56 lines
1.8 KiB
Python
56 lines
1.8 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 numpy as np
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import torch
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from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
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from diffusers.utils.testing_utils import require_torch, slow, torch_device
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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class DDPMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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# FIXME: add fast tests
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pass
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@slow
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@require_torch
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class DDPMPipelineIntegrationTests(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 = DDPMScheduler.from_config(model_id)
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ddpm = DDPMPipeline(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="numpy").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([0.41995, 0.35885, 0.19385, 0.38475, 0.3382, 0.2647, 0.41545, 0.3582, 0.33845])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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