You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Thank you for sharing the code! Just one quick question regarding the format of h data throughout the training and sampling process.
At first, h is defined as {'categorical': one_hot, 'integer': charges} and the data is concatenated with categorical at the front of integer. However, at line 1310 of the EnLatentDiffusion model, z_h is formatted as z_h = {'categorical': torch.zeros(0).to(z_h), 'integer': z_h}, meaning that the charges part is placed before the categorical part.
Then, take sampling for instance: here z0[:, :, -1:] is used as charges, meaning z0 has a format different from that of z_h in the diffusion model.
Should z_h = {'categorical': torch.zeros(0).to(z_h), 'integer': z_h} be changed to z_h = {'categorical': z_h, 'integer': torch.zeros(0).to(z_h)} instead?
Thanks!
Tianyi
The text was updated successfully, but these errors were encountered:
Hi Minkai!
Thank you for sharing the code! Just one quick question regarding the format of h data throughout the training and sampling process.
At first, h is defined as {'categorical': one_hot, 'integer': charges} and the data is concatenated with categorical at the front of integer. However, at line 1310 of the EnLatentDiffusion model, z_h is formatted as z_h = {'categorical': torch.zeros(0).to(z_h), 'integer': z_h}, meaning that the charges part is placed before the categorical part.
Then, take sampling for instance: here z0[:, :, -1:] is used as charges, meaning z0 has a format different from that of z_h in the diffusion model.
Should z_h = {'categorical': torch.zeros(0).to(z_h), 'integer': z_h} be changed to z_h = {'categorical': z_h, 'integer': torch.zeros(0).to(z_h)} instead?
Thanks!
Tianyi
The text was updated successfully, but these errors were encountered: