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Zero
Running
on
Zero
| import tempfile | |
| import torch | |
| from diffusers import IPNDMScheduler | |
| from .test_schedulers import SchedulerCommonTest | |
| class IPNDMSchedulerTest(SchedulerCommonTest): | |
| scheduler_classes = (IPNDMScheduler,) | |
| forward_default_kwargs = (("num_inference_steps", 50),) | |
| def get_scheduler_config(self, **kwargs): | |
| config = {"num_train_timesteps": 1000} | |
| config.update(**kwargs) | |
| return config | |
| def check_over_configs(self, time_step=0, **config): | |
| kwargs = dict(self.forward_default_kwargs) | |
| num_inference_steps = kwargs.pop("num_inference_steps", None) | |
| sample = self.dummy_sample | |
| residual = 0.1 * sample | |
| dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] | |
| for scheduler_class in self.scheduler_classes: | |
| scheduler_config = self.get_scheduler_config(**config) | |
| scheduler = scheduler_class(**scheduler_config) | |
| scheduler.set_timesteps(num_inference_steps) | |
| # copy over dummy past residuals | |
| scheduler.ets = dummy_past_residuals[:] | |
| if time_step is None: | |
| time_step = scheduler.timesteps[len(scheduler.timesteps) // 2] | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| scheduler.save_config(tmpdirname) | |
| new_scheduler = scheduler_class.from_pretrained(tmpdirname) | |
| new_scheduler.set_timesteps(num_inference_steps) | |
| # copy over dummy past residuals | |
| new_scheduler.ets = dummy_past_residuals[:] | |
| output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample | |
| new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample | |
| assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" | |
| output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample | |
| new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample | |
| assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" | |
| def test_from_save_pretrained(self): | |
| pass | |
| def check_over_forward(self, time_step=0, **forward_kwargs): | |
| kwargs = dict(self.forward_default_kwargs) | |
| num_inference_steps = kwargs.pop("num_inference_steps", None) | |
| sample = self.dummy_sample | |
| residual = 0.1 * sample | |
| dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] | |
| for scheduler_class in self.scheduler_classes: | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| scheduler.set_timesteps(num_inference_steps) | |
| # copy over dummy past residuals (must be after setting timesteps) | |
| scheduler.ets = dummy_past_residuals[:] | |
| if time_step is None: | |
| time_step = scheduler.timesteps[len(scheduler.timesteps) // 2] | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| scheduler.save_config(tmpdirname) | |
| new_scheduler = scheduler_class.from_pretrained(tmpdirname) | |
| # copy over dummy past residuals | |
| new_scheduler.set_timesteps(num_inference_steps) | |
| # copy over dummy past residual (must be after setting timesteps) | |
| new_scheduler.ets = dummy_past_residuals[:] | |
| output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample | |
| new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample | |
| assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" | |
| output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample | |
| new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample | |
| assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" | |
| 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 i, t in enumerate(scheduler.timesteps): | |
| residual = model(sample, t) | |
| sample = scheduler.step(residual, t, sample).prev_sample | |
| for i, t in enumerate(scheduler.timesteps): | |
| residual = model(sample, t) | |
| sample = scheduler.step(residual, t, sample).prev_sample | |
| return sample | |
| def test_step_shape(self): | |
| kwargs = dict(self.forward_default_kwargs) | |
| num_inference_steps = kwargs.pop("num_inference_steps", None) | |
| for scheduler_class in self.scheduler_classes: | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| sample = self.dummy_sample | |
| residual = 0.1 * sample | |
| if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): | |
| scheduler.set_timesteps(num_inference_steps) | |
| elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): | |
| kwargs["num_inference_steps"] = num_inference_steps | |
| # copy over dummy past residuals (must be done after set_timesteps) | |
| dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] | |
| scheduler.ets = dummy_past_residuals[:] | |
| time_step_0 = scheduler.timesteps[5] | |
| time_step_1 = scheduler.timesteps[6] | |
| output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample | |
| output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample | |
| self.assertEqual(output_0.shape, sample.shape) | |
| self.assertEqual(output_0.shape, output_1.shape) | |
| output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample | |
| output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample | |
| self.assertEqual(output_0.shape, sample.shape) | |
| self.assertEqual(output_0.shape, output_1.shape) | |
| def test_timesteps(self): | |
| for timesteps in [100, 1000]: | |
| self.check_over_configs(num_train_timesteps=timesteps, time_step=None) | |
| def test_inference_steps(self): | |
| for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]): | |
| self.check_over_forward(num_inference_steps=num_inference_steps, time_step=None) | |
| def test_full_loop_no_noise(self): | |
| sample = self.full_loop() | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_mean.item() - 2540529) < 10 | |