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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>
97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
"""
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Test using controlnet in the upscaling workflow.
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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 setup_utils import execute
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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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CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_"))
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CONTROLNET_TILE_OUTPUT_IMAGE = "controlnet_tile" + EXT
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TEST_CONTROLNET_TILE_MODEL = "control_v11f1e_sd15_tile.pth"
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class TestControlNet:
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"""Integration tests for the upscaling workflow with ControlNet."""
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@pytest.fixture(scope="class")
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def controlnet_upscaled_image(
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self,
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base_image,
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loaded_checkpoint,
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upscale_model,
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node_classes,
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seed,
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test_dirs,
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):
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"""Generate upscaled images using ControlNet."""
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image, positive, negative = base_image
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model, clip, vae = loaded_checkpoint
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image = image[0:1]
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(controlnet_tile_model,) = execute(
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node_classes["ControlNetLoader"], TEST_CONTROLNET_TILE_MODEL
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)
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(positive,) = execute(
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node_classes["ControlNetApply"], positive, controlnet_tile_model, image, 1.0
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)
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with torch.inference_mode():
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# Run upscale with ControlNet
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usdu = node_classes["UltimateSDUpscale"]
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(upscaled,) = usdu().upscale(
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image=image,
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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=2.0,
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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=1.0,
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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=32,
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seam_fix_mode="None",
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seam_fix_denoise=1.0,
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seam_fix_width=64,
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seam_fix_mask_blur=8,
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seam_fix_padding=16,
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force_uniform_tiles=True,
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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 = CATEGORY / CONTROLNET_TILE_OUTPUT_IMAGE
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save_image(upscaled[0], sample_dir / filename)
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upscaled = load_image(sample_dir / filename)
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return upscaled
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def test_controlnet_upscaled_image_matches_reference(
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self, controlnet_upscaled_image, test_dirs: DirectoryConfig
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):
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"""
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Verify ControlNet upscaled images match reference images.
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"""
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logger = logging.getLogger("test_controlnet_upscaled_image_matches_reference")
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test_img_dir = test_dirs.test_images
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test_img = load_image(test_img_dir / CATEGORY / CONTROLNET_TILE_OUTPUT_IMAGE)
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# Reduce high-frequency noise differences with gaussian blur
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diff = img_tensor_mae(blur(controlnet_upscaled_image), blur(test_img))
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logger.info(f"ControlNet Upscaled Image Diff: {diff}")
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assert diff < 0.05, "ControlNet upscaled image does not match its test image."
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