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| import tempfile | |
| from typing import Dict, List, Tuple | |
| import torch | |
| from diffusers import LCMScheduler | |
| from diffusers.utils.testing_utils import torch_device | |
| from .test_schedulers import SchedulerCommonTest | |
| class LCMSchedulerTest(SchedulerCommonTest): | |
| scheduler_classes = (LCMScheduler,) | |
| forward_default_kwargs = (("num_inference_steps", 10),) | |
| def get_scheduler_config(self, **kwargs): | |
| config = { | |
| "num_train_timesteps": 1000, | |
| "beta_start": 0.00085, | |
| "beta_end": 0.0120, | |
| "beta_schedule": "scaled_linear", | |
| "prediction_type": "epsilon", | |
| } | |
| config.update(**kwargs) | |
| return config | |
| def default_valid_timestep(self): | |
| kwargs = dict(self.forward_default_kwargs) | |
| num_inference_steps = kwargs.pop("num_inference_steps", None) | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = self.scheduler_classes[0](**scheduler_config) | |
| scheduler.set_timesteps(num_inference_steps) | |
| timestep = scheduler.timesteps[-1] | |
| return timestep | |
| def test_timesteps(self): | |
| for timesteps in [100, 500, 1000]: | |
| # 0 is not guaranteed to be in the timestep schedule, but timesteps - 1 is | |
| self.check_over_configs(time_step=timesteps - 1, num_train_timesteps=timesteps) | |
| def test_betas(self): | |
| for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]): | |
| self.check_over_configs(time_step=self.default_valid_timestep, beta_start=beta_start, beta_end=beta_end) | |
| def test_schedules(self): | |
| for schedule in ["linear", "scaled_linear", "squaredcos_cap_v2"]: | |
| self.check_over_configs(time_step=self.default_valid_timestep, beta_schedule=schedule) | |
| def test_prediction_type(self): | |
| for prediction_type in ["epsilon", "v_prediction"]: | |
| self.check_over_configs(time_step=self.default_valid_timestep, prediction_type=prediction_type) | |
| def test_clip_sample(self): | |
| for clip_sample in [True, False]: | |
| self.check_over_configs(time_step=self.default_valid_timestep, clip_sample=clip_sample) | |
| def test_thresholding(self): | |
| self.check_over_configs(time_step=self.default_valid_timestep, thresholding=False) | |
| for threshold in [0.5, 1.0, 2.0]: | |
| for prediction_type in ["epsilon", "v_prediction"]: | |
| self.check_over_configs( | |
| time_step=self.default_valid_timestep, | |
| thresholding=True, | |
| prediction_type=prediction_type, | |
| sample_max_value=threshold, | |
| ) | |
| def test_time_indices(self): | |
| # Get default timestep schedule. | |
| kwargs = dict(self.forward_default_kwargs) | |
| num_inference_steps = kwargs.pop("num_inference_steps", None) | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = self.scheduler_classes[0](**scheduler_config) | |
| scheduler.set_timesteps(num_inference_steps) | |
| timesteps = scheduler.timesteps | |
| for t in timesteps: | |
| self.check_over_forward(time_step=t) | |
| def test_inference_steps(self): | |
| # Hardcoded for now | |
| for t, num_inference_steps in zip([99, 39, 39, 19], [10, 25, 26, 50]): | |
| self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps) | |
| # Override test_add_noise_device because the hardcoded num_inference_steps of 100 doesn't work | |
| # for LCMScheduler under default settings | |
| def test_add_noise_device(self, num_inference_steps=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) | |
| sample = self.dummy_sample.to(torch_device) | |
| scaled_sample = scheduler.scale_model_input(sample, 0.0) | |
| self.assertEqual(sample.shape, scaled_sample.shape) | |
| noise = torch.randn(scaled_sample.shape).to(torch_device) | |
| t = scheduler.timesteps[5][None] | |
| noised = scheduler.add_noise(scaled_sample, noise, t) | |
| self.assertEqual(noised.shape, scaled_sample.shape) | |
| # Override test_from_save_pretrained because it hardcodes a timestep of 1 | |
| 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: | |
| timestep = self.default_valid_timestep | |
| 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) | |
