stable-diffusion-v1-5-tst_chair
/
diffusers
/tests
/schedulers
/test_scheduler_edm_dpmsolver_multistep.py
| import tempfile | |
| import unittest | |
| import torch | |
| from diffusers import ( | |
| EDMDPMSolverMultistepScheduler, | |
| ) | |
| from .test_schedulers import SchedulerCommonTest | |
| class EDMDPMSolverMultistepSchedulerTest(SchedulerCommonTest): | |
| scheduler_classes = (EDMDPMSolverMultistepScheduler,) | |
| forward_default_kwargs = (("num_inference_steps", 25),) | |
| def get_scheduler_config(self, **kwargs): | |
| config = { | |
| "sigma_min": 0.002, | |
| "sigma_max": 80.0, | |
| "sigma_data": 0.5, | |
| "num_train_timesteps": 1000, | |
| "solver_order": 2, | |
| "prediction_type": "epsilon", | |
| "thresholding": False, | |
| "sample_max_value": 1.0, | |
| "algorithm_type": "dpmsolver++", | |
| "solver_type": "midpoint", | |
| "lower_order_final": False, | |
| "euler_at_final": False, | |
| "final_sigmas_type": "sigma_min", | |
| } | |
| 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 = new_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] | |
| time_step = new_scheduler.timesteps[time_step] | |
| 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", "v_prediction"]: | |
| 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) | |
| # TODO (patil-suraj): Fix this test | |
| def test_solver_order_and_type(self): | |
| for algorithm_type in ["dpmsolver++", "sde-dpmsolver++"]: | |
| for solver_type in ["midpoint", "heun"]: | |
| for order in [1, 2, 3]: | |
| for prediction_type in ["epsilon", "v_prediction"]: | |
| if algorithm_type == "sde-dpmsolver++": | |
| if order == 3: | |
| continue | |
| else: | |
| 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() | |
| ), f"Samples have nan numbers, {order}, {solver_type}, {prediction_type}, {algorithm_type}" | |
| def test_lower_order_final(self): | |
| self.check_over_configs(lower_order_final=True) | |
| self.check_over_configs(lower_order_final=False) | |
| def test_euler_at_final(self): | |
| self.check_over_configs(euler_at_final=True) | |
| self.check_over_configs(euler_at_final=False) | |
| 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() - 0.0001) < 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 = 10 | |
| t_start = 5 | |
| 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 i, t in enumerate(timesteps): | |
| residual = model(sample, t) | |
| sample = scheduler.step(residual, t, sample).prev_sample | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 8.1661) < 1e-2, f" expected result sum 8.1661, but get {result_sum}" | |
| assert abs(result_mean.item() - 0.0106) < 1e-3, f" expected result mean 0.0106, but get {result_mean}" | |
| 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() - 0.0080) < 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() - 0.0092) < 1e-3 | |
| def test_duplicated_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) == scheduler.num_inference_steps | |
| def test_trained_betas(self): | |
| pass | |