--- title: MOSS-Image-Base canonical_url: "https://www.modelscope.cn/models/openmoss/MOSS-Image-Base" md_url: "https://www.modelscope.cn/models/openmoss/MOSS-Image-Base.md" repository: openmoss/MOSS-Image-Base last_updated: 2026-10-16 license: apache-2.0 pipeline_tag: text-to-image-synthesis tasks: - text-to-image-synthesis parameters: 13.5B tensor_type: - I64 - BF16 - F32 library_name: - pytorch - diffusers - lora - safetensors frameworks: - pytorch language: - en - zh downloads: 47 stars: 2 tags: - moss-image - diffusers - text-to-image --- # MOSS-Image-Base > MOSS-Image-Base - openmoss 在 ModelScope 开源的模型。 User guide · 中文用户指南 · ComfyUI openmoss/MOSS-Image-Base 是 ModelScope 魔搭社区上的 13.5B 参数text-to-image-synthesis模型，采用 apache-2.0 许可。 - **Repository**: openmoss/MOSS-Image-Base - **License**: apache-2.0 - **Tasks**: text-to-image-synthesis - **Parameters**: 13.5B - **Tags**: moss-image, diffusers, text-to-image - **Downloads**: 47 - **Stars**: 2 - **Last updated**: 2026-10-16 Source: https://www.modelscope.cn/models/openmoss/MOSS-Image-Base --- # MOSS-Image-Base

User guide · 中文用户指南 · ComfyUI

![MOSS-Image-Base and MOSS-Image-SFT](assets/teaser.webp) 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 | | --- | --- | | ![MOSS-Image-Base](assets/comparison/base.webp) | ![MOSS-Image-SFT](assets/comparison/sft.webp) | ## 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. ![Layout control](assets/showcase/layout-bboxes.webp) ## Showcase ![Examples overview](assets/showcase/overview.webp) ![Examples overview 2](assets/showcase/overview2.webp) ## 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.