1042 lines
46 KiB
Python
1042 lines
46 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any
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import math
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import torch
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from .slrd_core import build_nested_noise, clone_latent, latent_manifold_compand, residual_lock_multiband, resize_4d_tensor
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from .slrd_runtime import (
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LOCK_SCHEDULE_OPTIONS,
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MID_SCHEDULE_OPTIONS,
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ScaleLockConfig,
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build_runtime_context_from_advanced,
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clean_latent,
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compat_sampler_names,
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compat_scheduler_names,
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fix_latent_channels,
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make_lowres_latent,
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prepare_noise,
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run_scale_locked_ksampler,
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sample_with_runtime,
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apply_scale_lock_to_guider,
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clone_guider_for_scale_lock,
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)
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class ScaleLockedResidualKSampler:
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CATEGORY = "sampling/scale_locked"
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RETURN_TYPES = ("LATENT", "LATENT", "LATENT")
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RETURN_NAMES = ("output", "lowres_planner", "denoised_output")
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FUNCTION = "sample"
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("MODEL",),
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"latent_image": ("LATENT",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
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"steps": ("INT", {"default": 24, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
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"sampler_name": (compat_sampler_names(),),
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"scheduler": (compat_scheduler_names(),),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"target_megapixels": ("FLOAT", {"default": 1.0, "min": 0.10, "max": 16.0, "step": 0.05, "round": 0.01}),
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"lock_strength": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"lock_strength_start": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"lock_strength_end": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"lock_schedule": (LOCK_SCHEDULE_OPTIONS,),
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"lock_schedule_hold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.95, "step": 0.01, "round": 0.001}),
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"lock_schedule_power": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1, "round": 0.01}),
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"coarse_cutoff": ("FLOAT", {"default": 0.33, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
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"mid_band_cutoff": ("FLOAT", {"default": 0.60, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
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"mid_band_strength": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 2.0, "step": 0.01, "round": 0.001}),
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"mid_band_strength_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"mid_band_strength_end": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"mid_band_schedule": (MID_SCHEDULE_OPTIONS,),
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"mid_band_schedule_hold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.95, "step": 0.01, "round": 0.001}),
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"mid_band_schedule_power": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1, "round": 0.01}),
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"manifold_enabled": ("BOOLEAN", {"default": False}),
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"manifold_strength": ("FLOAT", {"default": 0.45, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_strength_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_strength_end": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_schedule": (LOCK_SCHEDULE_OPTIONS,),
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"manifold_schedule_hold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.95, "step": 0.01, "round": 0.001}),
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"manifold_schedule_power": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1, "round": 0.01}),
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"manifold_cutoff": ("FLOAT", {"default": 0.18, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_radial_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01, "round": 0.001}),
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"manifold_anisotropy": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 2.0, "step": 0.01, "round": 0.001}),
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"manifold_translation_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01, "round": 0.001}),
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"manifold_anchor_mix": ("FLOAT", {"default": 0.18, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_mean_anchor_mix": ("FLOAT", {"default": 0.12, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_contrast_restore": ("FLOAT", {"default": 0.10, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_energy_tether": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_channel_tether": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_energy_gain_cap": ("FLOAT", {"default": 1.75, "min": 1.0, "max": 4.0, "step": 0.05, "round": 0.01}),
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"manifold_max_shift_px": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 64.0, "step": 0.1, "round": 0.01}),
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"nested_noise_strength": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 4.0, "step": 0.01, "round": 0.001}),
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"add_noise": ("BOOLEAN", {"default": True}),
