tests/pipelines/versatile_diffusion/test_versatile_diffusion_image_variation.py - main - ai-forever/diffusers

tests/pipelines/versatile_diffusion/test_versatile_diffusion_image_variation.py

57 строк2 KB

Dhruv Nair

move tests to nightly (#4451)

03 авг 2023, 16:25

03 авг 2023, 16:251d2587b

# 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, nightly, require_torch_gpu, torch_device


torch.backends.cuda.matmul.allow_tf32 = False


class VersatileDiffusionImageVariationPipelineFastTests(unittest.TestCase):
    pass


@nightly
@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