import torch.nn import ldm.modules.diffusionmodules.openaimodel from modules import script_callbacks, shared, devices unet_options = [] current_unet_option = None current_unet = None def list_unets(): new_unets = script_callbacks.list_unets_callback() unet_options.clear() unet_options.extend(new_unets) def get_unet_option(option=None): option = option or shared.opts.sd_unet if option == "None": return None if option == "Automatic": name = shared.sd_model.sd_checkpoint_info.model_name options = [x for x in unet_options if x.model_name == name] option = options[0].label if options else "None" return next(iter([x for x in unet_options if x.label == option]), None) def apply_unet(option=None): global current_unet_option global current_unet new_option = get_unet_option(option) if new_option == current_unet_option: return if current_unet is not None: print(f"Dectivating unet: {current_unet.option.label}") current_unet.deactivate() current_unet_option = new_option if current_unet_option is None: current_unet = None if not (shared.cmd_opts.lowvram or shared.cmd_opts.medvram): shared.sd_model.model.diffusion_model.to(devices.device) return shared.sd_model.model.diffusion_model.to(devices.cpu) devices.torch_gc() current_unet = current_unet_option.create_unet() current_unet.option = current_unet_option print(f"Activating unet: {current_unet.option.label}") current_unet.activate() class SdUnetOption: model_name = None """name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this""" label = None """name of the unet in UI""" def create_unet(self): """returns SdUnet object to be used as a Unet instead of built-in unet when making pictures""" raise NotImplementedError() class SdUnet(torch.nn.Module): def forward(self, x, timesteps, context, *args, **kwargs): raise NotImplementedError() def activate(self): pass def deactivate(self): pass def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs): if current_unet is not None: return current_unet.forward(x, timesteps, context, *args, **kwargs) return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)