MOSS-Image, the 5B-parameter open-source image generation model released by MOSI and the OpenMOSS team, has drawn about 1.3 million downloads in its first four days, according to the team and QbitAI. That figure is summed across several repositories, including the Comfy-Org repository on Hugging Face, rather than counted from a single model page. The project also gathered about 1,400 stars on GitHub and quickly reached the top of Hugging Face's trending list for text-to-image models, placing near the top of the all-category list as well.
Much of the attention has come from overseas developer communities. On Reddit's r/StableDiffusion, MOSS-Image became one of the most discussed new models, and an Ask Me Anything thread hosted by its core developers was pinned by moderators. Ostris, a well-known ComfyUI community developer, suggested the model's more natural look may come from the team's decision to remove AI-generated images from its training data. Developers have been comparing it with FLUX.3-klein, Krea2 and Qwen Image 2.1 on style, human detail, text rendering and complex composition. Some testers praised its realism and detail, while others said stability in certain styles still needs work.
The community has also moved quickly to build on the release. Developers have published 8-step and 4-step accelerated versions, compared with the official 50-step reference setting, along with INT8, GGUF and MLX builds and style LoRAs ranging from phone photography and film grain to gouache, anime portraits and pixel art. Diffusers and an official ComfyUI plugin were available at launch, SGLang and vLLM-Omni added day-zero support, and training tools such as Ostris AI Toolkit and sd-scripts are adding support.
The features drawing the most interest are the ones the team built for design work: dense text rendering in Chinese and English, structured JSON layout input with bounding boxes, and prompts of up to 4,096 tokens. The model pairs a 5B diffusion transformer trained from scratch with a frozen Qwen3.5-9B text encoder and the FLUX.2 VAE, and the team says it was trained on a domestic PPU compute platform.
MOSI has also launched a MOSS-Image API, and the model can be used through its desktop voice assistant MosMos. The team says it plans to release a full-duplex real-time interaction system, MOSS-Live, and a next-generation speech model, MOSS-TTS 2.0.
The download and ranking figures come from the team and Chinese media reports and have not been independently audited.
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