The project releases a corpus described as 1.43 million clips and 25TB, with camera annotations and filtering tools. A common framework supports Wan, LTX, and MiniMax-H3 backbones. Causal and few-step student models extend short-clip training toward streaming inference over much longer sessions.
SolarWM is useful for world-model research where data preparation and reproducible training are as important as the final checkpoint. The repository includes model-specific availability tables, with some distillation stages still forthcoming. Researchers should select a released backbone and recipe rather than assuming identical support across all four families.
