CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance

Hanyang Wang^*, Yiyang Liu^*, Jiawei Chi, Fangfu Liu, Ran Xue, Yueqi Duan^†
Tsinghua University

_{* Equal contribution    † Corresponding author}

CVPR 2026

Abstract

Classifier-Free Guidance (CFG) has emerged as a central approach for enhancing semantic alignment in flow-based diffusion models. In this paper, we explore a unified framework called CFG-Ctrl, which reinterprets CFG as a control applied to the first-order continuous-time generative flow, using the conditional-unconditional discrepancy as an error signal to adjust the velocity field. From this perspective, we summarize vanilla CFG as a proportional controller (P-control) with fixed gain, and typical follow-up variants develop extended control-law designs derived from it. However, existing methods mainly rely on linear control, inherently leading to instability, overshooting, and degraded semantic fidelity especially on large guidance scales. To address this, we introduce Sliding Mode Control CFG (SMC-CFG), which enforces the generative flow toward a rapidly convergent sliding manifold. Specifically, we define an exponential sliding mode surface over the semantic prediction error and introduce a switching control term to establish nonlinear feedback-guided correction. Moreover, we provide a Lyapunov stability analysis to theoretically support finite-time convergence. Experiments across text-to-image generation models including Stable Diffusion 3.5, Flux, and Qwen-Image demonstrate that SMC-CFG outperforms standard CFG in semantic alignment and enhances robustness across a wide range of guidance scales.

Updates

• 2026/02/26 Code released.
• 2026/03/04 ArXiv paper released: 2603.03281

TODO List

• Release inference code and pipeline implementations
• Release ArXiv paper

Table of Contents

• Abstract
• Updates
• TODO List
• Table of Contents
• Method Overview
• Installation
  • Prerequisites
  • Option A: Conda (recommended)
  • Option B: pip
• Quick Start
  • Text-to-Image (FLUX)
  • Text-to-Image (Qwen-Image)
  • Text-to-Image (SD3)
  • Text-to-Video (Wan Video)
• Supported Models
• Parameters
• Citation
• Acknowledgements

Method Overview

CFG-Ctrl provides a unified control-theoretic framework for Classifier-Free Guidance:

• Vanilla CFG is equivalent to a proportional controller (P-control) with fixed gain w:

  v_guided = v_uncond + w * (v_cond - v_uncond)
  

• SMC-CFG introduces a nonlinear sliding mode controller that enforces the guidance error to converge along an exponential sliding surface:

  s_t = (e_t - e_{t-1}) + lambda * e_{t-1}    (sliding surface)
  u_sw = -K * sign(s_t)                         (switching control)
  

  where e_t = v_cond - v_uncond is the guidance error signal, lambda controls the exponential decay rate, and K is the switching gain.

This design prevents the instability and overshooting observed with standard CFG at large guidance scales.

Installation

Prerequisites

• Python >= 3.10 (recommended: 3.10)
• CUDA-compatible GPU

Option A: Conda (recommended)

git clone https://github.com/MaxLiuyy/CFG-Ctrl.git
cd CFG-Ctrl
conda env create -f environment.yml
conda activate cfg-ctrl

Option B: pip

conda create -n cfg-ctrl python=3.10 -y
conda activate cfg-ctrl
git clone https://github.com/MaxLiuyy/CFG-Ctrl.git
cd CFG-Ctrl
pip install -r requirements.txt

Quick Start

All examples should be run from the CFG-Ctrl/ directory.

Text-to-Image (FLUX)

python examples/flux_cfg_ctrl_example.py \
  --prompt "A cinematic portrait of a cat astronaut" \
  --cfg-scale 3 \
  --steps 30 \
  --smc-cfg-enable \
  --smc-cfg-lambda 5.0 \
  --smc-cfg-k 0.2 \
  --no-cfg-warmup-steps 2

Text-to-Image (Qwen-Image)

python examples/qwen_cfg_ctrl_example.py \
  --prompt "A ginger cat sitting by a rainy window, cinematic lighting" \
  --cfg-scale 4 \
  --steps 30 \
  --smc-cfg-enable \
  --smc-cfg-lambda 5.0 \
  --smc-cfg-k 0.2

Text-to-Image (SD3)

python examples/sd3_cfg_ctrl_example.py \
  --prompt "A futuristic city at sunrise, cinematic, ultra detailed" \
  --cfg-scale 7.5 \
  --steps 30 \
  --smc-cfg-enable \
  --smc-cfg-lambda 5.0 \
  --smc-cfg-k 0.2

Text-to-Video (Wan Video)

python examples/wan_video_cfg_ctrl_example.py \
  --prompt "A golden retriever running on a beach at sunset" \
  --cfg-scale 5 \
  --steps 50 \
  --smc-cfg-enable \
  --smc-cfg-lambda 5.0 \
  --smc-cfg-k 0.2

For Image-to-Video mode, add --input-image path/to/image.png.

Supported Models

Model	Pipeline Class	Task
FLUX.1-dev	FluxImagePipelineCFGCtrl	Text-to-Image
Qwen-Image	QwenImagePipelineCFGCtrl	Text-to-Image
SD3 / SD3.5	SD3ImagePipelineCFGCtrl	Text-to-Image
Wan2.1 / Wan2.2	WanVideoPipelineCFGCtrl	Text-to-Video / Image-to-Video

Parameters

Parameter	Type	Default	Description
smc_cfg_enable	bool	False	Enable SMC-CFG stabilization
smc_cfg_lambda	float	0.05	Exponential decay coefficient for sliding surface
smc_cfg_k / smc_cfg_K	float	0.3	Switching gain
no_cfg_warmup_steps	int	0	Number of initial steps without CFG

Recommended settings:

• FLUX: smc_cfg_lambda=5.0, smc_cfg_k=0.2, cfg_scale=2~3
• Qwen-Image: smc_cfg_lambda=5.0, smc_cfg_k=0.2, cfg_scale=4
• SD3/SD3.5: smc_cfg_lambda=5.0, smc_cfg_k=0.2, cfg_scale=7.5
• Wan Video: smc_cfg_lambda=5.0, smc_cfg_k=0.2, cfg_scale=5

Citation

@misc{wang2026cfgctrlcontrolbasedclassifierfreediffusion,
      title={CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance}, 
      author={Hanyang Wang and Yiyang Liu and Jiawei Chi and Fangfu Liu and Ran Xue and Yueqi Duan},
      year={2026},
      eprint={2603.03281},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.03281}, 
}

Acknowledgements

This project is built on DiffSynth-Studio.