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"pin_anchors": ("BOOLEAN", {"default": True}),
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"sampler_guard": (["warn", "error", "off"],),
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},
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"optional": {
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"lock_mask": ("MASK",),
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"manifold_mask": ("MASK",),
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},
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}
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def sample(
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self,
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model,
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positive,
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negative,
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latent_image,
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seed,
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steps,
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cfg,
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sampler_name,
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scheduler,
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denoise,
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target_megapixels,
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lock_strength,
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lock_strength_start,
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lock_strength_end,
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lock_schedule,
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lock_schedule_hold,
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lock_schedule_power,
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coarse_cutoff,
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mid_band_cutoff,
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mid_band_strength,
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mid_band_strength_start,
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mid_band_strength_end,
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mid_band_schedule,
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mid_band_schedule_hold,
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mid_band_schedule_power,
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manifold_enabled,
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manifold_strength,
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manifold_strength_start,
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manifold_strength_end,
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manifold_schedule,
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manifold_schedule_hold,
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manifold_schedule_power,
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manifold_cutoff,
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manifold_radial_strength,
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manifold_anisotropy,
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manifold_translation_strength,
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manifold_anchor_mix,
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manifold_mean_anchor_mix,
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manifold_contrast_restore,
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manifold_energy_tether,
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manifold_channel_tether,
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manifold_energy_gain_cap,
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manifold_max_shift_px,
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nested_noise_strength,
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add_noise,
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pin_anchors,
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sampler_guard,
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lock_mask=None,
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manifold_mask=None,
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):
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result = run_scale_locked_ksampler(
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model=model,
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positive=positive,
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negative=negative,
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latent_image=latent_image,
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seed=seed,
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steps=steps,
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cfg=cfg,
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sampler_name=sampler_name,
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scheduler=scheduler,
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denoise=denoise,
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target_megapixels=target_megapixels,
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nested_noise_strength=nested_noise_strength,
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add_noise=add_noise,
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pin_anchors=pin_anchors,
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sampler_guard=sampler_guard,
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config=_scale_lock_config(
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lock_strength=lock_strength,
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lock_strength_start=lock_strength_start,
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lock_strength_end=lock_strength_end,
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coarse_cutoff=coarse_cutoff,
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mid_band_cutoff=mid_band_cutoff,
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mid_band_strength=mid_band_strength,
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lock_schedule=lock_schedule,
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lock_schedule_hold=lock_schedule_hold,
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lock_schedule_power=lock_schedule_power,
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mid_band_strength_start=mid_band_strength_start,
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mid_band_strength_end=mid_band_strength_end,
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mid_band_schedule=mid_band_schedule,
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mid_band_schedule_hold=mid_band_schedule_hold,
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mid_band_schedule_power=mid_band_schedule_power,
