facok
724f5fa153
Experimentally validated combined mechanism: 1. Butterworth LPF erases HF spatial anchoring (composition sketch) 2. Random 4x4 DCT field applied after blur redistributes latent energy Push runs after cleanup in single post-cfg hook — ensures signal survives. Defaults: strength=0.5, n_periods=2, noise_type=pink.
72 lines
2.9 KiB
Python
72 lines
2.9 KiB
Python
"""DiversityBoost node — HF attenuation + DCT composition push."""
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import time
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from comfy_api.latest import io
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from .core import build_diversity_fn
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class DiversityBoostCore(io.ComfyNode):
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"""Restore composition diversity for distilled diffusion models.
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Single post-cfg hook at step 0: first attenuates HF amplitude
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(Butterworth LPF), then applies a random low-frequency DCT spatial
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field to the blurred result. Push runs AFTER cleanup so its signal
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cannot be erased by downstream processing.
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"""
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="DiversityBoostCore",
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display_name="Diversity Boost",
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category="sampling",
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description="Restore composition diversity for distilled models. "
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"HF attenuation + DCT composition push at step 0.",
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inputs=[
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io.Model.Input("model"),
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io.Float.Input("strength", default=0.50, min=0.0, max=2.0, step=0.05,
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tooltip="Composition push amplitude. "
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"0 = cleanup only. 0.5 = moderate. 1.0 = strong."),
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io.Float.Input("clamp", default=1.0, min=0.1, max=3.0, step=0.1,
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tooltip="Safety clamp for DCT field values."),
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io.Combo.Input("noise_type",
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options=["pink", "white", "blue"],
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default="pink",
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tooltip="Frequency spectrum of random DCT coefficients. "
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"pink = stronger composition push (recommended)."),
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io.Int.Input("n_periods", default=2, min=1, max=10, step=1,
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tooltip="Butterworth cutoff. 2 = preserves DCT signal."),
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io.Float.Input("dc_preserve", default=0.0, min=0.0, max=1.0, step=0.1,
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tooltip="DC amplitude preservation (0 = max diversity)."),
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io.Boolean.Input("energy_compensate", default=False,
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tooltip="Rescale output energy to match original."),
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],
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outputs=[
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io.Model.Output(display_name="model"),
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],
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)
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@classmethod
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def fingerprint_inputs(cls, **kwargs):
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return time.time()
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@classmethod
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def execute(cls, model, strength, clamp, noise_type,
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n_periods, dc_preserve, energy_compensate) -> io.NodeOutput:
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m = model.clone()
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m.set_model_sampler_post_cfg_function(
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build_diversity_fn(
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strength=strength,
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clamp_val=clamp,
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noise_type=noise_type,
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n_periods=n_periods,
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dc_preserve=dc_preserve,
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energy_compensate=energy_compensate,
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),
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)
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return io.NodeOutput(m)
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