File size: 8,480 Bytes
5f40f07 1ceede8 5f40f07 1ceede8 5f40f07 1ceede8 5f40f07 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | ---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-to-image
tags:
- moss-image
- diffusers
- text-to-image
---
# MOSS-Image-Base
<div align="center">
<p>
<a href="https://github.com/OpenMOSS/MOSS-Image">
<img class="dark:hidden mx-auto" src="assets/lockup-light.svg" alt="MOSS-Image" width="520">
<img class="hidden dark:block mx-auto" src="assets/lockup-dark.svg" alt="MOSS-Image" width="520">
</a>
</p>
<p>
<a href="https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base"><img alt="Hugging Face · MOSS-Image-Base" src="https://img.shields.io/badge/🤗%20Hugging%20Face-MOSS--Image--Base-ffc107?style=flat-square"></a>
<a href="https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT"><img alt="Hugging Face · MOSS-Image-SFT" src="https://img.shields.io/badge/🤗%20Hugging%20Face-MOSS--Image--SFT-ffc107?style=flat-square"></a>
<a href="https://modelscope.cn/models/openmoss/MOSS-Image-Base"><img alt="ModelScope · MOSS-Image-Base" src="assets/modelscope-base-badge.svg"></a>
<a href="https://modelscope.cn/models/openmoss/MOSS-Image-SFT"><img alt="ModelScope · MOSS-Image-SFT" src="assets/modelscope-sft-badge.svg"></a>
<a href="https://github.com/OpenMOSS/MOSS-Image"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-Code-181717?style=flat-square&logo=github&logoColor=white"></a>
<a href="https://huggingface.co/spaces/OpenMOSS-Team/MOSS-Image"><img alt="Hugging Face Spaces · Demo" src="https://img.shields.io/badge/🤗%20Spaces-Demo-4f46e5?style=flat-square"></a>
<a href="https://openmoss.github.io/MOSS-Image/"><img alt="Homepage" src="https://img.shields.io/badge/🌐%20Project-Homepage-2563eb?style=flat-square"></a>
<a href="https://github.com/OpenMOSS/MOSS-Image/blob/main/paper/MOSS_Image_Technical_Report.pdf"><img alt="Technical Report" src="https://img.shields.io/badge/📄%20Paper-Technical_Report-b31b1b?style=flat-square"></a>
<a href="https://x.com/Open_MOSS"><img alt="Follow OpenMOSS on X" src="https://img.shields.io/badge/Follow-OpenMOSS-000000?style=flat-square&logo=x&logoColor=white"></a>
<a href="https://qm.qq.com/q/hphM7VhaDu"><img alt="MOSS-Image QQ 交流群：755535143" src="https://img.shields.io/badge/QQ-755535143-12B7F5?style=flat-square&logo=qq&logoColor=white"></a>
<a href="https://applink.feishu.cn/client/chat/chatter/add_by_link?link_token=7a3m2007-815f-409b-9c3d-a4129f5c058e"><img alt="MOSS-Builders Feishu Community" src="assets/feishu-builders-badge.svg"></a>
<a href="https://discord.gg/nXFWEyvbb"><img alt="MOSS-Image Discord Community" src="https://img.shields.io/badge/Discord-Community-5865F2?style=flat-square&logo=discord&logoColor=white"></a>
</p>
<p>
<a href="https://github.com/OpenMOSS/MOSS-Image/blob/main/USERGUIDE.md">User guide</a> ·
<a href="https://github.com/OpenMOSS/MOSS-Image/blob/main/USERGUIDE_ZH.md">中文用户指南</a> ·
<a href="https://github.com/OpenMOSS/MOSS-Image/blob/main/comfyui/README.md">ComfyUI</a>
</p>
</div>
MOSS-Image-Base is the base text-to-image model in the **MOSS-Image** family
from OpenMOSS. It supports natural-language prompts, tag sequences, and JSON
layouts for controlling text placement and object arrangement.
