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 @property def default_num_inference_steps(self): return 10 @property 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)