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| # Copyright 2023 ParaDiGMS authors and The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import torch | |
| from diffusers import DDIMParallelScheduler | |
| from .test_schedulers import SchedulerCommonTest | |
| class DDIMParallelSchedulerTest(SchedulerCommonTest): | |
| scheduler_classes = (DDIMParallelScheduler,) | |
| forward_default_kwargs = (("eta", 0.0), ("num_inference_steps", 50)) | |
| def get_scheduler_config(self, **kwargs): | |
| config = { | |
| "num_train_timesteps": 1000, | |
| "beta_start": 0.0001, | |
| "beta_end": 0.02, | |
| "beta_schedule": "linear", | |
| "clip_sample": True, | |
| } | |
| config.update(**kwargs) | |
| return config | |
| def full_loop(self, **config): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config(**config) | |
| scheduler = scheduler_class(**scheduler_config) | |
| num_inference_steps, eta = 10, 0.0 | |
| model = self.dummy_model() | |
| sample = self.dummy_sample_deter | |
| scheduler.set_timesteps(num_inference_steps) | |
| for t in scheduler.timesteps: | |
| residual = model(sample, t) | |
| sample = scheduler.step(residual, t, sample, eta).prev_sample | |
| return sample | |
| def test_timesteps(self): | |
| for timesteps in [100, 500, 1000]: | |
| self.check_over_configs(num_train_timesteps=timesteps) | |
| def test_steps_offset(self): | |
| for steps_offset in [0, 1]: | |
| self.check_over_configs(steps_offset=steps_offset) | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config(steps_offset=1) | |
| scheduler = scheduler_class(**scheduler_config) | |
| scheduler.set_timesteps(5) | |
| assert torch.equal(scheduler.timesteps, torch.LongTensor([801, 601, 401, 201, 1])) | |
| 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(beta_start=beta_start, beta_end=beta_end) | |
| def test_schedules(self): | |
| for schedule in ["linear", "squaredcos_cap_v2"]: | |
| self.check_over_configs(beta_schedule=schedule) | |
| def test_prediction_type(self): | |
| for prediction_type in ["epsilon", "v_prediction"]: | |
| self.check_over_configs(prediction_type=prediction_type) | |
| def test_clip_sample(self): | |
| for clip_sample in [True, False]: | |
| self.check_over_configs(clip_sample=clip_sample) | |
| def test_timestep_spacing(self): | |
| for timestep_spacing in ["trailing", "leading"]: | |
| self.check_over_configs(timestep_spacing=timestep_spacing) | |
| def test_rescale_betas_zero_snr(self): | |
| for rescale_betas_zero_snr in [True, False]: | |
| self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) | |
| def test_thresholding(self): | |
| self.check_over_configs(thresholding=False) | |
| for threshold in [0.5, 1.0, 2.0]: | |
| for prediction_type in ["epsilon", "v_prediction"]: | |
| self.check_over_configs( | |
| thresholding=True, | |
| prediction_type=prediction_type, | |
| sample_max_value=threshold, | |
| ) | |
| def test_time_indices(self): | |
| for t in [1, 10, 49]: | |
| self.check_over_forward(time_step=t) | |
| def test_inference_steps(self): | |
| for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]): | |
| self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps) | |
| def test_eta(self): | |
| for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]): | |
| self.check_over_forward(time_step=t, eta=eta) | |
| def test_variance(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5 | |
| assert torch.sum(torch.abs(scheduler._get_variance(420, 400) - 0.14771)) < 1e-5 | |
| assert torch.sum(torch.abs(scheduler._get_variance(980, 960) - 0.32460)) < 1e-5 | |
| assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5 | |
| assert torch.sum(torch.abs(scheduler._get_variance(487, 486) - 0.00979)) < 1e-5 | |
| assert torch.sum(torch.abs(scheduler._get_variance(999, 998) - 0.02)) < 1e-5 | |
| def test_batch_step_no_noise(self): | |
| scheduler_class = self.scheduler_classes[0] | |
| scheduler_config = self.get_scheduler_config() | |
| scheduler = scheduler_class(**scheduler_config) | |
| num_inference_steps, eta = 10, 0.0 | |
| scheduler.set_timesteps(num_inference_steps) | |
| model = self.dummy_model() | |
| sample1 = self.dummy_sample_deter | |
| sample2 = self.dummy_sample_deter + 0.1 | |
| sample3 = self.dummy_sample_deter - 0.1 | |
| per_sample_batch = sample1.shape[0] | |
| samples = torch.stack([sample1, sample2, sample3], dim=0) | |
| timesteps = torch.arange(num_inference_steps)[0:3, None].repeat(1, per_sample_batch) | |
| residual = model(samples.flatten(0, 1), timesteps.flatten(0, 1)) | |
| pred_prev_sample = scheduler.batch_step_no_noise(residual, timesteps.flatten(0, 1), samples.flatten(0, 1), eta) | |
| result_sum = torch.sum(torch.abs(pred_prev_sample)) | |
| result_mean = torch.mean(torch.abs(pred_prev_sample)) | |
| assert abs(result_sum.item() - 1147.7904) < 1e-2 | |
| assert abs(result_mean.item() - 0.4982) < 1e-3 | |
| def test_full_loop_no_noise(self): | |
| sample = self.full_loop() | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 172.0067) < 1e-2 | |
| assert abs(result_mean.item() - 0.223967) < 1e-3 | |
| def test_full_loop_with_v_prediction(self): | |
| sample = self.full_loop(prediction_type="v_prediction") | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 52.5302) < 1e-2 | |
| assert abs(result_mean.item() - 0.0684) < 1e-3 | |
| def test_full_loop_with_set_alpha_to_one(self): | |
| # We specify different beta, so that the first alpha is 0.99 | |
| sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01) | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 149.8295) < 1e-2 | |
| assert abs(result_mean.item() - 0.1951) < 1e-3 | |
| def test_full_loop_with_no_set_alpha_to_one(self): | |
| # We specify different beta, so that the first alpha is 0.99 | |
| sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01) | |
| result_sum = torch.sum(torch.abs(sample)) | |
| result_mean = torch.mean(torch.abs(sample)) | |
| assert abs(result_sum.item() - 149.0784) < 1e-2 | |
| assert abs(result_mean.item() - 0.1941) < 1e-3 | |