Add hybrid Euler 2S sampler node
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aimfordeb
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# FunPack Hybrid Euler 2S Sampler
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This node outputs a ComfyUI `SAMPLER` object that can be connected directly into `CustomSamplerAdvanced` / `SamplerCustomAdvanced`.
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## Purpose
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It is designed as a quality/speed compromise between:
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- `euler_ancestral`: fast, reliable, one model evaluation per step
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- full 2S-style samplers: higher quality, but more expensive because they need an extra evaluation every step
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This sampler keeps classic Euler ancestral for most of the schedule, then applies a DPM-Solver++(2S)-style correction only on the late denoise steps where fine detail and cleanup usually matter most.
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## Parameters
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**eta**: Ancestral stochasticity. `1.0` keeps normal ancestral behavior.
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**s_noise**: Noise multiplier for ancestral noise injection.
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**high_quality_pct**: Fraction of the *late* denoise steps that receive the extra 2S correction.
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Example: `0.35` means only the last 35% of steps pay the extra compute cost.
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**correction_blend**: Blend between pure Euler ancestral and the late-step 2S correction.
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- `0.0` = behaves like Euler ancestral
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- `1.0` = full late-step correction
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## Recommended starting values
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- `eta = 1.0`
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- `s_noise = 1.0`
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- `high_quality_pct = 0.30` to `0.40`
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- `correction_blend = 1.0`
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For a safer first test, try:
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- `high_quality_pct = 0.25`
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- `correction_blend = 0.7`
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## Expected behavior
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Compared to plain `euler_ancestral`, this sampler should usually:
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- preserve more late-stage detail
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- clean up texture and edge quality better
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- cost noticeably less than a full 2S sampler
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## Limitation
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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`.
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+151
-16
@@ -4,12 +4,14 @@ from transformers import LlamaConfig, LlamaForCausalLM, LlavaForConditionalGener
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from huggingface_hub import hf_hub_download, snapshot_download
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from safetensors.torch import load_file, save_file, safe_open
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import glob
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import comfy.model_management as mm
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import comfy.sd as sd
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import folder_paths
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import nodes
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import tempfile
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import gc # Import garbage collector
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import comfy.model_management as mm
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import comfy.model_sampling
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import comfy.samplers
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import comfy.sd as sd
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import folder_paths
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import nodes
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import tempfile
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import gc # Import garbage collector
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import torch.nn as nn
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import torch.nn.functional as F
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from typing import List, Tuple, Optional
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@@ -17,10 +19,12 @@ import numpy as np
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from PIL import Image, ImageDraw, ImageFont
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import folder_paths
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from comfy.utils import ProgressBar
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import comfy.clip_vision
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import math
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import json
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from comfy.utils import ProgressBar
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import comfy.clip_vision
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import comfy.k_diffusion.sampling as k_diffusion_sampling
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import comfy.utils
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import math
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import json
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from comfy.utils import ProgressBar
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import random
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import re
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import base64
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@@ -1906,11 +1910,140 @@ class FunPackGemmaEmbeddingRefiner:
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modified_positive = [(new_positive, positive_meta)]
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return (modified_positive, status, feedback_question_output, training_info, loss_graph, refined_sigmas)
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@torch.no_grad()
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def sample_funpack_hybrid_euler_2s(model, x, sigmas, extra_args=None, callback=None,
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disable=None, eta=1.0, s_noise=1.0,
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high_quality_pct=0.35, correction_blend=1.0):
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"""
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Hybrid sampler:
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- Early schedule: classic Euler ancestral (fast).
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- Late schedule: DPM-Solver++(2S)-style correction (higher quality) blended onto
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the Euler ancestral proposal only where late-step detail matters most.
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This keeps the cost close to Euler ancestral while paying the extra model
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evaluation only on the tail of the schedule.