| kwargs["generator"] = torch.manual_seed(0) | |
| output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample | |
| kwargs["generator"] = torch.manual_seed(0) | |
| new_output = new_scheduler.step(residual, timestep, sample, **kwargs).prev_sample | |
| assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" | |
| # Override test_step_shape because uses 0 and 1 as hardcoded timesteps | |
| 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 | |
| scheduler.set_timesteps(num_inference_steps) | |
| timestep_0 = scheduler.timesteps[-2] | |
| timestep_1 = scheduler.timesteps[-1] | |
| output_0 = scheduler.step(residual, timestep_0, sample, **kwargs).prev_sample | |
| output_1 = scheduler.step(residual, timestep_1, sample, **kwargs).prev_sample | |
| self.assertEqual(output_0.shape, sample.shape) | |
| self.assertEqual(output_0.shape, output_1.shape) | |
| # Override test_set_scheduler_outputs_equivalence since it uses 0 as a hardcoded timestep | |
| 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 = self.default_valid_timestep | |
| 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 | |
| scheduler.set_timesteps(num_inference_steps) | |
| kwargs["generator"] = torch.manual_seed(0) | |
| outputs_dict = scheduler.step(residual, timestep, sample, **kwargs) | |
| scheduler.set_timesteps(num_inference_steps) | |
| kwargs["generator"] = torch.manual_seed(0) | |
| outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs) | |
| recursive_check(outputs_tuple, outputs_dict) | |
| def full_loop(self, num_inference_steps=10, seed=0, **config): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config(**config) | |
| scheduler = scheduler_class(**scheduler_config) | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter | |
| generator = torch.manual_seed(seed) | |
| scheduler.set_timesteps(num_inference_steps) | |
| for t in scheduler.timesteps: | |
| residual = model(sample, t) | |
| sample = scheduler.step(residual, t, sample, generator).prev_sample | |
| return sample | |
| def test_full_loop_onestep(self): | |
| sample = self.full_loop(num_inference_steps=1) | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| # TODO: get expected sum and mean | |
| assert abs(result_sum.item() - 18.7097) < 1e-3 | |
| assert abs(result_mean.item() - 0.0244) < 1e-3 | |
| def test_full_loop_multistep(self): | |
| sample = self.full_loop(num_inference_steps=10) | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| # TODO: get expected sum and mean | |
| assert abs(result_sum.item() - 197.7616) < 1e-3 | |
| assert abs(result_mean.item() - 0.2575) < 1e-3 | |
| def test_custom_timesteps(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| timesteps = [100, 87, 50, 1, 0] | |
| scheduler.set_timesteps(timesteps=timesteps) | |
| scheduler_timesteps = scheduler.timesteps | |
| for i, timestep in enumerate(scheduler_timesteps): | |
| if i == len(timesteps) - 1: | |
| expected_prev_t = -1 | |
| else: | |
| expected_prev_t = timesteps[i + 1] | |
| prev_t = scheduler.previous_timestep(timestep) | |
| prev_t = prev_t.item() | |
| self.assertEqual(prev_t, expected_prev_t) | |
| def test_custom_timesteps_increasing_order(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| timesteps = [100, 87, 50, 51, 0] | |
| with self.assertRaises(ValueError, msg="`custom_timesteps` must be in descending order."): | |
| scheduler.set_timesteps(timesteps=timesteps) | |
| def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| timesteps = [100, 87, 50, 1, 0] | |
| num_inference_steps = len(timesteps) | |
| with self.assertRaises(ValueError, msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."): | |
| scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps) | |
| def test_custom_timesteps_too_large(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| timesteps = [scheduler.config.num_train_timesteps] | |
| with self.assertRaises( | |
| ValueError, | |
| msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}", | |
| ): | |
| scheduler.set_timesteps(timesteps=timesteps) | |