InGuard: Towards Generalized Inner Guardrail for Safe Text-to-Image Generation

arXiv:2609.27620 · cs.CV, cs.AI · Submitted 2026-09-23 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/notAI-tech/NudeNet

Terminology

• Embeddings — 35× in this paper · explained in 2 episodes
• Benchmark — 30× in this paper · explained in 2 episodes
• Distillation — 16× in this paper · explained in 2 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Diffusion Model — 13× in this paper · explained in 8 episodes
• Diffusion Models — 12× in this paper · explained in 6 episodes
• Fine-Tuning — 11× in this paper · explained in 5 episodes
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Latent Representation — 6× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• International Conference on Learning Representations — 5× in this paper
• Alignment — 4× in this paper · explained in 1 episode
• Clustering — 4× in this paper · explained in 2 episodes
• Latent Representations — 4× in this paper · explained in 3 episodes
• Verification — 4× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 3× in this paper
• Grounding — 3× in this paper · explained in 3 episodes
• Soft-gated Asymmetric Guardrail for Embeddings — 3× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Diffusion Process — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Self-Validating Filtering — 2× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Accountability — 1× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Two-Stage Training Strategy — 1× in this paper · explained in 3 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Qwen2.5-VL Technical Report
• The Vendi Score: A Diversity Evaluation Metric for Machine Learning
• DINOv2: Learning Robust Visual Features without Supervision
• InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing
• Sparse Autoencoder as a Zero-Shot Classifier for Concept Erasing in Text-to-Image Diffusion Models
• Multilingual E5 Text Embeddings: A Technical Report
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• Qwen-Image Technical Report
• Qwen3 Technical Report
• FlowGuard: Towards Lightweight In-Generation Safety Detection for Diffusion Models via Linear Latent Decoding
• Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer

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