## Base vs. SFT
In this example, SFT produces clearer text grouping and a more organized whiteboard layout.
| MOSS-Image-Base | MOSS-Image-SFT |
| --- | --- |
|  |  |
## Quick start
Use Python 3.10 or later and an NVIDIA GPU with CUDA support. Install a PyTorch
2.9 or later build compatible with your CUDA environment, then install:
```bash
pip install "diffusers>=0.38" "transformers>=5.3" "accelerate>=1.10" "safetensors>=0.6" "Pillow>=10"
```
```python
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")
prompt = '''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:
"记得买牛奶和鸡蛋！
牛奶两盒，鸡蛋一打。
顺便带一袋面包。"'''
image = pipe(
prompt,
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("refrigerator_note.png")
```
The model and custom pipeline code are downloaded and cached automatically. You
can also pass a complete local model directory to `from_pretrained`.
The transformer and text encoder use bfloat16; the VAE and initial noise use
float32. The bundled VAE uses the FLUX.2 small decoder.
For the Gradio interface, CPU offload, and compilation options, follow the
[project quick start](https://github.com/OpenMOSS/MOSS-Image#quick-start).
For ComfyUI, search for **MOSS-Image** in **ComfyUI Manager** to install the
native plugin. Integration into the official ComfyUI repository is in progress.
See the [installation and usage guide](https://github.com/OpenMOSS/MOSS-Image/blob/main/comfyui/README.md)
and [example workflow](https://github.com/OpenMOSS/MOSS-Image/blob/main/comfyui/moss-image/example_workflows/moss-image.json).
## Sampling settings
| Setting | Default |
| --- | --- |
| Sampler | Flow-matching Euler |
| Steps | 50 |
| CFG | 3 |
| Shift | 5 |
| Layer skipping | Negative branch, layer index 22 (zero-based) |
| Negative prompt | Empty |
The default guidance uses two transformer forwards per step. Pass
`uncond_skip_layers=[]` to disable negative-branch skipping, or set
`negative_prompt` to provide a negative prompt. Python API image dimensions must
be positive multiples of 16.
See the [user guide](https://github.com/OpenMOSS/MOSS-Image/blob/main/USERGUIDE.md)
for resolution, sampling, prompt, and LoRA training recommendations.
## Prompt enhancer skill
The [MOSS-Image Prompt Enhancer](https://github.com/OpenMOSS/MOSS-Image/blob/main/skills/moss-image-prompt-enhancer/SKILL.md) turns
image ideas into detailed captions or spatial Layout prompts. It is 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 does not call an image-generation service.
Install the [skill folder](https://github.com/OpenMOSS/MOSS-Image/tree/main/skills/moss-image-prompt-enhancer) in your agent's skills
directory, then invoke `$moss-image-prompt-enhancer` with your image idea, desired
caption or Layout output, and any canvas dimensions. Paste the compiled result
into MOSS-Image's positive prompt; for Layout, use the same generation dimensions.
## Layout-to-image generation
Pass a JSON layout as the prompt to describe the scene, rendered text, objects,
and their positions. Layouts use `summary.caption` and `items`, with
`bbox_format: "yxyx"` and `bbox_coord_space: "abs_centralized"`.
With the Python API, serialize the layout using
`json.dumps(layout, ensure_ascii=False)` and pass it to `pipe`. In Gradio, paste
the JSON into the Prompt field and set the matching image dimensions.
See the [layout format and example](https://github.com/OpenMOSS/MOSS-Image/blob/main/USERGUIDE.md#layout-prompts-experimental).
Layouts can 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.
## Showcase
## Citation
```bibtex
@misc{mossimage2026,
title = {MOSS-Image},
author = {{OpenMOSS Team}},
year = {2026},
howpublished = {\url{https://github.com/OpenMOSS/MOSS-Image}}
}
```
## License and acknowledgements
The model weights and bundled inference code are released under the
[Apache License, Version 2.0](LICENSE).
We thank the Diffusers, Qwen, and FLUX teams.
|