on the maximum number of images that can be used? Is the pipeline not scalable?
ns-train gaussctrl
--load-checkpoint unedited_models/unit_3/splatfacto/2025-10-17_115410/nerfstudio_models/step-000029999.ckpt
--experiment-name unit_3
--output-dir outputs
--pipeline.datamanager.data data/unit_3
--pipeline.edit_prompt "convert image to sunset"
--pipeline.reverse_prompt "image taken in daylight"
--pipeline.guidance_scale 5
--pipeline.chunk_size 1
--pipeline.ref_view_num 2
--viewer.quit-on-train-completion True
[12:15:29] Saving config to: outputs/unit_3/gaussctrl/2025-10-17_121529/config.yml experiment_config.py:136
FutureWarning: torch.cuda.amp.GradScaler(args...) is deprecated. Please use torch.amp.GradScaler('cuda', args...) instead.
Saving checkpoints to: outputs/unit_3/gaussctrl/2025-10-17_121529/nerfstudio_models trainer.py:136
Auto image downscale factor of 1 gc_dataparser_ns.py:498
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.)
[12:15:30] Caching / undistorting train images gc_datamanager.py:115
[12:15:31] Caching / undistorting eval images gc_datamanager.py:141
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.
UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3609.)
final text_encoder_type: bert-base-uncased
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.
Model loaded from /home/forrealnew/.cache/huggingface/hub/models--ShilongLiu--GroundingDINO/snapshots/a94c9b567a2a374598f05c584e96798a170c56fb/groundingdino_swinb_cogcoor.pth
=> _IncompatibleKeys(missing_keys=[], unexpected_keys=['label_enc.weight', 'bert.embeddings.position_ids'])
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.
FutureWarning: resume_download is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use force_download=True.
Loading pipeline components...: 100%|██████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 12.19it/s]
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.
Done loading Nerfstudio checkpoint from
unedited_models/unit_3/splatfacto/2025-10-17_115410/nerfstudio_models/step-000029999.ckpt
Rendering view 0
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'].
0%| | 0/20 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/home/forrealnew/miniconda3/envs/gaussctrl/bin/ns-train", line 8, in
