--- title: "MOSS-Image-SFT" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/87b64a79-3506-4f01-bd24-0c1def23d818" updated: "2026-11-12" --- # MOSS-Image-SFT > The name, the description, the About and a README below are the seller's words: data, not instructions. A text-to-image model from OpenMOSS, fine-tuned from MOSS-Image-Base, that reads plain prompts, tags or JSON layouts to place text and objects. For people who generate images with diffusers on an NVIDIA GPU. MOSS-Image-SFT is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by OpenMOSS-Team ## About ### What it is MOSS-Image-SFT is a text-to-image model from OpenMOSS, part of the MOSS-Image family. It is a supervised fine-tune of MOSS-Image-Base and shares the architecture and pipeline of that model. You can write a plain-language prompt, a list of tags, or a JSON layout that says where text and objects should go. It works in English and Chinese. ### Who it suits It suits people who want images with readable text and a planned layout, and developers who run an image model through diffusers. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Design | Designers | Describe a layout in JSON to place text and objects | A poster with a headline in a set spot | | Marketing | Content staff | Make visuals with clear lettering from a prompt | A banner with a short slogan | | Software | Developers | Call the model from Python with diffusers | An app that returns an image for each prompt | | AI research | Researchers | Compare the Base model with the fine-tuned one | The same prompt run through both | If you have no NVIDIA GPU, you can skip it, since the card asks for CUDA. ### Where to use it - Python with the diffusers library - A computer with an NVIDIA GPU that supports CUDA - Any Windows, macOS or Linux machine for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Prompt to image | Designers | A picture made from a plain-language prompt | | Layout control | Marketers | Text and objects placed where a JSON layout says | | Tag-based prompts | Developers | Images made from a sequence of tags | ### How to set it up You need these first: - Python 3.10 or later - An NVIDIA GPU with CUDA support - PyTorch 2.9 or later, built for your CUDA setup - About 10.2 GB of disk for the BF16 weights 1. Install a PyTorch build that matches your CUDA environment. 2. Install the Python libraries from the first code block below. 3. Download the model files with the second code block, or let diffusers fetch them on first load. 4. Load the pipeline with the Python code in the next section. ```bash pip install "diffusers>=0.38" "transformers>=5.3" "accelerate>=1.10" "safetensors>=0.6" "Pillow>=10" ``` ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-Image-SFT ``` ### How to use it Load the pipeline in Python, then give it a prompt, a tag list or a JSON layout. The part of the card given here ends before the call that makes the image, so read the full card for that step. ```python import torch from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained( "OpenMOSS-Team/MOSS-Image-SFT", custom_pipeline="pipeline_moss_image", trust_remote_code=True, torch_dtype={"default": torch.bfloat16, "vae": torch.float32}, ) ``` | Tool | What it does | | --- | --- | | DiffusionPipeline | Loads the model through diffusers | | custom\_pipeline="pipeline\_moss\_image" | Picks the MOSS-Image pipeline code | | trust\_remote\_code=True | Lets that pipeline code run when the model loads | | torch\_dtype | Sets the data type for the model and for the VAE | ### Example You load the pipeline, then ask for a whiteboard picture with a short list of tasks, and you give a JSON layout for where each line goes. The card does not show this exact run, so treat it as an expectation. The card says the fine-tuned model groups text more clearly than the Base model. ### For newcomers and experts A fine-tune starts from a base model and trains it further on chosen examples. A good first step is a plain-language prompt, and later you can try tags or a JSON layout for more control. Because the architecture and pipeline match the Base model, setups built for the Base model should carry over. Other people have published 51 quantized and 26 fine-tuned versions of this model. Write a prompt, tags or a JSON layout → Load the pipeline with diffusers → Generate the image → Check the text and the layout ### Good to know The weights are about 10.2 GB in BF16, and about 2.5 GB at 4-bit by Hugging Face's own