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78 lines
2.8 KiB
Python
78 lines
2.8 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 gc
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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 StableDiffusionKDiffusionPipeline
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from diffusers.utils import slow, torch_device
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from diffusers.utils.testing_utils import require_torch_gpu
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torch.backends.cuda.matmul.allow_tf32 = False
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@slow
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@require_torch_gpu
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class StableDiffusionPipelineIntegrationTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def test_stable_diffusion_1(self):
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sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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sd_pipe.set_scheduler("sample_euler")
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, num_inference_steps=20, output_type="np")
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.8887, 0.915, 0.91, 0.894, 0.909, 0.912, 0.919, 0.925, 0.883])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_2(self):
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sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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sd_pipe.set_scheduler("sample_euler")
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, num_inference_steps=20, output_type="np")
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array(
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[0.826810, 0.81958747, 0.8510199, 0.8376758, 0.83958465, 0.8682068, 0.84370345, 0.85251087, 0.85884345]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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