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import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
# UnCLIPScheduler is a modified DDPMScheduler with a subset of the configuration.
class UnCLIPSchedulerTest(SchedulerCommonTest):
scheduler_classes = (UnCLIPScheduler,)
def get_scheduler_config(self, **kwargs):
config = {
"num_train_timesteps": 1000,
"variance_type": "fixed_small_log",
"clip_sample": True,
"clip_sample_range": 1.0,
"prediction_type": "epsilon",
}
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_variance_type(self):
for variance in ["fixed_small_log", "learned_range"]:
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_clip_sample_range(self):
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=clip_sample_range)
def test_prediction_type(self):
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=prediction_type)
def test_time_indices(self):
for time_step in [0, 500, 999]:
for prev_timestep in [None, 5, 100, 250, 500, 750]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=time_step, prev_timestep=prev_timestep)
def test_variance_fixed_small_log(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config(variance_type="fixed_small_log")
scheduler = scheduler_class(**scheduler_config)
assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0000e-10)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0549625)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9994987)) < 1e-5
def test_variance_learned_range(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config(variance_type="learned_range")
scheduler = scheduler_class(**scheduler_config)
predicted_variance = 0.5
assert scheduler._get_variance(1, predicted_variance=predicted_variance) - -10.1712790 < 1e-5
assert scheduler._get_variance(487, predicted_variance=predicted_variance) - -5.7998052 < 1e-5
assert scheduler._get_variance(999, predicted_variance=predicted_variance) - -0.0010011 < 1e-5
def test_full_loop(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
timesteps = scheduler.timesteps
model = self.dummy_model()
sample = self.dummy_sample_deter
generator = torch.manual_seed(0)
for i, t in enumerate(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() - 252.2682495) < 1e-2
assert abs(result_mean.item() - 0.3284743) < 1e-3
def test_full_loop_skip_timesteps(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(25)
timesteps = scheduler.timesteps
model = self.dummy_model()
sample = self.dummy_sample_deter
generator = torch.manual_seed(0)
for i, t in enumerate(timesteps):
# 1. predict noise residual
residual = model(sample, t)
if i + 1 == timesteps.shape[0]:
prev_timestep = None
else:
prev_timestep = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
pred_prev_sample = scheduler.step(
residual, t, sample, prev_timestep=prev_timestep, 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() - 258.2044983) < 1e-2
assert abs(result_mean.item() - 0.3362038) < 1e-3
def test_trained_betas(self):
pass
def test_add_noise_device(self):
pass
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