89 lines
3.4 KiB
Python
89 lines
3.4 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import torch
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from diffusers import DDIMScheduler, DDPMScheduler, UNet2DModel
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from diffusers.testing_utils import slow
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from diffusers.training_utils import set_seed
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torch.backends.cuda.matmul.allow_tf32 = False
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class TrainingTests(unittest.TestCase):
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def get_model_optimizer(self, resolution=32):
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set_seed(0)
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model = UNet2DModel(sample_size=resolution, in_channels=3, out_channels=3)
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optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)
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return model, optimizer
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@slow
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def test_training_step_equality(self):
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device = "cpu" # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
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ddpm_scheduler = DDPMScheduler(
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num_train_timesteps=1000,
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beta_start=0.0001,
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beta_end=0.02,
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beta_schedule="linear",
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clip_sample=True,
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tensor_format="pt",
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)
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ddim_scheduler = DDIMScheduler(
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num_train_timesteps=1000,
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beta_start=0.0001,
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beta_end=0.02,
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beta_schedule="linear",
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clip_sample=True,
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tensor_format="pt",
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)
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assert ddpm_scheduler.num_train_timesteps == ddim_scheduler.num_train_timesteps
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# shared batches for DDPM and DDIM
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set_seed(0)
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clean_images = [torch.randn((4, 3, 32, 32)).clip(-1, 1).to(device) for _ in range(4)]
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noise = [torch.randn((4, 3, 32, 32)).to(device) for _ in range(4)]
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timesteps = [torch.randint(0, 1000, (4,)).long().to(device) for _ in range(4)]
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# train with a DDPM scheduler
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model, optimizer = self.get_model_optimizer(resolution=32)
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model.train().to(device)
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for i in range(4):
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optimizer.zero_grad()
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ddpm_noisy_images = ddpm_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])
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ddpm_noise_pred = model(ddpm_noisy_images, timesteps[i]).sample
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loss = torch.nn.functional.mse_loss(ddpm_noise_pred, noise[i])
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loss.backward()
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optimizer.step()
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del model, optimizer
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# recreate the model and optimizer, and retry with DDIM
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model, optimizer = self.get_model_optimizer(resolution=32)
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model.train().to(device)
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for i in range(4):
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optimizer.zero_grad()
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ddim_noisy_images = ddim_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])
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ddim_noise_pred = model(ddim_noisy_images, timesteps[i]).sample
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loss = torch.nn.functional.mse_loss(ddim_noise_pred, noise[i])
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loss.backward()
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optimizer.step()
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del model, optimizer
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self.assertTrue(torch.allclose(ddpm_noisy_images, ddim_noisy_images, atol=1e-5))
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self.assertTrue(torch.allclose(ddpm_noise_pred, ddim_noise_pred, atol=1e-5))
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