  Avoiding synthesizing specific visual concepts is an essential challenge in
responsible visual synthesis. However, the visual concept that needs to be
avoided for responsible visual synthesis tends to be diverse, depending on the
region, context, and usage scenarios. In this work, we formalize a new task,
Open-vocabulary Responsible Visual Synthesis (ORES), where the synthesis model
is able to avoid forbidden visual concepts while allowing users to input any
desired content. To address this problem, we present a Two-stage Intervention
(TIN) framework. By introducing 1) rewriting with learnable instruction through
a large-scale language model (LLM) and 2) synthesizing with prompt intervention
on a diffusion synthesis model, it can effectively synthesize images avoiding
any concepts but following the user's query as much as possible. To evaluate on
ORES, we provide a publicly available dataset, baseline models, and benchmark.
Experimental results demonstrate the effectiveness of our method in reducing
risks of image generation. Our work highlights the potential of LLMs in
responsible visual synthesis. Our code and dataset is public available.
