ComfyUI-ApplySpectralLock

Apply Spectral Lock (Flux/USDU) — a ComfyUI model patch that performs a Latent Spectral Handoff on 16-channel Flux latents during sampling. It uses two decoupled controls:

• Structure lock — low frequencies are anchored to a reference latent (latent_orig). Active from step 0, so composition / color / lighting are preserved even at 0.8 denoise.
• Detail gate (variance matching) — instead of blanket dampening, the high frequencies are renormalized to match the reference's per-channel contrast/energy. This keeps real, crisp detail while stopping the Rectified Flow solver from overshooting into digital grain ("Turbo paradox"). The gate follows a schedule-aware cosine (or linear) envelope, engaging mostly over the final steps.

Requirements

• ComfyUI (recent master)
• PyTorch
• Flux-family models with 16 latent channels (SD 4-channel latents are passed through unchanged)

Install

Clone into ComfyUI/custom_nodes/:

ComfyUI/custom_nodes/ComfyUI-ApplySpectralLock/

Restart ComfyUI.

Usage

1. Load your MODEL as usual.
2. Provide latent_orig: the full-resolution (or reference) latent corresponding to the uncorrupted base structure you want to lock to (image2latent / VAE encode of the guide image).
3. Run ApplySpectralLock → connect patched_model into your sampler / Ultimate SD Upscale chain before sampling.

Parameters

| Input | Role | |--------|------| | kernel_size | Odd kernel size for separable Gaussian low-pass (default 15). | | blur_sigma | Gaussian sigma for the 1D kernels (default 3.0). | | alpha_lock | Structure lock: blend weight for anchoring low frequencies to latent_orig (0–1, default 0.85). Active from step 0. | | gamma_max | Detail gate: maximum blend toward the soft-clipped high band over the final steps (0–1, default 0.70). Higher = stronger grain suppression. | | clip_k | Soft-clip knee in units of the high-band sigma (default 2.0). Spikes beyond k*sigma are tamed; ordinary detail (|H| << knee) passes through. Lower = more aggressive grain removal; higher = gentler / more detail kept. | | decay_type | cosine (default, slow start then firm asymptote) or linear progress scaling for the detail gate. | | edge_taper | Spatial edge-gating width in latent cells (default 48, 0 disables). The spectral adjustment is cosine-tapered to zero at the tile border so USDU seam blending sees no discontinuity. Auto-clamped to half the tile size. |

Ultimate SD Upscale (USDU)

For tiled runs, the patcher estimates the tile’s (y, x) offset in latent space via normalized template matching on channel-mean maps, then crops latent_orig once per tile and caches the blurred reference low-pass for the rest of that tile’s steps.

Note: Very large Gaussian radii relative to USDU overlap can still stress seams; keep kernel_size coherent with your tile overlap.

Flux CFG = 1.0

The hook is registered with disable_cfg1_optimization=True so the post-CFG callback still runs when CFG is disabled (common for distilled / single-stream Flux runs).

Implementation notes

• Depthwise separable Gaussian: two F.conv2d passes with groups=16, F.pad(..., mode='reflect') before each pass.
• Detail-preserving grain suppression: the high band is soft-clipped with a tanh knee at clip_k * sigma. Grain/"deep-fry" is sparse extreme spikes, so clipping tames them while ordinary structured detail (|H| << knee) passes through unchanged — it does NOT normalize energy toward the reference (which would blur detail down to a soft base).
• Spatial edge-gating: a cached cosine taper mask zeroes the spectral delta (X_new - denoised) at tile borders (edge_taper), so USDU seam blending never reconciles a discontinuity introduced here.
• Decoupled envelopes: structure lock (alpha) is applied every step; the variance gate (gamma) ramps with schedule progress.
• Float32 for blur / variance / NCC internals; outputs are cast back to denoised.dtype and device.
• Sigma progress: schedule-agnostic progress = 1 - sigma / max_sigma_seen for the current tile/run.

License

MIT (c) 2026 lookuters22.