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# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNet2DModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
torch.backends.cuda.matmul.allow_tf32 = False
class TrainingTests(unittest.TestCase):
def get_model_optimizer(self, resolution=32):
set_seed(0)
model = UNet2DModel(sample_size=resolution, in_channels=3, out_channels=3)
optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)
return model, optimizer
@slow
def test_training_step_equality(self):
device = "cpu" # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
ddpm_scheduler = DDPMScheduler(
num_train_timesteps=1000,
beta_start=0.0001,
beta_end=0.02,
beta_schedule="linear",
clip_sample=True,
)
ddim_scheduler = DDIMScheduler(
num_train_timesteps=1000,
beta_start=0.0001,
beta_end=0.02,
beta_schedule="linear",
clip_sample=True,
)
assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps
# shared batches for DDPM and DDIM
set_seed(0)
clean_images = [torch.randn((4, 3, 32, 32)).clip(-1, 1).to(device) for _ in range(4)]
noise = [torch.randn((4, 3, 32, 32)).to(device) for _ in range(4)]
timesteps = [torch.randint(0, 1000, (4,)).long().to(device) for _ in range(4)]
# train with a DDPM scheduler
model, optimizer = self.get_model_optimizer(resolution=32)
model.train().to(device)
for i in range(4):
optimizer.zero_grad()
ddpm_noisy_images = ddpm_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])
ddpm_noise_pred = model(ddpm_noisy_images, timesteps[i]).sample
loss = torch.nn.functional.mse_loss(ddpm_noise_pred, noise[i])
loss.backward()
optimizer.step()
del model, optimizer
# recreate the model and optimizer, and retry with DDIM
model, optimizer = self.get_model_optimizer(resolution=32)
model.train().to(device)
for i in range(4):
optimizer.zero_grad()
ddim_noisy_images = ddim_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])
ddim_noise_pred = model(ddim_noisy_images, timesteps[i]).sample
loss = torch.nn.functional.mse_loss(ddim_noise_pred, noise[i])
loss.backward()
optimizer.step()
del model, optimizer
self.assertTrue(torch.allclose(ddpm_noisy_images, ddim_noisy_images, atol=1e-5))
self.assertTrue(torch.allclose(ddpm_noise_pred, ddim_noise_pred, atol=1e-5))
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