mirror of
https://github.com/finegrain-ai/refiners.git
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53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
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import torch
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import pytest
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from warnings import warn
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from PIL import Image
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from pathlib import Path
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from refiners.fluxion.utils import load_from_safetensors
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from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
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from tests.utils import ensure_similar_images
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@pytest.fixture(scope="module")
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def ref_path() -> Path:
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return Path(__file__).parent / "test_auto_encoder_ref"
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@pytest.fixture(scope="module")
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def encoder(test_weights_path: Path, test_device: torch.device) -> LatentDiffusionAutoencoder:
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lda_weights = test_weights_path / "lda.safetensors"
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if not lda_weights.is_file():
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warn(f"could not find weights at {lda_weights}, skipping")
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pytest.skip(allow_module_level=True)
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encoder = LatentDiffusionAutoencoder(device=test_device)
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tensors = load_from_safetensors(lda_weights)
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encoder.load_state_dict(tensors)
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return encoder
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@pytest.fixture(scope="module")
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def sample_image(ref_path: Path) -> Image.Image:
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test_image = ref_path / "macaw.png"
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if not test_image.is_file():
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warn(f"could not reference image at {test_image}, skipping")
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pytest.skip(allow_module_level=True)
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img = Image.open(test_image)
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assert img.size == (512, 512)
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return img
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@torch.no_grad()
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def test_encode_decode(encoder: LatentDiffusionAutoencoder, sample_image: Image.Image):
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encoded = encoder.encode_image(sample_image)
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decoded = encoder.decode_latents(encoded)
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assert decoded.mode == "RGB"
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# Ensure no saturation. The green channel (band = 1) must not max out.
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assert max(iter(decoded.getdata(band=1))) < 255 # type: ignore
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ensure_similar_images(sample_image, decoded, min_psnr=20, min_ssim=0.9)
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