OpenMOSS-Team/MOSS-Image-Base — new model trending #30 on Hugging Face

 

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<p>

<a href="https://github.com/OpenMOSS/MOSS-Image/blob/main/USERGUIDE.md">User guide</a> &nbsp; · &nbsp;

<a href="https://github.com/OpenMOSS/MOSS-Image/blob/main/USERGUIDE_ZH.md">中文用户指南</a> &nbsp; · &nbsp;

<a href="https://github.com/OpenMOSS/MOSS-Image/blob/main/comfyui/README.md">ComfyUI</a>

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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

Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base