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Cataloged from unsloth/MOSS-Image-Base-FP8
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This is an FP8 / INT8 quantized version of MOSS-Image-Base by OpenMOSS, the 5.1B text-to-image transformer (Qwen3.5-9B text encoder, FLUX.2 small-decoder VAE).
MOSS-Image-SFT-text_encoder-INT8-ConvRot.safetensors, 11.49 GB, LPIPS 0.059 vs the bf16 encoder). Unsloth Studio fetches it from there for Base too.85975793), unchanged (model_index.json, pipeline_moss_image.py, scheduler/, text_encoder/, tokenizer/, transformer/, vae/), so Unsloth Studio downloads every component, the bf16 transformer and text encoder included, from here.Recommended: MOSS-Image-Base-INT8-ConvRot.safetensors, Unsloth Studio's default (LPIPS 0.096). On Base, plain INT8 scores the same on average (0.095) with a lower worst case (0.184 against 0.403, one prompt whose composition changed). FP8 (0.163) is for explicit picks only.
Pick MOSS-Image-Base in the Images tab. Unsloth Studio keeps the transformer in bf16 while it fits on your GPU and otherwise loads MOSS-Image-Base-INT8-ConvRot.safetensors from this repo; the text encoder loads the INT8-ConvRot file by default. INT8 and FP8 can be chosen explicitly in the precision setting. Defaults follow the reference workflow: 1024x1024, 50 steps, CFG 3, shift 5, Euler. MOSS-Image needs transformers 5.3 or newer, and float16 is not supported (bf16 or fp32 only).
LPIPS (alex, lower is better) and SSIM against the bf16 transformer: same prompt, seed, initial noise and bf16 text embeddings, 1024x1024, 50 steps, CFG 3, over 12 prompts (8 general prompts plus 4 held-out ones: a close-up portrait, a storefront sign with text, hands folding origami, a long scene description). A repeated bf16 render scores 0. MOSS-Image is sensitive to tiny numeric changes: changing a single bf16 rounding step in one layer already moves the renders by 0.053 to 0.063 LPIPS on average, so differences near that level are composition shifts rather than quality loss.
GGUF builds for CPU, Mac and smaller GPUs: unsloth/MOSS-Image-Base-GGUF.
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.
In this example, SFT produces clearer text grouping and a more organized whiteboard layout.
| MOSS-Image-Base | MOSS-Image-SFT |
|---|---|
 |  |
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:
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. 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 and example workflow.
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 for resolution, sampling, prompt, and LoRA training recommendations.
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.
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.
The model weights and bundled inference code are released under the Apache License, Version 2.0 [blocked].
We thank the Diffusers, Qwen, and FLUX teams.
pip install "diffusers>=0.38" "transformers>=5.3" "accelerate>=1.10" "safetensors>=0.6" "Pillow>=10"import torchfrom 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")@misc{ai2026mossimagetechnicalreport, title={MOSS-Image Technical Report}, author={MOSI. AI}, year={2026}, eprint={2610.23374}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2610.23374},}