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Despite their impressive capabilities, diffusion-based text-to-image (T2I)models can lack faithfulness to the text prompt, where generated images may notcontain all the mentioned objects, attributes or relations. To alleviate theseissues, recent works proposed post-hoc methods to improve model faithfulnesswithout costly retraining, by modifying how the model utilizes the inputprompt. In this work, we take a step back and show that large T2I diffusionmodels are more faithful than usually assumed, and can generate images faithfulto even complex prompts without the need to manipulate the generative process.Based on that, we show how faithfulness can be simply treated as a candidateselection problem instead, and introduce a straightforward pipeline thatgenerates candidate images for a text prompt and picks the best one accordingto an automatic scoring system that can leverage already existing T2Ievaluation metrics. Quantitative comparisons alongside user studies on diversebenchmarks show consistently improved faithfulness over post-hoc enhancementmethods, with comparable or lower computational cost. Code is available at\url{https://github.com/ExplainableML/ImageSelect}.

If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection