--- title: "MOSS-Image-SFT-GGUF" kind: "Kit" category: "Marketing & SEO" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/23976aeb-2fe8-48dc-a5b5-a0088c187f17" updated: "2026-11-13" --- # MOSS-Image-SFT-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs the MOSS-Image text to image model from compressed GGUF files on CPU, Mac or GPU, through Unsloth Studio or stable-diffusion.cpp. For people who make images on their own computer. MOSS-Image-SFT-GGUF is a kit in the Marketing \& SEO category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by unsloth ## About ### What it is This is a quantized GGUF copy of MOSS-Image-SFT from OpenMOSS, a text to image transformer with 5.1 billion parameters. Unsloth made the files so the model fits on ordinary hardware. They hold only the transformer, so the VAE, tokenizer and text encoder come from other repositories. The record lists English and Chinese. ### Who it suits It suits people who want to generate pictures locally from a text prompt, from casual users of Unsloth Studio to developers who script the command line. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Design | Designers and illustrators | Generate pictures from text on their own computer | A daytime storefront sign with bold clean letters | | Marketing | Small marketing teams | Draft visuals without sending prompts to a hosted service | A quick mock-up of a shop sign | | Software | Developers | Run the model from the stable-diffusion.cpp command line | A scripted render with a fixed prompt and size | | Research | Model testers | Compare quant levels against the bf16 version | Check Q8\_0 against UD-Q4\_K\_XL | People who want text chat or code help can skip it, since this model only makes images. ### Where to use it - Unsloth Studio, in its Images tab - A stable-diffusion.cpp release at master-975 or later, with MOSS-Image support - CPU, Mac and GPU - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Images inside an app | Non-technical users | Pictures from a prompt in Unsloth Studio, which fetches the extra parts for you | | Command line renders | Developers | A png file per command, with size, steps and prompt set by flags | | Quality comparison | Testers | Scores for each quant file against the bf16 version | ### How to set it up You need a few things first. - Python and pip for the Hugging Face hub tool - Unsloth Studio, or a stable-diffusion.cpp build with MOSS-Image support - Room on disk for the transformer, a text encoder, a VAE and a tokenizer 1. Install the hub tool and download this repository. 2. In Unsloth Studio, open the Images tab, select the MOSS-Image-SFT model and one of this repository's GGUF files. Studio then fetches the missing VAE, tokenizer and text encoder by itself. 3. With stable-diffusion.cpp, download the recommended transformer file, the Qwen3.5-9B Q8\_0 text encoder, and the VAE and tokenizer from the FP8 repository. ```sh pip install -U huggingface_hub hf download unsloth/MOSS-Image-SFT-GGUF ``` ```bash hf download unsloth/MOSS-Image-SFT-GGUF MOSS-Image-SFT-UD-Q4_K_XL.gguf --local-dir moss hf download unsloth/Qwen3.5-9B-GGUF Qwen3.5-9B-Q8_0.gguf --local-dir moss hf download unsloth/MOSS-Image-SFT-FP8 vae/diffusion_pytorch_model.safetensors tokenizer/tokenizer.json --local-dir moss ``` ### How to use it In Unsloth Studio you type a prompt in the Images tab. With stable-diffusion.cpp you run sd-cli once per image, pointing it at the four downloaded files. ```bash ./sd-cli --diffusion-model moss/MOSS-Image-SFT-UD-Q4_K_XL.gguf \ --llm moss/Qwen3.5-9B-Q8_0.gguf \ --vae moss/vae/diffusion_pytorch_model.safetensors \ --tokenizer moss/tokenizer/tokenizer.json \ --cfg-scale 3.0 --steps 50 -H 1024 -W 1024 -s 42 \ -p "a storefront sign that says \"UNSLOTH\" in bold clean letters, daytime, sharp focus" -o moss.png ``` | Tool | What it does | | --- | --- | | Unsloth Studio | Runs MOSS-Image GGUFs with a bundled engine and starts at 1024x1024, 50 steps, CFG 3, shift 5 | | sd-cli | The stable-diffusion.cpp program that renders one image per run | | --diffusion-model | Points to the transformer GGUF file | | --llm | Points to the text encoder file | | --vae and --tokenizer | Point to the VAE and tokenizer files | | --cfg-scale, --steps, -H, -W | Set guidance strength, step count, height and width | | -p and -o | Give the prompt text and the output file name | ### Example You ask for a daytime storefront sign that reads UNSLOTH in bold clean letters, with sharp focus, and run the command above. It writes a 1024x1024 image to a file named moss.png in your current folder. ### For newcomers and experts GGUF is a compact packaging of a model so it runs on smaller machines. If you are new, start with Unsloth