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import torch
from diffusers import HeunDiscreteScheduler
from diffusers.utils.testing_utils import torch_device
from .test_schedulers import SchedulerCommonTest
class HeunDiscreteSchedulerTest(SchedulerCommonTest):
scheduler_classes = (HeunDiscreteScheduler,)
num_inference_steps = 10
def get_scheduler_config(self, **kwargs):
config = {
"num_train_timesteps": 1100,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**kwargs)
return config
def test_timesteps(self):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=timesteps)
def test_betas(self):
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
def test_schedules(self):
for schedule in ["linear", "scaled_linear", "exp"]:
self.check_over_configs(beta_schedule=schedule)
def test_clip_sample(self):
for clip_sample_range in [1.0, 2.0, 3.0]:
self.check_over_configs(clip_sample_range=clip_sample_range, clip_sample=True)
def test_prediction_type(self):
for prediction_type in ["epsilon", "v_prediction", "sample"]:
self.check_over_configs(prediction_type=prediction_type)
def full_loop(self, **config):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config(**config)
scheduler = scheduler_class(**scheduler_config)
num_inference_steps = self.num_inference_steps
scheduler.set_timesteps(num_inference_steps)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
sample = sample.to(torch_device)
for i, t in enumerate(scheduler.timesteps):
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
return sample
def full_loop_custom_timesteps(self, **config):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config(**config)
scheduler = scheduler_class(**scheduler_config)
num_inference_steps = self.num_inference_steps
scheduler.set_timesteps(num_inference_steps)
timesteps = scheduler.timesteps
timesteps = torch.cat([timesteps[:1], timesteps[1::2]])
# reset the timesteps using `timesteps`
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(num_inference_steps=None, timesteps=timesteps)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
sample = sample.to(torch_device)
for i, t in enumerate(scheduler.timesteps):
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
return sample
def test_full_loop_no_noise(self):
sample = self.full_loop()
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 0.1233) < 1e-2
assert abs(result_mean.item() - 0.0002) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 0.1233) < 1e-2
assert abs(result_mean.item() - 0.0002) < 1e-3
def test_full_loop_with_v_prediction(self):
sample = self.full_loop(prediction_type="v_prediction")
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.6934e-07) < 1e-2
assert abs(result_mean.item() - 6.1112e-10) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2
assert abs(result_mean.item() - 0.0002) < 1e-3
def test_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)
model = self.dummy_model()
sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
if str(torch_device).startswith("cpu"):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 0.1233) < 1e-2
assert abs(result_mean.item() - 0.0002) < 1e-3
elif str(torch_device).startswith("mps"):
# Larger tolerance on mps
assert abs(result_mean.item() - 0.0002) < 1e-2
else:
# CUDA
assert abs(result_sum.item() - 0.1233) < 1e-2
assert abs(result_mean.item() - 0.0002) < 1e-3
def test_full_loop_device_karras_sigmas(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True)
scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
model = self.dummy_model()
sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
sample = sample.to(torch_device)
for t in scheduler.timesteps:
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
assert abs(result_sum.item() - 0.00015) < 1e-2
assert abs(result_mean.item() - 1.9869554535034695e-07) < 1e-2
def test_full_loop_with_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)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
sample = sample.to(torch_device)
t_start = self.num_inference_steps - 2
noise = self.dummy_noise_deter
noise = noise.to(torch_device)
timesteps = scheduler.timesteps[t_start * scheduler.order :]
sample = scheduler.add_noise(sample, noise, timesteps[:1])
for i, t in enumerate(timesteps):
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
assert abs(result_sum.item() - 75074.8906) < 1e-2, f" expected result sum 75074.8906, but get {result_sum}"
assert abs(result_mean.item() - 97.7538) < 1e-3, f" expected result mean 97.7538, but get {result_mean}"
def test_custom_timesteps(self):
for prediction_type in ["epsilon", "sample", "v_prediction"]:
for timestep_spacing in ["linspace", "leading"]:
sample = self.full_loop(
prediction_type=prediction_type,
timestep_spacing=timestep_spacing,
)
sample_custom_timesteps = self.full_loop_custom_timesteps(
prediction_type=prediction_type,
timestep_spacing=timestep_spacing,
)
assert (
torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5
), f"Scheduler outputs are not identical for prediction_type: {prediction_type}, timestep_spacing: {timestep_spacing}"
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