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"""
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if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
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return k_diffusion_sampling.sample_euler_ancestral(
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model, x, sigmas, extra_args=extra_args, callback=callback,
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disable=disable, eta=eta, s_noise=s_noise
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)
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extra_args = {} if extra_args is None else extra_args
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seed = extra_args.get("seed", None)
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noise_sampler = k_diffusion_sampling.default_noise_sampler(x, seed=seed)
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total_steps = max(0, len(sigmas) - 1)
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if total_steps <= 0:
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return x
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high_quality_pct = max(0.0, min(1.0, float(high_quality_pct)))
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correction_blend = max(0.0, min(1.0, float(correction_blend)))
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late_steps = max(1, int(math.ceil(total_steps * high_quality_pct))) if high_quality_pct > 0.0 else 0
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late_start = max(0, total_steps - late_steps)
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sigma_fn = lambda t: t.neg().exp()
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t_fn = lambda sigma: sigma.log().neg()
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s_in = x.new_ones([x.shape[0]])
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for i in comfy.utils.model_trange(total_steps, disable=disable):
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sigma = sigmas[i]
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sigma_next = sigmas[i + 1]
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denoised = model(x, sigma * s_in, **extra_args)
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if callback is not None:
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callback({"x": x, "i": i, "sigma": sigma, "sigma_hat": sigma, "denoised": denoised})
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sigma_down, sigma_up = k_diffusion_sampling.get_ancestral_step(sigma, sigma_next, eta=eta)
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if sigma_down == 0:
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x = denoised
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else:
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d = k_diffusion_sampling.to_d(x, sigma, denoised)
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dt = sigma_down - sigma
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x_euler = x + d * dt
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if i >= late_start and correction_blend > 0.0:
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t = t_fn(sigma)
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t_next = t_fn(sigma_down)
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h = t_next - t
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r = 0.5
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s = t + r * h
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x_mid = (sigma_fn(s) / sigma_fn(t)) * x - torch.expm1(-h * r) * denoised
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denoised_mid = model(x_mid, sigma_fn(s) * s_in, **extra_args)
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x_2s = (sigma_fn(t_next) / sigma_fn(t)) * x - torch.expm1(-h) * denoised_mid
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x = x_euler.lerp(x_2s, correction_blend)
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else:
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x = x_euler
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if sigma_next > 0 and eta > 0 and s_noise > 0:
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x = x + noise_sampler(sigma, sigma_next) * s_noise * sigma_up
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return x
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class FunPackHybridEuler2SSampler:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"eta": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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"tooltip": "Ancestral stochasticity. Keep at 1.0 for classic ancestral behaviour."
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}),
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"s_noise": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 10.0,
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"step": 0.01,
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"tooltip": "Noise scale for ancestral noise injection."
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}),
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"high_quality_pct": ("FLOAT", {
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"default": 0.35,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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"tooltip": "Fraction of late denoise steps that receive the slower 2S quality correction."
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}),
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"correction_blend": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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"tooltip": "Blend between Euler ancestral (0.0) and late-step 2S correction (1.0)."
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}),
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}
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}
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RETURN_TYPES = ("SAMPLER",)
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RETURN_NAMES = ("sampler",)
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FUNCTION = "get_sampler"
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CATEGORY = "FunPack/Sampling"
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DESCRIPTION = "Hybrid sampler: Euler ancestral for most steps, DPM-Solver++(2S)-style correction only on late steps for a better quality/speed tradeoff."
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def get_sampler(self, eta, s_noise, high_quality_pct, correction_blend):
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sampler = comfy.samplers.KSAMPLER(
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sample_funpack_hybrid_euler_2s,
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extra_options={
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"eta": eta,
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"s_noise": s_noise,
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"high_quality_pct": high_quality_pct,
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"correction_blend": correction_blend,
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}
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)
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return (sampler,)
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# Constants from StoryMem
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IMAGE_FACTOR = 28
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VIDEO_MIN_PIXELS = 48 * IMAGE_FACTOR * IMAGE_FACTOR # 37,632
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MIN_FRAME_SIMILARITY = 0.9
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MAX_KEYFRAME_NUM = 3
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MIN_FRAME_SIMILARITY = 0.9
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MAX_KEYFRAME_NUM = 3
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ADAPTIVE_ALPHA = 0.01
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HPSV3_QUALITY_THRESHOLD = 3.0
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@@ -2998,11 +3131,12 @@ NODE_CLASS_MAPPINGS = {
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"FunPackContinueVideo": FunPackContinueVideo,
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"FunPackLorebookEnhancer": FunPackLorebookEnhancer,
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"FunPackGemmaEmbeddingRefiner": FunPackGemmaEmbeddingRefiner,
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"FunPackHybridEuler2SSampler": FunPackHybridEuler2SSampler,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"FunPackPromptCombiner": "FunPack Prompt Combiner",
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"FunPackStoryMemKeyframeExtractor": "FunPack StoryMem Keyframe Extractor",
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NODE_DISPLAY_NAME_MAPPINGS = {
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"FunPackPromptCombiner": "FunPack Prompt Combiner",
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"FunPackStoryMemKeyframeExtractor": "FunPack StoryMem Keyframe Extractor",
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"FunPackStoryMemLastFrameExtractor": "FunPack StoryMem Last Frame Extractor",
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"FunPackPromptEnhancer": "FunPack Prompt Enhancer (Standalone)",
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"FunPackStoryWriter": "FunPack Story Writer",
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@@ -3010,4 +3144,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"FunPackContinueVideo": "FunPack Continue Video",
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"FunPackLorebookEnhancer": "FunPack Lorebook Enhancer",
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"FunPackGemmaEmbeddingRefiner": "FunPack Gemma Embedding Refiner (Self-Refinement)",
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"FunPackHybridEuler2SSampler": "FunPack Hybrid Euler 2S Sampler",
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}
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