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Zero
Running
on
Zero
| import torch | |
| from diffusers import TCDScheduler | |
| from .test_schedulers import SchedulerCommonTest | |
| class TCDSchedulerTest(SchedulerCommonTest): | |
| scheduler_classes = (TCDScheduler,) | |
| forward_default_kwargs = (("num_inference_steps", 10),) | |
| def get_scheduler_config(self, **kwargs): | |
| config = { | |
| "num_train_timesteps": 1000, | |
| "beta_start": 0.00085, | |
| "beta_end": 0.0120, | |
| "beta_schedule": "scaled_linear", | |
| "prediction_type": "epsilon", | |
| } | |
| config.update(**kwargs) | |
| return config | |
| def default_num_inference_steps(self): | |
| return 10 | |
| def default_valid_timestep(self): | |
| kwargs = dict(self.forward_default_kwargs) | |
| num_inference_steps = kwargs.pop("num_inference_steps", None) | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = self.scheduler_classes[0](**scheduler_config) | |
| scheduler.set_timesteps(num_inference_steps) | |
| timestep = scheduler.timesteps[-1] | |
| return timestep | |
| def test_timesteps(self): | |
| for timesteps in [100, 500, 1000]: | |
| # 0 is not guaranteed to be in the timestep schedule, but timesteps - 1 is | |
| self.check_over_configs(time_step=timesteps - 1, 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(time_step=self.default_valid_timestep, beta_start=beta_start, beta_end=beta_end) | |
| def test_schedules(self): | |
| for schedule in ["linear", "scaled_linear", "squaredcos_cap_v2"]: | |
| self.check_over_configs(time_step=self.default_valid_timestep, beta_schedule=schedule) | |
| def test_prediction_type(self): | |
| for prediction_type in ["epsilon", "v_prediction"]: | |
| self.check_over_configs(time_step=self.default_valid_timestep, prediction_type=prediction_type) | |
| def test_clip_sample(self): | |
| for clip_sample in [True, False]: | |
| self.check_over_configs(time_step=self.default_valid_timestep, clip_sample=clip_sample) | |
| def test_thresholding(self): | |
| self.check_over_configs(time_step=self.default_valid_timestep, thresholding=False) | |
| for threshold in [0.5, 1.0, 2.0]: | |
| for prediction_type in ["epsilon", "v_prediction"]: | |
| self.check_over_configs( | |
| time_step=self.default_valid_timestep, | |
| thresholding=True, | |
| prediction_type=prediction_type, | |
| sample_max_value=threshold, | |
| ) | |
| def test_time_indices(self): | |
| # Get default timestep schedule. | |
| kwargs = dict(self.forward_default_kwargs) | |
| num_inference_steps = kwargs.pop("num_inference_steps", None) | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = self.scheduler_classes[0](**scheduler_config) | |
| scheduler.set_timesteps(num_inference_steps) | |
| timesteps = scheduler.timesteps | |
| for t in timesteps: | |
| self.check_over_forward(time_step=t) | |
| def test_inference_steps(self): | |
| # Hardcoded for now | |
| for t, num_inference_steps in zip([99, 39, 39, 19], [10, 25, 26, 50]): | |
| self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps) | |
| def full_loop(self, num_inference_steps=10, seed=0, **config): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config(**config) | |
| scheduler = scheduler_class(**scheduler_config) | |
| eta = 0.0 # refer to gamma in the paper | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter | |
| generator = torch.manual_seed(seed) | |
| scheduler.set_timesteps(num_inference_steps) | |
| for t in scheduler.timesteps: | |
| residual = model(sample, t) | |
| sample = scheduler.step(residual, t, sample, eta, generator).prev_sample | |
| return sample | |
| def test_full_loop_onestep_deter(self): | |
| sample = self.full_loop(num_inference_steps=1) | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 29.8715) < 1e-3 # 0.0778918 | |
| assert abs(result_mean.item() - 0.0389) < 1e-3 | |
| def test_full_loop_multistep_deter(self): | |
| sample = self.full_loop(num_inference_steps=10) | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 181.2040) < 1e-3 | |
| assert abs(result_mean.item() - 0.2359) < 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) | |