Recently, a myriad of conditional image generation and editing models have been developed to serve different downstream tasks, including text-to-image generation, text-guided image editing, subject-driven image generation, control-guided image generation, etc. However, we observe huge inconsistencies in experimental conditions: datasets, inference, and evaluation metrics - render fair comparisons difficult. This paper proposes ImagenHub, which is a one-stop library to standardize the inference and evaluation of all the conditional image generation models. Firstly, we define seven prominent tasks and curate high-quality evaluation datasets for them. Secondly, we built a unified inference pipeline to ensure fair comparison. Thirdly, we design two human evaluation scores, i.e. Semantic Consistency and Perceptual Quality, along with comprehensive guidelines to evaluate generated images. We train expert raters to evaluate the model outputs based on the proposed metrics. Our human evaluation achieves a high inter-worker agreement of Krippendorff's alpha on 76% models with a value higher than 0.4. We comprehensively evaluated a total of around 30 models and observed three key takeaways: (1) the existing models' performance is generally unsatisfying except for Text-guided Image Generation and Subject-driven Image Generation, with 74% models achieving an overall score lower than 0.5. (2) we examined the claims from published papers and found 83% of them hold with a few exceptions. (3) None of the existing automatic metrics has a Spearman's correlation higher than 0.2 except subject-driven image generation. Moving forward, we will continue our efforts to evaluate newly published models and update our leaderboard to keep track of the progress in conditional image generation.

翻译：近期，大量条件图像生成与编辑模型被开发用于服务不同的下游任务，包括文本到图像生成、文本引导图像编辑、主体驱动图像生成、控制引导图像生成等。然而，我们观察到实验条件中存在巨大的不一致性：数据集、推理过程和评价指标的差异使得公平比较较为困难。本文提出ImagenHub，这是一个一站式库，旨在标准化所有条件图像生成模型的推理与评估流程。首先，我们定义了七个主要任务，并为每个任务精选了高质量评估数据集。其次，我们构建了统一的推理流程以确保公平比较。第三，我们设计了两种人工评价指标，即语义一致性与感知质量，并附有全面的指南来评估生成的图像。我们训练专业评分员基于所提出的指标对模型输出进行评价。我们的人工评估在76%的模型上实现了较高的评分者间信度（Krippendorff's alpha值高于0.4）。我们全面评估了约30个模型，并发现三个关键要点：（1）除文本引导图像生成和主体驱动图像生成外，现有模型的性能普遍不尽人意，其中74%的模型总体得分低于0.5。（2）我们审查了已发表论文中的主张，发现83%的主张成立，仅有少数例外。（3）除主体驱动图像生成外，现有自动评估指标与Spearman相关系数均未超过0.2。展望未来，我们将继续评估新发布的模型，并更新排行榜以跟踪条件图像生成领域的发展。