olivv-cs-ComfyUI-FunPack/docs/FunPackHybridEuler2SSampler.md

FunPack Hybrid Euler 2S Sampler

This node outputs a ComfyUI SAMPLER and can also take optional SIGMAS input and output modified SIGMAS for use with CustomSamplerAdvanced / SamplerCustomAdvanced.

Purpose

It is designed as a quality/speed compromise between:

• euler_ancestral: fast, reliable, one model evaluation per step
• deterministic 2S-style ODE refinement: higher quality, but more expensive because it needs extra evaluations

This sampler keeps classic Euler ancestral for the early "structure building" stage where motion, anatomy and main composition are forming. On the late denoise steps it switches to a deterministic Euler / DPM-Solver++(2S) ODE refinement path where fine detail and cleanup usually matter most.

It can also build an optional paper-style Restart schedule at the progress point you choose:

• detect a restart anchor across the full sigma schedule
• insert upward sigma jumps back to a higher sigma
• replay the chosen sigma interval one or more times

Unlike the older implementation, Restart is now encoded directly into the outgoing SIGMAS schedule, so the visible schedule passed into SamplerCustomAdvanced is the actual schedule that gets executed.

Recommended wiring

Use this node with SamplerCustomAdvanced or CustomSamplerAdvanced:

• connect your base scheduler output into this node's optional sigmas input
• connect this node's sampler output into the sampler input
• connect this node's sigmas output into the sigmas input

This node is not intended for KSampler.

Parameters

eta: Ancestral stochasticity. 1.0 keeps normal ancestral behavior.

s_noise: Noise multiplier for ancestral noise injection.

high_quality_pct: Fraction of the late denoise steps that switch from Euler ancestral into deterministic quality refinement.
Example: 0.35 means only the last 35% of steps use the ODE refinement path.

correction_blend: Blend between late-step Euler ODE and late-step 2S correction.

• 0.0 = pure late-step Euler ODE
• 1.0 = full late-step DPM++(2S)-style correction

restart_steps: Target size of the restarted sigma interval. The node interprets this against the actual sigma curve and expands the outgoing SIGMAS schedule accordingly.

restart_repeats: How many Restart loops to run. 0 disables Restart.

restart_trigger_pct: Sampling progress point where Restart is triggered across the full schedule. This is independent from high_quality_pct, so values such as 0.30 can trigger an early restart instead of being clamped to the Euler-to-2S transition point.

restart_noise: Noise strength used when re-noising the latent up to the restart interval's higher sigma.

sigmas: Optional incoming sigma schedule. If connected, the node outputs a restart-expanded sigma schedule that matches the sampler settings. If not connected, the sampler output still works, but there is no schedule for the node to rewrite.

Recommended starting values

• eta = 1.0
• s_noise = 1.0
• high_quality_pct = 0.30 to 0.40
• correction_blend = 1.0
• restart_repeats = 0 for baseline testing

For a first Restart test, try:

• high_quality_pct = 0.35
• correction_blend = 1.0
• restart_steps = 3
• restart_repeats = 1
• restart_trigger_pct = 0.85
• restart_noise = 1.0

Example: with 8 base sampling steps, restart_steps = 3, and restart_repeats = 1, a normal evenly spaced schedule will usually expand into a schedule with about 3 extra restart transitions. With custom sigmas, the exact replay step count can shift slightly because Restart is anchored to the real sigma values rather than raw step indices.

Expected behavior

Compared to plain euler_ancestral, this sampler should usually:

• preserve more late-stage detail
• clean up texture and edge quality better
• keep the early motion/anatomy formation more lively
• cost less than running a heavier deterministic solver for the whole schedule

With Restart enabled, it may further improve late cleanup and detail consistency, but it will increase runtime and may be too aggressive for some video workflows if overused.

Because Restart is now represented in the outgoing SIGMAS, the total number of executed transitions should match what the expanded schedule shows in the downstream custom sampler.

Limitation

This node improves the sampler-side quality/speed tradeoff. It does not reduce the underlying cost of CFG++ guidance itself. If the goal becomes specifically "CFG++ quality at lower guidance cost", that likely requires a custom GUIDER, not just a custom SAMPLER.