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import torch |
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from diffusers import DDIMScheduler |
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from .test_schedulers import SchedulerCommonTest |
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class DDIMSchedulerTest(SchedulerCommonTest): |
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scheduler_classes = (DDIMScheduler,) |
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forward_default_kwargs = (("eta", 0.0), ("num_inference_steps", 50)) |
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def get_scheduler_config(self, **kwargs): |
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config = { |
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"num_train_timesteps": 1000, |
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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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"clip_sample": True, |
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} |
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config.update(**kwargs) |
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return config |
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def full_loop(self, **config): |
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scheduler_class = self.scheduler_classes[0] |
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scheduler_config = self.get_scheduler_config(**config) |
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scheduler = scheduler_class(**scheduler_config) |
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num_inference_steps, eta = 10, 0.0 |
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model = self.dummy_model() |
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sample = self.dummy_sample_deter |
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scheduler.set_timesteps(num_inference_steps) |
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for t in scheduler.timesteps: |
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residual = model(sample, t) |
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sample = scheduler.step(residual, t, sample, eta).prev_sample |
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return sample |
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def test_timesteps(self): |
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for timesteps in [100, 500, 1000]: |
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self.check_over_configs(num_train_timesteps=timesteps) |
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def test_steps_offset(self): |
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for steps_offset in [0, 1]: |
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self.check_over_configs(steps_offset=steps_offset) |
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scheduler_class = self.scheduler_classes[0] |
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scheduler_config = self.get_scheduler_config(steps_offset=1) |
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scheduler = scheduler_class(**scheduler_config) |
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scheduler.set_timesteps(5) |
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assert torch.equal(scheduler.timesteps, torch.LongTensor([801, 601, 401, 201, 1])) |
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def test_betas(self): |
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for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]): |
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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", "squaredcos_cap_v2"]: |
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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_clip_sample(self): |
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for clip_sample in [True, False]: |
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self.check_over_configs(clip_sample=clip_sample) |
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def test_timestep_spacing(self): |
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for timestep_spacing in ["trailing", "leading"]: |
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self.check_over_configs(timestep_spacing=timestep_spacing) |
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def test_rescale_betas_zero_snr(self): |
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for rescale_betas_zero_snr in [True, False]: |
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self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) |
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def test_thresholding(self): |
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self.check_over_configs(thresholding=False) |
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for threshold in [0.5, 1.0, 2.0]: |
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for prediction_type in ["epsilon", "v_prediction"]: |
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self.check_over_configs( |
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thresholding=True, |
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prediction_type=prediction_type, |
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sample_max_value=threshold, |
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) |
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def test_time_indices(self): |
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for t in [1, 10, 49]: |
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self.check_over_forward(time_step=t) |
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def test_inference_steps(self): |
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for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]): |
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self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps) |
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def test_eta(self): |
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for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]): |
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self.check_over_forward(time_step=t, eta=eta) |
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def test_variance(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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assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5 |
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assert torch.sum(torch.abs(scheduler._get_variance(420, 400) - 0.14771)) < 1e-5 |
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assert torch.sum(torch.abs(scheduler._get_variance(980, 960) - 0.32460)) < 1e-5 |
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assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5 |
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assert torch.sum(torch.abs(scheduler._get_variance(487, 486) - 0.00979)) < 1e-5 |
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assert torch.sum(torch.abs(scheduler._get_variance(999, 998) - 0.02)) < 1e-5 |
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def test_full_loop_no_noise(self): |
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sample = self.full_loop() |
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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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assert abs(result_sum.item() - 172.0067) < 1e-2 |
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assert abs(result_mean.item() - 0.223967) < 1e-3 |
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def test_full_loop_with_v_prediction(self): |
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sample = self.full_loop(prediction_type="v_prediction") |
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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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assert abs(result_sum.item() - 52.5302) < 1e-2 |
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assert abs(result_mean.item() - 0.0684) < 1e-3 |
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def test_full_loop_with_set_alpha_to_one(self): |
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sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01) |
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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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assert abs(result_sum.item() - 149.8295) < 1e-2 |
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assert abs(result_mean.item() - 0.1951) < 1e-3 |
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def test_full_loop_with_no_set_alpha_to_one(self): |
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sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01) |
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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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assert abs(result_sum.item() - 149.0784) < 1e-2 |
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assert abs(result_mean.item() - 0.1941) < 1e-3 |
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def test_full_loop_with_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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num_inference_steps, eta = 10, 0.0 |
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t_start = 8 |
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model = self.dummy_model() |
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sample = self.dummy_sample_deter |
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scheduler.set_timesteps(num_inference_steps) |
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noise = self.dummy_noise_deter |
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timesteps = scheduler.timesteps[t_start * scheduler.order :] |
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sample = scheduler.add_noise(sample, noise, timesteps[:1]) |
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for t in timesteps: |
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residual = model(sample, t) |
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sample = scheduler.step(residual, t, sample, eta).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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assert abs(result_sum.item() - 354.5418) < 1e-2, f" expected result sum 218.4379, but get {result_sum}" |
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assert abs(result_mean.item() - 0.4616) < 1e-3, f" expected result mean 0.2844, but get {result_mean}" |
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