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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
"""
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Tests for base image generation.
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"""
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import logging
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from configs import DirectoryConfig
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from tensor_utils import img_tensor_mae, blur
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from io_utils import load_image
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from fixtures_images import BASE_IMAGE_1, BASE_IMAGE_2
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def test_base_image_matches_reference(base_image, test_dirs: DirectoryConfig):
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"""
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Verify generated base images match reference images.
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This is just to check if the checkpoint and generation pipeline are as expected for the tests dependent on their behavior.
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"""
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logger = logging.getLogger("test_base_image_matches_reference")
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image, _, _ = base_image
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test_image_dir = test_dirs.test_images
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im1 = image[0:1]
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im2 = image[1:2]
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test_im1 = load_image(test_image_dir / BASE_IMAGE_1)
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test_im2 = load_image(test_image_dir / BASE_IMAGE_2)
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# Reduce high-frequency noise differences with gaussian blur. Using perceptual metrics are probably overkill.
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diff1 = img_tensor_mae(blur(im1), blur(test_im1))
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diff2 = img_tensor_mae(blur(im2), blur(test_im2))
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logger.info(f"Base Image Diff1: {diff1}, Diff2: {diff2}")
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assert diff1 < 0.05, "Image 1 does not match its test image."
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assert diff2 < 0.05, "Image 2 does not match its test image."
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