JIG: JSON-to-Image Generation in Pixel and Latent Spaces

Text conditioning is fundamental to image generation and has evolved from short alt-text to dense synthetic captions and, more recently, structured JSON, with caption lengths growing from tens to thousands of tokens. However, the large-scale JSON-annotated corpora used by recent generators remain unreleased, limiting reproducible studies of how annotation content, textual format, and generator design contribute to performance. In this paper, we present JIG, an open model family and empirical study of JSON-to-image generation in pixel and latent spaces. First, we introduce JIG-14M, a dataset of 13.7M images paired with rich structured annotations that capture global scene information, visual styles, and spatially grounded element details. Second, we investigate the influence of annotation content and textual format. Both JSON and natural-language prose realizations of the structured annotations outperform conventional dense captions, suggesting that richer annotation content is a key contributor to improved generation quality across textual formats. Third, we train and compare four JIG models spanning pixel and latent spaces using decoupled diffusion and unified Transfusion-style architectures. Under the baseline training setup, latent-space models outperform their pixel-space counterparts, while differences between architectures are comparatively small. We further analyze this gap and develop a pixel-specific training recipe that substantially narrows it in both architectures. With this improvement, pixel models approach their latent counterparts in prompt following, with the largest remaining gap in aesthetic quality. With image-generation training on JIG-14M, the resulting systems also achieve competitive performance against compact open-source generators, with strong compositional generation and prompt alignment. We will release the dataset JIG-14M and all four JIG models to support the research community.