import torch from diffusers import DDIMScheduler from .test_schedulers import SchedulerCommonTest class DDIMSchedulerTest(SchedulerCommonTest): scheduler_classes = (DDIMScheduler,) forward_default_kwargs = (("eta", 0.0), ("num_inference_steps", 50)) def get_scheduler_config(self, **kwargs): config = { "num_train_timesteps": 1000, "beta_start": 0.0001, "beta_end": 0.02, "beta_schedule": "linear", "clip_sample": True, } config.update(**kwargs) return config 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, eta = 10, 0.0 model = self.dummy_model() sample = self.dummy_sample_deter scheduler.set_timesteps(num_inference_steps) for t in scheduler.timesteps: residual = model(sample, t) sample = scheduler.step(residual, t, sample, eta).prev_sample return sample def test_timesteps(self): for timesteps in [100, 500, 1000]: self.check_over_configs(num_train_timesteps=timesteps) def test_steps_offset(self): for steps_offset in [0, 1]: self.check_over_configs(steps_offset=steps_offset) scheduler_class = self.scheduler_classes[0] scheduler_config = self.get_scheduler_config(steps_offset=1) scheduler = scheduler_class(**scheduler_config) scheduler.set_timesteps(5) assert torch.equal(scheduler.timesteps, torch.LongTensor([801, 601, 401, 201, 1])) 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_prediction_type(self): for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=prediction_type) def test_clip_sample(self): for clip_sample in [True, False]: self.check_over_configs(clip_sample=clip_sample) def test_timestep_spacing(self): for timestep_spacing in ["trailing", "leading"]: self.check_over_configs(timestep_spacing=timestep_spacing) 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_thresholding(self): self.check_over_configs(thresholding=False) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs( thresholding=True, prediction_type=prediction_type, sample_max_value=threshold, ) def test_time_indices(self): for t in [1, 10, 49]: self.check_over_forward(time_step=t) def test_inference_steps(self): for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]): self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps) def test_eta(self): for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]): self.check_over_forward(time_step=t, eta=eta) 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) - 0.0)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(420, 400) - 0.14771)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(980, 960) - 0.32460)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(487, 486) - 0.00979)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(999, 998) - 0.02)) < 1e-5 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() - 172.0067) < 1e-2 assert abs(result_mean.item() - 0.223967) < 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() - 52.5302) < 1e-2 assert abs(result_mean.item() - 0.0684) < 1e-3 def test_full_loop_with_set_alpha_to_one(self): # We specify different beta, so that the first alpha is 0.99 sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01) result_sum = torch.sum(torch.abs(sample)) result_mean = torch.mean(torch.abs(sample)) assert abs(result_sum.item() - 149.8295) < 1e-2 assert abs(result_mean.item() - 0.1951) < 1e-3 def test_full_loop_with_no_set_alpha_to_one(self): # We specify different beta, so that the first alpha is 0.99 sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01) result_sum = torch.sum(torch.abs(sample)) result_mean = torch.mean(torch.abs(sample)) assert abs(result_sum.item() - 149.0784) < 1e-2 assert abs(result_mean.item() - 0.1941) < 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) num_inference_steps, eta = 10, 0.0 t_start = 8 model = self.dummy_model() sample = self.dummy_sample_deter scheduler.set_timesteps(num_inference_steps) # 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: residual = model(sample, t) sample = scheduler.step(residual, t, sample, eta).prev_sample result_sum = torch.sum(torch.abs(sample)) result_mean = torch.mean(torch.abs(sample)) assert abs(result_sum.item() - 354.5418) < 1e-2, f" expected result sum 218.4379, but get {result_sum}" assert abs(result_mean.item() - 0.4616) < 1e-3, f" expected result mean 0.2844, but get {result_mean}"