import inspect import tempfile import unittest from typing import Dict, List, Tuple import torch from diffusers import EDMEulerScheduler from .test_schedulers import SchedulerCommonTest class EDMEulerSchedulerTest(SchedulerCommonTest): scheduler_classes = (EDMEulerScheduler,) forward_default_kwargs = (("num_inference_steps", 10),) def get_scheduler_config(self, **kwargs): config = { "num_train_timesteps": 256, "sigma_min": 0.002, "sigma_max": 80.0, } config.update(**kwargs) return config def test_timesteps(self): for timesteps in [10, 50, 100, 1000]: self.check_over_configs(num_train_timesteps=timesteps) def test_prediction_type(self): for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=prediction_type) def test_full_loop_no_noise(self, num_inference_steps=10, seed=0): scheduler_class = self.scheduler_classes[0] scheduler_config = self.get_scheduler_config() scheduler = scheduler_class(**scheduler_config) scheduler.set_timesteps(num_inference_steps) model = self.dummy_model() sample = self.dummy_sample_deter * scheduler.init_noise_sigma for i, t in enumerate(scheduler.timesteps): scaled_sample = scheduler.scale_model_input(sample, t) model_output = model(scaled_sample, t) output = scheduler.step(model_output, t, sample) sample = output.prev_sample result_sum = torch.sum(torch.abs(sample)) result_mean = torch.mean(torch.abs(sample)) assert abs(result_sum.item() - 34.1855) < 1e-3 assert abs(result_mean.item() - 0.044) < 1e-3 def test_full_loop_device(self, num_inference_steps=10, seed=0): scheduler_class = self.scheduler_classes[0] scheduler_config = self.get_scheduler_config() scheduler = scheduler_class(**scheduler_config) scheduler.set_timesteps(num_inference_steps) model = self.dummy_model() sample = self.dummy_sample_deter * scheduler.init_noise_sigma for i, t in enumerate(scheduler.timesteps): scaled_sample = scheduler.scale_model_input(sample, t) model_output = model(scaled_sample, t) output = scheduler.step(model_output, t, sample) sample = output.prev_sample result_sum = torch.sum(torch.abs(sample)) result_mean = torch.mean(torch.abs(sample)) assert abs(result_sum.item() - 34.1855) < 1e-3 assert abs(result_mean.item() - 0.044) < 1e-3 # Override test_from_save_pretrined to use EDMEulerScheduler-specific logic def test_from_save_pretrained(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 with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(tmpdirname) new_scheduler = scheduler_class.from_pretrained(tmpdirname) scheduler.set_timesteps(num_inference_steps) new_scheduler.set_timesteps(num_inference_steps) timestep = scheduler.timesteps[0] sample = self.dummy_sample scaled_sample = scheduler.scale_model_input(sample, timestep) residual = 0.1 * scaled_sample new_scaled_sample = new_scheduler.scale_model_input(sample, timestep) new_residual = 0.1 * new_scaled_sample if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): kwargs["generator"] = torch.manual_seed(0) output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): kwargs["generator"] = torch.manual_seed(0) new_output = new_scheduler.step(new_residual, timestep, sample, **kwargs).prev_sample assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" # Override test_from_save_pretrined to use EDMEulerScheduler-specific logic def test_step_shape(self): num_inference_steps = 10 scheduler_config = self.get_scheduler_config() scheduler = self.scheduler_classes[0](**scheduler_config) scheduler.set_timesteps(num_inference_steps) timestep_0 = scheduler.timesteps[0] timestep_1 = scheduler.timesteps[1] sample = self.dummy_sample scaled_sample = scheduler.scale_model_input(sample, timestep_0) residual = 0.1 * scaled_sample output_0 = scheduler.step(residual, timestep_0, sample).prev_sample output_1 = scheduler.step(residual, timestep_1, sample).prev_sample self.assertEqual(output_0.shape, sample.shape) self.assertEqual(output_0.shape, output_1.shape) # Override test_from_save_pretrained to use EDMEulerScheduler-specific logic def test_scheduler_outputs_equivalence(self): def set_nan_tensor_to_zero(t): t[t != t] = 0 return t def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (List, Tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()): recursive_check(tuple_iterable_value, dict_iterable_value) elif isinstance(tuple_object, Dict): for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( torch.allclose( set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 ), msg=( "Tuple and dict output are not equal. Difference:" f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." ), ) kwargs = dict(self.forward_default_kwargs) num_inference_steps = kwargs.pop("num_inference_steps", 50) timestep = 0 for scheduler_class in self.scheduler_classes: scheduler_config = self.get_scheduler_config() scheduler = scheduler_class(**scheduler_config) scheduler.set_timesteps(num_inference_steps) timestep = scheduler.timesteps[0] sample = self.dummy_sample scaled_sample = scheduler.scale_model_input(sample, timestep) residual = 0.1 * scaled_sample # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): kwargs["generator"] = torch.manual_seed(0) outputs_dict = scheduler.step(residual, timestep, sample, **kwargs) scheduler.set_timesteps(num_inference_steps) scaled_sample = scheduler.scale_model_input(sample, timestep) residual = 0.1 * scaled_sample # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): kwargs["generator"] = torch.manual_seed(0) outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs) recursive_check(outputs_tuple, outputs_dict) @unittest.skip(reason="EDMEulerScheduler does not support beta schedules.") def test_trained_betas(self): pass