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| import torch | |
| from diffusers import DPMSolverSDEScheduler | |
| from diffusers.utils.testing_utils import require_torchsde, torch_device | |
| from .test_schedulers import SchedulerCommonTest | |
| class DPMSolverSDESchedulerTest(SchedulerCommonTest): | |
| scheduler_classes = (DPMSolverSDEScheduler,) | |
| num_inference_steps = 10 | |
| def get_scheduler_config(self, **kwargs): | |
| config = { | |
| "num_train_timesteps": 1100, | |
| "beta_start": 0.0001, | |
| "beta_end": 0.02, | |
| "beta_schedule": "linear", | |
| "noise_sampler_seed": 0, | |
| } | |
| config.update(**kwargs) | |
| return config | |
| def test_timesteps(self): | |
| for timesteps in [10, 50, 100, 1000]: | |
| self.check_over_configs(num_train_timesteps=timesteps) | |
| def test_betas(self): | |
| for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]): | |
| self.check_over_configs(beta_start=beta_start, beta_end=beta_end) | |
| def test_schedules(self): | |
| for schedule in ["linear", "scaled_linear"]: | |
| self.check_over_configs(beta_schedule=schedule) | |
| def test_prediction_type(self): | |
| for prediction_type in ["epsilon", "v_prediction"]: | |
| self.check_over_configs(prediction_type=prediction_type) | |
| def test_full_loop_no_noise(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| scheduler.set_timesteps(self.num_inference_steps) | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter * scheduler.init_noise_sigma | |
| sample = sample.to(torch_device) | |
| for i, t in enumerate(scheduler.timesteps): | |
| sample = scheduler.scale_model_input(sample, t) | |
| model_output = model(sample, t) | |
| output = scheduler.step(model_output, t, sample) | |
| sample = output.prev_sample | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| if torch_device in ["mps"]: | |
| assert abs(result_sum.item() - 167.47821044921875) < 1e-2 | |
| assert abs(result_mean.item() - 0.2178705964565277) < 1e-3 | |
| elif torch_device in ["cuda"]: | |
| assert abs(result_sum.item() - 171.59352111816406) < 1e-2 | |
| assert abs(result_mean.item() - 0.22342906892299652) < 1e-3 | |
| else: | |
| assert abs(result_sum.item() - 162.52383422851562) < 1e-2 | |
| assert abs(result_mean.item() - 0.211619570851326) < 1e-3 | |
| def test_full_loop_with_v_prediction(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config(prediction_type="v_prediction") | |
| scheduler = scheduler_class(**scheduler_config) | |
| scheduler.set_timesteps(self.num_inference_steps) | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter * scheduler.init_noise_sigma | |
| sample = sample.to(torch_device) | |
| for i, t in enumerate(scheduler.timesteps): | |
| sample = scheduler.scale_model_input(sample, t) | |
| model_output = model(sample, t) | |
| output = scheduler.step(model_output, t, sample) | |
| sample = output.prev_sample | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| if torch_device in ["mps"]: | |
| assert abs(result_sum.item() - 124.77149200439453) < 1e-2 | |
| assert abs(result_mean.item() - 0.16226289014816284) < 1e-3 | |
| elif torch_device in ["cuda"]: | |
| assert abs(result_sum.item() - 128.1663360595703) < 1e-2 | |
| assert abs(result_mean.item() - 0.16688326001167297) < 1e-3 | |
| else: | |
| assert abs(result_sum.item() - 119.8487548828125) < 1e-2 | |
| assert abs(result_mean.item() - 0.1560530662536621) < 1e-3 | |
| def test_full_loop_device(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| scheduler.set_timesteps(self.num_inference_steps, device=torch_device) | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma | |
| for t in scheduler.timesteps: | |
| sample = scheduler.scale_model_input(sample, t) | |
| model_output = model(sample, t) | |
| output = scheduler.step(model_output, t, sample) | |
| sample = output.prev_sample | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| if torch_device in ["mps"]: | |
| assert abs(result_sum.item() - 167.46957397460938) < 1e-2 | |
| assert abs(result_mean.item() - 0.21805934607982635) < 1e-3 | |
| elif torch_device in ["cuda"]: | |
| assert abs(result_sum.item() - 171.59353637695312) < 1e-2 | |
| assert abs(result_mean.item() - 0.22342908382415771) < 1e-3 | |
| else: | |
| assert abs(result_sum.item() - 162.52383422851562) < 1e-2 | |
| assert abs(result_mean.item() - 0.211619570851326) < 1e-3 | |
| def test_full_loop_device_karras_sigmas(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True) | |
| scheduler.set_timesteps(self.num_inference_steps, device=torch_device) | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma | |
| sample = sample.to(torch_device) | |
| for t in scheduler.timesteps: | |
| sample = scheduler.scale_model_input(sample, t) | |
| model_output = model(sample, t) | |
| output = scheduler.step(model_output, t, sample) | |
| sample = output.prev_sample | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| if torch_device in ["mps"]: | |
| assert abs(result_sum.item() - 176.66974135742188) < 1e-2 | |
| assert abs(result_mean.item() - 0.23003872730981811) < 1e-2 | |
| elif torch_device in ["cuda"]: | |
| assert abs(result_sum.item() - 177.63653564453125) < 1e-2 | |
| assert abs(result_mean.item() - 0.23003872730981811) < 1e-2 | |
| else: | |
| assert abs(result_sum.item() - 170.3135223388672) < 1e-2 | |
| assert abs(result_mean.item() - 0.23003872730981811) < 1e-2 | |