82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
"""
|
|
Test for other settings included in the upscaling nodes.
|
|
"""
|
|
|
|
import logging
|
|
import pathlib
|
|
import pytest
|
|
import torch
|
|
from contextlib import nullcontext
|
|
|
|
from tensor_utils import img_tensor_mae, blur
|
|
from io_utils import save_image, load_image, image_name_format
|
|
from configs import DirectoryConfig
|
|
from fixtures_images import EXT
|
|
|
|
# Image file names
|
|
CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_"))
|
|
|
|
|
|
@pytest.mark.parametrize("batch_size", [1, 2])
|
|
def test_minimal_tile_sizes(
|
|
base_image,
|
|
loaded_checkpoint,
|
|
node_classes,
|
|
seed,
|
|
batch_size,
|
|
test_dirs: DirectoryConfig,
|
|
):
|
|
"""Test upscaling with minimal tile sizes."""
|
|
image, positive, negative = base_image
|
|
image = image[0:1] # 1 image for simplicity
|
|
model, clip, vae = loaded_checkpoint
|
|
|
|
with torch.inference_mode():
|
|
with pytest.raises(AssertionError) if batch_size > 1 else nullcontext():
|
|
usdu = node_classes["UltimateSDUpscale"]
|
|
(upscaled,) = usdu().upscale(
|
|
image=image,
|
|
model=model,
|
|
positive=positive,
|
|
negative=negative,
|
|
vae=vae,
|
|
upscale_by=1.5,
|
|
seed=seed,
|
|
steps=5,
|
|
cfg=8,
|
|
sampler_name="euler",
|
|
scheduler="normal",
|
|
denoise=0.6,
|
|
upscale_model=None,
|
|
mode_type="Chess",
|
|
tile_width=512,
|
|
tile_height=512,
|
|
mask_blur=8,
|
|
tile_padding=8,
|
|
seam_fix_mode="None",
|
|
seam_fix_denoise=1.0,
|
|
seam_fix_width=16,
|
|
seam_fix_mask_blur=8,
|
|
seam_fix_padding=4,
|
|
force_uniform_tiles=False, # This should trigger the assertion for batch_size > 1
|
|
tiled_decode=False,
|
|
batch_size=batch_size,
|
|
)
|
|
|
|
if batch_size > 1:
|
|
return # Test passed if assertion was raised
|
|
|
|
# Save and reload sample image
|
|
sample_dir = test_dirs.sample_images
|
|
filename = CATEGORY / image_name_format("non_uniform_tiles", EXT, batch_size)
|
|
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)
|
|
logger = logging.getLogger(__name__)
|
|
diff = img_tensor_mae(blur(upscaled), blur(test_image))
|
|
logger.info(f"{filename} MAE: {diff}")
|
|
assert diff < 0.02, f"{filename} does not match reference (MAE {diff})"
|