capacap
46ccfc0ab0
The patch node hooked set_model_unet_function_wrapper to inject sigma adjustments, but that wrapper sits below apply_model's input scaling and timestep transformation. The second forward pass therefore ran with input prepped for the original sigma while the outer layer's calculate_denoised also used the original sigma, so the adjustment produced near-zero visible effect regardless of intensity. Keep only the SAMPLER variant (used via SamplerCustom), which operates at the correct layer.
20 lines
752 B
Python
20 lines
752 B
Python
from .selective_sigma_detailer import (
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SelectiveSigmaDetailerDebugNode,
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SelectiveSigmaDetailerDeltaV2Node,
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SelectiveSigmaDetailerMaskPreviewNode,
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)
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NODE_CLASS_MAPPINGS = {
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"SelectiveSigmaDetailerDeltaV2Node": SelectiveSigmaDetailerDeltaV2Node,
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"SelectiveSigmaDetailerDebugNode": SelectiveSigmaDetailerDebugNode,
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"SelectiveSigmaDetailerMaskPreviewNode": SelectiveSigmaDetailerMaskPreviewNode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SelectiveSigmaDetailerDeltaV2Node": "Selective Sigma Detailer",
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"SelectiveSigmaDetailerDebugNode": "Selective Sigma Detailer (Debug)",
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"SelectiveSigmaDetailerMaskPreviewNode": "Selective Sigma Detailer (Debug Preview)",
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}
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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