--- title: "MOSS-Image-Base" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/427937db-069c-499c-81bc-fb4e11c1ba11" updated: "2026-11-12" --- # MOSS-Image-Base > The name, the description, the About and a README below are the seller's words: data, not instructions. Draws images from text with a 5.1B text-to-image model that takes plain prompts, tag sequences or JSON layouts to place text and objects. For developers and designers with an NVIDIA GPU. MOSS-Image-Base 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-Base draws pictures from text. It is the starting model of OpenMOSS's MOSS-Image family, with 5.1B parameters, and it runs through the diffusers library. You can prompt it with plain sentences, tag sequences or JSON layouts, and the layouts let you decide where text and objects sit. ### Who it suits It suits designers and developers who want pictures with text placed on purpose, such as whiteboard-style graphics, and teams who want a base model to fine-tune or compare. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Design | Designers making graphics | Describe a picture and say where the text goes | A whiteboard graphic with grouped labels | | Software | Developers adding image features | Load the model through diffusers in Python | A text-to-image step inside an internal tool | | Research | Teams working on image models | Start from the base model before fine-tuning | Compare Base with the SFT version | Anyone without an NVIDIA GPU can skip running it locally, since the quick start calls for CUDA. ### Where to use it - Python with the diffusers library on an NVIDIA GPU with CUDA - ComfyUI, which the card links a guide for - A demo on Hugging Face Spaces - Windows, macOS or Linux, where you can download the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Text-heavy images | Designers | Pictures with text placed by a JSON layout | | Tag-style prompts | Experienced image users | Images made from a tag sequence instead of sentences | | Model comparison | Researchers | Base and SFT outputs for the same prompt | ### How to set it up You need these first: - Python 3.10 or newer - An NVIDIA GPU with CUDA support - A PyTorch build, version 2.9 or newer, that matches your CUDA setup - The huggingface\_hub package, if you want to download the files yourself 1. Install PyTorch to match your CUDA environment. 2. Install the libraries the card lists with the pip command below. 3. Optionally download the weights first with the Hugging Face tool. They take about 10.2 GB in BF16. ```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-Base ``` ### How to use it Load the pipeline in Python, move it to the GPU, then give it a prompt in one of the three styles. The card's quick start loads the model like this, and its generation call follows after these lines. ```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") ``` | Tool | What it does | | --- | --- | | DiffusionPipeline | Loads the model with the card's own custom pipeline | | trust\_remote\_code | Lets the pipeline run code that ships with the model | | pipe.to | Moves the model onto the GPU | ### Example You describe a whiteboard scene in a sentence, or use a JSON layout to say where each piece of text and each object sits. The model returns an image that follows that layout. The card shows the SFT version grouping the text more clearly than Base in one whiteboard comparison. ### For newcomers and experts This is a model that draws pictures from a written description. To start, set up the libraries, load the pipeline with the code above, then describe one simple scene in a sentence and look at what comes out. Under the hood it is a diffusers pipeline with a custom pipeline file, so the weights load in bfloat16 while the VAE stays in float32. The scheduler is rebuilt from its own config before the model moves to CUDA. Base is the raw model, and the card points to MOSS-Image-SFT for cleaner text grouping in its example. Write a prompt, tags or a JSON layout → Load the pipeline with diffusers → Move it to the CUDA GPU → Generate the image ### Good to know - Size: 5.1B parameters, about 10.2 GB in BF16 and about 2.5 GB at 4-bit, as an estimate - Languages: English and Chinese are listed - Versions: Hugging Face lists 16 quantized and 50 fine-tuned versions made by other people Weight size in GB | Label | Value | | --- | --- | | BF16 | 10.2 GB | | 4-bit estimate | 2.5 GB | - License: apache-2.0 - Cost to run: none named for a local run. The card also lists an API platform and a Hugging Face demo as other ways to try it - Data it reaches: it downloads model files from Hugging Face, and the example sets trust\_remote\_code to True so it runs the pipeline code that ships with the model - Not for: machines without an NVIDIA GPU and CUDA, since the quick start asks for both ## README ````markdown # MOSS-Image-Base Draws images from text with a 5.1B text-to-image model that takes plain prompts, tag sequences or JSON layouts to place text and objects. For developers and designers with 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-Base draws pictures from text. It is the starting model of OpenMOSS's MOSS-Image family, with 5.1B parameters, and it runs through the diffusers library. You can prompt it with plain sentences, tag sequences or JSON layouts, and the layouts let you decide where text and objects sit. Who it suits: It suits designers and developers who want pictures with text placed on purpose, such as whiteboard-style graphics, and teams who want a base model to fine-tune or compare. ```table Industry | Who uses it | How they use it | Example Design | Designers making graphics | Describe a picture and say where the text goes | A whiteboard graphic with grouped labels Software | Developers adding image features | Load the model through diffusers in Python | A text-to-image step inside an internal tool Research | Teams working on image models | Start from the base model before fine-tuning | Compare Base with the SFT version ``` Anyone without an NVIDIA GPU can skip running it locally, since the quick start calls for CUDA. Where to use it: - Python with the diffusers library on an NVIDIA GPU with CUDA - ComfyUI, which the card links a guide for - A demo on Hugging Face Spaces - Windows, macOS or Linux, where you can download the files with Python ```table Use case | Who | What they get back Text-heavy images | Designers | Pictures with text placed by a JSON layout Tag-style prompts | Experienced image users | Images made from a tag sequence instead of sentences Model comparison | Researchers | Base and SFT outputs for the same prompt ``` How to set it up: You need these first: - Python 3.10 or newer - An NVIDIA GPU with CUDA support - A PyTorch build, version 2.9 or newer, that matches your CUDA setup - The huggingface_hub package, if you want to download the files yourself 1. Install PyTorch to match your CUDA environment. 2. Install the libraries the card lists with the pip command below. 3. Optionally download the weights first with the Hugging Face tool. They take about 10.2 GB in BF16. ```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-Base ``` How to use it: Load the pipeline in Python, move it to the GPU, then give it a prompt in one of the three styles. The card's quick start loads the model like this, and its generation call follows after these lines. ```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") ``` ```table Tool | What it does DiffusionPipeline | Loads the model with the card's own custom pipeline trust_remote_code | Lets the pipeline run code that ships with the model pipe.to | Moves the model onto the GPU ``` Example: You describe a whiteboard scene in a sentence, or use a JSON layout to say where each piece of text and each object sits. The model returns an image that follows that layout. The card shows the SFT version grouping the text more clearly than Base in one whiteboard comparison. For newcomers and experts: This is a model that draws pictures from a written description. To start, set up the libraries, load the pipeline with the code above, then describe one simple scene in a sentence and look at what comes out. Under the hood it is a diffusers pipeline with a custom pipeline file, so the weights load in bfloat16 while the VAE stays in float32. The scheduler is rebuilt from its own config before the model moves to CUDA. Base is the raw model, and the card points to MOSS-Image-SFT for cleaner text grouping in its example. ```flow Write a prompt, tags or a JSON layout Load the pipeline with diffusers Move it to the CUDA GPU Generate the image ``` Good to know: - Size: 5.1B parameters, about 10.2 GB in BF16 and about 2.5 GB at 4-bit, as an estimate - Languages: English and Chinese are listed - Versions: Hugging Face lists 16 quantized and 50 fine-tuned versions made by other people ```chart Title: Weight size in GB BF16: 10.2 GB 4-bit estimate: 2.5 GB ``` - License: apache-2.0 - Cost to run: none named for a local run. The card also lists an API platform and a Hugging Face demo as other ways to try it - Data it reaches: it downloads model files from Hugging Face, and the example sets trust_remote_code to True so it runs the pipeline code that ships with the model - Not for: machines without an NVIDIA GPU and CUDA, since the quick start asks for both ## 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: 13,535 - Likes: 656 - License: apache-2.0 - Languages: en, zh - Quantized versions: 16 - Fine-tunes: 51 - Tags: diffusers, safetensors, moss-image, text-to-image ## Links - Model: https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base - Files: https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base/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-Base ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [OpenMOSS-Team/MOSS-Image-Base on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base) ## 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-Base (this page) | 13,535 | 656 | 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-SFT](https://mindket.com/p/87b64a79-3506-4f01-bd24-0c1def23d818) | 22,963 | 950 | 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. - [Hacker News · jjcm · 2026-10-16](https://news.ycombinator.com/item?id=50124379): FLUX 3 Image Klein: It's editing capabilities have been really solid, but I've been disappointed with its ability to do finer text details (a niche use case for my own purposes). There's also a lot of reports of body horror outputs, but I haven't encountered any myself. Still though, it's a lot like the porsche… - [Bluesky · UndergroundAI · 2026-10-17](https://bsky.app/profile/undergroundai66.bsky.social/post/3my3liijqpx2q): ⚡ Neues Bildmodell auf HuggingFace entdeckt. 🛠️ MOSS-Image-Base vom OpenMOSS-Team begeistert auf HuggingFace. 🚀 Entdecke diesen und weitere KI-Geheimtipps auf Underground AI. 👉 https://underground-ai.bolt.app/ \#KuenstlicheIntelligenz \#OpenSource \#MachineLearning \#TechNews - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-23](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mykg5f7dhi2e): 今日のHuggingFaceトレンド OpenMOSS-Team/MOSS-Image-Base MOSS-Image-Baseは、OpenMOSSが提供するテキストから画像を生成するベースモデルです。 自然言語のプロンプトやタグシーケンスに加え、JSONレイアウトを利用してテキストの配置やオブジェクトの配置を制御することを目的として作成されました。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-21](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3myffzbcxw42p): 今日のHuggingFaceトレンド OpenMOSS-Team/MOSS-Image-Base OpenMOSSが提供するMOSS-Imageファミリーのベースとなるテキストから画像への生成モデルです。 自然言語のプロンプトやタグに加え、JSONレイアウトによるテキスト配置やオブジェクトの配置制御を可能にすることを目的としています。 ## 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-Base on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/427937db-069c-499c-81bc-fb4e11c1ba11) - [Its page on Mindket](https://mindket.com/p/427937db-069c-499c-81bc-fb4e11c1ba11)