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
As explained here, Real-CUGAN pro models have a weird trick to them. The authors added a pro key to the state dict, and that is the only difference to regular Real-CUGAN models. The issue with this pro key is that it is not part of the architecture itself. This means that saving a pro model will not include the pro key. The goal of this issue is to find a solution to this problem.
Maybe we could have something like the call API where we have descriptor methods for saving that are model specific?
My response:
[...] the issue with that is that we would have 2 (potentially) different state dicts per model. One state dict of the model itself (model_desc.model.state_dict()) and the modified state dict of the new API.
The text was updated successfully, but these errors were encountered:
Good idea. The official repo sets pro to the int 1, but it doesn't actually check that value, it only checks whether the key exists. So we have a tensor (size [1]) and assign it to the pro field. PyTorch would then save this tensor just like any other tensor and add the pro key we need to the state dict.
As explained here, Real-CUGAN pro models have a weird trick to them. The authors added a
pro
key to the state dict, and that is the only difference to regular Real-CUGAN models. The issue with thispro
key is that it is not part of the architecture itself. This means that saving a pro model will not include thepro
key. The goal of this issue is to find a solution to this problem.@joeyballentine suggested the following solution:
My response:
The text was updated successfully, but these errors were encountered: