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https://github.com/huggingface/diffusers.git
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7aa6af1138
* update
* update
* update
* update
* update
* merge main
* Revert "merge main"
This reverts commit 65efbcead5.
143 lines
4.9 KiB
Python
143 lines
4.9 KiB
Python
# coding=utf-8
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# Copyright 2025 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 numpy as np
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import torch
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from diffusers import DDIMPipeline, DDIMScheduler, UNet2DModel
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from ...testing_utils import enable_full_determinism, require_torch_accelerator, slow, torch_device
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from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
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from ..test_pipelines_common import PipelineTesterMixin
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enable_full_determinism()
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class DDIMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = DDIMPipeline
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params = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
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required_optional_params = PipelineTesterMixin.required_optional_params - {
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"num_images_per_prompt",
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"latents",
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"callback",
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"callback_steps",
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}
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batch_params = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
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def get_dummy_components(self):
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torch.manual_seed(0)
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unet = UNet2DModel(
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block_out_channels=(4, 8),
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layers_per_block=1,
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norm_num_groups=4,
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sample_size=8,
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in_channels=3,
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out_channels=3,
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down_block_types=("DownBlock2D", "AttnDownBlock2D"),
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up_block_types=("AttnUpBlock2D", "UpBlock2D"),
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)
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scheduler = DDIMScheduler()
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components = {"unet": unet, "scheduler": scheduler}
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return components
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def get_dummy_inputs(self, device, seed=0):
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if str(device).startswith("mps"):
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generator = torch.manual_seed(seed)
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else:
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generator = torch.Generator(device=device).manual_seed(seed)
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inputs = {
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"batch_size": 1,
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"generator": generator,
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"num_inference_steps": 2,
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"output_type": "np",
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}
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return inputs
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def test_inference(self):
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device = "cpu"
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components = self.get_dummy_components()
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pipe = self.pipeline_class(**components)
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pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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self.assertEqual(image.shape, (1, 8, 8, 3))
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expected_slice = np.array([0.0, 9.979e-01, 0.0, 9.999e-01, 9.986e-01, 9.991e-01, 7.106e-04, 0.0, 0.0])
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max_diff = np.abs(image_slice.flatten() - expected_slice).max()
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self.assertLessEqual(max_diff, 1e-3)
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def test_dict_tuple_outputs_equivalent(self):
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super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)
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def test_save_load_local(self):
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super().test_save_load_local(expected_max_difference=3e-3)
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def test_save_load_optional_components(self):
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super().test_save_load_optional_components(expected_max_difference=3e-3)
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def test_inference_batch_single_identical(self):
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super().test_inference_batch_single_identical(expected_max_diff=3e-3)
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@slow
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@require_torch_accelerator
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class DDIMPipelineIntegrationTests(unittest.TestCase):
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def test_inference_cifar10(self):
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model_id = "google/ddpm-cifar10-32"
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unet = UNet2DModel.from_pretrained(model_id)
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scheduler = DDIMScheduler()
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ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
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ddim.to(torch_device)
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ddim.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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image = ddim(generator=generator, eta=0.0, output_type="np").images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 32, 32, 3)
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expected_slice = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_inference_ema_bedroom(self):
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model_id = "google/ddpm-ema-bedroom-256"
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unet = UNet2DModel.from_pretrained(model_id)
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scheduler = DDIMScheduler.from_pretrained(model_id)
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ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
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ddpm.to(torch_device)
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ddpm.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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image = ddpm(generator=generator, output_type="np").images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 256, 256, 3)
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expected_slice = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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