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manifold_enabled=manifold_enabled,
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manifold_strength=manifold_strength,
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manifold_strength_start=manifold_strength_start,
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manifold_strength_end=manifold_strength_end,
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manifold_schedule=manifold_schedule,
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manifold_schedule_hold=manifold_schedule_hold,
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manifold_schedule_power=manifold_schedule_power,
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manifold_cutoff=manifold_cutoff,
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manifold_radial_strength=manifold_radial_strength,
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manifold_anisotropy=manifold_anisotropy,
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manifold_translation_strength=manifold_translation_strength,
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manifold_anchor_mix=manifold_anchor_mix,
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manifold_mean_anchor_mix=manifold_mean_anchor_mix,
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manifold_contrast_restore=manifold_contrast_restore,
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manifold_energy_tether=manifold_energy_tether,
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manifold_channel_tether=manifold_channel_tether,
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manifold_energy_gain_cap=manifold_energy_gain_cap,
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manifold_max_shift_px=manifold_max_shift_px,
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lock_mask=lock_mask,
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manifold_mask=manifold_mask,
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),
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)
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return (result.output, result.lowres_planner, result.denoised_output)
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class ScaleLockedRuntimeContextBuilder:
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CATEGORY = "sampling/scale_locked"
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RETURN_TYPES = ("SLRD_RUNTIME", "NOISE", "LATENT")
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RETURN_NAMES = ("runtime", "prepared_noise", "lowres_planner")
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FUNCTION = "build"
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"noise": ("NOISE",),
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"guider": ("GUIDER",),
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"sampler": ("SAMPLER",),
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"sigmas": ("SIGMAS",),
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"latent_image": ("LATENT",),
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"target_megapixels": ("FLOAT", {"default": 1.0, "min": 0.10, "max": 16.0, "step": 0.05, "round": 0.01}),
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"nested_noise_strength": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 4.0, "step": 0.01, "round": 0.001}),
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"pin_anchors": ("BOOLEAN", {"default": True}),
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}
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}
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def build(self, noise, guider, sampler, sigmas, latent_image, target_megapixels, nested_noise_strength, pin_anchors):
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runtime = build_runtime_context_from_advanced(
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noise=noise,
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guider=guider,
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sampler=sampler,
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sigmas=sigmas,
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latent_image=latent_image,
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target_megapixels=target_megapixels,
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nested_noise_strength=nested_noise_strength,
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pin_anchors=pin_anchors,
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)
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return (runtime, runtime.prepared_noise(), clean_latent(runtime.lowres_out))
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class ScaleLockedCFGGuiderNode:
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CATEGORY = "sampling/scale_locked"
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RETURN_TYPES = ("GUIDER",)
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RETURN_NAMES = ("guider",)
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FUNCTION = "build"
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"guider": ("GUIDER",),
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"runtime": ("SLRD_RUNTIME",),
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"lock_strength": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"lock_strength_start": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"lock_strength_end": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"lock_schedule": (LOCK_SCHEDULE_OPTIONS,),
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"lock_schedule_hold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.95, "step": 0.01, "round": 0.001}),
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"lock_schedule_power": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1, "round": 0.01}),
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"coarse_cutoff": ("FLOAT", {"default": 0.33, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
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"mid_band_cutoff": ("FLOAT", {"default": 0.60, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
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"mid_band_strength": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 2.0, "step": 0.01, "round": 0.001}),
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"mid_band_strength_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"mid_band_strength_end": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"mid_band_schedule": (MID_SCHEDULE_OPTIONS,),
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"mid_band_schedule_hold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.95, "step": 0.01, "round": 0.001}),
