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import torch |
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from diffusers import SASolverScheduler |
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from diffusers.utils.testing_utils import require_torchsde, torch_device |
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from .test_schedulers import SchedulerCommonTest |
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@require_torchsde |
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class SASolverSchedulerTest(SchedulerCommonTest): |
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scheduler_classes = (SASolverScheduler,) |
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forward_default_kwargs = (("num_inference_steps", 10),) |
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num_inference_steps = 10 |
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def get_scheduler_config(self, **kwargs): |
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config = { |
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"num_train_timesteps": 1100, |
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"beta_start": 0.0001, |
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"beta_end": 0.02, |
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"beta_schedule": "linear", |
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} |
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config.update(**kwargs) |
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return config |
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def test_step_shape(self): |
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kwargs = dict(self.forward_default_kwargs) |
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num_inference_steps = kwargs.pop("num_inference_steps", None) |
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for scheduler_class in self.scheduler_classes: |
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scheduler_config = self.get_scheduler_config() |
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scheduler = scheduler_class(**scheduler_config) |
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sample = self.dummy_sample |
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residual = 0.1 * sample |
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): |
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scheduler.set_timesteps(num_inference_steps) |
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): |
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kwargs["num_inference_steps"] = num_inference_steps |
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dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] |
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scheduler.model_outputs = dummy_past_residuals[ |
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: max( |
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scheduler.config.predictor_order, |
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scheduler.config.corrector_order - 1, |
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) |
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] |
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time_step_0 = scheduler.timesteps[5] |
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time_step_1 = scheduler.timesteps[6] |
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output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample |
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output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample |
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self.assertEqual(output_0.shape, sample.shape) |
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self.assertEqual(output_0.shape, output_1.shape) |
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def test_timesteps(self): |
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for timesteps in [10, 50, 100, 1000]: |
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self.check_over_configs(num_train_timesteps=timesteps) |
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def test_betas(self): |
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for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]): |
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self.check_over_configs(beta_start=beta_start, beta_end=beta_end) |
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def test_schedules(self): |
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for schedule in ["linear", "scaled_linear"]: |
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self.check_over_configs(beta_schedule=schedule) |
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def test_prediction_type(self): |
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for prediction_type in ["epsilon", "v_prediction"]: |
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self.check_over_configs(prediction_type=prediction_type) |
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def test_full_loop_no_noise(self): |
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scheduler_class = self.scheduler_classes[0] |
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scheduler_config = self.get_scheduler_config() |
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scheduler = scheduler_class(**scheduler_config) |
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scheduler.set_timesteps(self.num_inference_steps) |
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model = self.dummy_model() |
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sample = self.dummy_sample_deter * scheduler.init_noise_sigma |
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sample = sample.to(torch_device) |
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generator = torch.manual_seed(0) |
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for i, t in enumerate(scheduler.timesteps): |
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sample = scheduler.scale_model_input(sample, t, generator=generator) |
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model_output = model(sample, t) |
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output = scheduler.step(model_output, t, sample) |
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sample = output.prev_sample |
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result_sum = torch.sum(torch.abs(sample)) |
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result_mean = torch.mean(torch.abs(sample)) |
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if torch_device in ["cpu"]: |
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assert abs(result_sum.item() - 337.394287109375) < 1e-2 |
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assert abs(result_mean.item() - 0.43931546807289124) < 1e-3 |
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elif torch_device in ["cuda"]: |
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assert abs(result_sum.item() - 329.1999816894531) < 1e-2 |
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assert abs(result_mean.item() - 0.4286458194255829) < 1e-3 |
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else: |
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print("None") |
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def test_full_loop_with_v_prediction(self): |
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scheduler_class = self.scheduler_classes[0] |
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scheduler_config = self.get_scheduler_config(prediction_type="v_prediction") |
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scheduler = scheduler_class(**scheduler_config) |
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scheduler.set_timesteps(self.num_inference_steps) |
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model = self.dummy_model() |
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sample = self.dummy_sample_deter * scheduler.init_noise_sigma |
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sample = sample.to(torch_device) |
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generator = torch.manual_seed(0) |
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for i, t in enumerate(scheduler.timesteps): |
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sample = scheduler.scale_model_input(sample, t, generator=generator) |
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model_output = model(sample, t) |
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output = scheduler.step(model_output, t, sample) |
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sample = output.prev_sample |
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result_sum = torch.sum(torch.abs(sample)) |
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result_mean = torch.mean(torch.abs(sample)) |
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if torch_device in ["cpu"]: |
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assert abs(result_sum.item() - 193.1467742919922) < 1e-2 |
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assert abs(result_mean.item() - 0.2514931857585907) < 1e-3 |
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elif torch_device in ["cuda"]: |
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assert abs(result_sum.item() - 193.4154052734375) < 1e-2 |
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assert abs(result_mean.item() - 0.2518429756164551) < 1e-3 |
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else: |
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print("None") |
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def test_full_loop_device(self): |
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scheduler_class = self.scheduler_classes[0] |
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scheduler_config = self.get_scheduler_config() |
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scheduler = scheduler_class(**scheduler_config) |
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scheduler.set_timesteps(self.num_inference_steps, device=torch_device) |
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model = self.dummy_model() |
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sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma |
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generator = torch.manual_seed(0) |
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for t in scheduler.timesteps: |
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sample = scheduler.scale_model_input(sample, t) |
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model_output = model(sample, t) |
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output = scheduler.step(model_output, t, sample, generator=generator) |
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sample = output.prev_sample |
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result_sum = torch.sum(torch.abs(sample)) |
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result_mean = torch.mean(torch.abs(sample)) |
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if torch_device in ["cpu"]: |
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assert abs(result_sum.item() - 337.394287109375) < 1e-2 |
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assert abs(result_mean.item() - 0.43931546807289124) < 1e-3 |
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elif torch_device in ["cuda"]: |
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assert abs(result_sum.item() - 337.394287109375) < 1e-2 |
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assert abs(result_mean.item() - 0.4393154978752136) < 1e-3 |
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else: |
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print("None") |
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def test_full_loop_device_karras_sigmas(self): |
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scheduler_class = self.scheduler_classes[0] |
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scheduler_config = self.get_scheduler_config() |
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scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True) |
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scheduler.set_timesteps(self.num_inference_steps, device=torch_device) |
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model = self.dummy_model() |
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sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma |
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sample = sample.to(torch_device) |
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generator = torch.manual_seed(0) |
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for t in scheduler.timesteps: |
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sample = scheduler.scale_model_input(sample, t) |
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model_output = model(sample, t) |
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output = scheduler.step(model_output, t, sample, generator=generator) |
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sample = output.prev_sample |
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result_sum = torch.sum(torch.abs(sample)) |
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result_mean = torch.mean(torch.abs(sample)) |
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if torch_device in ["cpu"]: |
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assert abs(result_sum.item() - 837.2554931640625) < 1e-2 |
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assert abs(result_mean.item() - 1.0901764631271362) < 1e-2 |
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elif torch_device in ["cuda"]: |
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assert abs(result_sum.item() - 837.25537109375) < 1e-2 |
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assert abs(result_mean.item() - 1.0901763439178467) < 1e-2 |
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else: |
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print("None") |
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