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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>
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
"""
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Test for other settings included in the upscaling nodes.
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"""
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import logging
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import pathlib
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import pytest
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import torch
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from tensor_utils import img_tensor_mae, blur
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from io_utils import save_image, load_image
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from configs import DirectoryConfig
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from fixtures_images import EXT
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# Image file names
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CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_"))
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def test_minimal_tile_sizes(
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base_image, loaded_checkpoint, node_classes, seed, test_dirs: DirectoryConfig
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):
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"""Test upscaling with minimal tile sizes."""
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filename = "non_uniform_tiles"
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image, positive, negative = base_image
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model, clip, vae = loaded_checkpoint
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with torch.inference_mode():
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usdu = node_classes["UltimateSDUpscale"]
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(upscaled,) = usdu().upscale(
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image=image[0:1],
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model=model,
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positive=positive,
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negative=negative,
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vae=vae,
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upscale_by=1.5,
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seed=seed,
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steps=5,
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cfg=8,
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sampler_name="euler",
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scheduler="normal",
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denoise=0.15,
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upscale_model=None,
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mode_type="Chess",
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tile_width=512,
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tile_height=512,
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mask_blur=8,
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tile_padding=8,
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seam_fix_mode="None",
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seam_fix_denoise=1.0,
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seam_fix_width=16,
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seam_fix_mask_blur=8,
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seam_fix_padding=4,
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force_uniform_tiles=False,
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tiled_decode=False,
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)
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# Save and reload sample image
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sample_dir = test_dirs.sample_images
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filename_path = CATEGORY / (filename + EXT)
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save_image(upscaled[0], sample_dir / filename_path)
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upscaled = load_image(sample_dir / filename_path)
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# Compare with reference
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test_image_dir = test_dirs.test_images
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test_image = load_image(test_image_dir / filename_path)
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diff = img_tensor_mae(blur(upscaled), blur(test_image))
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logger = logging.getLogger(__name__)
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logger.info(f"{filename} MAE: {diff}")
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assert diff < 0.05, f"{filename} output doesn't match reference"
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