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"mid_band_schedule_power": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1, "round": 0.01}),
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"manifold_enabled": ("BOOLEAN", {"default": False}),
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"manifold_strength": ("FLOAT", {"default": 0.45, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_strength_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_strength_end": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_schedule": (LOCK_SCHEDULE_OPTIONS,),
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"manifold_schedule_hold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.95, "step": 0.01, "round": 0.001}),
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"manifold_schedule_power": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1, "round": 0.01}),
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"manifold_cutoff": ("FLOAT", {"default": 0.18, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_radial_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01, "round": 0.001}),
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"manifold_anisotropy": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 2.0, "step": 0.01, "round": 0.001}),
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"manifold_translation_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01, "round": 0.001}),
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"manifold_anchor_mix": ("FLOAT", {"default": 0.18, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_mean_anchor_mix": ("FLOAT", {"default": 0.12, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_contrast_restore": ("FLOAT", {"default": 0.10, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_energy_tether": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_channel_tether": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
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"manifold_energy_gain_cap": ("FLOAT", {"default": 1.75, "min": 1.0, "max": 4.0, "step": 0.05, "round": 0.01}),
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"manifold_max_shift_px": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 64.0, "step": 0.1, "round": 0.01}),
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},
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"optional": {
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"lock_mask": ("MASK",),
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"manifold_mask": ("MASK",),
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},
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}
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def build(
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self,
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guider,
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runtime,
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lock_strength,
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lock_strength_start,
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lock_strength_end,
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lock_schedule,
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lock_schedule_hold,
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lock_schedule_power,
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coarse_cutoff,
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mid_band_cutoff,
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mid_band_strength,
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mid_band_strength_start,
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mid_band_strength_end,
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mid_band_schedule,
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mid_band_schedule_hold,
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mid_band_schedule_power,
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manifold_enabled,
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manifold_strength,
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manifold_strength_start,
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manifold_strength_end,
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manifold_schedule,
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manifold_schedule_hold,
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manifold_schedule_power,
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manifold_cutoff,
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manifold_radial_strength,
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manifold_anisotropy,
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manifold_translation_strength,
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manifold_anchor_mix,
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manifold_mean_anchor_mix,
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manifold_contrast_restore,
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manifold_energy_tether,
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manifold_channel_tether,
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manifold_energy_gain_cap,
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manifold_max_shift_px,
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lock_mask=None,
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manifold_mask=None,
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):
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patched_guider = clone_guider_for_scale_lock(guider)
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apply_scale_lock_to_guider(
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patched_guider,
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runtime,
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_scale_lock_config(
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lock_strength=lock_strength,
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lock_strength_start=lock_strength_start,
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lock_strength_end=lock_strength_end,
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coarse_cutoff=coarse_cutoff,
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mid_band_cutoff=mid_band_cutoff,
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mid_band_strength=mid_band_strength,
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lock_schedule=lock_schedule,
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lock_schedule_hold=lock_schedule_hold,
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lock_schedule_power=lock_schedule_power,
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mid_band_strength_start=mid_band_strength_start,
