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
spectrum_solver_step_id
spectrum_time_coord

Optional key:

spectrum_actual_forward
spectrum_total_steps

The built-in outer-step controller injects run_id, solver_step_id, time_coord, and total_steps automatically for normal ComfyUI sampling. In the current implementation, time_coord is the same ordinal outer-step index used by the forecaster, not the raw sigma value. 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
debug

window_size, flex_window, and warmup_steps are now consumed by the built-in outer-step controller path. External controllers can also provide explicit per-step decisions through spectrum_actual_forward.

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 ordinal outer-step space
Step identity: spectrum_solver_step_id
Run boundary: spectrum_run_id
Stream isolation: uuids + cond_or_uncond + input shape

The runtime may still use sample_sigmas length as a fallback for normalization when spectrum_total_steps is absent, but it does not use sigma values to decide solver steps or forecast eligibility. Raw sigma-space forecasting is not implemented in this tree.

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

Expected output:

ok

References

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