| | import torch |
| |
|
| | from diffusers import SASolverScheduler |
| | from diffusers.utils.testing_utils import require_torchsde, torch_device |
| |
|
| | from .test_schedulers import SchedulerCommonTest |
| |
|
| |
|
| | @require_torchsde |
| | 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 |
| |
|
| | |
| | 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") |
| |
|
