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Running
on
Zero
| import torch | |
| from diffusers import DDPMScheduler | |
| from .test_schedulers import SchedulerCommonTest | |
| class DDPMSchedulerTest(SchedulerCommonTest): | |
| scheduler_classes = (DDPMScheduler,) | |
| def get_scheduler_config(self, **kwargs): | |
| config = { | |
| "num_train_timesteps": 1000, | |
| "beta_start": 0.0001, | |
| "beta_end": 0.02, | |
| "beta_schedule": "linear", | |
| "variance_type": "fixed_small", | |
| "clip_sample": True, | |
| } | |
| 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_betas(self): | |
| for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]): | |
| self.check_over_configs(beta_start=beta_start, beta_end=beta_end) | |
| def test_schedules(self): | |
| for schedule in ["linear", "squaredcos_cap_v2"]: | |
| self.check_over_configs(beta_schedule=schedule) | |
| def test_variance_type(self): | |
| for variance in ["fixed_small", "fixed_large", "other"]: | |
| 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_thresholding(self): | |
| self.check_over_configs(thresholding=False) | |
| for threshold in [0.5, 1.0, 2.0]: | |
| for prediction_type in ["epsilon", "sample", "v_prediction"]: | |
| self.check_over_configs( | |
| thresholding=True, | |
| prediction_type=prediction_type, | |
| sample_max_value=threshold, | |
| ) | |
| def test_prediction_type(self): | |
| for prediction_type in ["epsilon", "sample", "v_prediction"]: | |
| self.check_over_configs(prediction_type=prediction_type) | |
| def test_time_indices(self): | |
| for t in [0, 500, 999]: | |
| self.check_over_forward(time_step=t) | |
| def test_variance(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5 | |
| assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.00979)) < 1e-5 | |
| assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5 | |
| 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 test_full_loop_no_noise(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| num_trained_timesteps = len(scheduler) | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter | |
| generator = torch.manual_seed(0) | |
| for t in reversed(range(num_trained_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 | |
| # if t > 0: | |
| # noise = self.dummy_sample_deter | |
| # variance = scheduler.get_variance(t) ** (0.5) * noise | |
| # | |
| # sample = pred_prev_sample + variance | |
| sample = pred_prev_sample | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 258.9606) < 1e-2 | |
| assert abs(result_mean.item() - 0.3372) < 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) | |
| num_trained_timesteps = len(scheduler) | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter | |
| generator = torch.manual_seed(0) | |
| for t in reversed(range(num_trained_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 | |
| # if t > 0: | |
| # noise = self.dummy_sample_deter | |
| # variance = scheduler.get_variance(t) ** (0.5) * noise | |
| # | |
| # sample = pred_prev_sample + variance | |
| sample = pred_prev_sample | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 202.0296) < 1e-2 | |
| assert abs(result_mean.item() - 0.2631) < 1e-3 | |
| def test_custom_timesteps(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| timesteps = [100, 87, 50, 1, 0] | |
| scheduler.set_timesteps(timesteps=timesteps) | |
| scheduler_timesteps = scheduler.timesteps | |
| for i, timestep in enumerate(scheduler_timesteps): | |
| if i == len(timesteps) - 1: | |
| expected_prev_t = -1 | |
| else: | |
| expected_prev_t = timesteps[i + 1] | |
| prev_t = scheduler.previous_timestep(timestep) | |
| prev_t = prev_t.item() | |
| self.assertEqual(prev_t, expected_prev_t) | |
| def test_custom_timesteps_increasing_order(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| timesteps = [100, 87, 50, 51, 0] | |
| with self.assertRaises(ValueError, msg="`custom_timesteps` must be in descending order."): | |
| scheduler.set_timesteps(timesteps=timesteps) | |
| def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| timesteps = [100, 87, 50, 1, 0] | |
| num_inference_steps = len(timesteps) | |
| with self.assertRaises(ValueError, msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."): | |
| scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps) | |
| def test_custom_timesteps_too_large(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| timesteps = [scheduler.config.num_train_timesteps] | |
| with self.assertRaises( | |
| ValueError, | |
| msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}", | |
| ): | |
| scheduler.set_timesteps(timesteps=timesteps) | |
| def test_full_loop_with_noise(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| num_trained_timesteps = len(scheduler) | |
| t_start = num_trained_timesteps - 2 | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter | |
| generator = torch.manual_seed(0) | |
| # add noise | |
| noise = self.dummy_noise_deter | |
| timesteps = scheduler.timesteps[t_start * scheduler.order :] | |
| sample = scheduler.add_noise(sample, noise, timesteps[:1]) | |
| for t in 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() - 387.9466) < 1e-2, f" expected result sum 387.9466, but get {result_sum}" | |
| assert abs(result_mean.item() - 0.5051) < 1e-3, f" expected result mean 0.5051, but get {result_mean}" | |