huochai
huochai
I am currently attempting to reproduce the training process described in your paper using Stable Diffusion v2. However, my RTX 4090 ran out of memory when training with batchsize 32...
I always run things on a remote sever and need to open obj files on the sever through this extension. This extension was working fine util I updated my VSCode...
``` label = np.zeros((len(batch_paths) * 2,1), dtype=np.int) label[:(len(batch_paths) - 1)] = 1 blobs['label'] = label`` ```
``` Traceback (most recent call last): File "test_from_init_pose_pickle.py", line 390, in main() File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 28, in decorate_context return func(*args, **kwargs) File "test_from_init_pose_pickle.py", line 343, in main outputs = model(Gs=pose_input,...
After runing conda env create --file environment.yml, I got the following error: ``` rasterize_points.cu(117): error: argument of type "int *" is incompatible with parameter of type "float *" rasterize_points.cu(118): error:...
Hello. It seems that scatter_sum is not precise compared to torch implementation. Is this a bug? I encountered this problem in my personal project which is sensitive to numerical precision...
```json { "objects": { "OXO_Cookie_Spatula": { "prim_path": "/World/objects/gso_OXO_Cookie_Spatula", "scale": [ 0.1175592169165613, 0.11755921691656132, 0.11755921691656115 ], "translation": [ -0.6190868020057678, -0.14792710542678833, 0.011477616615593433 ], "rotation_matrix": [ [ 0.11553694271286183, 0.02170424800426942, 0.0005567487486819248 ], [ -0.02170064164052224, 0.11534694907408455,...