Patrick von Platen
6ba2231d72
* make tests deterministic * run slow tests * prepare for testing * finish * refactor * add print statements * finish more * correct some test failures * more fixes * set up to correct tests * more corrections * up * fix more * more prints * add * up * up * up * uP * uP * more fixes * uP * up * up * up * up * fix more * up * up * clean tests * up * up * up * more fixes * Apply suggestions from code review Co-authored-by: Suraj Patil <surajp815@gmail.com> * make * correct * finish * finish Co-authored-by: Suraj Patil <surajp815@gmail.com>
58 lines
1.9 KiB
Python
58 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.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([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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