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53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
from pathlib import Path
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import pytest
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import torch
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from PIL import Image
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from tests.utils import ensure_similar_images
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from refiners.fluxion.utils import image_to_tensor, no_grad, tensor_to_image
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from refiners.foundationals.latent_diffusion.preprocessors.informative_drawings import InformativeDrawings
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def _img_open(path: Path) -> Image.Image:
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return Image.open(path) # type: ignore
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@pytest.fixture(scope="module")
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def diffusion_ref_path(test_e2e_path: Path) -> Path:
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return test_e2e_path / "test_diffusion_ref"
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@pytest.fixture(scope="module")
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def cutecat_init(diffusion_ref_path: Path) -> Image.Image:
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return _img_open(diffusion_ref_path / "cutecat_init.png").convert("RGB")
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@pytest.fixture
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def expected_image_informative_drawings(diffusion_ref_path: Path) -> Image.Image:
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return _img_open(diffusion_ref_path / "cutecat_guide_lineart.png").convert("RGB")
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@pytest.fixture
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def informative_drawings_model(
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controlnet_preprocessor_info_drawings_weights_path: Path,
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test_device: torch.device,
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) -> InformativeDrawings:
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model = InformativeDrawings(device=test_device)
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model.load_from_safetensors(controlnet_preprocessor_info_drawings_weights_path)
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return model
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@no_grad()
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def test_preprocessor_informative_drawing(
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informative_drawings_model: InformativeDrawings,
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cutecat_init: Image.Image,
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expected_image_informative_drawings: Image.Image,
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test_device: torch.device,
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):
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in_tensor = image_to_tensor(cutecat_init.convert("RGB"), device=test_device)
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out_tensor = informative_drawings_model(in_tensor)
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rgb_tensor = out_tensor.repeat(1, 3, 1, 1) # grayscale to RGB
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image = tensor_to_image(rgb_tensor)
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ensure_similar_images(image, expected_image_informative_drawings)
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