Given a command, humans can directly execute the action after thinking or choose to reject it, with reasonable feedback at the same time. However, the behavior of existing text-to-image generation methods are uncontrollable and irresponsible. In this paper, we construct extensive experiments to verify whether they can be accountable (say no and explain why) for those prohibited instructions. To this end, we define a novel text-based visual re-creation task and construct new synthetic CLEVR-NOT dataset (620K) and manually pictured Fruit-NOT dataset (50K). In our method, one text-image pair as the query is fed into the machine, and the model gives a yes or no answer after visual and textual reasoning. If the answer is yes, the image auto-encoder and auto-regressive transformer must complete the visual re-creation under the premise of ensuring image quality, otherwise the system needs to explain why the commands cannot be completed or prohibited. We provide a detailed analysis of experimental results in image quality, answer accuracy, and model behavior in the face of uncertainty and imperfect user queries. Our results demonstrate the difficulty of a single model for both textual and visual reasoning. We also hope our explorations and findings can bring valuable insights about the accountability of text-based image generation models. Code and datasets can be found at https://matrix-alpha.github.io.

翻译：给定指令，人类可以思考后直接执行动作，或选择拒绝并给出合理反馈。然而，现有文本到图像生成方法的行为具有不可控性和不负责任性。本文构建了大量实验，验证这些方法是否能够对违禁指令负责（即拒绝并解释原因）。为此，我们定义了一项新颖的文本驱动图像重创任务，并构建了全新的合成数据集CLEVR-NOT（62万样本）与人工拍摄数据集Fruit-NOT（5万样本）。方法中，模型接收一个文本-图像对作为查询，经过视觉与文本推理后输出"是/否"答案。若答案为"是"，则图像自编码器与自回归变换器须在保证图像质量的前提下完成图像重创；否则系统需解释指令无法执行或被禁止的原因。我们从图像质量、答案准确性及模型面对不确定性与不完美用户查询时的行为等方面进行了详细实验分析。结果表明，单一模型同时完成文本与视觉推理存在显著困难。我们期望本次探索与发现能为文本驱动图像生成模型的可问责性提供有价值的参考。代码与数据集见https://matrix-alpha.github.io。