mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-29 07:22:12 +03:00
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
# 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
|
|
|
|
|
|
@slow
|
|
@require_torch_gpu
|
|
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
|