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import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class DDPMSchedulerTest(SchedulerCommonTest):
scheduler_classes = (DDPMScheduler,)
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_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
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
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
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
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
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