# Copyright 2024 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 DDPMParallelScheduler from .test_schedulers import SchedulerCommonTest class DDPMParallelSchedulerTest(SchedulerCommonTest): scheduler_classes = (DDPMParallelScheduler,) def get_scheduler_config(self, **kwargs): config = { "num_train_timesteps": 1000, "beta_start": 0.0001, "beta_end": 0.02, "beta_schedule": "linear", "variance_type": "fixed_small", "clip_sample": True, } config.update(**kwargs) return config def test_timesteps(self): for timesteps in [1, 5, 100, 1000]: self.check_over_configs(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(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_variance_type(self): for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=variance) def test_clip_sample(self): for clip_sample in [True, False]: self.check_over_configs(clip_sample=clip_sample) def test_thresholding(self): self.check_over_configs(thresholding=False) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=True, prediction_type=prediction_type, sample_max_value=threshold, ) def test_prediction_type(self): for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=prediction_type) def test_time_indices(self): for t in [0, 500, 999]: self.check_over_forward(time_step=t) 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)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.00979)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5 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_batch_step_no_noise(self): scheduler_class = self.scheduler_classes[0] scheduler_config = self.get_scheduler_config() scheduler = scheduler_class(**scheduler_config) num_trained_timesteps = len(scheduler) 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_trained_timesteps)[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)) result_sum = torch.sum(torch.abs(pred_prev_sample)) result_mean = torch.mean(torch.abs(pred_prev_sample)) assert abs(result_sum.item() - 1153.1833) < 1e-2 assert abs(result_mean.item() - 0.5005) < 1e-3 def test_full_loop_no_noise(self): scheduler_class = self.scheduler_classes[0] scheduler_config = self.get_scheduler_config() scheduler = scheduler_class(**scheduler_config) num_trained_timesteps = len(scheduler) model = self.dummy_model() sample = self.dummy_sample_deter generator = torch.manual_seed(0) for t in reversed(range(num_trained_timesteps)): # 1. predict noise residual residual = model(sample, t) # 2. predict previous mean of sample x_t-1 pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample sample = pred_prev_sample result_sum = torch.sum(torch.abs(sample)) result_mean = torch.mean(torch.abs(sample)) assert abs(result_sum.item() - 258.9606) < 1e-2 assert abs(result_mean.item() - 0.3372) < 1e-3 def test_full_loop_with_v_prediction(self): scheduler_class = self.scheduler_classes[0] scheduler_config = self.get_scheduler_config(prediction_type="v_prediction") scheduler = scheduler_class(**scheduler_config) num_trained_timesteps = len(scheduler) model = self.dummy_model() sample = self.dummy_sample_deter generator = torch.manual_seed(0) for t in reversed(range(num_trained_timesteps)): # 1. predict noise residual residual = model(sample, t) # 2. predict previous mean of sample x_t-1 pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample sample = pred_prev_sample result_sum = torch.sum(torch.abs(sample)) result_mean = torch.mean(torch.abs(sample)) assert abs(result_sum.item() - 202.0296) < 1e-2 assert abs(result_mean.item() - 0.2631) < 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) def test_full_loop_with_noise(self): scheduler_class = self.scheduler_classes[0] scheduler_config = self.get_scheduler_config() scheduler = scheduler_class(**scheduler_config) num_trained_timesteps = len(scheduler) t_start = num_trained_timesteps - 2 model = self.dummy_model() sample = self.dummy_sample_deter generator = torch.manual_seed(0) # add noise noise = self.dummy_noise_deter timesteps = scheduler.timesteps[t_start * scheduler.order :] sample = scheduler.add_noise(sample, noise, timesteps[:1]) for t in timesteps: # 1. predict noise residual residual = model(sample, t) # 2. predict previous mean of sample x_t-1 pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample sample = pred_prev_sample result_sum = torch.sum(torch.abs(sample)) result_mean = torch.mean(torch.abs(sample)) assert abs(result_sum.item() - 387.9466) < 1e-2, f" expected result sum 387.9466, but get {result_sum}" assert abs(result_mean.item() - 0.5051) < 1e-3, f" expected result mean 0.5051, but get {result_mean}"