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mid_band_strength_end=mid_band_strength_end,
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mid_band_schedule=mid_band_schedule,
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mid_band_schedule_hold=mid_band_schedule_hold,
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mid_band_schedule_power=mid_band_schedule_power,
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manifold_enabled=manifold_enabled,
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manifold_strength=manifold_strength,
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manifold_strength_start=manifold_strength_start,
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manifold_strength_end=manifold_strength_end,
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manifold_schedule=manifold_schedule,
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manifold_schedule_hold=manifold_schedule_hold,
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manifold_schedule_power=manifold_schedule_power,
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manifold_cutoff=manifold_cutoff,
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manifold_radial_strength=manifold_radial_strength,
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manifold_anisotropy=manifold_anisotropy,
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manifold_translation_strength=manifold_translation_strength,
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manifold_anchor_mix=manifold_anchor_mix,
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manifold_mean_anchor_mix=manifold_mean_anchor_mix,
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manifold_contrast_restore=manifold_contrast_restore,
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manifold_energy_tether=manifold_energy_tether,
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manifold_channel_tether=manifold_channel_tether,
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manifold_energy_gain_cap=manifold_energy_gain_cap,
|
|
manifold_max_shift_px=manifold_max_shift_px,
|
|
lock_mask=lock_mask,
|
|
manifold_mask=manifold_mask,
|
|
),
|
|
)
|
|
return (patched_guider,)
|
|
|
|
|
|
class ScaleLockedResidualSamplerCustomAdvanced:
|
|
CATEGORY = "sampling/scale_locked"
|
|
RETURN_TYPES = ("LATENT", "LATENT", "LATENT")
|
|
RETURN_NAMES = ("output", "lowres_planner", "denoised_output")
|
|
FUNCTION = "sample"
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"noise": ("NOISE",),
|
|
"guider": ("GUIDER",),
|
|
"sampler": ("SAMPLER",),
|
|
"sigmas": ("SIGMAS",),
|
|
"latent_image": ("LATENT",),
|
|
"target_megapixels": ("FLOAT", {"default": 1.0, "min": 0.10, "max": 16.0, "step": 0.05, "round": 0.01}),
|
|
"lock_strength": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"lock_strength_start": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"lock_strength_end": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"lock_schedule": (LOCK_SCHEDULE_OPTIONS,),
|
|
"lock_schedule_hold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.95, "step": 0.01, "round": 0.001}),
|
|
"lock_schedule_power": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1, "round": 0.01}),
|
|
"coarse_cutoff": ("FLOAT", {"default": 0.33, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"mid_band_cutoff": ("FLOAT", {"default": 0.60, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"mid_band_strength": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 2.0, "step": 0.01, "round": 0.001}),
|
|
"mid_band_strength_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"mid_band_strength_end": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"mid_band_schedule": (MID_SCHEDULE_OPTIONS,),
|
|
"mid_band_schedule_hold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.95, "step": 0.01, "round": 0.001}),
|
|
"mid_band_schedule_power": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1, "round": 0.01}),
|
|
"manifold_enabled": ("BOOLEAN", {"default": False}),
|
|
"manifold_strength": ("FLOAT", {"default": 0.45, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_strength_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_strength_end": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_schedule": (LOCK_SCHEDULE_OPTIONS,),
|
|
"manifold_schedule_hold": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 0.95, "step": 0.01, "round": 0.001}),
|
|
"manifold_schedule_power": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 8.0, "step": 0.1, "round": 0.01}),
|
|
"manifold_cutoff": ("FLOAT", {"default": 0.18, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_radial_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_anisotropy": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 2.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_translation_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_anchor_mix": ("FLOAT", {"default": 0.18, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_mean_anchor_mix": ("FLOAT", {"default": 0.12, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_contrast_restore": ("FLOAT", {"default": 0.10, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_energy_tether": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_channel_tether": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_energy_gain_cap": ("FLOAT", {"default": 1.75, "min": 1.0, "max": 4.0, "step": 0.05, "round": 0.01}),
|
|
"manifold_max_shift_px": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 64.0, "step": 0.1, "round": 0.01}),
|
|
"nested_noise_strength": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 4.0, "step": 0.01, "round": 0.001}),
|
|
"pin_anchors": ("BOOLEAN", {"default": True}),
|
|
},
|
|
"optional": {
|
|
"lock_mask": ("MASK",),
|
|
"manifold_mask": ("MASK",),
|
|
},
|
|
}
|
|
|
|
def sample(
|
|
self,
|
|
noise,
|
|
guider,
|
|
sampler,
|
|
sigmas,
|
|
latent_image,
|
|
target_megapixels,
|
|
lock_strength,
|
|
lock_strength_start,
|
|
lock_strength_end,
|
|
lock_schedule,
|
|
lock_schedule_hold,
|
|
lock_schedule_power,
|
|
coarse_cutoff,
|
|
mid_band_cutoff,
|
|
mid_band_strength,
|
|
mid_band_strength_start,
|
|
mid_band_strength_end,
|
|
mid_band_schedule,
|
|
mid_band_schedule_hold,
|
|
mid_band_schedule_power,
|
|
manifold_enabled,
|
|
manifold_strength,
|
|
manifold_strength_start,
|
|
manifold_strength_end,
|
|
manifold_schedule,
|
|
manifold_schedule_hold,
|
|
manifold_schedule_power,
|
|
manifold_cutoff,
|
|
manifold_radial_strength,
|
|
manifold_anisotropy,
|
|
manifold_translation_strength,
|
|
manifold_anchor_mix,
|
|
manifold_mean_anchor_mix,
|
|
manifold_contrast_restore,
|
|
manifold_energy_tether,
|
|
manifold_channel_tether,
|
|
manifold_energy_gain_cap,
|
|
manifold_max_shift_px,
|
|
nested_noise_strength,
|
|
pin_anchors,
|
|
lock_mask=None,
|
|
