support freezing text_encoder layers for OpenCLIP

[bghira]

At least for SD2.1, freezing the first n layers (typically, 17) allows it to learn more effectively without catastrophic loss / destructive moves to the base layers.

This helps the model keep concepts that aren't trained in, but are colocated to the tensor subgroups that we're training.

It can be done like so:

    first_frozen_layer = 0
    last_frozen_layer = 0
    total_count = 0
    for name, param in text_encoder.named_parameters():
        total_count += 1
        pieces = name.split(".")
        if pieces[1] != "encoder" and pieces[2] != "layers":
            print(f"Ignoring non-encoder layer: {name}")
            continue
        print(f'Pieces: {pieces}')
        current_layer = int(pieces[3])
        if (
            current_layer >= first_frozen_layer and current_layer < 21
        ):  # choose whatever you like to freeze, here
            last_frozen_layer = current_layer
            if hasattr(param, 'requires_grad'):
                param.requires_grad = False
                print(f'Froze layer: {name}')
            else:
                print(f'Ignoring layer that does not mark as gradient capable: {name}')