Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific modeling and data choices: state-of-the-art open-weight models provide limited ablations, and do not disclose their training data and full training details. The research community needs fully open (weights, data, and code) models as a foundation for further research; yet existing fully open models still fall significantly short of leading models in performance. In this project, we conduct a systematic investigation of the modeling and data design choices in text-to-image diffusion training and inference with 300+ controlled experiments totaling 700K+ TPU v6e hours. Our experiments highlight several empirical findings (e.g., equal weighting is a strong default for mixing curated datasets) and simple design decisions (e.g., larger text encoder adapters improve performance with minimal added parameters) for training strong models. Guided by these insights, we train i1, a 3B-parameter text-to-image diffusion model using only publicly available datasets. i1 is competitive with leading models on five representative benchmarks (GenEval, DPG, PRISM, CVTG-2K, and LongText), and outperforms the best existing fully open model by 29.5 absolute percentage points on average. We provide the i1 checkpoints, training and inference code, and the data processing pipeline. Together, our findings and the i1 recipe establish a practical foundation for future open research in text-to-image diffusion models. Our code is available at https://github.com/zlab-princeton/i1.

翻译：扩散模型持续推动着文本到图像生成领域的进步。然而，我们难以将近期进展归因于特定的建模与数据选择：当前最先进的开源模型仅提供有限的消融实验，且未公开其训练数据及全部训练细节。研究社区亟需完全开源（权重、数据及代码）的模型作为后续研究的基础，但现有完全开源模型的性能仍显著落后于领先模型。本研究通过300余组受控实验（总计消耗超过70万TPU v6e小时）系统探究了文本到图像扩散训练与推理中的建模及数据设计选择。我们的实验凸显了若干经验性发现（例如，在混合精选数据集时，等权重加权是一种强有效的默认设置）和简洁设计决策（例如，更大的文本编码适配器能以极少的参数增加提升性能）对训练强模型的作用。基于这些洞见，我们训练了i1模型——一个仅使用公开数据集的30亿参数文本到图像扩散模型。i1在五项代表性基准测试（GenEval、DPG、PRISM、CVTG-2K及LongText）中与领先模型性能相当，并在平均表现上比现有最佳完全开源模型高出29.5个百分点。我们开源了i1模型检查点、训练与推理代码以及数据处理流程。本研究结合发现与i1配方，为未来文本到图像扩散模型的开放研究奠定了实践基础。代码已发布于https://github.com/zlab-princeton/i1。