FSampler is a training‑free, sampler‑agnostic acceleration layer for diffusion sampling that reduces model calls by predicting each step’s epsilon
(noise) from recent real calls and feeding it into the existing integrator. It provides fixed history modes (h2/h3/h4) and an aggressive adaptive
mode that, per step, builds two predictions (e.g., h3 vs h2), compares their predicted next states to get a relative error, and skips the model call
when that error is below a hardcoded tolerance. Predicted epsilons are validated and scaled by a universal learning stabilizer L, skips are bounded
by guard rails, and the sampler math (Euler, RES 2M/2S, DDIM, DPM++ 2M/2S, LMS) is unchanged.
- Manual step selection with extrapolation method control (Good for low step count workflows)
- Take precise control over which steps to skip and how predictions are made using the
skip_indicesparameter (available in FSampler Advanced node).
- Training‑free acceleration that skips full model calls using predicted epsilon (noise) from recent REAL steps.
- Works with existing samplers: Euler, RES 2M/2S, DDIM, DPM++ 2M/2S, LMS, RES_Multistep.
- Stability via a universal learning stabilizer L and strict validators; clear per‑step diagnostics.
- Since all equations are deterministic, running high skips will still produce very similar results as if ran with no skips menaing you can generate alot more test quicker before using a single seed for production.
- Very simple extrapolation
- Open/enlarge the picture below and note how generations change with the more predictions and steps between them. We dont see as much quality loss but rather the direction of where the model goes. Thats not to say there isnt any quality loss but instead this method creates more variations in the image.
- All tests were done using comfy cache to prevent time distortions and create a fairer test. This means that model loading time i sthe same for each generation. If you do tests please do the same.
Method 1: Git Clone
cd ComfyUI/custom_nodes
git clone https://github.com/obisin/comfyui-FSampler
# Restart ComfyUIMethod 2: Manual Download
- Download the repository as ZIP
- Extract to
ComfyUI/custom_nodes/comfyui-FSampler/ - Restart ComfyUI
- Fixed modes: h2 (~25%), h3 (~16%), h4 (~12%) NFE reduction with parity on standard configs.
- Adaptive mode: aggressive gate; can reach 40–60%+ reduction on smooth runs while preserving quality.
- none: baseline (no skipping)
- hN/sK: s=history used for predictor, s=steps/calls before skip
- h2/s2..s5: linear predictor; common picks h2/s2 (~24%) or h2/s3 (~20%+)
- h3/s3..s5: Richardson; common picks h3/s3 (~16%) or h3/s4 (~12%+)
- h4/s4..s5: cubic; conservative, quality-sensitive; typically h4/s4
- adaptive: aggressive skip gate using two predictors (h3 vs h2) in predicted‑state space
- For quick usage start with the Fsampler rather than the FSampler Advanced as the simpler version only need noise and adaption mode to operate.
- Swap with your normal KSampler node.
- Add the FSampler node (or FSampler Advanced for more control)
- Choose your sampler and scheduler as usual
- Set skip_mode:
none— baseline (no skipping, use this first to validate)h2— conservative, ~20-30% speedup (recommended starting point)h3— more conservative, ~16% speeduph4— very conservative, ~12% speedupadaptive— aggressive, 40-60%+ speedup (may degrade on tough configs)
- Adjust protect_first_steps / protect_last_steps if needed (defaults are usually fine)
- Validators: finite checks, magnitude clamp vs history, cosine vs last REAL epsilon.
- Learning stabilizer L: scales predicted epsilon by 1/L on skipped steps; updates on REAL steps only.
- Diagnostics: per‑step timing + concise line showing σ targets, h/weights (where relevant), epsilon norms, x_rms, and [RISK].
All samplers now support four adaptive modes for intelligent step skipping:
- Standard operation without adaptive corrections
- SKIP steps use cached predictions directly
- Learns a stabilization factor
Lfrom REAL steps - Scales cached predictions by
1/Lon SKIP steps - L is learned via exponential moving average and clamped to [0.5, 2.0]
- Helps adapt to model behavior across the denoising trajectory
- Applies gradient estimation correction on SKIP steps only
- Adds directional correction based on gradient differences
- No L scaling applied
- Best for maintaining prediction directionality
- Combines both approaches for maximum adaptivity
- Applies L scaling AND gradient correction on SKIP steps
- Recommended for best quality when using aggressive skip patterns
New Feature: Manual step selection with extrapolation method control
Take precise control over which steps to skip and how predictions are made using the skip_indices parameter (available in FSampler Advanced node).
Write configs like: "h2, 3, 4, 7, 9" or "h4, 10, 12" or simply "3, 6, 8"
Components:
- Predictor token (optional):
h2,h3, orh4- specifies the extrapolation method - Step indices: Comma or space-separated integers (0-based)
- Empty/whitespace:
""or"[]"- ignored, uses other skip settings
FSampler adapts when history is insufficient:
h4 requested → uses h4 if ≥4 history, else h3 if ≥3, else h2 if ≥2, else cancels skip
h3 requested → uses h3 if ≥3 history, else h2 if ≥2, else cancels skip
h2 requested → uses h2 if ≥2 history, else cancels skip
Never falls below h2 - minimum 2 REAL epsilon history required for any skip.
Always Enforced:
- Steps 0 and 1 never skipped (need initial history)
- Final step can be skipped (explicit indices bypass protect windows)
Low-step generation (5 steps):
skip_indices = "4"
- Skip only step 4 using h2 (linear)
- Steps 0,1,2,3 are REAL → saves ~20% compute
For Low Step Counts (4-8 steps):
- Modern models excel at 4-6 step generation
- Each step has outsized impact on final quality
- Early steps establish composition, final steps add detail
- Manual control preserves the most critical steps
For Medium Step Counts (10-20 steps):
Steps: 15
skip_indices = "h2, 5, 7, 9, 11, 13" # Skip ~40% with better extrapolation
- h2/h3/h4 are conservative and deterministic; adaptive is aggressive and may show degradation on tough configs — validators and L minimize artifacts.
- Protect first/last windows guard early/late critical regions.
- Anchors and max consecutive skips are internal to adaptive to bound drift.
- Please incude the verbose output for the run so I can see what the calculations are doing and diagnose the problem
- Not all schedulers and samplr combos will produce results- some will produce nonsense on some models as is the case without skipping
Q: Does this work with LoRAs/ControlNet/IP-Adapter? A: Yes! FSampler sits between the scheduler and sampler, so it's transparent to conditioning.
Q: Will this work on SDXL Turbo / LCM? A: Potentially, but low-step models (<10 steps) won't benefit much since there's less history to extrapolate from.
Q: Can I use this with custom schedulers? A: Yes, FSampler works with any scheduler that produces sigma values.
Q: I'm getting artifacts/weird images A: Try these in order:
- Use
skip_mode=nonefirst to verify baseline quality - Switch to
h2orh3(more conservative than adaptive) - Increase
protect_first_stepsandprotect_last_steps - Some sampler+scheduler combos produce nonsense even without skipping — try different combinations
Q: How does this compare to other speedup methods? A: FSampler is complementary to:
- Distillation (LCM, Turbo): Use both together
- Quantization: Use both together
- Dynamic CFG: Use both together
- FSampler specifically reduces sampling steps, not model inference cost