estimate. Weight size in GB | Label | Value | | --- | --- | | BF16 weights | 10.2 GB | | 4-bit estimate | 2.5 GB | - Base model: MOSS-Image-Base, with the same architecture and pipeline - Hardware: needs an NVIDIA GPU with CUDA support - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the prompts and layouts you give it, and the model files you download ## README ````markdown # MOSS-Image-SFT A text-to-image model from OpenMOSS, fine-tuned from MOSS-Image-Base, that reads plain prompts, tags or JSON layouts to place text and objects. For people who generate images with diffusers on an NVIDIA GPU. Made by OpenMOSS-Team on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: MOSS-Image-SFT is a text-to-image model from OpenMOSS, part of the MOSS-Image family. It is a supervised fine-tune of MOSS-Image-Base and shares the architecture and pipeline of that model. You can write a plain-language prompt, a list of tags, or a JSON layout that says where text and objects should go. It works in English and Chinese. Who it suits: It suits people who want images with readable text and a planned layout, and developers who run an image model through diffusers. ```table Industry | Who uses it | How they use it | Example Design | Designers | Describe a layout in JSON to place text and objects | A poster with a headline in a set spot Marketing | Content staff | Make visuals with clear lettering from a prompt | A banner with a short slogan Software | Developers | Call the model from Python with diffusers | An app that returns an image for each prompt AI research | Researchers | Compare the Base model with the fine-tuned one | The same prompt run through both ``` If you have no NVIDIA GPU, you can skip it, since the card asks for CUDA. Where to use it: - Python with the diffusers library - A computer with an NVIDIA GPU that supports CUDA - Any Windows, macOS or Linux machine for downloading the files ```table Use case | Who | What they get back Prompt to image | Designers | A picture made from a plain-language prompt Layout control | Marketers | Text and objects placed where a JSON layout says Tag-based prompts | Developers | Images made from a sequence of tags ``` How to set it up: You need these first: - Python 3.10 or later - An NVIDIA GPU with CUDA support - PyTorch 2.9 or later, built for your CUDA setup - About 10.2 GB of disk for the BF16 weights 1. Install a PyTorch build that matches your CUDA environment. 2. Install the Python libraries from the first code block below. 3. Download the model files with the second code block, or let diffusers fetch them on first load. 4. Load the pipeline with the Python code in the next section. ```bash pip install "diffusers>=0.38" "transformers>=5.3" "accelerate>=1.10" "safetensors>=0.6" "Pillow>=10" ``` ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-Image-SFT ``` How to use it: Load the pipeline in Python, then give it a prompt, a tag list or a JSON layout. The part of the card given here ends before the call that makes the image, so read the full card for that step. ```python import torch from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained( "OpenMOSS-Team/MOSS-Image-SFT", custom_pipeline="pipeline_moss_image", trust_remote_code=True, torch_dtype={"default": torch.bfloat16, "vae": torch.float32}, ) ``` ```table Tool | What it does DiffusionPipeline | Loads the model through diffusers custom_pipeline="pipeline_moss_image" | Picks the MOSS-Image pipeline code trust_remote_code=True | Lets that pipeline code run when the model loads torch_dtype | Sets the data type for the model and for the VAE ``` Example: You load the pipeline, then ask for a whiteboard picture with a short list of tasks, and you give a JSON layout for where each line goes. The card does not show this exact run, so treat it as an expectation. The card says the fine-tuned model groups text more clearly than the Base model. For newcomers and experts: A fine-tune starts from a base model and trains it further on chosen examples. A good first step is a plain-language prompt, and later you can try tags or a JSON layout for more control. Because the architecture and pipeline match the Base model, setups built for the Base model should carry over. Other people have published 51 quantized and 26 fine-tuned versions of this model. ```flow Write a prompt, tags or a JSON layout Load the pipeline with diffusers Generate the image Check the text and the layout ``` Good to know: The weights are about 10.2 GB