Studio, since it fetches the extra pieces itself. The card names UD-Q4\_K\_XL, at 3.42 GB, as the pick for most machines. The UD files mix precisions across layers. Unsloth picked each layer's type by measuring how sensitive it was, using image generation runs to gather an importance matrix. Quality is scored with LPIPS against the bf16 transformer, where lower is closer. UD-Q4\_K\_XL scores 0.213 and Q4\_K\_M 0.294. The card warns that one bf16 rounding change in one layer already moves renders by about 0.041, so small gaps are composition shifts. Prompt goes to the Qwen3.5-9B text encoder → Transformer GGUF runs the 50 steps → VAE turns the result into pixels → Image is saved as a png file ### Good to know - License: Apache 2.0. - Cost to run: no API key or paid service named. You need your own hardware and enough disk space for the extra files. - Data it reaches: the extra model files it downloads from Hugging Face, and the image files you write to disk. - Not for: use on its own. These files hold only the transformer, so you also need the text encoder, VAE and tokenizer. Quant files and how close they are to bf16 | File | Size | Note | | --- | --- | --- | | UD-Q3\_K\_XL | 2.84 GB | The smallest in the list | | Q4\_K\_M | 3.20 GB | Scores 0.294 LPIPS against bf16 | | UD-Q4\_K\_XL | 3.42 GB | The card's pick for most machines | | UD-Q5\_K\_XL | 3.95 GB | Closer to bf16, 0.121 LPIPS | | UD-Q6\_K\_XL | 4.53 GB | Closer again, 0.106 LPIPS | | Q8\_0 | 5.48 GB | The closest quantized file, 0.054 LPIPS | | BF16 | 10.17 GB | The full precision reference | ## README ````markdown # MOSS-Image-SFT-GGUF Runs the MOSS-Image text to image model from compressed GGUF files on CPU, Mac or GPU, through Unsloth Studio or stable-diffusion.cpp. For people who make images on their own computer. Made by unsloth 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: This is a quantized GGUF copy of MOSS-Image-SFT from OpenMOSS, a text to image transformer with 5.1 billion parameters. Unsloth made the files so the model fits on ordinary hardware. They hold only the transformer, so the VAE, tokenizer and text encoder come from other repositories. The record lists English and Chinese. Who it suits: It suits people who want to generate pictures locally from a text prompt, from casual users of Unsloth Studio to developers who script the command line. ```table Industry | Who uses it | How they use it | Example Design | Designers and illustrators | Generate pictures from text on their own computer | A daytime storefront sign with bold clean letters Marketing | Small marketing teams | Draft visuals without sending prompts to a hosted service | A quick mock-up of a shop sign Software | Developers | Run the model from the stable-diffusion.cpp command line | A scripted render with a fixed prompt and size Research | Model testers | Compare quant levels against the bf16 version | Check Q8_0 against UD-Q4_K_XL ``` People who want text chat or code help can skip it, since this model only makes images. Where to use it: - Unsloth Studio, in its Images tab - A stable-diffusion.cpp release at master-975 or later, with MOSS-Image support - CPU, Mac and GPU - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Images inside an app | Non-technical users | Pictures from a prompt in Unsloth Studio, which fetches the extra parts for you Command line renders | Developers | A png file per command, with size, steps and prompt set by flags Quality comparison | Testers | Scores for each quant file against the bf16 version ``` How to set it up: You need a few things first. - Python and pip for the Hugging Face hub tool - Unsloth Studio, or a stable-diffusion.cpp build with MOSS-Image support - Room on disk for the transformer, a text encoder, a VAE and a tokenizer 1. Install the hub tool and download this repository. 2. In Unsloth Studio, open the Images tab, select the MOSS-Image-SFT model and one of this repository's GGUF files. Studio then fetches the missing VAE, tokenizer and text encoder by itself. 