# Copyright 2023 ParaDiGMS authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class DDIMParallelSchedulerTest(SchedulerCommonTest):
scheduler_classes = (DDIMParallelScheduler,)
forward_default_kwargs = (("eta", 0.0), ("num_inference_steps", 50))
def get_scheduler_config(self, **kwargs):
config = {
"num_train_timesteps": 1000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"clip_sample": True,
}
config.update(**kwargs)
return config
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, eta = 10, 0.0
model = self.dummy_model()
sample = self.dummy_sample_deter
scheduler.set_timesteps(num_inference_steps)
for t in scheduler.timesteps:
residual = model(sample, t)
sample = scheduler.step(residual, t, sample, eta).prev_sample
return sample
def test_timesteps(self):
for timesteps in [100, 500, 1000]:
self.check_over_configs(num_train_timesteps=timesteps)
def test_steps_offset(self):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=steps_offset)
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config(steps_offset=1)
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(5)
assert torch.equal(scheduler.timesteps, torch.LongTensor([801, 601, 401, 201, 1]))
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_prediction_type(self):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=prediction_type)
def test_clip_sample(self):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=clip_sample)
def test_timestep_spacing(self):
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=timestep_spacing)
def test_rescale_betas_zero_snr(self):
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)
def test_thresholding(self):
self.check_over_configs(thresholding=False)
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=True,
prediction_type=prediction_type,
sample_max_value=threshold,
)
def test_time_indices(self):
for t in [1, 10, 49]:
self.check_over_forward(time_step=t)
def test_inference_steps(self):
for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]):
self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)
def test_eta(self):
for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]):
self.check_over_forward(time_step=t, eta=eta)
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) - 0.0)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(420, 400) - 0.14771)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(980, 960) - 0.32460)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487, 486) - 0.00979)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999, 998) - 0.02)) < 1e-5
def test_batch_step_no_noise(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
num_inference_steps, eta = 10, 0.0
scheduler.set_timesteps(num_inference_steps)
model = self.dummy_model()
sample1 = self.dummy_sample_deter
sample2 = self.dummy_sample_deter + 0.1
sample3 = self.dummy_sample_deter - 0.1
per_sample_batch = sample1.shape[0]
samples = torch.stack([sample1, sample2, sample3], dim=0)
timesteps = torch.arange(num_inference_steps)[0:3, None].repeat(1, per_sample_batch)
residual = model(samples.flatten(0, 1), timesteps.flatten(0, 1))
pred_prev_sample = scheduler.batch_step_no_noise(residual, timesteps.flatten(0, 1), samples.flatten(0, 1), eta)
result_sum = torch.sum(torch.abs(pred_prev_sample))
result_mean = torch.mean(torch.abs(pred_prev_sample))
assert abs(result_sum.item() - 1147.7904) < 1e-2
assert abs(result_mean.item() - 0.4982) < 1e-3
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))
assert abs(result_sum.item() - 172.0067) < 1e-2
assert abs(result_mean.item() - 0.223967) < 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))
assert abs(result_sum.item() - 52.5302) < 1e-2
assert abs(result_mean.item() - 0.0684) < 1e-3
def test_full_loop_with_set_alpha_to_one(self):
# We specify different beta, so that the first alpha is 0.99
sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
assert abs(result_sum.item() - 149.8295) < 1e-2
assert abs(result_mean.item() - 0.1951) < 1e-3
def test_full_loop_with_no_set_alpha_to_one(self):
# We specify different beta, so that the first alpha is 0.99
sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
assert abs(result_sum.item() - 149.0784) < 1e-2
assert abs(result_mean.item() - 0.1941) < 1e-3