capacap
d02e85f267
Extract shared sampler-wrapping and mask postprocessing into ssd_core.py. Add three experimental variants alongside the original (variance snapshot): dynamic variance with EMA, delta (inter-step prediction difference) with EMA, and edges (Sobel). Each sampler node now also emits a mask handle consumed by a new mask-preview node that renders the captured mask inline after sampling runs. Use reflect-padding in variance, Sobel, and blur ops to eliminate the border-aura artifact produced by zero-padded convolutions.
26 lines
1.1 KiB
Python
26 lines
1.1 KiB
Python
from .selective_sigma_detailer import (
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SelectiveSigmaDetailerDeltaNode,
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SelectiveSigmaDetailerDynamicVarianceNode,
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SelectiveSigmaDetailerEdgesNode,
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SelectiveSigmaDetailerMaskPreviewNode,
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SelectiveSigmaDetailerNode,
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)
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NODE_CLASS_MAPPINGS = {
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"SelectiveSigmaDetailerNode": SelectiveSigmaDetailerNode,
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"SelectiveSigmaDetailerDynamicVarianceNode": SelectiveSigmaDetailerDynamicVarianceNode,
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"SelectiveSigmaDetailerDeltaNode": SelectiveSigmaDetailerDeltaNode,
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"SelectiveSigmaDetailerEdgesNode": SelectiveSigmaDetailerEdgesNode,
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"SelectiveSigmaDetailerMaskPreviewNode": SelectiveSigmaDetailerMaskPreviewNode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SelectiveSigmaDetailerNode": "Selective Sigma Detailer (Variance Snapshot)",
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"SelectiveSigmaDetailerDynamicVarianceNode": "Selective Sigma Detailer (Dynamic Variance)",
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"SelectiveSigmaDetailerDeltaNode": "Selective Sigma Detailer (Delta)",
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"SelectiveSigmaDetailerEdgesNode": "Selective Sigma Detailer (Edges)",
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"SelectiveSigmaDetailerMaskPreviewNode": "Selective Sigma Detailer Mask Preview",
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}
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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