| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class EmptyClass(PretrainedConfig): | |
| def __init__(self): | |
| pass | |
| class SDConfig(PretrainedConfig): | |
| def __init__(self, | |
| override_total_steps = -1, | |
| freeze_vae = True, | |
| use_flash = False, | |
| adapt_topk = -1, | |
| loss = 'mse', | |
| mean = [0.485, 0.456, 0.406], | |
| std = [0.229, 0.224, 0.225], | |
| use_same_noise_among_timesteps = False, | |
| random_timestep_per_iteration = True, | |
| rand_timestep_equal_int = False, | |
| output_dir = './outputs/First_Start', | |
| do_center_crop_size = 384, | |
| architectures = None, | |
| input = None, | |
| model = None, | |
| tta = None, | |
| **kwargs | |
| ): | |
| super().__init__() | |
| self.model = EmptyClass() | |
| self.model.override_total_steps = override_total_steps | |
| self.model.freeze_vae = freeze_vae | |
| self.model.use_flash = use_flash | |
| self.tta = EmptyClass() | |
| self.tta.gradient_descent = EmptyClass() | |
| self.tta.adapt_topk = adapt_topk | |
| self.tta.loss = loss | |
| self.tta.use_same_noise_among_timesteps = use_same_noise_among_timesteps | |
| self.tta.random_timestep_per_iteration = random_timestep_per_iteration | |
| self.tta.rand_timestep_equal_int = rand_timestep_equal_int | |
| self.input = EmptyClass() | |
| self.input.mean = mean | |
| self.input.std = std | |
| self.output_dir = output_dir | |
| self.do_center_crop_size = do_center_crop_size | |
| self.architectures = architectures | |
| for k, v in kwargs.items(): | |
| setattr(self, k, v) | |
| if __name__ =='__main__': | |
| SDConfig() | |