A new arXiv paper proposes DSS, a method for removing unwanted concepts from text-to-image diffusion models. The approach steers the model's internal semantic representations dynamically rather than relying on static interventions, aiming to make erasure more robust against adversarial prompts. The work targets safety concerns such as generated NSFW material or copyrighted content.
A new arXiv paper introduces LEAPSC, a method that combines deep joint source-channel coding with in-loop concept erasure to prevent learned semantic features from leaking sensitive attributes like gender, race, or speaker identity. The approach aims to preserve transmission efficiency while removing private information during encoding. It is a research contribution rather than a released product.
A new arXiv paper introduces AcFlow, a technique that controls text-to-image diffusion transformers by learning conditional activation flow at inference time. The approach aims to give users adjustable control over style intensity and to suppress unwanted concepts that direct prompting handles poorly. The work targets the limits of prompt-based interfaces for generative image models.