MOSS-Image, a 5B open-weights image model, passes one million downloads in four days

MOSS-Image, an open-weights text-to-image model from MOSS (模思智能) and the OpenMOSS team, has passed one million downloads in four days, according to a report in Chinese by QbitAI (量子位). The model uses a 5B-parameter diffusion transformer (DiT) backbone. The article puts the download total across overseas communities, including Hugging Face's Comfy-Org repository, at 130万 (130 wan). The GitHub repository has 1.4k stars, and the model reached the top of Hugging Face's text-to-image trending list.

Architecture and training

MOSS-Image is a single-stream diffusion transformer pretrained from scratch. The DiT backbone has 5B parameters. It is paired with a frozen Qwen3.5-9B text encoder and the FLUX.2 VAE. Qwen3.5-9B extracts text features, the VAE handles image latents, and the DiT performs the diffusion modeling.

The team says it deduplicated and quality-filtered the training data, removed AI-generated images, and annotated attributes such as watermarks and blur. Ostris, a developer in the ComfyUI community, speculated that excluding AI-generated images may explain why outputs look less like typical AI imagery. According to MOSS, training ran on a domestic PPU compute platform.

Capabilities

• Text rendering: Optimized for dense Chinese and English text, preserving line breaks and similar structure. On the Long Long Text Benchmark (LLTB), MOSS-Image scored 96.3% Mean-6 text coverage, first among the open-weights models tested. QbitAI notes that coverage measures how much of the target text appears and is not the same as character-level accuracy.
• Layout control: Accepts structured JSON input with a "summary" for the overall scene and "items" for text and objects, each with visual attributes and a bounding box. On 7Bench, it scored 0.88 target localization mIoU and 0.70 layout AUC.
• Long prompts: Supports text conditioning up to 4096 tokens. OpenMOSS also provides a MOSS-Image Prompt Enhancer Skill that expands short ideas into detailed captions or structured layout prompts.

Design evaluation

In the RLA real-design blind evaluation with only short design requirements, MOSS-Image's Elo point estimate was 1698.70, second among the models tested and behind GPT Image 2. In a separate comparison with unified enhanced prompts, it scored 1807.34, the highest in that group, ahead of ERNIE Image and SenseNova U1. These are results reported by MOSS and QbitAI.

Community and tooling

The official reference configuration uses 50-step sampling. Community variants appeared within days: an 8-step DMD accelerated version from Lodestone, a 4-step attempt called Rapid-MOSS, INT8 quantization, GGUF, MLX weights, and LoRAs for styles including phone photography, film grain, gouache, anime portraits and pixel art. Support exists for Diffusers, an official ComfyUI plugin, SGLang and vLLM-Omni (the last two on day 0), and training tools such as Ostris AI Toolkit and sd-scripts.

Developers on X and Reddit compared it with FLUX.3-klein, Krea2 and Qwen Image 2.1. Reactions were mixed: some praised naturalness and detail, while others said stability in specific art styles has room to improve. A core developer's AMA thread on r/StableDiffusion was pinned.

Availability

Base and SFT weights are on Hugging Face under OpenMOSS-Team, and a technical report is on GitHub. A MOSS-Image API is live at platform.mosi.cn; pricing not yet disclosed. Users can also reach the model through MosMos, MOSS's desktop voice assistant.

Company context

MOSS announced a Pre-A round of over 1 billion yuan on October 12, led by 华控基金. QbitAI reports the company released more than 20 models in the first half of 2026, and that its MOSS-TTS series has passed 3 million cumulative downloads. MOSS-Live and MOSS-TTS 2.0 are expected soon, according to QbitAI.

What this means

MOSS-Image targets design work where text accuracy and element placement matter, such as posters and e-commerce images. The JSON layout input with bounding boxes turns composition requirements into explicit spatial constraints instead of natural-language descriptions. The reported results, including the LLTB score and the second-place Elo behind GPT Image 2, come from MOSS and QbitAI and rest on specific benchmark conditions. Community comparisons so far are informal. Open Base and SFT weights, day-0 inference support and fast community derivatives lower the barrier to fine-tuning and local deployment.