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Zero
Running
on
Zero
| import torch | |
| from diffusers import SASolverScheduler | |
| from diffusers.utils.testing_utils import require_torchsde, torch_device | |
| from .test_schedulers import SchedulerCommonTest | |
| class SASolverSchedulerTest(SchedulerCommonTest): | |
| scheduler_classes = (SASolverScheduler,) | |
| forward_default_kwargs = (("num_inference_steps", 10),) | |
| 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", | |
| } | |
| config.update(**kwargs) | |
| return config | |
| def test_step_shape(self): | |
| kwargs = dict(self.forward_default_kwargs) | |
| num_inference_steps = kwargs.pop("num_inference_steps", None) | |
| for scheduler_class in self.scheduler_classes: | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| sample = self.dummy_sample | |
| residual = 0.1 * sample | |
| if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): | |
| scheduler.set_timesteps(num_inference_steps) | |
| elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): | |
| kwargs["num_inference_steps"] = num_inference_steps | |
| # copy over dummy past residuals (must be done after set_timesteps) | |
| dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] | |
| scheduler.model_outputs = dummy_past_residuals[ | |
| : max( | |
| scheduler.config.predictor_order, | |
| scheduler.config.corrector_order - 1, | |
| ) | |
| ] | |
| time_step_0 = scheduler.timesteps[5] | |
| time_step_1 = scheduler.timesteps[6] | |
| output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample | |
| output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample | |
| self.assertEqual(output_0.shape, sample.shape) | |
| self.assertEqual(output_0.shape, output_1.shape) | |
| 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) | |
| generator = torch.manual_seed(0) | |
| for i, t in enumerate(scheduler.timesteps): | |
| sample = scheduler.scale_model_input(sample, t, generator=generator) | |
| 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 ["cpu"]: | |
| assert abs(result_sum.item() - 337.394287109375) < 1e-2 | |
| assert abs(result_mean.item() - 0.43931546807289124) < 1e-3 | |
| elif torch_device in ["cuda"]: | |
| assert abs(result_sum.item() - 329.1999816894531) < 1e-2 | |
| assert abs(result_mean.item() - 0.4286458194255829) < 1e-3 | |
| else: | |
| print("None") | |
| 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) | |
| generator = torch.manual_seed(0) | |
| for i, t in enumerate(scheduler.timesteps): | |
| sample = scheduler.scale_model_input(sample, t, generator=generator) | |
| 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 ["cpu"]: | |
| assert abs(result_sum.item() - 193.1467742919922) < 1e-2 | |
| assert abs(result_mean.item() - 0.2514931857585907) < 1e-3 | |
| elif torch_device in ["cuda"]: | |
| assert abs(result_sum.item() - 193.4154052734375) < 1e-2 | |
| assert abs(result_mean.item() - 0.2518429756164551) < 1e-3 | |
| else: | |
| print("None") | |
| 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 | |
| generator = torch.manual_seed(0) | |
| for t in scheduler.timesteps: | |
| sample = scheduler.scale_model_input(sample, t) | |
| model_output = model(sample, t) | |
| output = scheduler.step(model_output, t, sample, generator=generator) | |
| sample = output.prev_sample | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| if torch_device in ["cpu"]: | |
| assert abs(result_sum.item() - 337.394287109375) < 1e-2 | |
| assert abs(result_mean.item() - 0.43931546807289124) < 1e-3 | |
| elif torch_device in ["cuda"]: | |
| assert abs(result_sum.item() - 337.394287109375) < 1e-2 | |
| assert abs(result_mean.item() - 0.4393154978752136) < 1e-3 | |
| else: | |
| print("None") | |
| 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) | |
| generator = torch.manual_seed(0) | |
| for t in scheduler.timesteps: | |
| sample = scheduler.scale_model_input(sample, t) | |
| model_output = model(sample, t) | |
| output = scheduler.step(model_output, t, sample, generator=generator) | |
| sample = output.prev_sample | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| if torch_device in ["cpu"]: | |
| assert abs(result_sum.item() - 837.2554931640625) < 1e-2 | |
| assert abs(result_mean.item() - 1.0901764631271362) < 1e-2 | |
| elif torch_device in ["cuda"]: | |
| assert abs(result_sum.item() - 837.25537109375) < 1e-2 | |
| assert abs(result_mean.item() - 1.0901763439178467) < 1e-2 | |
| else: | |
| print("None") | |