| | import torch |
| |
|
| | from diffusers import UnCLIPScheduler |
| |
|
| | from .test_schedulers import SchedulerCommonTest |
| |
|
| |
|
| | |
| | class UnCLIPSchedulerTest(SchedulerCommonTest): |
| | scheduler_classes = (UnCLIPScheduler,) |
| |
|
| | def get_scheduler_config(self, **kwargs): |
| | config = { |
| | "num_train_timesteps": 1000, |
| | "variance_type": "fixed_small_log", |
| | "clip_sample": True, |
| | "clip_sample_range": 1.0, |
| | "prediction_type": "epsilon", |
| | } |
| |
|
| | config.update(**kwargs) |
| | return config |
| |
|
| | def test_timesteps(self): |
| | for timesteps in [1, 5, 100, 1000]: |
| | self.check_over_configs(num_train_timesteps=timesteps) |
| |
|
| | def test_variance_type(self): |
| | for variance in ["fixed_small_log", "learned_range"]: |
| | self.check_over_configs(variance_type=variance) |
| |
|
| | def test_clip_sample(self): |
| | for clip_sample in [True, False]: |
| | self.check_over_configs(clip_sample=clip_sample) |
| |
|
| | def test_clip_sample_range(self): |
| | for clip_sample_range in [1, 5, 10, 20]: |
| | self.check_over_configs(clip_sample_range=clip_sample_range) |
| |
|
| | def test_prediction_type(self): |
| | for prediction_type in ["epsilon", "sample"]: |
| | self.check_over_configs(prediction_type=prediction_type) |
| |
|
| | def test_time_indices(self): |
| | for time_step in [0, 500, 999]: |
| | for prev_timestep in [None, 5, 100, 250, 500, 750]: |
| | if prev_timestep is not None and prev_timestep >= time_step: |
| | continue |
| |
|
| | self.check_over_forward(time_step=time_step, prev_timestep=prev_timestep) |
| |
|
| | def test_variance_fixed_small_log(self): |
| | scheduler_class = self.scheduler_classes[0] |
| | scheduler_config = self.get_scheduler_config(variance_type="fixed_small_log") |
| | scheduler = scheduler_class(**scheduler_config) |
| |
|
| | assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0000e-10)) < 1e-5 |
| | assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0549625)) < 1e-5 |
| | assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9994987)) < 1e-5 |
| |
|
| | def test_variance_learned_range(self): |
| | scheduler_class = self.scheduler_classes[0] |
| | scheduler_config = self.get_scheduler_config(variance_type="learned_range") |
| | scheduler = scheduler_class(**scheduler_config) |
| |
|
| | predicted_variance = 0.5 |
| |
|
| | assert scheduler._get_variance(1, predicted_variance=predicted_variance) - -10.1712790 < 1e-5 |
| | assert scheduler._get_variance(487, predicted_variance=predicted_variance) - -5.7998052 < 1e-5 |
| | assert scheduler._get_variance(999, predicted_variance=predicted_variance) - -0.0010011 < 1e-5 |
| |
|
| | def test_full_loop(self): |
| | scheduler_class = self.scheduler_classes[0] |
| | scheduler_config = self.get_scheduler_config() |
| | scheduler = scheduler_class(**scheduler_config) |
| |
|
| | timesteps = scheduler.timesteps |
| |
|
| | model = self.dummy_model() |
| | sample = self.dummy_sample_deter |
| | generator = torch.manual_seed(0) |
| |
|
| | for i, t in enumerate(timesteps): |
| | |
| | residual = model(sample, t) |
| |
|
| | |
| | pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample |
| |
|
| | sample = pred_prev_sample |
| |
|
| | result_sum = torch.sum(torch.abs(sample)) |
| | result_mean = torch.mean(torch.abs(sample)) |
| |
|
| | assert abs(result_sum.item() - 252.2682495) < 1e-2 |
| | assert abs(result_mean.item() - 0.3284743) < 1e-3 |
| |
|
| | def test_full_loop_skip_timesteps(self): |
| | scheduler_class = self.scheduler_classes[0] |
| | scheduler_config = self.get_scheduler_config() |
| | scheduler = scheduler_class(**scheduler_config) |
| |
|
| | scheduler.set_timesteps(25) |
| |
|
| | timesteps = scheduler.timesteps |
| |
|
| | model = self.dummy_model() |
| | sample = self.dummy_sample_deter |
| | generator = torch.manual_seed(0) |
| |
|
| | for i, t in enumerate(timesteps): |
| | |
| | residual = model(sample, t) |
| |
|
| | if i + 1 == timesteps.shape[0]: |
| | prev_timestep = None |
| | else: |
| | prev_timestep = timesteps[i + 1] |
| |
|
| | |
| | pred_prev_sample = scheduler.step( |
| | residual, t, sample, prev_timestep=prev_timestep, generator=generator |
| | ).prev_sample |
| |
|
| | sample = pred_prev_sample |
| |
|
| | result_sum = torch.sum(torch.abs(sample)) |
| | result_mean = torch.mean(torch.abs(sample)) |
| |
|
| | assert abs(result_sum.item() - 258.2044983) < 1e-2 |
| | assert abs(result_mean.item() - 0.3362038) < 1e-3 |
| |
|
| | def test_trained_betas(self): |
| | pass |
| |
|
| | def test_add_noise_device(self): |
| | pass |
| |
|
