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Zero
Running
on
Zero
| import torch | |
| from diffusers import UnCLIPScheduler | |
| from .test_schedulers import SchedulerCommonTest | |
| # UnCLIPScheduler is a modified DDPMScheduler with a subset of the configuration. | |
| class UnCLIPSchedulerTest(SchedulerCommonTest): | |
| scheduler_classes = (UnCLIPScheduler,) | |
| def get_scheduler_config(self, **kwargs): | |
| config = { | |
| "num_train_timesteps": 1000, | |
| "variance_type": "fixed_small_log", | |
| "clip_sample": True, | |
| "clip_sample_range": 1.0, | |
| "prediction_type": "epsilon", | |
| } | |
| config.update(**kwargs) | |
| return config | |
| def test_timesteps(self): | |
| for timesteps in [1, 5, 100, 1000]: | |
| self.check_over_configs(num_train_timesteps=timesteps) | |
| def test_variance_type(self): | |
| for variance in ["fixed_small_log", "learned_range"]: | |
| self.check_over_configs(variance_type=variance) | |
| def test_clip_sample(self): | |
| for clip_sample in [True, False]: | |
| self.check_over_configs(clip_sample=clip_sample) | |
| def test_clip_sample_range(self): | |
| for clip_sample_range in [1, 5, 10, 20]: | |
| self.check_over_configs(clip_sample_range=clip_sample_range) | |
| def test_prediction_type(self): | |
| for prediction_type in ["epsilon", "sample"]: | |
| self.check_over_configs(prediction_type=prediction_type) | |
| def test_time_indices(self): | |
| for time_step in [0, 500, 999]: | |
| for prev_timestep in [None, 5, 100, 250, 500, 750]: | |
| if prev_timestep is not None and prev_timestep >= time_step: | |
| continue | |
| self.check_over_forward(time_step=time_step, prev_timestep=prev_timestep) | |
| def test_variance_fixed_small_log(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config(variance_type="fixed_small_log") | |
| scheduler = scheduler_class(**scheduler_config) | |
| assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0000e-10)) < 1e-5 | |
| assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0549625)) < 1e-5 | |
| assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9994987)) < 1e-5 | |
| def test_variance_learned_range(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config(variance_type="learned_range") | |
| scheduler = scheduler_class(**scheduler_config) | |
| predicted_variance = 0.5 | |
| assert scheduler._get_variance(1, predicted_variance=predicted_variance) - -10.1712790 < 1e-5 | |
| assert scheduler._get_variance(487, predicted_variance=predicted_variance) - -5.7998052 < 1e-5 | |
| assert scheduler._get_variance(999, predicted_variance=predicted_variance) - -0.0010011 < 1e-5 | |
| def test_full_loop(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| timesteps = scheduler.timesteps | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter | |
| generator = torch.manual_seed(0) | |
| for i, t in enumerate(timesteps): | |
| # 1. predict noise residual | |
| residual = model(sample, t) | |
| # 2. predict previous mean of sample x_t-1 | |
| pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample | |
| sample = pred_prev_sample | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 252.2682495) < 1e-2 | |
| assert abs(result_mean.item() - 0.3284743) < 1e-3 | |
| def test_full_loop_skip_timesteps(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| scheduler.set_timesteps(25) | |
| timesteps = scheduler.timesteps | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter | |
| generator = torch.manual_seed(0) | |
| for i, t in enumerate(timesteps): | |
| # 1. predict noise residual | |
| residual = model(sample, t) | |
| if i + 1 == timesteps.shape[0]: | |
| prev_timestep = None | |
| else: | |
| prev_timestep = timesteps[i + 1] | |
| # 2. predict previous mean of sample x_t-1 | |
| pred_prev_sample = scheduler.step( | |
| residual, t, sample, prev_timestep=prev_timestep, generator=generator | |
| ).prev_sample | |
| sample = pred_prev_sample | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 258.2044983) < 1e-2 | |
| assert abs(result_mean.item() - 0.3362038) < 1e-3 | |
| def test_trained_betas(self): | |
| pass | |
| def test_add_noise_device(self): | |
| pass | |