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import torch |
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from diffusers import DPMSolverSDEScheduler |
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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 DPMSolverSDESchedulerTest(SchedulerCommonTest): |
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scheduler_classes = (DPMSolverSDEScheduler,) |
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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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"noise_sampler_seed": 0, |
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} |
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config.update(**kwargs) |
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return config |
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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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for i, t in enumerate(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) |
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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 ["mps"]: |
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assert abs(result_sum.item() - 167.47821044921875) < 1e-2 |
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assert abs(result_mean.item() - 0.2178705964565277) < 1e-3 |
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elif torch_device in ["cuda"]: |
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assert abs(result_sum.item() - 171.59352111816406) < 1e-2 |
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assert abs(result_mean.item() - 0.22342906892299652) < 1e-3 |
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else: |
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assert abs(result_sum.item() - 162.52383422851562) < 1e-2 |
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assert abs(result_mean.item() - 0.211619570851326) < 1e-3 |
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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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for i, t in enumerate(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) |
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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 ["mps"]: |
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assert abs(result_sum.item() - 124.77149200439453) < 1e-2 |
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assert abs(result_mean.item() - 0.16226289014816284) < 1e-3 |
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elif torch_device in ["cuda"]: |
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assert abs(result_sum.item() - 128.1663360595703) < 1e-2 |
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assert abs(result_mean.item() - 0.16688326001167297) < 1e-3 |
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else: |
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assert abs(result_sum.item() - 119.8487548828125) < 1e-2 |
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assert abs(result_mean.item() - 0.1560530662536621) < 1e-3 |
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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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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) |
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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 ["mps"]: |
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assert abs(result_sum.item() - 167.46957397460938) < 1e-2 |
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assert abs(result_mean.item() - 0.21805934607982635) < 1e-3 |
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elif torch_device in ["cuda"]: |
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assert abs(result_sum.item() - 171.59353637695312) < 1e-2 |
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assert abs(result_mean.item() - 0.22342908382415771) < 1e-3 |
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else: |
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assert abs(result_sum.item() - 162.52383422851562) < 1e-2 |
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assert abs(result_mean.item() - 0.211619570851326) < 1e-3 |
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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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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) |
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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 ["mps"]: |
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assert abs(result_sum.item() - 176.66974135742188) < 1e-2 |
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assert abs(result_mean.item() - 0.23003872730981811) < 1e-2 |
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elif torch_device in ["cuda"]: |
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assert abs(result_sum.item() - 177.63653564453125) < 1e-2 |
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assert abs(result_mean.item() - 0.23003872730981811) < 1e-2 |
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else: |
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assert abs(result_sum.item() - 170.3135223388672) < 1e-2 |
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assert abs(result_mean.item() - 0.23003872730981811) < 1e-2 |
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