manifold_mask=None,
|
|
):
|
|
runtime = build_runtime_context_from_advanced(
|
|
noise=noise,
|
|
guider=guider,
|
|
sampler=sampler,
|
|
sigmas=sigmas,
|
|
latent_image=latent_image,
|
|
target_megapixels=target_megapixels,
|
|
nested_noise_strength=nested_noise_strength,
|
|
pin_anchors=pin_anchors,
|
|
)
|
|
result = sample_with_runtime(
|
|
guider=guider,
|
|
sampler=sampler,
|
|
runtime=runtime,
|
|
config=_scale_lock_config(
|
|
lock_strength=lock_strength,
|
|
lock_strength_start=lock_strength_start,
|
|
lock_strength_end=lock_strength_end,
|
|
coarse_cutoff=coarse_cutoff,
|
|
mid_band_cutoff=mid_band_cutoff,
|
|
mid_band_strength=mid_band_strength,
|
|
lock_schedule=lock_schedule,
|
|
lock_schedule_hold=lock_schedule_hold,
|
|
lock_schedule_power=lock_schedule_power,
|
|
mid_band_strength_start=mid_band_strength_start,
|
|
mid_band_strength_end=mid_band_strength_end,
|
|
mid_band_schedule=mid_band_schedule,
|
|
mid_band_schedule_hold=mid_band_schedule_hold,
|
|
mid_band_schedule_power=mid_band_schedule_power,
|
|
manifold_enabled=manifold_enabled,
|
|
manifold_strength=manifold_strength,
|
|
manifold_strength_start=manifold_strength_start,
|
|
manifold_strength_end=manifold_strength_end,
|
|
manifold_schedule=manifold_schedule,
|
|
manifold_schedule_hold=manifold_schedule_hold,
|
|
manifold_schedule_power=manifold_schedule_power,
|
|
manifold_cutoff=manifold_cutoff,
|
|
manifold_radial_strength=manifold_radial_strength,
|
|
manifold_anisotropy=manifold_anisotropy,
|
|
manifold_translation_strength=manifold_translation_strength,
|
|
manifold_anchor_mix=manifold_anchor_mix,
|
|
manifold_mean_anchor_mix=manifold_mean_anchor_mix,
|
|
manifold_contrast_restore=manifold_contrast_restore,
|
|
manifold_energy_tether=manifold_energy_tether,
|
|
manifold_channel_tether=manifold_channel_tether,
|
|
manifold_energy_gain_cap=manifold_energy_gain_cap,
|
|
manifold_max_shift_px=manifold_max_shift_px,
|
|
lock_mask=lock_mask,
|
|
manifold_mask=manifold_mask,
|
|
),
|
|
)
|
|
return (result.output, result.lowres_planner, result.denoised_output)
|
|
|
|
|
|
class ScaleLockedNestedNoisePreview:
|
|
CATEGORY = "sampling/scale_locked"
|
|
RETURN_TYPES = ("LATENT", "LATENT")
|
|
RETURN_NAMES = ("nested_noise_latent", "lowres_reference_latent")
|
|
FUNCTION = "build"
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"latent_image": ("LATENT",),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
|
|
"target_megapixels": ("FLOAT", {"default": 1.0, "min": 0.10, "max": 16.0, "step": 0.05, "round": 0.01}),
|
|
"nested_noise_strength": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 4.0, "step": 0.01, "round": 0.001}),
|
|
}
|
|
}
|
|
|
|
def build(self, latent_image, seed, target_megapixels, nested_noise_strength):
|
|
high = clone_latent(latent_image)
|
|
low = make_lowres_latent(high, target_megapixels)
|
|
|
|
low_samples = low["samples"]
|
|
high_samples = high["samples"]
|
|
low_noise = prepare_noise(low_samples, int(seed), low.get("batch_index", None))
|
|
high_noise = build_nested_noise(
|
|
lowres_noise=low_noise,
|
|
target_shape=tuple(high_samples.shape),
|
|
seed=int(seed) ^ 0x9E3779B97F4A7C15,
|
|
hf_strength=nested_noise_strength,
|
|
)
|
|
|
|
out = clone_latent(high)
|
|
out["samples"] = high_noise
|
|
return (out, low)
|
|
|
|
|
|
@dataclass
|
|
class _ImpactSampleRequest:
|
|
model: Any
|
|
seed: int
|
|
steps: int
|
|
cfg: float
|
|
sampler_name: str
|
|
scheduler: str
|
|
positive: Any
|
|
negative: Any
|
|
latent: dict
|
|
denoise: float
|
|
|
|
|
|
_IMPACT_FIELD_ALIASES = {
|
|
"model": ("model",),
|
|
"seed": ("seed",),
|
|
"steps": ("steps",),
|
|
"cfg": ("cfg",),
|
|
"sampler_name": ("sampler_name", "sampler"),
|
|
"scheduler": ("scheduler",),
|
|
"positive": ("positive", "positive_cond", "cond"),
|
|
"negative": ("negative", "negative_cond", "uncond"),
|
|
"latent": ("latent", "latent_image"),
|
|
"denoise": ("denoise",),
|
|
}
|
|
|
|
|
|
@dataclass
|
|
class _ImpactHookSettings:
|
|
config: ScaleLockConfig
|
|
|
|
|
|
def _clone_latent_with_samples(latent: dict, samples: torch.Tensor) -> dict:
|
|
out = clone_latent(latent)
|
|
out["samples"] = samples
|
|
return out
|
|
|
|
|
|
def _expand_mask_for_like(mask: torch.Tensor | None, like: torch.Tensor) -> torch.Tensor | None:
|
|
if mask is None:
|
|
return None
|
|
|
|
m = mask
|
|
if m.ndim == 2:
|
|
m = m.unsqueeze(0).unsqueeze(0)
|
|
elif m.ndim == 3:
|
|
m = m.unsqueeze(1)
|
|
elif m.ndim == 4:
|
|
if m.shape[-1] == 1 and m.shape[1] != 1:
|
|
m = m.permute(0, 3, 1, 2)
|
|
elif m.shape[1] != 1:
|
|
m = m.mean(dim=1, keepdim=True)
|
|
else:
|
|
raise ValueError(f"Unsupported mask rank {m.ndim} for shape {tuple(m.shape)}")
|
|
|
|
m = m.to(device=like.device, dtype=like.dtype, non_blocking=True)
|
|
if tuple(m.shape[-2:]) != tuple(like.shape[-2:]):
|
|
m = resize_4d_tensor(m, tuple(like.shape[-2:]))
|
|
if m.shape[0] < like.shape[0]:
|
|
repeat = math.ceil(like.shape[0] / max(1, m.shape[0]))
|
|
m = m.repeat(repeat, 1, 1, 1)[: like.shape[0]]
|
|
elif m.shape[0] > like.shape[0]:
|
|
m = m[: like.shape[0]]
|
|
return m.clamp(0.0, 1.0)
|
|
|
|
|
|
def _combine_masks(*masks: torch.Tensor | None) -> torch.Tensor | None:
|
|
combined = None
|
|
for mask in masks:
|
|
if mask is None:
|
|
continue
|
|
combined = mask if combined is None else combined * mask
|
|
return None if combined is None else combined.clamp(0.0, 1.0)
|
|
|
|
|
|
def _impact_request_tuple_from_kwargs(kwargs: dict[str, Any]) -> tuple[Any, ...]:
|
|
if not kwargs:
|
|
return ()
|
|
|
|
data = {}
|
|
for field, aliases in _IMPACT_FIELD_ALIASES.items():
|
|
for alias in aliases:
|
|
if alias in kwargs:
|
|
data[field] = kwargs[alias]
|
|
break
|
|
if not data:
|
|
return ()
|
|
|
|
names = ("model", "seed", "steps", "cfg", "sampler_name", "scheduler", "positive", "negative", "latent", "denoise")
|
|
return tuple(data.get(name) for name in names)
|
|
|
|
|
|
class _ScaleLockedDetailerHook:
|
|
def __init__(self, settings: _ImpactHookSettings):
|
|
self._settings = settings
|
|
self._pending_request: _ImpactSampleRequest | None = None
|
|
self._step_info: Any | None = None
|
|
self._upscale_mask: torch.Tensor | None = None
|
|
self._anchor_latent: torch.Tensor | None = None
|
|
self._latent_mask: torch.Tensor | None = None
|
|
|
|
def _clear_detailer_state(self):
|
|
self._upscale_mask = None
|
|
self._anchor_latent = None
|
|
self._latent_mask = None
|
|
|
|
def get_skip_sampling(self):
|
|
return False
|
|
|
|
def set_steps(self, info):
|
|
self._step_info = info
|
|
|
|
def post_crop_region(self, w, h, item_bbox, crop_region):
|
|
del w, h, item_bbox
|
|
return crop_region
|
|
|
|
def post_detection(self, segs):
|
|
return segs
|
|
|
|
def touch_scaled_size(self, w, h):
|
|
return w, h
|
|
|
|
def post_upscale(self, pixels, mask=None):
