  Despite their impressive capabilities, diffusion-based text-to-image (T2I)
models can lack faithfulness to the text prompt, where generated images may not
contain all the mentioned objects, attributes or relations. To alleviate these
issues, recent works proposed post-hoc methods to improve model faithfulness
without costly retraining, by modifying how the model utilizes the input
prompt. In this work, we take a step back and show that large T2I diffusion
models are more faithful than usually assumed, and can generate images faithful
to even complex prompts without the need to manipulate the generative process.
Based on that, we show how faithfulness can be simply treated as a candidate
selection problem instead, and introduce a straightforward pipeline that
generates candidate images for a text prompt and picks the best one according
to an automatic scoring system that can leverage already existing T2I
evaluation metrics. Quantitative comparisons alongside user studies on diverse
benchmarks show consistently improved faithfulness over post-hoc enhancement
methods, with comparable or lower computational cost. Code is available at
\url{https://github.com/ExplainableML/ImageSelect}.
