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test_scheduler_euler_ancestral.py
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importtorch
fromdiffusersimportEulerAncestralDiscreteScheduler
fromdiffusers.utils.testing_utilsimporttorch_device
from .test_schedulersimportSchedulerCommonTest
classEulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
scheduler_classes= (EulerAncestralDiscreteScheduler,)
num_inference_steps=10
defget_scheduler_config(self, **kwargs):
config= {
"num_train_timesteps": 1100,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**kwargs)
returnconfig
deftest_timesteps(self):
fortimestepsin [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=timesteps)
deftest_betas(self):
forbeta_start, beta_endinzip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
deftest_schedules(self):
forschedulein ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=schedule)
deftest_prediction_type(self):
forprediction_typein ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=prediction_type)
deftest_rescale_betas_zero_snr(self):
forrescale_betas_zero_snrin [True, False]:
self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)
deftest_full_loop_no_noise(self):
scheduler_class=self.scheduler_classes[0]
scheduler_config=self.get_scheduler_config()
scheduler=scheduler_class(**scheduler_config)
scheduler.set_timesteps(self.num_inference_steps)
generator=torch.manual_seed(0)
model=self.dummy_model()
sample=self.dummy_sample_deter*scheduler.init_noise_sigma.cpu()
sample=sample.to(torch_device)
fori, tinenumerate(scheduler.timesteps):
sample=scheduler.scale_model_input(sample, t)
model_output=model(sample, t)
output=scheduler.step(model_output, t, sample, generator=generator)
sample=output.prev_sample
result_sum=torch.sum(torch.abs(sample))
result_mean=torch.mean(torch.abs(sample))
assertabs(result_sum.item() -152.3192) <1e-2
assertabs(result_mean.item() -0.1983) <1e-3
deftest_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)
scheduler.set_timesteps(self.num_inference_steps)
generator=torch.manual_seed(0)
model=self.dummy_model()
sample=self.dummy_sample_deter*scheduler.init_noise_sigma
sample=sample.to(torch_device)
fori, tinenumerate(scheduler.timesteps):
sample=scheduler.scale_model_input(sample, t)
model_output=model(sample, t)
output=scheduler.step(model_output, t, sample, generator=generator)
sample=output.prev_sample
result_sum=torch.sum(torch.abs(sample))
result_mean=torch.mean(torch.abs(sample))
assertabs(result_sum.item() -108.4439) <1e-2
assertabs(result_mean.item() -0.1412) <1e-3
deftest_full_loop_device(self):
scheduler_class=self.scheduler_classes[0]
scheduler_config=self.get_scheduler_config()
scheduler=scheduler_class(**scheduler_config)
scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
generator=torch.manual_seed(0)
model=self.dummy_model()
sample=self.dummy_sample_deter*scheduler.init_noise_sigma.cpu()
sample=sample.to(torch_device)
fortinscheduler.timesteps:
sample=scheduler.scale_model_input(sample, t)
model_output=model(sample, t)
output=scheduler.step(model_output, t, sample, generator=generator)
sample=output.prev_sample
result_sum=torch.sum(torch.abs(sample))
result_mean=torch.mean(torch.abs(sample))
assertabs(result_sum.item() -152.3192) <1e-2
assertabs(result_mean.item() -0.1983) <1e-3
deftest_full_loop_with_noise(self):
scheduler_class=self.scheduler_classes[0]
scheduler_config=self.get_scheduler_config()
scheduler=scheduler_class(**scheduler_config)
t_start=self.num_inference_steps-2
scheduler.set_timesteps(self.num_inference_steps)
generator=torch.manual_seed(0)
model=self.dummy_model()
sample=self.dummy_sample_deter*scheduler.init_noise_sigma
# add noise
noise=self.dummy_noise_deter
noise=noise.to(sample.device)
timesteps=scheduler.timesteps[t_start*scheduler.order :]
sample=scheduler.add_noise(sample, noise, timesteps[:1])
fori, tinenumerate(timesteps):
sample=scheduler.scale_model_input(sample, t)
model_output=model(sample, t)
output=scheduler.step(model_output, t, sample, generator=generator)
sample=output.prev_sample
result_sum=torch.sum(torch.abs(sample))
result_mean=torch.mean(torch.abs(sample))
assertabs(result_sum.item() -56163.0508) <1e-2, f" expected result sum 56163.0508, but get {result_sum}"
assertabs(result_mean.item() -73.1290) <1e-3, f" expected result mean 73.1290, but get {result_mean}"