|
|
self._clear_detailer_state()
|
|
self._upscale_mask = None if mask is None else mask.detach().clone()
|
|
return pixels
|
|
|
|
def post_encode(self, samples):
|
|
if isinstance(samples, dict) and isinstance(samples.get("samples", None), torch.Tensor):
|
|
self._anchor_latent = samples["samples"].detach().clone()
|
|
self._latent_mask = _expand_mask_for_like(self._upscale_mask, samples["samples"])
|
|
else:
|
|
self._anchor_latent = None
|
|
self._latent_mask = None
|
|
return samples
|
|
|
|
def pre_decode(self, samples):
|
|
if self._anchor_latent is None:
|
|
return samples
|
|
if not isinstance(samples, dict) or "samples" not in samples or not isinstance(samples["samples"], torch.Tensor):
|
|
return samples
|
|
|
|
cfg = self._settings.config
|
|
base = samples["samples"]
|
|
anchor = self._anchor_latent.to(device=base.device, dtype=base.dtype, non_blocking=True)
|
|
if tuple(anchor.shape[-2:]) != tuple(base.shape[-2:]):
|
|
anchor = resize_4d_tensor(anchor, tuple(base.shape[-2:]))
|
|
face_mask = _expand_mask_for_like(self._latent_mask, base)
|
|
lock_mask = _combine_masks(face_mask, _expand_mask_for_like(cfg.spatial_mask, base))
|
|
manifold_source_mask = cfg.manifold_spatial_mask if cfg.manifold_spatial_mask is not None else cfg.spatial_mask
|
|
manifold_mask = _combine_masks(face_mask, _expand_mask_for_like(manifold_source_mask, base))
|
|
|
|
corrected = base
|
|
if cfg.lock_strength > 0.0 or cfg.mid_strength > 0.0:
|
|
locked = residual_lock_multiband(
|
|
corrected,
|
|
anchor,
|
|
low_strength=float(max(0.0, min(1.0, cfg.lock_strength))),
|
|
mid_strength=float(max(0.0, min(1.0, cfg.mid_strength))),
|
|
low_cutoff=cfg.cutoff,
|
|
mid_cutoff=cfg.mid_cutoff,
|
|
)
|
|
corrected = corrected + lock_mask * (locked - corrected) if lock_mask is not None else locked
|
|
|
|
if cfg.manifold_enabled and cfg.manifold_strength > 0.0:
|
|
manifold = latent_manifold_compand(
|
|
corrected,
|
|
anchor,
|
|
mask=manifold_mask,
|
|
strength=float(max(0.0, min(1.0, cfg.manifold_strength))),
|
|
cutoff=cfg.manifold_cutoff,
|
|
radial_strength=cfg.manifold_radial_strength,
|
|
anisotropy=cfg.manifold_anisotropy,
|
|
translation_strength=cfg.manifold_translation_strength,
|
|
anchor_mix=cfg.manifold_anchor_mix,
|
|
mean_anchor_mix=cfg.manifold_mean_anchor_mix,
|
|
contrast_restore=cfg.manifold_contrast_restore,
|
|
energy_tether=cfg.manifold_energy_tether,
|
|
channel_tether=cfg.manifold_channel_tether,
|
|
energy_gain_cap=cfg.manifold_energy_gain_cap,
|
|
max_shift_px=cfg.manifold_max_shift_px,
|
|
)
|
|
corrected = corrected + manifold_mask * (manifold - corrected) if manifold_mask is not None else manifold
|
|
|
|
return _clone_latent_with_samples(samples, corrected)
|
|
|
|
def post_decode(self, pixels):
|
|
return pixels
|
|
|
|
def cycle_latent(self, latent):
|
|
return latent
|
|
|
|
def post_paste(self, image):
|
|
return image
|
|
|
|
def get_custom_noise(self, seed, noise, is_touched):
|
|
del seed
|
|
return noise, is_touched
|
|
|
|
def should_retry_patch(self, image):
|
|
del image
|
|
return False
|
|
|
|
def get_custom_sampler(self, *args, **kwargs):
|
|
self._remember_request(args, kwargs, strict=False)
|
|
return None
|
|
|
|
def get_custom_sampler_provider(self, *args, **kwargs):
|
|
return self.get_custom_sampler(*args, **kwargs)
|
|
|
|
def get_custom_ksampler_provider(self, *args, **kwargs):
|
|
return self.get_custom_sampler(*args, **kwargs)
|
|
|
|
def pre_ksample(self, *args, **kwargs):
|
|
request = self._remember_request(args, kwargs, strict=False)
|
|
if request is None:
|
|
return _impact_request_tuple_from_kwargs(kwargs) if kwargs else args
|
|
return (
|
|
request.model,
|
|
request.seed,
|
|
request.steps,
|
|
request.cfg,
|
|
request.sampler_name,
|
|
request.scheduler,
|
|
request.positive,
|
|
request.negative,
|
|
request.latent,
|
|
request.denoise,
|
|
)
|
|
|
|
def post_ksample(self, *args, **kwargs):
|
|
self._pending_request = None
|
|
self._clear_detailer_state()
|
|
if kwargs:
|
|
return kwargs
|
|
if len(args) == 1:
|
|
return args[0]
|
|
return args
|
|
|
|
def _remember_request(self, args, kwargs, strict: bool) -> _ImpactSampleRequest | None:
|
|
try:
|
|
request = _coerce_impact_request(args, kwargs, fallback=self._pending_request)
|
|
except TypeError:
|
|
if strict:
|
|
raise
|
|
request = self._pending_request
|
|
if request is not None:
|
|
self._pending_request = request
|
|
return request
|
|
|
|
|
|
class ScaleLockedDetailerHookProvider:
|
|
CATEGORY = "sampling/scale_locked/impact"
|
|
RETURN_TYPES = ("DETAILER_HOOK",)
|
|
RETURN_NAMES = ("detailer_hook",)
|
|
FUNCTION = "build"
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"lock_strength": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"coarse_cutoff": ("FLOAT", {"default": 0.33, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"mid_band_cutoff": ("FLOAT", {"default": 0.60, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"mid_band_strength": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 2.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_enabled": ("BOOLEAN", {"default": False}),
|
|
"manifold_strength": ("FLOAT", {"default": 0.45, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_cutoff": ("FLOAT", {"default": 0.18, "min": 0.05, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_radial_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_anisotropy": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 2.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_translation_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 3.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_anchor_mix": ("FLOAT", {"default": 0.18, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_mean_anchor_mix": ("FLOAT", {"default": 0.12, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_contrast_restore": ("FLOAT", {"default": 0.10, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_energy_tether": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_channel_tether": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
|
|
"manifold_energy_gain_cap": ("FLOAT", {"default": 1.75, "min": 1.0, "max": 4.0, "step": 0.05, "round": 0.01}),
|
|
"manifold_max_shift_px": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 64.0, "step": 0.1, "round": 0.01}),
|
|
},
|
|
"optional": {
|
|
"lock_mask": ("MASK",),
|
|
"manifold_mask": ("MASK",),
|
|
},
|
|
}
|
|
|
|
def build(
|
|
self,
|
|
lock_strength,
|
|
coarse_cutoff,
|
|
mid_band_cutoff,
|
|
mid_band_strength,
|
|
manifold_enabled,
|
|
manifold_strength,
|
|
manifold_cutoff,
|
|
manifold_radial_strength,
|
|
manifold_anisotropy,
|
|
manifold_translation_strength,
|
|
manifold_anchor_mix,
|
|
manifold_mean_anchor_mix,
|
|
manifold_contrast_restore,
|
|
manifold_energy_tether,
|
|
manifold_channel_tether,
