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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 numpy as np | |
| import torch | |
| from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel | |
| from diffusers.utils.testing_utils import enable_full_determinism, require_torch_accelerator, slow, torch_device | |
| enable_full_determinism() | |
| class DDPMPipelineFastTests(unittest.TestCase): | |
| def dummy_uncond_unet(self): | |
| torch.manual_seed(0) | |
| model = UNet2DModel( | |
| block_out_channels=(4, 8), | |
| layers_per_block=1, | |
| norm_num_groups=4, | |
| sample_size=8, | |
| in_channels=3, | |
| out_channels=3, | |
| down_block_types=("DownBlock2D", "AttnDownBlock2D"), | |
| up_block_types=("AttnUpBlock2D", "UpBlock2D"), | |
| ) | |
| return model | |
| def test_fast_inference(self): | |
| device = "cpu" | |
| unet = self.dummy_uncond_unet | |
| scheduler = DDPMScheduler() | |
| ddpm = DDPMPipeline(unet=unet, scheduler=scheduler) | |
| ddpm.to(device) | |
| ddpm.set_progress_bar_config(disable=None) | |
| generator = torch.Generator(device=device).manual_seed(0) | |
| image = ddpm(generator=generator, num_inference_steps=2, output_type="np").images | |
| generator = torch.Generator(device=device).manual_seed(0) | |
| image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="np", return_dict=False)[0] | |
| image_slice = image[0, -3:, -3:, -1] | |
| image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] | |
| assert image.shape == (1, 8, 8, 3) | |
| expected_slice = np.array([0.0, 0.9996672, 0.00329116, 1.0, 0.9995991, 1.0, 0.0060907, 0.00115037, 0.0]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_inference_predict_sample(self): | |
| unet = self.dummy_uncond_unet | |
| scheduler = DDPMScheduler(prediction_type="sample") | |
| ddpm = DDPMPipeline(unet=unet, scheduler=scheduler) | |
| ddpm.to(torch_device) | |
| ddpm.set_progress_bar_config(disable=None) | |
| generator = torch.manual_seed(0) | |
| image = ddpm(generator=generator, num_inference_steps=2, output_type="np").images | |
| generator = torch.manual_seed(0) | |
| image_eps = ddpm(generator=generator, num_inference_steps=2, output_type="np")[0] | |
| image_slice = image[0, -3:, -3:, -1] | |
| image_eps_slice = image_eps[0, -3:, -3:, -1] | |
| assert image.shape == (1, 8, 8, 3) | |
| tolerance = 1e-2 if torch_device != "mps" else 3e-2 | |
| assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance | |
| class DDPMPipelineIntegrationTests(unittest.TestCase): | |
| def test_inference_cifar10(self): | |
| model_id = "google/ddpm-cifar10-32" | |
| unet = UNet2DModel.from_pretrained(model_id) | |
| scheduler = DDPMScheduler.from_pretrained(model_id) | |
| ddpm = DDPMPipeline(unet=unet, scheduler=scheduler) | |
| ddpm.to(torch_device) | |
| ddpm.set_progress_bar_config(disable=None) | |
| generator = torch.manual_seed(0) | |
| image = ddpm(generator=generator, output_type="np").images | |
| image_slice = image[0, -3:, -3:, -1] | |
| assert image.shape == (1, 32, 32, 3) | |
| expected_slice = np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |