According to the paper "MOSS-Image Technical Report," the MOSI. AI team proposes the open-source image generation system MOSS-Image, which combines high-fidelity visual synthesis with intent-aware composition. The research claims that the system pre-trains from scratch a 5B single-stream diffusion Transformer (DiT) that supports bilingual Chinese-English long-text rendering and precise layout control; it also develops an intent-aware intelligent prompt enhancement module that transforms user requests into detailed content and layout specifications. The team further proposes the long-text rendering benchmark LLTB and the layout generation benchmark RLA. The paper states that MOSS-Image achieves the best performance among open-weight models on LLTB, with an average text coverage rate of 96.3%; it ranks second in the RLA end-to-end system comparison, and its backbone network ranks first in the generator evaluation with shared enhanced prompts. The authors state that they will release the model, training recipe, and evaluation framework.
MOSS-Image Technical Report
HF Papers
Sourcesarxiv.org