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 @property 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, 19], [10, 25, 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_like(scaled_sample).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-2 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() - 280.5618) < 1e-2 assert abs(result_mean.item() - 0.3653) < 1e-3