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Classifier-Free Guidance

5 articles · Page 1 of 1

Oct 15, 2026 · Artificial Intelligence

RA-CFGCache: Risk-Aligned Caching for CFG-Guided Diffusion Models

RA-CFGCache introduces a risk-aligned caching framework for classifier-free guided diffusion models that jointly models cross-branch error alignment and timestep-dependent error propagation, achieving superior efficiency-fidelity trade-offs on FLUX.1-dev, Wan2.1, and CogVideoX while remaining compatible with existing cache estimators.

CachingClassifier-Free GuidanceGenerative AI

0 likes · 19 min read

RA-CFGCache: Risk-Aligned Caching for CFG-Guided Diffusion Models

Machine Learning Algorithms & Natural Language Processing

Jul 8, 2026 · Artificial Intelligence

Why the One‑Step “Drift Model” by He Kaiming’s Team Was Rejected at ICML (Scores 5‑4‑4‑3)

The drift model moves the distribution evolution into training to achieve single‑step image synthesis with near‑SOTA FID scores, but ICML reviewers gave it 5/4/4/3 and rejected it, citing heavy reliance on a pretrained feature encoder, unclear contribution of the drift field versus classifier‑free guidance, and insufficient comparison to prior work.

Classifier-Free GuidanceFIDICML rejection

0 likes · 9 min read

Why the One‑Step “Drift Model” by He Kaiming’s Team Was Rejected at ICML (Scores 5‑4‑4‑3)

Machine Learning Algorithms & Natural Language Processing

Apr 27, 2026 · Artificial Intelligence

From Parameter Tuning to Control: CFG‑Ctrl Boosts Stability and Precision in Text‑to‑Image Generation

The paper introduces CFG‑Ctrl, a control‑theoretic redesign of classifier‑free diffusion guidance that treats the generation process as a dynamic system, achieving more stable and accurate text‑to‑image results across multiple model scales and evaluation metrics.

CFG-CtrlClassifier-Free Guidancecontrol theory

0 likes · 15 min read

From Parameter Tuning to Control: CFG‑Ctrl Boosts Stability and Precision in Text‑to‑Image Generation

vivo Internet Technology

Apr 1, 2026 · Artificial Intelligence

Why Fixed CFG Fails and How Time‑Adaptive C²FG Boosts Diffusion Image Generation

This article introduces C²FG, a training‑free, plug‑and‑play time‑adaptive exponential control function that replaces the fixed classifier‑free guidance scale, theoretically justifies its superiority with score discrepancy bounds, and demonstrates significant FID and IS improvements across multiple diffusion architectures on ImageNet.

CVPR 2026Classifier-Free GuidanceImage Generation

0 likes · 7 min read

Why Fixed CFG Fails and How Time‑Adaptive C²FG Boosts Diffusion Image Generation

AI Engineering

Jan 28, 2026 · Artificial Intelligence

Alibaba Tongyi Unveils Z-Image Non‑Distilled Base Model with Full CFG and Negative Prompt Support

Alibaba's Tongyi releases the Z-Image base model, a non‑distilled diffusion transformer that supports full classifier‑free guidance, negative prompts, higher diversity, and fine‑tuning, contrasting with the faster Turbo variant and providing detailed usage instructions and community resources.

AlibabaClassifier-Free GuidanceImage Generation

0 likes · 4 min read

Alibaba Tongyi Unveils Z-Image Non‑Distilled Base Model with Full CFG and Negative Prompt Support