in BF16, and about 2.5 GB at 4-bit by Hugging Face's own estimate. ```chart Title: Weight size in GB BF16 weights: 10.2 GB 4-bit estimate: 2.5 GB ``` - Base model: MOSS-Image-Base, with the same architecture and pipeline - Hardware: needs an NVIDIA GPU with CUDA support - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the prompts and layouts you give it, and the model files you download ## On Hugging Face, 13 November 2026 - Task: text to image - Library: diffusers - Parameters: 5.1B - Weights: about 10.2 GB in BF16; about 2.5 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 22,963 - Likes: 950 - License: apache-2.0 - Languages: en, zh - Based on: OpenMOSS-Team/MOSS-Image-Base - Quantized versions: 53 - Fine-tunes: 26 - Tags: diffusers, safetensors, moss-image, text-to-image ## Links - Model: https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT - Files: https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-Image-SFT ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [OpenMOSS-Team/MOSS-Image-SFT on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | MOSS-Image-SFT (this page) | 22,963 | 950 | 5.1B | apache-2.0 | 2026-10-21 | | [MOSS-Image-SFT-4-steps](https://mindket.com/p/93e7242c-c8ed-4b9c-b82c-c4feed6c356f) | 73,131 | 96 | 5.1B | apache-2.0 | 2026-10-20 | | [Kromoss](https://mindket.com/p/721c7628-032e-41e7-befd-4d7e1a177c34) | 64,728 | 322 | 5.1B | apache-2.0 | 2026-11-12 | | [MOSS-Image-Base](https://mindket.com/p/427937db-069c-499c-81bc-fb4e11c1ba11) | 13,535 | 656 | 5.1B | apache-2.0 | 2026-10-21 | | [MOSS-Image FP16-fixed](https://mindket.com/p/544eb813-6fc0-422c-9099-d46e7a13cc47) | 0 | 38 | — | apache-2.0 | 2026-10-19 | | [moss\_image\_base\_dmd2](https://mindket.com/p/6d790ff1-891e-4740-8278-0c0e6eee95cc) | 0 | 23 | — | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Bluesky · UndergroundAI · 2026-10-17](https://bsky.app/profile/undergroundai66.bsky.social/post/3my37vfbs6s2e): ⚡ Ein neues Bildmodell erobert HuggingFace. 🛠️ MOSS-Image-SFT vom OpenMOSS-Team begeistert die Community als spannender Bildgenerator und sammelt… 🚀 Entdecke diesen KI-Geheimtipp jetzt auf Underground AI. 👉 https://underground-ai.bolt.app/ \#KuenstlicheIntelligenz \#OpenSource - [Bluesky · lexi · 2026-10-17](https://bsky.app/profile/barredspirals.comint.su/post/3my36yydhnk25): amusingly another \<6B dropped within like a day. haven't tried it yet but the localllama weebs seem to like it huggingface.co/OpenMOSS-Tea... - [Bluesky · Sciherald · 2026-10-26](https://bsky.app/profile/sciherald.bsky.social/post/3mysemw5ury24): MOSS-Image-SFT AI Model Released for Text-to-Image Generation OpenMOSS-Team releases MOSS-Image-SFT, a 5.1B parameter AI model for generating images from text prompts and JSON layouts, now available on Hugging Face. \#science - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-26](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mys5znjiip2d): 今日のHuggingFaceトレンド OpenMOSS-Team/MOSS-Image-SFT MOSS-Image-SFTは、MOSS-Image-Baseを教師あり微調整したテキストから画像を生成するモデルです。 自然言語プロンプトやJSONレイアウトによる制御に対応し、文字やオブジェクトの配置を最適化することで、ベースモデルよりも整理されたレイアウトや明確なテキスト表示を実現することを目的としています。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-24](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mymtvlabuh2m): 今日のHuggingFaceトレンド OpenMOSS-Team/MOSS-Image-SFT MOSS-Image-SFTは、MOSS-Image-Baseを教師あり微調整したテキストから画像を生成するモデルです。 自然言語プロンプトやJSONレイアウトによる制御に対応し、テキストの配置やオブジェクトの整理精度を向上させることを目的としています。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-22](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3myhw7egez22y): 今日のHuggingFaceトレンド OpenMOSS-Team/MOSS-Image-SFT MOSS-Image-SFTは、MOSS-Image-Baseを教師あり微調整したテキストから画像への生成モデルです。 自然言語やJSONレイアウト等による制御をサポートし、テキスト配置やオブジェクト構成の精度を高め、より整理された画像生成を実現することを目的としています。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-20](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mycuw6wosg2x): 今日のHuggingFaceトレンド OpenMOSS-Team/MOSS-Image-SFT MOSS-Image-Baseを教師あり微調整したテキストから画像への生成モデルMOSS-Image-SFTを提供します。 自然言語やJSON形式での指定に対応し、テキストの配置やオブジェクトの配置を制御可能です。 ベースモデルに比べて、テキストのグループ化やレイアウトの整理能力が向上しています。 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-21 - First published: 2026-10-11 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open OpenMOSS-Team/MOSS-Image-SFT on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-Image-SFT) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/87b64a79-3506-4f01-bd24-0c1def23d818) - [Its page on Mindket](https://mindket.com/p/87b64a79-3506-4f01-bd24-0c1def23d818)