Hugging Face Trending Papers
Haste Makes Waste? Towards Safe Few-Step Text-to-Image Models
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arXiv Computer Vision
Oct 28
Haste Makes Waste? Towards Safe Few-Step Text-to-Image Models
The paper investigates the safety implications of few-step distilled text-to-image models, finding that faster generation often leads to higher unsafe content rates and that existing concept-erasure methods are less effective on these models. The authors diagnose why erasure fails, attributing it to the fragile generation process and the way guidance is distilled. They propose DICE, a post-distillation erasure technique that aligns with the distillation process, achieving a significant reduction in unsafe-generation rates while preserving quality.
arXiv AI
Jun 16
SACE: Concept Erasure at the Semantic Singularity in Visual Autoregressive Models
arXiv:2606. 15819v1 Announce Type: cross Abstract: The rapid progress of visual autoregressive (VAR) models has unlocked a transformative frontier for high-fidelity text-to-image synthesis, while heightening concerns over the safety alignment of generated content.
arXiv AI
Sep 21
GUARD: Natural Forgetting in Large Reasoning Models via Guided Answer-Reasoning Distillation
The paper introduces GUARD, a method for natural forgetting in large reasoning models that transforms unsafe disclosures into safe-exit trajectories using guided answer‑reasoning distillation. It aligns a frozen model with guidance tokens and distills this behavior into the parameters, aiming for a coherent, non‑disclosing chain of thought followed by a refusal‑style answer. The authors also propose the Natural Forgetting Reasoning Score (NFRS) to evaluate structural stability, fluency, and unsupported substitutes, and demonstrate GUARD’s effectiveness on R‑TOFU and a STAR‑1‑derived harmful‑intent setting.
arXiv AI
Jul 23
CGCE: Classifier-Guided Concept Erasure in Generative Models
arXiv:2511. 05865v3 Announce Type: replace-cross Abstract: Recent advancements in large-scale generative models have enabled the creation of high-quality images and videos, but have also raised significant safety concerns regarding the generation of unsafe content.
arXiv Machine Learning
Oct 9
Enabling Preference-driven Unlearning in Few-step Distilled Text-to-Image Diffusion Models
The paper presents a preference-driven unlearning framework for few-step distilled text-to-image diffusion models. It identifies that standard Direct Preference Optimization (DPO) fails on these models due to altered generation dynamics and proposes a modified formulation aligned with few-step properties. The method enables direct concept removal while preserving desirable capabilities, and is evaluated on identity, NSFW, and object-level unlearning tasks with strong forgetting and retention results.
Hugging Face Trending Papers
Oct 7
Enabling Preference-driven Unlearning in Few-step Distilled Text-to-Image Diffusion Models
Text-to-image diffusion models are increasingly distilled into few-step variants and being deployed to enable fast inference. However, their ability to generate harmful or undesired content poses sign...