|
|
manifold_energy_gain_cap,
|
|
manifold_max_shift_px,
|
|
lock_mask=None,
|
|
manifold_mask=None,
|
|
):
|
|
settings = _ImpactHookSettings(
|
|
config=_scale_lock_config(
|
|
lock_strength=lock_strength,
|
|
lock_strength_start=lock_strength,
|
|
lock_strength_end=lock_strength,
|
|
coarse_cutoff=coarse_cutoff,
|
|
mid_band_cutoff=mid_band_cutoff,
|
|
mid_band_strength=mid_band_strength,
|
|
lock_schedule="flat",
|
|
lock_schedule_hold=0.0,
|
|
lock_schedule_power=1.0,
|
|
mid_band_strength_start=mid_band_strength,
|
|
mid_band_strength_end=mid_band_strength,
|
|
mid_band_schedule="flat",
|
|
mid_band_schedule_hold=0.0,
|
|
mid_band_schedule_power=1.0,
|
|
manifold_enabled=manifold_enabled,
|
|
manifold_strength=manifold_strength,
|
|
manifold_strength_start=manifold_strength,
|
|
manifold_strength_end=manifold_strength,
|
|
manifold_schedule="flat",
|
|
manifold_schedule_hold=0.0,
|
|
manifold_schedule_power=1.0,
|
|
manifold_cutoff=manifold_cutoff,
|
|
manifold_radial_strength=manifold_radial_strength,
|
|
manifold_anisotropy=manifold_anisotropy,
|
|
manifold_translation_strength=manifold_translation_strength,
|
|
manifold_anchor_mix=manifold_anchor_mix,
|
|
manifold_mean_anchor_mix=manifold_mean_anchor_mix,
|
|
manifold_contrast_restore=manifold_contrast_restore,
|
|
manifold_energy_tether=manifold_energy_tether,
|
|
manifold_channel_tether=manifold_channel_tether,
|
|
manifold_energy_gain_cap=manifold_energy_gain_cap,
|
|
manifold_max_shift_px=manifold_max_shift_px,
|
|
lock_mask=lock_mask,
|
|
manifold_mask=manifold_mask,
|
|
),
|
|
)
|
|
return (_ScaleLockedDetailerHook(settings),)
|
|
|
|
|
|
def _scale_lock_config(
|
|
*,
|
|
lock_strength,
|
|
lock_strength_start,
|
|
lock_strength_end,
|
|
coarse_cutoff,
|
|
mid_band_cutoff,
|
|
mid_band_strength,
|
|
lock_schedule,
|
|
lock_schedule_hold,
|
|
lock_schedule_power,
|
|
mid_band_strength_start,
|
|
mid_band_strength_end,
|
|
mid_band_schedule,
|
|
mid_band_schedule_hold,
|
|
mid_band_schedule_power,
|
|
lock_mask,
|
|
manifold_enabled=False,
|
|
manifold_strength=0.0,
|
|
manifold_strength_start=1.0,
|
|
manifold_strength_end=0.0,
|
|
manifold_schedule="ease_out",
|
|
manifold_schedule_hold=0.0,
|
|
manifold_schedule_power=2.0,
|
|
manifold_cutoff=0.18,
|
|
manifold_radial_strength=1.0,
|
|
manifold_anisotropy=0.15,
|
|
manifold_translation_strength=1.0,
|
|
manifold_anchor_mix=0.18,
|
|
manifold_mean_anchor_mix=0.12,
|
|
manifold_contrast_restore=0.10,
|
|
manifold_energy_tether=0.0,
|
|
manifold_channel_tether=0.0,
|
|
manifold_energy_gain_cap=1.75,
|
|
manifold_max_shift_px=3.0,
|
|
manifold_mask=None,
|
|
):
|
|
return ScaleLockConfig(
|
|
lock_strength=float(lock_strength),
|
|
lock_strength_start=float(lock_strength_start),
|
|
lock_strength_end=float(lock_strength_end),
|
|
cutoff=float(coarse_cutoff),
|
|
mid_cutoff=float(mid_band_cutoff),
|
|
mid_strength=float(mid_band_strength),
|
|
schedule=lock_schedule,
|
|
schedule_power=float(lock_schedule_power),
|
|
schedule_hold=float(lock_schedule_hold),
|
|
mid_strength_start=float(mid_band_strength_start),
|
|
mid_strength_end=float(mid_band_strength_end),
|
|
mid_schedule=mid_band_schedule,
|
|
mid_schedule_power=float(mid_band_schedule_power),
|
|
mid_schedule_hold=float(mid_band_schedule_hold),
|
|
spatial_mask=lock_mask,
|
|
manifold_enabled=bool(manifold_enabled),
|
|
manifold_strength=float(manifold_strength),
|
|
manifold_strength_start=float(manifold_strength_start),
|
|
manifold_strength_end=float(manifold_strength_end),
|
|
manifold_schedule=manifold_schedule,
|
|
manifold_schedule_power=float(manifold_schedule_power),
|
|
manifold_schedule_hold=float(manifold_schedule_hold),
|
|
manifold_cutoff=float(manifold_cutoff),
|
|
manifold_radial_strength=float(manifold_radial_strength),
|
|
manifold_anisotropy=float(manifold_anisotropy),
|
|
manifold_translation_strength=float(manifold_translation_strength),
|
|
manifold_anchor_mix=float(manifold_anchor_mix),
|
|
manifold_mean_anchor_mix=float(manifold_mean_anchor_mix),
|
|
manifold_contrast_restore=float(manifold_contrast_restore),
|
|
manifold_energy_tether=float(manifold_energy_tether),
|
|
manifold_channel_tether=float(manifold_channel_tether),
|
|
manifold_energy_gain_cap=float(manifold_energy_gain_cap),
|
|
manifold_max_shift_px=float(manifold_max_shift_px),
|
|
manifold_spatial_mask=manifold_mask,
|
|
)
|
|
|
|
|
|
def _coerce_impact_request(args, kwargs, fallback=None) -> _ImpactSampleRequest:
|
|
data = {}
|
|
if fallback is not None:
|
|
data.update(fallback.__dict__)
|
|
|
|
if len(args) >= 10:
|
|
names = ("model", "seed", "steps", "cfg", "sampler_name", "scheduler", "positive", "negative", "latent", "denoise")
|
|
for name, value in zip(names, args[:10]):
|
|
data[name] = value
|
|
|
|
for field, aliases in _IMPACT_FIELD_ALIASES.items():
|
|
for alias in aliases:
|
|
if alias in kwargs:
|
|
data[field] = kwargs[alias]
|
|
break
|
|
|
|
required = ("model", "seed", "steps", "cfg", "sampler_name", "scheduler", "positive", "negative", "latent", "denoise")
|
|
missing = [field for field in required if field not in data or data[field] is None]
|
|
if missing:
|
|
raise TypeError(f"Unable to resolve Impact detailer sample request fields: {', '.join(missing)}")
|
|
|
|
latent = data["latent"]
|
|
if not isinstance(latent, dict) or "samples" not in latent:
|
|
raise TypeError("Impact detailer sample request did not provide a LATENT dict with a 'samples' entry.")
|
|
|
|
return _ImpactSampleRequest(
|
|
model=data["model"],
|
|
seed=int(data["seed"]),
|
|
steps=int(data["steps"]),
|
|
cfg=float(data["cfg"]),
|
|
sampler_name=str(data["sampler_name"]),
|
|
scheduler=str(data["scheduler"]),
|
|
positive=data["positive"],
|
|
negative=data["negative"],
|
|
latent=data["latent"],
|
|
denoise=float(data["denoise"]),
|
|
)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"ScaleLockedResidualKSampler": ScaleLockedResidualKSampler,
|
|
"ScaleLockedRuntimeContextBuilder": ScaleLockedRuntimeContextBuilder,
|
|
"ScaleLockedCFGGuider": ScaleLockedCFGGuiderNode,
|
|
"ScaleLockedResidualSamplerCustomAdvanced": ScaleLockedResidualSamplerCustomAdvanced,
|
|
"ScaleLockedNestedNoisePreview": ScaleLockedNestedNoisePreview,
|
|
"ScaleLockedDetailerHookProvider": ScaleLockedDetailerHookProvider,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"ScaleLockedResidualKSampler": "Scale-Locked Residual KSampler",
|
|
"ScaleLockedRuntimeContextBuilder": "Scale-Locked Runtime Context",
|
|
"ScaleLockedCFGGuider": "Scale-Locked CFG Guider",
|
|
"ScaleLockedResidualSamplerCustomAdvanced": "Scale-Locked Residual SamplerCustomAdvanced",
|
|
"ScaleLockedNestedNoisePreview": "Scale-Locked Nested Noise Preview",
|
|
"ScaleLockedDetailerHookProvider": "Scale-Locked Detailer Hook Provider",
|
|
}
|
|
|