| import torch |
|
|
| from diffusers import EulerDiscreteScheduler |
| from diffusers.utils.testing_utils import torch_device |
|
|
| from .test_schedulers import SchedulerCommonTest |
|
|
|
|
| class EulerDiscreteSchedulerTest(SchedulerCommonTest): |
| scheduler_classes = (EulerDiscreteScheduler,) |
| 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_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_timestep_type(self): |
| timestep_types = ["discrete", "continuous"] |
| for timestep_type in timestep_types: |
| self.check_over_configs(timestep_type=timestep_type) |
|
|
| def test_karras_sigmas(self): |
| self.check_over_configs(use_karras_sigmas=True, sigma_min=0.02, sigma_max=700.0) |
|
|
| def test_rescale_betas_zero_snr(self): |
| for rescale_betas_zero_snr in [True, False]: |
| self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) |
|
|
| def full_loop(self, **config): |
| scheduler_class = self.scheduler_classes[0] |
| scheduler_config = self.get_scheduler_config(**config) |
| scheduler = scheduler_class(**scheduler_config) |
|
|
| num_inference_steps = self.num_inference_steps |
| scheduler.set_timesteps(num_inference_steps) |
|
|
| generator = torch.manual_seed(0) |
|
|
| 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, generator=generator) |
| sample = output.prev_sample |
| return sample |
|
|
| def full_loop_custom_timesteps(self, **config): |
| scheduler_class = self.scheduler_classes[0] |
| scheduler_config = self.get_scheduler_config(**config) |
| scheduler = scheduler_class(**scheduler_config) |
|
|
| num_inference_steps = self.num_inference_steps |
| scheduler.set_timesteps(num_inference_steps) |
| timesteps = scheduler.timesteps |
| |
| scheduler = scheduler_class(**scheduler_config) |
| scheduler.set_timesteps(num_inference_steps=None, timesteps=timesteps) |
|
|
| generator = torch.manual_seed(0) |
|
|
| 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, generator=generator) |
| sample = output.prev_sample |
| return sample |
|
|
| def full_loop_custom_sigmas(self, **config): |
| scheduler_class = self.scheduler_classes[0] |
| scheduler_config = self.get_scheduler_config(**config) |
| scheduler = scheduler_class(**scheduler_config) |
|
|
| num_inference_steps = self.num_inference_steps |
| scheduler.set_timesteps(num_inference_steps) |
| sigmas = scheduler.sigmas |
| |
| scheduler = scheduler_class(**scheduler_config) |
| scheduler.set_timesteps(num_inference_steps=None, sigmas=sigmas) |
|
|
| generator = torch.manual_seed(0) |
|
|
| 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, generator=generator) |
| sample = output.prev_sample |
| return sample |
|
|
| def test_full_loop_no_noise(self): |
| sample = self.full_loop() |
|
|
| result_sum = torch.sum(torch.abs(sample)) |
| result_mean = torch.mean(torch.abs(sample)) |
|
|
| assert abs(result_sum.item() - 10.0807) < 1e-2 |
| assert abs(result_mean.item() - 0.0131) < 1e-3 |
|
|
| def test_full_loop_with_v_prediction(self): |
| sample = self.full_loop(prediction_type="v_prediction") |
|
|
| result_sum = torch.sum(torch.abs(sample)) |
| result_mean = torch.mean(torch.abs(sample)) |
|
|
| assert abs(result_sum.item() - 0.0002) < 1e-2 |
| assert abs(result_mean.item() - 2.2676e-06) < 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) |
|
|
| generator = torch.manual_seed(0) |
|
|
| model = self.dummy_model() |
| sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu() |
| 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, generator=generator) |
| sample = output.prev_sample |
|
|
| result_sum = torch.sum(torch.abs(sample)) |
| result_mean = torch.mean(torch.abs(sample)) |
|
|
| assert abs(result_sum.item() - 10.0807) < 1e-2 |
| assert abs(result_mean.item() - 0.0131) < 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) |
|
|
| generator = torch.manual_seed(0) |
|
|
| model = self.dummy_model() |
| sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu() |
| 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, generator=generator) |
| sample = output.prev_sample |
|
|
| result_sum = torch.sum(torch.abs(sample)) |
| result_mean = torch.mean(torch.abs(sample)) |
|
|
| assert abs(result_sum.item() - 124.52299499511719) < 1e-2 |
| assert abs(result_mean.item() - 0.16213932633399963) < 1e-3 |
|
|
| def test_full_loop_with_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) |
|
|
| generator = torch.manual_seed(0) |
|
|
| model = self.dummy_model() |
| sample = self.dummy_sample_deter * scheduler.init_noise_sigma |
|
|
| |
| t_start = self.num_inference_steps - 2 |
| noise = self.dummy_noise_deter |
| noise = noise.to(sample.device) |
| timesteps = scheduler.timesteps[t_start * scheduler.order :] |
| sample = scheduler.add_noise(sample, noise, timesteps[:1]) |
|
|
| for i, t in enumerate(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)) |
|
|
| assert abs(result_sum.item() - 57062.9297) < 1e-2, f" expected result sum 57062.9297, but get {result_sum}" |
| assert abs(result_mean.item() - 74.3007) < 1e-3, f" expected result mean 74.3007, but get {result_mean}" |
|
|
| def test_custom_timesteps(self): |
| for prediction_type in ["epsilon", "sample", "v_prediction"]: |
| for interpolation_type in ["linear", "log_linear"]: |
| for final_sigmas_type in ["sigma_min", "zero"]: |
| sample = self.full_loop( |
| prediction_type=prediction_type, |
| interpolation_type=interpolation_type, |
| final_sigmas_type=final_sigmas_type, |
| ) |
| sample_custom_timesteps = self.full_loop_custom_timesteps( |
| prediction_type=prediction_type, |
| interpolation_type=interpolation_type, |
| final_sigmas_type=final_sigmas_type, |
| ) |
| assert ( |
| torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5 |
| ), f"Scheduler outputs are not identical for prediction_type: {prediction_type}, interpolation_type: {interpolation_type} and final_sigmas_type: {final_sigmas_type}" |
|
|
| def test_custom_sigmas(self): |
| for prediction_type in ["epsilon", "sample", "v_prediction"]: |
| for final_sigmas_type in ["sigma_min", "zero"]: |
| sample = self.full_loop( |
| prediction_type=prediction_type, |
| final_sigmas_type=final_sigmas_type, |
| ) |
| sample_custom_timesteps = self.full_loop_custom_sigmas( |
| prediction_type=prediction_type, |
| final_sigmas_type=final_sigmas_type, |
| ) |
| assert ( |
| torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5 |
| ), f"Scheduler outputs are not identical for prediction_type: {prediction_type} and final_sigmas_type: {final_sigmas_type}" |
|
|
