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FSampler Changelog:
2026-01-01
v1.5.0
Sigma-Aware Extrapolation
New sigma_aware toggle (FSampler Advanced / FSampler Select) — uses actual sigma coordinates for Lagrange interpolation
Improves skip prediction accuracy with non-uniform schedulers (bong_tangent, karras, exponential, beta, etc.)
Enabled by default in the simple FSampler node
Denoised Extrapolation
New extrapolate_denoised toggle — extrapolates the model's denoised (clean image) output instead of epsilon
Denoised converges smoothly toward the final image, can produce more stable skip predictions
Enabled by default in the simple FSampler node
Composes with sigma_aware (orthogonal: sigma_aware controls weights, denoised controls what quantity is extrapolated)
New Node: FSampler Select
Outputs SAMPLER + SIGMAS for use with ComfyUI's SamplerCustom (KSamplerCustom) node
Lets you plug FSampler's skip-aware sampling into the standard modular workflow alongside any guider (CFGGuider, DualCFGGuider, BasicGuider, etc.)
Wire sampler → SamplerCustom's sampler slot, sigmas → SamplerCustom's sigmas slot
All FSampler skip/adaptive controls available; model/conditioning/cfg/noise handled by SamplerCustom
Live Preview
FSampler and FSampler Advanced now show live image previews during sampling (Latent2RGB, auto-enabled)

Author: obisin

README.md

@@ -11,17 +11,29 @@ when that error is below a hardcoded tolerance. Predicted epsilons are validated
 by guard rails, and the sampler math (Euler, RES 2M/2S, DDIM, DPM++ 2M/2S, LMS) is unchanged.
 
 ## FSampler Changelog:
+## 2026-01-01
 
-## 2025-10-12
-### New Samplers Added
+## v1.5.0
+### Sigma-Aware Extrapolation
+- New `sigma_aware` toggle (FSampler Advanced / FSampler Select) — uses actual sigma coordinates for Lagrange interpolation
+- Improves skip prediction accuracy with non-uniform schedulers (bong_tangent, karras, exponential, beta, etc.)
+- Enabled by default in the simple FSampler node
 
-- #### `res_multistep_ancestral`
-- #### `heun`
-- #### `gradient_estimation`
+### Denoised Extrapolation
+- New `extrapolate_denoised` toggle — extrapolates the model's denoised (clean image) output instead of epsilon
+- Denoised converges smoothly toward the final image, can produce more stable skip predictions
+- Enabled by default in the simple FSampler node
+- Composes with sigma_aware (orthogonal: sigma_aware controls weights, denoised controls what quantity is extrapolated)
 
-### Adaptive Skip Modes
-- #### `grad_est`
-- #### `learn+grad_est`
+
+### New Node: FSampler Select
+- Outputs `SAMPLER` + `SIGMAS` for use with ComfyUI's **SamplerCustom** (KSamplerCustom) node
+- Lets you plug FSampler's skip-aware sampling into the standard modular workflow alongside any guider (CFGGuider, DualCFGGuider, BasicGuider, etc.)
+- Wire `sampler` → SamplerCustom's sampler slot, `sigmas` → SamplerCustom's sigmas slot
+- All FSampler skip/adaptive controls available; model/conditioning/cfg/noise handled by SamplerCustom
+
+### Live Preview
+- FSampler and FSampler Advanced now show live image previews during sampling (Latent2RGB, auto-enabled)
 
 ### Explicit Skip Indices with Predictor Selection
 -  Manual step selection with extrapolation method control (Good for low step  count workflows)
@@ -78,7 +90,7 @@ Method 2: Manual Download
 ## Usage (ComfyUI)
 
 - 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. 
+- Swap with your normal KSampler node.
 
 1. Add the **FSampler** node (or **FSampler Advanced** for more control)
 2. Choose your **sampler** and **scheduler** as usual
@@ -90,6 +102,50 @@ Method 2: Manual Download
    - `adaptive` — aggressive, 40-60%+ speedup (may degrade on tough configs)
 4. Adjust **protect_first_steps** / **protect_last_steps** if needed (defaults are usually fine)
 
+### FSampler Select (Modular / KSamplerCustom)
+
+For advanced workflows using ComfyUI's modular sampling system:
+
+1. Add **FSampler Select** — configure your sampler, scheduler, steps, and skip settings
+2. Add **SamplerCustom** (ComfyUI's KSamplerCustom node)
+3. Wire FSampler Select's `sampler` output → SamplerCustom's `sampler` input
+4. Wire FSampler Select's `sigmas` output → SamplerCustom's `sigmas` input
+5. Wire your model, positive/negative conditioning, cfg, and latent into SamplerCustom as normal
+
+This lets you use FSampler with any guider (CFGGuider, DualCFGGuider, BasicGuider) and the rest of the modular sampling ecosystem.
+
+## Extrapolation Settings (v1.5.0)
+
+FSampler Advanced and FSampler Select expose two new boolean toggles that can help reduce prediction drift on skipped steps. Both are enabled by default in the simple FSampler node.
+
+### Sigma-Aware Extrapolation (`sigma_aware`)
+
+Standard extrapolation assumes steps are uniformly spaced. Many schedulers (bong_tangent, karras, exponential, beta, etc.) have highly non-uniform spacing — dense in some regions, sparse in others. With uniform assumptions, the Lagrange basis weights can be wrong and predictions may drift.
+
+When `sigma_aware` is enabled, FSampler uses the actual sigma values as coordinates for Lagrange interpolation. This can give more accurate polynomial weights when scheduler spacing is non-uniform.
+
+- **Best for:** non-uniform schedulers (bong_tangent, karras, exponential, beta)
+- **No effect on:** uniform schedulers (normal) — weights are identical either way
+- **Default:** OFF in Advanced/Select, ON in simple FSampler
+
+### Denoised Extrapolation (`extrapolate_denoised`)
+
+Standard FSampler extrapolates **epsilon** (noise prediction) = `denoised - x`. Epsilon depends on both the model's output AND the changing noisy latent `x`, which can introduce variability from both sources.
+
+When `extrapolate_denoised` is enabled, FSampler extrapolates the model's **denoised** output directly — its best guess at the clean image. Denoised tends to converge more smoothly toward the final image as noise decreases, which can make it a more stable quantity to extrapolate and may reduce prediction drift. The predicted denoised is then converted back to epsilon for the sampler.
+
+- **Orthogonal to sigma_aware:** sigma_aware controls interpolation weights, denoised controls what quantity is extrapolated. Both can be used together.
+- **Default:** OFF in Advanced/Select, ON in simple FSampler
+
+### Recommended Combinations
+
+| Setting | Best For |
+|---------|----------|
+| Both OFF | Baseline comparison, uniform schedulers with simple samplers |
+| `sigma_aware` only | Non-uniform schedulers where epsilon extrapolation already works well |
+| `extrapolate_denoised` only | Any scheduler where skip quality needs improvement |
+| Both ON (simple FSampler default) | Generally good results, especially with non-uniform schedulers |
+
 ## Quality & Safety
 - 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.