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* [WIP] Standardize fast pipeline tests with PipelineTestMixin * refactor the sd tests a bit * add more common tests * add xformers * add progressbar test * cleanup * upd fp16 * CycleDiffusionPipelineFastTests * DanceDiffusionPipelineFastTests * AltDiffusionPipelineFastTests * StableDiffusion2PipelineFastTests * StableDiffusion2InpaintPipelineFastTests * StableDiffusionImageVariationPipelineFastTests * StableDiffusionImg2ImgPipelineFastTests * StableDiffusionInpaintPipelineFastTests * remove unused mixins * quality * add missing inits * try to fix mps tests * fix mps tests * add mps warmups * skip for some pipelines * style * Update tests/test_pipelines_common.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
57 lines
1.9 KiB
Python
57 lines
1.9 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 VersatileDiffusionImageVariationPipeline
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from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
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torch.backends.cuda.matmul.allow_tf32 = False
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class VersatileDiffusionImageVariationPipelineFastTests(unittest.TestCase):
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pass
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@slow
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@require_torch_gpu
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class VersatileDiffusionImageVariationPipelineIntegrationTests(unittest.TestCase):
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def test_inference_image_variations(self):
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pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion")
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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image_prompt = load_image(
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"https://raw.githubusercontent.com/SHI-Labs/Versatile-Diffusion/master/assets/benz.jpg"
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)
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generator = torch.Generator(device=torch_device).manual_seed(0)
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image = pipe(
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image=image_prompt,
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generator=generator,
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guidance_scale=7.5,
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num_inference_steps=50,
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output_type="numpy",
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).images
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image_slice = image[0, 253:256, 253:256, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.1205, 0.1914, 0.2289, 0.0883, 0.1595, 0.1683, 0.0703, 0.1493, 0.1298])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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