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tango2-full
/
diffusers
/tests
/pipelines
/versatile_diffusion
/test_versatile_diffusion_image_variation.py
# coding=utf-8 | |
# Copyright 2023 HuggingFace Inc. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import unittest | |
import numpy as np | |
import torch | |
from diffusers import VersatileDiffusionImageVariationPipeline | |
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device | |
torch.backends.cuda.matmul.allow_tf32 = False | |
class VersatileDiffusionImageVariationPipelineFastTests(unittest.TestCase): | |
pass | |
class VersatileDiffusionImageVariationPipelineIntegrationTests(unittest.TestCase): | |
def test_inference_image_variations(self): | |
pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion") | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
image_prompt = load_image( | |
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" | |
) | |
generator = torch.manual_seed(0) | |
image = pipe( | |
image=image_prompt, | |
generator=generator, | |
guidance_scale=7.5, | |
num_inference_steps=50, | |
output_type="numpy", | |
).images | |
image_slice = image[0, 253:256, 253:256, -1] | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |