when running stable-zero123 I get torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 338.00 MiB. GPU 0 has a total capacty of 6.00 GiB of which 0 bytes is free. Of the allocated memory 4.91 GiB is allocated by PyTorch, and 77.87 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
(base) C:\Users\user\threestudio> I have a 'NVIDIA GeForce RTX 4050 Laptop GPU'
when running stable-zero123 I get torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 338.00 MiB. GPU 0 has a total capacty of 6.00 GiB of which 0 bytes is free. Of the allocated memory 4.91 GiB is allocated by PyTorch, and 77.87 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF (base) C:\Users\user\threestudio> I have a 'NVIDIA GeForce RTX 4050 Laptop GPU'
and another thing could you help me figure out how to do this: [INFO] You are using a CUDA device ('NVIDIA GeForce RTX 4050 Laptop GPU') that has Tensor Cores. To properly utilize them, you should set
torch.set_float32_matmul_precision('medium' | 'high')which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision