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| # coding=utf-8 | |
| # Copyright 2023 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 DDIMPipeline, DDIMScheduler, UNet2DModel | |
| from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device | |
| from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS | |
| from ..test_pipelines_common import PipelineTesterMixin | |
| enable_full_determinism() | |
| class DDIMPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
| pipeline_class = DDIMPipeline | |
| params = UNCONDITIONAL_IMAGE_GENERATION_PARAMS | |
| required_optional_params = PipelineTesterMixin.required_optional_params - { | |
| "num_images_per_prompt", | |
| "latents", | |
| "callback", | |
| "callback_steps", | |
| } | |
| batch_params = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS | |
| def get_dummy_components(self): | |
| torch.manual_seed(0) | |
| unet = UNet2DModel( | |
| block_out_channels=(32, 64), | |
| layers_per_block=2, | |
| sample_size=32, | |
| in_channels=3, | |
| out_channels=3, | |
| down_block_types=("DownBlock2D", "AttnDownBlock2D"), | |
| up_block_types=("AttnUpBlock2D", "UpBlock2D"), | |
| ) | |
| scheduler = DDIMScheduler() | |
| components = {"unet": unet, "scheduler": scheduler} | |
| return components | |
| def get_dummy_inputs(self, device, seed=0): | |
| if str(device).startswith("mps"): | |
| generator = torch.manual_seed(seed) | |
| else: | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| inputs = { | |
| "batch_size": 1, | |
| "generator": generator, | |
| "num_inference_steps": 2, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| def test_inference(self): | |
| device = "cpu" | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe.to(device) | |
| pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| image = pipe(**inputs).images | |
| image_slice = image[0, -3:, -3:, -1] | |
| self.assertEqual(image.shape, (1, 32, 32, 3)) | |
| expected_slice = np.array( | |
| [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04] | |
| ) | |
| max_diff = np.abs(image_slice.flatten() - expected_slice).max() | |
| self.assertLessEqual(max_diff, 1e-3) | |
| def test_dict_tuple_outputs_equivalent(self): | |
| super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3) | |
| def test_save_load_local(self): | |
| super().test_save_load_local(expected_max_difference=3e-3) | |
| def test_save_load_optional_components(self): | |
| super().test_save_load_optional_components(expected_max_difference=3e-3) | |
| def test_inference_batch_single_identical(self): | |
| super().test_inference_batch_single_identical(expected_max_diff=3e-3) | |
| class DDIMPipelineIntegrationTests(unittest.TestCase): | |
| def test_inference_cifar10(self): | |
| model_id = "google/ddpm-cifar10-32" | |
| unet = UNet2DModel.from_pretrained(model_id) | |
| scheduler = DDIMScheduler() | |
| ddim = DDIMPipeline(unet=unet, scheduler=scheduler) | |
| ddim.to(torch_device) | |
| ddim.set_progress_bar_config(disable=None) | |
| generator = torch.manual_seed(0) | |
| image = ddim(generator=generator, eta=0.0, output_type="numpy").images | |
| image_slice = image[0, -3:, -3:, -1] | |
| assert image.shape == (1, 32, 32, 3) | |
| expected_slice = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_inference_ema_bedroom(self): | |
| model_id = "google/ddpm-ema-bedroom-256" | |
| unet = UNet2DModel.from_pretrained(model_id) | |
| scheduler = DDIMScheduler.from_pretrained(model_id) | |
| ddpm = DDIMPipeline(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="numpy").images | |
| image_slice = image[0, -3:, -3:, -1] | |
| assert image.shape == (1, 256, 256, 3) | |
| expected_slice = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |