import torch from diffusers import HeunDiscreteScheduler from diffusers.utils.testing_utils import torch_device from .test_schedulers import SchedulerCommonTest class HeunDiscreteSchedulerTest(SchedulerCommonTest): scheduler_classes = (HeunDiscreteScheduler,) 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", "exp"]: self.check_over_configs(beta_schedule=schedule) def test_clip_sample(self): for clip_sample_range in [1.0, 2.0, 3.0]: self.check_over_configs(clip_sample_range=clip_sample_range, clip_sample=True) def test_prediction_type(self): for prediction_type in ["epsilon", "v_prediction", "sample"]: self.check_over_configs(prediction_type=prediction_type) 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) 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) 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 timesteps = torch.cat([timesteps[:1], timesteps[1::2]]) # reset the timesteps using `timesteps` scheduler = scheduler_class(**scheduler_config) scheduler.set_timesteps(num_inference_steps=None, timesteps=timesteps) 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) 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)) if torch_device in ["cpu", "mps"]: assert abs(result_sum.item() - 0.1233) < 1e-2 assert abs(result_mean.item() - 0.0002) < 1e-3 else: # CUDA assert abs(result_sum.item() - 0.1233) < 1e-2 assert abs(result_mean.item() - 0.0002) < 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)) if torch_device in ["cpu", "mps"]: assert abs(result_sum.item() - 4.6934e-07) < 1e-2 assert abs(result_mean.item() - 6.1112e-10) < 1e-3 else: # CUDA assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2 assert abs(result_mean.item() - 0.0002) < 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) model = self.dummy_model() sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma for t in scheduler.timesteps: sample = scheduler.scale_model_input(sample, t) 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 str(torch_device).startswith("cpu"): # The following sum varies between 148 and 156 on mps. Why? assert abs(result_sum.item() - 0.1233) < 1e-2 assert abs(result_mean.item() - 0.0002) < 1e-3 elif str(torch_device).startswith("mps"): # Larger tolerance on mps assert abs(result_mean.item() - 0.0002) < 1e-2 else: # CUDA assert abs(result_sum.item() - 0.1233) < 1e-2 assert abs(result_mean.item() - 0.0002) < 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) model = self.dummy_model() sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma 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) sample = output.prev_sample result_sum = torch.sum(torch.abs(sample)) result_mean = torch.mean(torch.abs(sample)) assert abs(result_sum.item() - 0.00015) < 1e-2 assert abs(result_mean.item() - 1.9869554535034695e-07) < 1e-2 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) model = self.dummy_model() sample = self.dummy_sample_deter * scheduler.init_noise_sigma sample = sample.to(torch_device) t_start = self.num_inference_steps - 2 noise = self.dummy_noise_deter noise = noise.to(torch_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) sample = output.prev_sample result_sum = torch.sum(torch.abs(sample)) result_mean = torch.mean(torch.abs(sample)) assert abs(result_sum.item() - 75074.8906) < 1e-2, f" expected result sum 75074.8906, but get {result_sum}" assert abs(result_mean.item() - 97.7538) < 1e-3, f" expected result mean 97.7538, but get {result_mean}" def test_custom_timesteps(self): for prediction_type in ["epsilon", "sample", "v_prediction"]: for timestep_spacing in ["linspace", "leading"]: sample = self.full_loop( prediction_type=prediction_type, timestep_spacing=timestep_spacing, ) sample_custom_timesteps = self.full_loop_custom_timesteps( prediction_type=prediction_type, timestep_spacing=timestep_spacing, ) assert ( torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5 ), f"Scheduler outputs are not identical for prediction_type: {prediction_type}, timestep_spacing: {timestep_spacing}"