| | import torch |
| |
|
| | from diffusers import DDIMInverseScheduler |
| |
|
| | from .test_schedulers import SchedulerCommonTest |
| |
|
| |
|
| | class DDIMInverseSchedulerTest(SchedulerCommonTest): |
| | scheduler_classes = (DDIMInverseScheduler,) |
| | forward_default_kwargs = (("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 = 10 |
| |
|
| | 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).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([1, 201, 401, 601, 801])) |
| |
|
| | 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_add_noise_device(self): |
| | pass |
| |
|
| | 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() - 671.6816) < 1e-2 |
| | assert abs(result_mean.item() - 0.8746) < 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() - 1394.2185) < 1e-2 |
| | assert abs(result_mean.item() - 1.8154) < 1e-3 |
| |
|
| | def test_full_loop_with_set_alpha_to_one(self): |
| | |
| | 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() - 539.9622) < 1e-2 |
| | assert abs(result_mean.item() - 0.7031) < 1e-3 |
| |
|
| | def test_full_loop_with_no_set_alpha_to_one(self): |
| | |
| | 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() - 542.6722) < 1e-2 |
| | assert abs(result_mean.item() - 0.7066) < 1e-3 |
| |
|
