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.

通过引入可学习指令的重写和扩散合成模型的提示干预，我们提出了一种两阶段干预（TIN）框架，用于有效合成图像，避免任何概念但尽可能地遵循用户的查询，实验证明我们的方法在减少图像生成风险方面有效。