Spandrel gives your project support for various PyTorch architectures meant for AI Super-Resolution, restoration, and inpainting. Based on the model support implemented in chaiNNer.
This PR adds an internal API for hyperparameters. The basic idea is that the model classes now have a @store_hyperparameters decorator that changes the class to automatically store all given hyperparameters in a hyperparameters field. Example:
@store_hyperparameters()
class CodeFormer(VQAutoEncoder):
hyperparameters = {}
Unfortunately, classes still have to declare the hyperparameters class variable for pyright to pick up on the field.
While all models now have a hyperparameters field, this field is not part of the public API (for now). In this PR, I just add implementation for hyperparameters and use them in tests.
Speaking of tests: assert_loads_correctly now uses the new hyperparameters to test whether a model was loaded correctly. This is much stricter than the old opt-in system via condition=... and already found a few minor differences between detected hyperparameters.
A future PR will deal with defining the public API for hyperparameters.
This PR adds an internal API for hyperparameters. The basic idea is that the model classes now have a
@store_hyperparametersdecorator that changes the class to automatically store all given hyperparameters in ahyperparametersfield. Example:Unfortunately, classes still have to declare the
hyperparametersclass variable for pyright to pick up on the field.While all models now have a
hyperparametersfield, this field is not part of the public API (for now). In this PR, I just add implementation for hyperparameters and use them in tests.Speaking of tests:
assert_loads_correctlynow uses the new hyperparameters to test whether a model was loaded correctly. This is much stricter than the old opt-in system viacondition=...and already found a few minor differences between detected hyperparameters.A future PR will deal with defining the public API for hyperparameters.