MOSS-Image: Text-to-Image Generation
with Native Layout Control

News

October 16, 2026 — Introducing MOSS-Image-Base, with code and model weights available.

Layout control, strong text rendering, prompts of up to 4K tokens, anime and photorealistic styles, and a 5B backbone designed for efficient generation and fine-tuning. Training uses heterogeneous GPU platforms.

Features

Showcases

Text, illustration, photography and visual design, composed as in the project showcase. Open either image to explore it at full resolution.

Layout-to-Image Generation

Layouts also support controlled editing and user-friendly layout tools. Edit text, element descriptions and bounding boxes to adjust image content and composition in a structured way.

MOSS-Image accepts layout specifications through the same prompt interface used for text-to-image generation. The generated result below illustrates layout control; the JSON example that follows describes a travel poster with a title and a mountain illustration.

{
  "summary": {
    "meta": ["poster"],
    "canvas": "single",
    "caption": "A minimalist travel poster with a cream background, a blue mountain illustration, and the title EXPLORE at the top."
  },
  "items": [
    {
      "label": "text",
      "caption": "Large dark-blue uppercase title centered at the top.",
      "text": "EXPLORE",
      "bbox": [-416, -384, -288, 384]
    },
    {
      "label": "object",
      "caption": "A flat blue mountain illustration in the lower center.",
      "bbox": [-192, -288, 384, 288]
    }
  ],
  "bbox_coord_space": "abs_centralized",
  "bbox_format": "yxyx"
}

import json
from pathlib import Path

layout = json.loads(Path("layout.json").read_text())
image = pipe(
    json.dumps(layout, ensure_ascii=False),
    height=1024, width=1024,
    num_inference_steps=50, guidance_scale=3.0,
    uncond_skip_layers=[22],
    generator=torch.Generator("cpu").manual_seed(42),
).images[0]
image.save("layout.png")

Models

The two checkpoints share the same architecture and sampling interface.

Model	Description	Weights
MOSS-Image-Base	Base text-to-image model	Hugging Face ↗ · ModelScope ↗
MOSS-Image-SFT	Fine-tuned from MOSS-Image-Base on curated data	Hugging Face ↗ · ModelScope ↗

Weights: Apache-2.0. The pipeline code and third-party components retain their respective licenses.

Base vs. SFT

In this example, SFT produces clearer text grouping and a more organized whiteboard layout.

Quick Start

Use Python 3.10+ and an NVIDIA GPU. In a dedicated Python environment, install a PyTorch build compatible with your CUDA environment, then run:

git clone https://github.com/OpenMOSS/MOSS-Image.git
cd MOSS-Image
python -m venv .venv
source .venv/bin/activate

pip install -r requirements.txt

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "OpenMOSS-Team/MOSS-Image-Base",
    custom_pipeline="pipeline_moss_image",
    trust_remote_code=True,
    torch_dtype={"default": torch.bfloat16, "vae": torch.float32},
)
pipe.scheduler = type(pipe.scheduler).from_config(pipe.scheduler.config)
pipe.to("cuda")
image = pipe(
    "A wide view of a refrigerator beside a kitchen counter with a fruit bowl. A small sticky note on the refrigerator door is handwritten in Chinese:“\n记得买牛奶和鸡蛋！\n牛奶两盒，鸡蛋一打。\n顺便带一袋面包。”",
    height=1456,
    width=1456,
    num_inference_steps=50,
    guidance_scale=3.0,
    uncond_skip_layers=[22],
    generator=torch.Generator("cpu").manual_seed(42),
).images[0]
image.save("refrigerator_note.png")

For prompts, layout inputs and LoRA training guidance, see the English user guide or 中文用户指南.

pip install -e '.[ui]'
python gradio_app.py --model OpenMOSS-Team/MOSS-Image-Base

Prompt Enhancer

Turn image ideas into detailed captions or spatial Layout prompts with the MOSS-Image Prompt Enhancer skill, designed for GPT-6-family agents with local file and Python tools.

The bundled script converts pixel-coordinate drafts into compact JSON with integer centered coordinates. It prepares prompts without calling an image-generation service.

1. Install the skill folder in your agent’s skills directory.
2. Invoke $moss-image-prompt-enhancer with your image idea, caption or Layout output requirements, and canvas dimensions.
3. Paste the compiled result into MOSS-Image’s positive prompt. For Layout prompts, use the same generation dimensions as the draft.

Prompt Enhancer instructions ↗

Citation

If you use MOSS-Image in your research, please cite the project repository.

@misc{mossimage2026,
  title = {MOSS-Image},
  author = {{OpenMOSS Team}},
  year = {2026},
  howpublished = {\url{https://github.com/OpenMOSS/MOSS-Image}}
}