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Add custom nodes, Civitai loras (LFS), and vast.ai setup script
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>
2026-02-09 00:56:42 +00:00

135 lines
4.0 KiB
Python

"""
Tests for different upscaling modes and seam fix modes.
"""
import logging
import pathlib
import pytest
import torch
from tensor_utils import img_tensor_mae, blur
from io_utils import save_image, load_image
from configs import DirectoryConfig
from fixtures_images import EXT
# Image file names
CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_"))
def image_name_format(prefix: str, mode: str) -> str:
"""Helper for the image name format for the tests below."""
return f"{prefix}_{mode.lower().replace(' ', '_')}{EXT}"
class TestTilingModes:
def _test_upscale_variant(
self,
base_image,
loaded_checkpoint,
node_classes,
seed,
test_dirs: DirectoryConfig,
mode_type,
seam_fix_mode,
seam_fix_denoise,
filename_prefix,
):
"""Helper method to test upscale variants with different parameters."""
logger = logging.getLogger(f"test_{filename_prefix}")
image, positive, negative = base_image
model, clip, vae = loaded_checkpoint
with torch.inference_mode():
usdu = node_classes["UltimateSDUpscale"]
(upscaled,) = usdu().upscale(
image=image[0:1],
model=model,
positive=positive,
negative=negative,
vae=vae,
upscale_by=2.0,
seed=seed,
steps=3,
cfg=8,
sampler_name="euler",
scheduler="normal",
denoise=0.2,
upscale_model=None,
mode_type=mode_type,
tile_width=512,
tile_height=512,
mask_blur=8,
tile_padding=32,
seam_fix_mode=seam_fix_mode,
seam_fix_denoise=seam_fix_denoise,
seam_fix_width=64,
seam_fix_mask_blur=8,
seam_fix_padding=16,
force_uniform_tiles=True,
tiled_decode=False,
)
# Save and reload sample image
sample_dir = test_dirs.sample_images
filename = CATEGORY / filename_prefix
save_image(upscaled[0], sample_dir / filename)
upscaled = load_image(sample_dir / filename)
# Compare with reference
test_image_dir = test_dirs.test_images
test_image = load_image(test_image_dir / filename)
diff = img_tensor_mae(blur(upscaled), blur(test_image))
logger.info(f"{filename_prefix} MAE: {diff}")
assert diff < 0.05, f"{filename_prefix} output doesn't match reference"
# "Chess" is tested in the main workflow test
@pytest.mark.parametrize("mode_type", ["Linear", "None"])
def test_mode_types(
self,
base_image,
loaded_checkpoint,
node_classes,
seed,
mode_type,
test_dirs: DirectoryConfig,
):
"""Test different tiling mode types."""
filename = image_name_format("mode", mode_type)
self._test_upscale_variant(
base_image,
loaded_checkpoint,
node_classes,
seed,
test_dirs,
mode_type=mode_type,
seam_fix_mode="None",
seam_fix_denoise=1.0,
filename_prefix=filename,
)
@pytest.mark.parametrize(
"seam_fix_mode", ["None", "Band Pass", "Half Tile", "Half Tile + Intersections"]
)
def test_seam_fix_modes(
self,
base_image,
loaded_checkpoint,
node_classes,
seed,
seam_fix_mode,
test_dirs: DirectoryConfig,
):
"""Test different seam fix modes."""
filename = image_name_format("seamfix", seam_fix_mode)
self._test_upscale_variant(
base_image,
loaded_checkpoint,
node_classes,
seed,
test_dirs,
mode_type="None",
seam_fix_mode=seam_fix_mode,
seam_fix_denoise=0.5,
filename_prefix=filename,
)