MOSS-Image

What is MOSS-Image?

MOSS-Image is a 5 billion parameter diffusion transformer model designed for high-quality text-to-image generation. It focuses on delivering clear and accurate text rendering within generated images while providing precise control over layout and composition. The model accepts prompts up to 4,000 tokens and supports diverse visual styles, including anime and photorealistic outputs.

The repository provides publicly available code and model weights under an Apache-2.0 license. It includes integration support for ComfyUI, SGLang, and vLLM-Omni, along with a Gradio-based interface for local testing. Training tools like KohakuMossTrainer enable full fine-tuning and adapter training with mixed precision and multi-GPU setups across NVIDIA and Intel hardware.

Community extensions offer additional capabilities such as vertical banding reduction, sampling acceleration, and format conversion for standard diffusion pipelines. The model is also accessible through the MOSI API Platform for cloud-based inference and playground testing.

MOSS-Image features

• Layout control for precise placement of text and visual elements
• Accurate text rendering inside generated images
• Support for long prompts up to 4,000 tokens
• Efficient 5 billion parameter diffusion transformer backbone
• Compatible with ComfyUI, SGLang, and vLLM-Omni
• Fine-tuning tools supporting LoRA and full model adaptation
• Multi-GPU and CPU offloading training across NVIDIA and Intel hardware

Who uses MOSS-Image and for what

• Generate product mockups with exact text placement and brand typography
• Create educational materials that require accurate diagrams and labels
• Produce comic panels or storyboards with controlled composition and dialogue
• Train custom style adapters for specific artistic domains or photography
• Run local image generation workflows using ComfyUI or Gradio interfaces

MOSS-Image FAQ

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