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shivamshrirao
GitHub Repository: shivamshrirao/diffusers
 Path: blob/main/tests/test_outputs.py
1440 views
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import unittest

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from dataclasses import dataclass

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from typing import List, Union

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import numpy as np

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import PIL.Image

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from diffusers.utils.outputs import BaseOutput

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@dataclass

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class CustomOutput(BaseOutput):

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    images: Union[List[PIL.Image.Image], np.ndarray]

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class ConfigTester(unittest.TestCase):

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    def test_outputs_single_attribute(self):

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        outputs = CustomOutput(images=np.random.rand(1, 3, 4, 4))

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        # check every way of getting the attribute

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        assert isinstance(outputs.images, np.ndarray)

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        assert outputs.images.shape == (1, 3, 4, 4)

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        assert isinstance(outputs["images"], np.ndarray)

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        assert outputs["images"].shape == (1, 3, 4, 4)

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        assert isinstance(outputs[0], np.ndarray)

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        assert outputs[0].shape == (1, 3, 4, 4)

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        # test with a non-tensor attribute

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        outputs = CustomOutput(images=[PIL.Image.new("RGB", (4, 4))])

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        # check every way of getting the attribute

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        assert isinstance(outputs.images, list)

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        assert isinstance(outputs.images[0], PIL.Image.Image)

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        assert isinstance(outputs["images"], list)

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        assert isinstance(outputs["images"][0], PIL.Image.Image)

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        assert isinstance(outputs[0], list)

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        assert isinstance(outputs[0][0], PIL.Image.Image)

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    def test_outputs_dict_init(self):

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        # test output reinitialization with a `dict` for compatibility with `accelerate`

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        outputs = CustomOutput({"images": np.random.rand(1, 3, 4, 4)})

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        # check every way of getting the attribute

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        assert isinstance(outputs.images, np.ndarray)

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        assert outputs.images.shape == (1, 3, 4, 4)

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        assert isinstance(outputs["images"], np.ndarray)

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        assert outputs["images"].shape == (1, 3, 4, 4)

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        assert isinstance(outputs[0], np.ndarray)

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        assert outputs[0].shape == (1, 3, 4, 4)

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        # test with a non-tensor attribute

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        outputs = CustomOutput({"images": [PIL.Image.new("RGB", (4, 4))]})

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        # check every way of getting the attribute

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        assert isinstance(outputs.images, list)

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        assert isinstance(outputs.images[0], PIL.Image.Image)

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        assert isinstance(outputs["images"], list)

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        assert isinstance(outputs["images"][0], PIL.Image.Image)

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        assert isinstance(outputs[0], list)

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        assert isinstance(outputs[0][0], PIL.Image.Image)

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