MOSS

MOSS Image is a 5B parameter text-to-image DiT from the OpenMOSS team, built for accurate in-image text, layout control and long prompts. It pairs a Qwen3.5 9B text encoder with the FLUX.2 VAE, accepts prompts of up to 4K tokens in natural language, booru-style tags or a JSON layout, and renders from 512px up to 2K at any aspect ratio. Originally released by on Hugging Face. All credit for the model goes to the OpenMOSS team. Civitai hosts a mirror so creators can generate with it on-site and train against it. Head to the for the reference pipeline, the user guide and updates. Two versions - SFT is the supervised fine-tune of Base on curated data. OpenMOSS recommends it for generation: cleaner text grouping and more organised layouts. - Base is the undistilled base model with no post-training. OpenMOSS recommends it for fine-tuning and LoRA training. LoRAs trained on Base can also be used on SFT, with partial compatibility. Both run at 50 steps with CFG 3 (raise it for short prompts), flow-matching Euler, shift 5. OpenMOSS recommends English prompts; Chinese and other languages are supported. Use line breaks and quotes around text you want rendered. Files Each version carries the diffusion model in bf16 and int8, from the . The Qwen3.5 text encoder and FLUX.2 VAE ship in the same repo. The original Diffusers weights are at and . Generating on Civitai Both versions are in the Civitai generator for text-to-image, with steps, CFG and a negative prompt exposed. JSON layout prompts work as plain prompt text. Image editing and reference images are not part of this release. License MOSS Image weights and inference code are released under the . Commercial use, derivatives and LoRAs are allowed; keep the licence notice with any redistribution. Links - - - - -