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Running
on
Zero
| import tempfile | |
| import torch | |
| from diffusers import DPMSolverMultistepInverseScheduler, DPMSolverMultistepScheduler | |
| from .test_schedulers import SchedulerCommonTest | |
| class DPMSolverMultistepSchedulerTest(SchedulerCommonTest): | |
| scheduler_classes = (DPMSolverMultistepInverseScheduler,) | |
| forward_default_kwargs = (("num_inference_steps", 25),) | |
| def get_scheduler_config(self, **kwargs): | |
| config = { | |
| "num_train_timesteps": 1000, | |
| "beta_start": 0.0001, | |
| "beta_end": 0.02, | |
| "beta_schedule": "linear", | |
| "solver_order": 2, | |
| "prediction_type": "epsilon", | |
| "thresholding": False, | |
| "sample_max_value": 1.0, | |
| "algorithm_type": "dpmsolver++", | |
| "solver_type": "midpoint", | |
| "lower_order_final": False, | |
| "lambda_min_clipped": -float("inf"), | |
| "variance_type": None, | |
| } | |
| 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 + 0.10] | |
| 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.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] | |
| 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.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] | |
| output, new_output = sample, sample | |
| for t in range(time_step, time_step + scheduler.config.solver_order + 1): | |
| t = scheduler.timesteps[t] | |
| output = scheduler.step(residual, t, output, **kwargs).prev_sample | |
| new_output = new_scheduler.step(residual, t, new_output, **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 + 0.10] | |
| 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.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] | |
| 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.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] | |
| 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, scheduler=None, **config): | |
| if scheduler is None: | |
| 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 | |
| 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 + 0.10] | |
| scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] | |
| 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) | |
| def test_timesteps(self): | |
| for timesteps in [25, 50, 100, 999, 1000]: | |
| self.check_over_configs(num_train_timesteps=timesteps) | |
| def test_thresholding(self): | |
| self.check_over_configs(thresholding=False) | |
| for order in [1, 2, 3]: | |
| for solver_type in ["midpoint", "heun"]: | |
| for threshold in [0.5, 1.0, 2.0]: | |
| for prediction_type in ["epsilon", "sample"]: | |
| self.check_over_configs( | |
| thresholding=True, | |
| prediction_type=prediction_type, | |
| sample_max_value=threshold, | |
| algorithm_type="dpmsolver++", | |
| solver_order=order, | |
| solver_type=solver_type, | |
| ) | |
| def test_prediction_type(self): | |
| for prediction_type in ["epsilon", "v_prediction"]: | |
| self.check_over_configs(prediction_type=prediction_type) | |
| def test_solver_order_and_type(self): | |
| for algorithm_type in ["dpmsolver", "dpmsolver++"]: | |
| for solver_type in ["midpoint", "heun"]: | |
| for order in [1, 2, 3]: | |
| for prediction_type in ["epsilon", "sample"]: | |
| self.check_over_configs( | |
| solver_order=order, | |
| solver_type=solver_type, | |
| prediction_type=prediction_type, | |
| algorithm_type=algorithm_type, | |
| ) | |
| sample = self.full_loop( | |
| solver_order=order, | |
| solver_type=solver_type, | |
| prediction_type=prediction_type, | |
| algorithm_type=algorithm_type, | |
| ) | |
| assert not torch.isnan(sample).any(), "Samples have nan numbers" | |
| def test_lower_order_final(self): | |
| self.check_over_configs(lower_order_final=True) | |
| self.check_over_configs(lower_order_final=False) | |
| def test_lambda_min_clipped(self): | |
| self.check_over_configs(lambda_min_clipped=-float("inf")) | |
| self.check_over_configs(lambda_min_clipped=-5.1) | |
| def test_variance_type(self): | |
| self.check_over_configs(variance_type=None) | |
| self.check_over_configs(variance_type="learned_range") | |
| def test_timestep_spacing(self): | |
| for timestep_spacing in ["trailing", "leading"]: | |
| self.check_over_configs(timestep_spacing=timestep_spacing) | |
| def test_inference_steps(self): | |
| for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]: | |
| self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0) | |
| def test_full_loop_no_noise(self): | |
| sample = self.full_loop() | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_mean.item() - 0.7047) < 1e-3 | |
| def test_full_loop_no_noise_thres(self): | |
| sample = self.full_loop(thresholding=True, dynamic_thresholding_ratio=0.87, sample_max_value=0.5) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_mean.item() - 19.8933) < 1e-3 | |
| def test_full_loop_with_v_prediction(self): | |
| sample = self.full_loop(prediction_type="v_prediction") | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_mean.item() - 1.5194) < 1e-3 | |
| def test_full_loop_with_karras_and_v_prediction(self): | |
| sample = self.full_loop(prediction_type="v_prediction", use_karras_sigmas=True) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_mean.item() - 1.7833) < 2e-3 | |
| def test_switch(self): | |
| # make sure that iterating over schedulers with same config names gives same results | |
| # for defaults | |
| scheduler = DPMSolverMultistepInverseScheduler(**self.get_scheduler_config()) | |
| sample = self.full_loop(scheduler=scheduler) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_mean.item() - 0.7047) < 1e-3 | |
| scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) | |
| scheduler = DPMSolverMultistepInverseScheduler.from_config(scheduler.config) | |
| sample = self.full_loop(scheduler=scheduler) | |
| new_result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(new_result_mean.item() - result_mean.item()) < 1e-3 | |
| def test_fp16_support(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0) | |
| scheduler = scheduler_class(**scheduler_config) | |
| num_inference_steps = 10 | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter.half() | |
| 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 | |
| assert sample.dtype == torch.float16 | |
| def test_unique_timesteps(self, **config): | |
| for scheduler_class in self.scheduler_classes: | |
| scheduler_config = self.get_scheduler_config(**config) | |
| scheduler = scheduler_class(**scheduler_config) | |
| scheduler.set_timesteps(scheduler.config.num_train_timesteps) | |
| assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps | |