ComfyUI Spectrum SDXL Proper

Native ComfyUI custom node for the SDXL U-Net path. It patches the native UNetModel._forward(...) boundary and targets the final hidden feature before the SDXL output head.

Current contract

Spectrum's scheduling invariant lives at the outer solver-step level, not inside repeated low-level model calls. This repository now enforces that conservatively:

• the node installs an outer-step controller at ComfyUI's sampler_calc_cond_batch_function hook
• the model hook does not reconstruct solver steps from sigmas, call order, or repeated timesteps
• forecasting is allowed only when transformer_options contains explicit outer-step context
• otherwise the runtime fails open and executes a real denoiser forward

Required outer-step keys:

• spectrum_run_id - unique identifier for the current sampling run
• spectrum_solver_step_id - ordinal solver step index (0, 1, 2, ...)
• spectrum_time_coord - raw sigma value (sigma-space coordinate)

Optional key:

• spectrum_actual_forward - explicit per-step decision override
• spectrum_total_steps - total number of diffusion steps

The built-in outer-step controller injects run_id, solver_step_id, time_coord, and total_steps automatically for normal ComfyUI sampling. The time_coord represents the raw sigma value from the noise schedule, not an ordinal step index. External controllers should supply raw sigma values in spectrum_time_coord to maintain compatibility with the sigma-space forecasting coordinate system. spectrum_actual_forward can still be supplied by an external controller if it wants to force the per-step decision explicitly.

What the model hook does

The SDXL wrapper is now only a thin execution layer:

• on actual steps, it runs the real U-Net path and records the final hidden feature
• on forecast steps, it reads the outer-step context and asks the runtime for a prediction
• if context is missing, malformed, or stream identity is missing, it falls back to an actual forward

The hook does not own solver-step identities. When spectrum_actual_forward is absent, the runtime schedules from the explicit outer step id instead of reconstructing steps from repeated low-level calls.

This still assumes the hooked sampler_calc_cond_batch_function path is called once per forecastable denoiser evaluation in the samplers you care about. That boundary is better than the U-Net hook, but it is not a universal proof for every sampler layout.

Node

Spectrum Apply SDXL returns a patched MODEL.

Inputs:

• model
• enabled
• blend_weight
• degree
• ridge_lambda
• window_size
• flex_window
• warmup_steps
• tail_actual_steps
• min_fit_points
• debug

window_size, flex_window, and warmup_steps are now consumed by the built-in outer-step controller path. min_fit_points sets the minimum number of actual observations required before the forecaster is even eligible; the runtime still requires at least max(3, degree + 1, min_fit_points) actual history points and also requires the recent one-step validation error to stay below a conservative internal threshold before a forecast can be used. tail_actual_steps reserves the last N solver steps for real forwards only, even if forecasting history is ready. Set it to 0 to preserve the older behavior with no protected real tail. External controllers can also provide explicit per-step decisions through spectrum_actual_forward, but the runtime still forces the protected tail onto the real path.

Usage

Typical placement:

CheckpointLoaderSimple
  -> LoRA / model patches
  -> Spectrum Apply SDXL
  -> sampler / guider

For normal ComfyUI sampling, the node installs its own outer-step controller and stamps explicit Spectrum step metadata before the U-Net hook runs.

Implementation notes

• Forecast target: the final hidden U-Net feature before self.out(h)
• Forecast history: populated only from actual forwards
• Forecast coordinate: spectrum_time_coord in sigma-space (raw sigma values)
• Step identity: spectrum_solver_step_id (ordinal step index)
• Run boundary: spectrum_run_id
• Stream isolation: uuids + cond_or_uncond + input shape

The forecaster normalizes sigma values to the Chebyshev domain using the actual min/max range of observed sigma coordinates. This allows the predictor to correctly handle arbitrary continuous sigma schedules without assuming ordinal step spacing. The runtime uses sample_sigmas length as a fallback for spectrum_total_steps when not explicitly provided.

Smoke test

Run:

python tests/smoke_runtime.py

The smoke suite covers:

• fail-open behavior without explicit solver-step context
• explicit-context forecasting
• outer-step controller context injection
• run-id-based state reset
• protected real-tail enforcement, including tail_actual_steps >= total_steps

Expected output:

ok

References

• Paper: Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration
• Spectrum project page: Spectrum
• Official implementation: hanjq17/Spectrum