3. With stable-diffusion.cpp, download the recommended transformer file, the Qwen3.5-9B Q8_0 text encoder, and the VAE and tokenizer from the FP8 repository. ```sh pip install -U huggingface_hub hf download unsloth/MOSS-Image-SFT-GGUF ``` ```bash hf download unsloth/MOSS-Image-SFT-GGUF MOSS-Image-SFT-UD-Q4_K_XL.gguf --local-dir moss hf download unsloth/Qwen3.5-9B-GGUF Qwen3.5-9B-Q8_0.gguf --local-dir moss hf download unsloth/MOSS-Image-SFT-FP8 vae/diffusion_pytorch_model.safetensors tokenizer/tokenizer.json --local-dir moss ``` How to use it: In Unsloth Studio you type a prompt in the Images tab. With stable-diffusion.cpp you run sd-cli once per image, pointing it at the four downloaded files. ```bash ./sd-cli --diffusion-model moss/MOSS-Image-SFT-UD-Q4_K_XL.gguf \ --llm moss/Qwen3.5-9B-Q8_0.gguf \ --vae moss/vae/diffusion_pytorch_model.safetensors \ --tokenizer moss/tokenizer/tokenizer.json \ --cfg-scale 3.0 --steps 50 -H 1024 -W 1024 -s 42 \ -p "a storefront sign that says \"UNSLOTH\" in bold clean letters, daytime, sharp focus" -o moss.png ``` ```table Tool | What it does Unsloth Studio | Runs MOSS-Image GGUFs with a bundled engine and starts at 1024x1024, 50 steps, CFG 3, shift 5 sd-cli | The stable-diffusion.cpp program that renders one image per run --diffusion-model | Points to the transformer GGUF file --llm | Points to the text encoder file --vae and --tokenizer | Point to the VAE and tokenizer files --cfg-scale, --steps, -H, -W | Set guidance strength, step count, height and width -p and -o | Give the prompt text and the output file name ``` Example: You ask for a daytime storefront sign that reads UNSLOTH in bold clean letters, with sharp focus, and run the command above. It writes a 1024x1024 image to a file named moss.png in your current folder. For newcomers and experts: GGUF is a compact packaging of a model so it runs on smaller machines. If you are new, start with Unsloth Studio, since it fetches the extra pieces itself. The card names UD-Q4_K_XL, at 3.42 GB, as the pick for most machines. The UD files mix precisions across layers. Unsloth picked each layer's type by measuring how sensitive it was, using image generation runs to gather an importance matrix. Quality is scored with LPIPS against the bf16 transformer, where lower is closer. UD-Q4_K_XL scores 0.213 and Q4_K_M 0.294. The card warns that one bf16 rounding change in one layer already moves renders by about 0.041, so small gaps are composition shifts. ```flow Prompt goes to the Qwen3.5-9B text encoder Transformer GGUF runs the 50 steps VAE turns the result into pixels Image is saved as a png file ``` Good to know: - License: Apache 2.0. - Cost to run: no API key or paid service named. You need your own hardware and enough disk space for the extra files. - Data it reaches: the extra model files it downloads from Hugging Face, and the image files you write to disk. - Not for: use on its own. These files hold only the transformer, so you also need the text encoder, VAE and tokenizer. ```table Title: Quant files and how close they are to bf16 File | Size | Note UD-Q3_K_XL | 2.84 GB | The smallest in the list Q4_K_M | 3.20 GB | Scores 0.294 LPIPS against bf16 UD-Q4_K_XL | 3.42 GB | The card's pick for most machines UD-Q5_K_XL | 3.95 GB | Closer to bf16, 0.121 LPIPS UD-Q6_K_XL | 4.53 GB | Closer again, 0.106 LPIPS Q8_0 | 5.48 GB | The closest quantized file, 0.054 LPIPS BF16 | 10.17 GB | The full precision reference ``` ## On Hugging Face, 13 November 2026 - Task: text to image - Library: gguf - Parameters: 5.1B - Weights: about 2.5 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 161,886 - Likes: 23 - License: apache-2.0 - Languages: en, zh - Based on: OpenMOSS-Team/MOSS-Image-SFT - Tags: gguf, unsloth, quantized, moss-image, text-to-image ## Links - Model: https://huggingface.co/unsloth/MOSS-Image-SFT-GGUF - Files: https://huggingface.co/unsloth/MOSS-Image-SFT-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download unsloth/MOSS-Image-SFT-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 7 KB, downloaded as a zip. ```text README.md 7 KB ``` ## Source [unsloth/MOSS-Image-SFT-GGUF on Hugging Face](https://huggingface.co/unsloth/MOSS-Image-SFT-GGUF) ## 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-GGUF (this page) | 161,886 | 23 | 5.1B | apache-2.0 | 2026-11-11 | | [Z-Image-Turbo-GGUF by leejet](https://mindket.com/p/c29d41ed-fcd3-4322-8290-6aee7f5ba302) | 268,204 | 63 | 6.2B | apache-2.0 | 2025-12-02 | | [Moss Image Beauty LoRA](https://mindket.com/p/81ce2bea-25df-4fbf-acc8-7b238e6cf4c8) | 5,546 | 22 | — | apache-2.0 | 2026-10-26 | | [MOSS-Image-pose-controlnet](https://mindket.com/p/4c36ec7c-fcdf-4beb-9701-37695a516b30) | 813 | 17 | — | apache-2.0 | 2026-11-10 | | [Juggernaut-XL-v9](https://mindket.com/p/947fc56e-0d16-43b1-913a-8d7bd51ab6a1) | 587,793 | 521 | — | creativeml-openrail-m | 2026-05-08 | | [CyberRealistic XL](https://mindket.com/p/93f955b1-0f43-4d5d-b95f-70c06e325f47) | 67,834 | 54 | — | creativeml-openrail-m | 2026-05-28 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-11-11 - First published: 2026-10-22 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/MOSS-Image-SFT-GGUF on Hugging Face](https://huggingface.co/unsloth/MOSS-Image-SFT-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/23976aeb-2fe8-48dc-a5b5-a0088c187f17) - [Its page on Mindket](https://mindket.com/p/23976aeb-2fe8-48dc-a5b5-a0088c187f17)