| Sampler | Description | |---------|-------------| | Adept Solver | Hybrid Predictor-Corrector pipeline using Adams-Bashforth integration (DEIS) with UniPC correction steps and dynamic thresholding | | Adept Ancestral Solver | Enhanced Euler Ancestral with phase-dependent step sizing, adaptive noise injection (Eta), context-aware derivative corrections, and adaptive noise scale | | AkashicSolver v2 | SA-Solver (Stochastic Adams) implementation integrated with SMEA (Sinusoidal Multipass) interpolation for high-res coherence, Combat CFG Drift, and adaptive noise scale. Use external rescaleCFG (0.7) for EQ-VAE models | | Mirror Correction Euler | Euler Ancestral with semantic reflection probe. Uses a 3-call Heun correction (x_probe = 2·D(x) − x) in the first correction_phase fraction of steps for improved curvature estimation. Probe norm limiting for stability. Optional smooth phase decay and adaptive noise scale |

CFG Fix Nodes (1)

| Node | Description | |------|-------------| | Adept Spectral Modulation (CFG) | Patches the model's CFG function to apply Clybius Spectral Modulation (frequency-domain correction) to the noise prediction. Connect MODEL → MODEL before your sampler. Based on ComfyUI-Latent-Modifiers |

Schedulers (19+)

| Category | Schedulers | |----------|-----------| | Anime-Optimized | AOS-V (v-prediction), AOS-ε (epsilon) | | EQ-VAE / Akashic | AkashicAOS (Continuous Power-Function), AkashicAOS Alt (stronger detail bias), AkashicEQFlow (pure CDF-based crossover-focused log-SNR) | | Research-Based | AYS-SDXL (Align Your Steps), JYS (Jump Your Steps), SNR-Optimized | | General Purpose | Entropic, Cosine-Annealed, LogSNR-Uniform, Constant-Rate, Adaptive-Optimized | | Experimental | Stochastic, Jittered-Karras, Hybrid JYS-Karras, Tanh Mid-Boost, Exponential Tail |

Installation

Navigate to your ComfyUI custom nodes folder:
```
cd ComfyUI/custom_nodes/
```

Clone or copy this repository:

git clone https://github.com/nawka12/ComfyUI-Adept-Sampler.git
# OR copy the folder directly

Restart ComfyUI

Usage

Using Schedulers

Connect a scheduler node to generate custom sigma schedules:

[Load Checkpoint] → [Adept Scheduler (AOS-V)] → [SamplerCustom] → [VAE Decode]
                                                      ↑
                                               [Sampler Node]

Using Samplers

Connect a sampler node to use custom sampling algorithms:

[Load Checkpoint] → [BasicScheduler] → [SamplerCustom] → [VAE Decode]
                                             ↑
                                [Adept Solver Sampler]

Using Spectral Modulation

Patch the model before passing it to SamplerCustom:

[Load Checkpoint] → [Adept Spectral Modulation (CFG)] → [SamplerCustom] → [VAE Decode]
                                                               ↑
                                                        [Sampler Node]

Node Reference

Scheduler Nodes

All scheduler nodes take a MODEL input and output SIGMAS.

| Node | Parameters | |------|------------| | Adept Scheduler (AOS-V) | steps | | Adept Scheduler (AOS-ε) | steps | | Adept Scheduler (AkashicAOS) | steps | | Adept Scheduler (AkashicAOS Alt) | steps | | Adept Scheduler (AkashicEQFlow) | steps | | Adept Scheduler (Entropic) | steps, power | | Adept Scheduler (JYS) | steps | | Adept Scheduler (AYS-SDXL) | steps | | Adept Scheduler (Stochastic) | steps, noise_type, noise_scale, base_schedule | | Adept Scheduler (Advanced) | steps, scheduler (dropdown), entropic_power |

Sampler Nodes

All sampler nodes output SAMPLER for use with SamplerCustom.

| Node | Key Parameters | |------|----------------| | Adept Solver Sampler | order (1-3), use_corrector, detail enhancement options | | Adept Ancestral Sampler | eta, s_noise, adaptive_eta, phase_noise, enhanced_derivative, adaptive_noise | | AkashicSolver v2 | tau (0-1), eta, s_noise, order, adaptive_eta, smea_strength, ndb_strength, combat_cfg_drift, combat_drift_intensity, adaptive_noise | | Mirror Correction Euler Sampler | eta, s_noise, correction_phase (0-1), smooth_phase, adaptive_noise |

CFG Fix Nodes

| Node | Key Parameters | Input → Output | |------|----------------|----------------| | Adept Spectral Modulation (CFG) | strength (0-2), percentile (1-15) | MODEL → MODEL |

Recommended Settings

For v-prediction models (e.g., SDXL)

Scheduler: AOS-V or AYS-SDXL
Sampler: Adept Solver (order=2, corrector=on)

For epsilon-prediction models

Scheduler: AOS-ε
Sampler: Adept Ancestral (eta=1.0, adaptive_eta=on)

For EQ-VAE models (e.g., AkashicPulse)

Scheduler: AkashicAOS, AkashicAOS Alt, or AkashicEQFlow
Sampler: AkashicSolver v2 (tau=0.5-0.6, order=2) + external rescaleCFG node (0.7)
Optional: add Adept Spectral Modulation and/or enable Combat CFG Drift

Mirror Correction Euler

Scheduler: Any (Karras, AOS-ε, AkashicAOS)
correction_phase=0.3–0.5 is a good starting point
Enable smooth_phase for EQ-VAE smooth latents

Adaptive Noise Scale

Available on Mirror Correction Euler, Adept Ancestral, and AkashicSolver. When enabled:

Collects noise excess metrics during a warmup phase (texture sigma region 0.5–5.0)
After calibration, restarts generation with a per-phase correction factor applied
Useful when the default s_noise is over- or under-shooting for a given model

Credits

Original reForge extension and algorithms developed for Stable Diffusion WebUI. Ported to ComfyUI as custom nodes.

License

This project is licensed under the GNU General Public License v3.0 (GPL-3.0).

License Summary

Permitted: Commercial use, Modification, Distribution, Patent use, Private use

Requirements: License and copyright notice, State changes, Disclose source, Same license

Limitations: No Liability or Warranty

Why GPL-3.0?

This license ensures compatibility with Stable Diffusion WebUI reForge and its ecosystem, while protecting the open-source nature of the project.

Full license text: https://www.gnu.org/licenses/gpl-3.0.html