KIMJUNGHOandClaude Sonnet 4.6
94929c6bd0
## Nodes - Unified node key prefix to Util* (UtilSwitch, UtilStringToList, etc.) - Added UtilSaveFile: combined Save Text & Image node with batch/list support, shared counter, subfolder, INPUT_IS_LIST - Added UtilJsonToList: JSON file/string to STRING list via dot-notation key - Added UtilEmptyLatent channels param (16=Flux/Lumina/SD3, 4=SD/SDXL) - Updated UtilLoadImageList: count-based dropdown selectors from Input folder - Added CNStepControl, CNPreprocessor (canny/depth/lineart/openpose/mlsd) - Added ImageCropResize, LoRAStepLoader - Added SBPromptFilter (Claude API style/controlnet filtering) - Removed layer_nodes.py, sampler_nodes.py (layer separation experiments) - Removed experiments/ directory ## Dependencies - Removed controlnet_aux — direct preprocessor implementations in preprocessors/ Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
"""ControlNet preprocessors without controlnet_aux dependency."""
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import torch
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_detector_cache = {}
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def _get_device():
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"""Get the best available device for inference."""
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if torch.cuda.is_available():
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return torch.device("cuda")
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if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
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return torch.device("mps")
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return torch.device("cpu")
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def get_detector(processor_id):
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"""Get or create a cached detector instance. Lazy imports to avoid loading unused models."""
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if processor_id in _detector_cache:
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return _detector_cache[processor_id]
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device = _get_device()
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if processor_id == "canny":
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from .canny import CannyDetector
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detector = CannyDetector()
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elif processor_id == "depth":
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from .depth import MidasDetector
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detector = MidasDetector.from_pretrained()
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detector.model.to(device)
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elif processor_id == "lineart":
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from .lineart import LineartDetector
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detector = LineartDetector.from_pretrained()
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detector.model.to(device)
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elif processor_id == "pose":
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from .openpose import OpenposeDetector
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detector = OpenposeDetector.from_pretrained()
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detector.to(device)
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elif processor_id == "mlsd":
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from .mlsd import MLSDdetector
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detector = MLSDdetector.from_pretrained()
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detector.model.to(device)
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else:
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raise ValueError(f"Unknown preprocessor: {processor_id}")
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_detector_cache[processor_id] = detector
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return detector
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PREPROCESSOR_IDS = ["canny", "depth", "lineart", "pose", "mlsd"]
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CALL_PARAMS = {
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"canny": {},
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"depth": {},
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"lineart": {"coarse": False},
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"pose": {},
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"mlsd": {},
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}
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