--- title: "MOSS-Image-SFT-4-steps" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/93e7242c-c8ed-4b9c-b82c-c4feed6c356f" updated: "2026-11-12" --- # MOSS-Image-SFT-4-steps > The name, the description, the About and a README below are the seller's words: data, not instructions. Makes the MOSS-Image-SFT text to image model finish a picture in 4 steps with no guidance, using one LoRA file. Renders English and Chinese text, for ComfyUI and diffusers users. MOSS-Image-SFT-4-steps 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 akhaliq ## About ### What it is MOSS-Image-SFT-4-steps is a distilled adapter for MOSS-Image-SFT, a text to image model with 5.1B parameters. It lets the model draw an image in 4 steps, with no classifier-free guidance, using one LoRA file and the standard flow-matching Euler sampler. The repository also holds the pipeline, text encoder and VAE. ### Who it suits Image makers and developers who want fast generations from MOSS-Image, especially pictures that contain readable English or Chinese text. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Design | Designers | Draft signs, menus and posters with text in the picture | A cafe menu board with prices | | Marketing | Content teams | Produce quick visuals in English and Chinese | A storefront image with Chinese lettering | | Software | Developers | Add fast image generation to an app through diffusers | A script that renders a prompt in four steps | | Research | ML researchers | Study few-step distillation of an image model | A comparison of 3-step and 4-step results | If you do not use MOSS-Image models, or want the slower full-quality sampling, you can skip it. ### Where to use it - ComfyUI, through its LoraLoaderModelOnly node - Python with diffusers, through the apply\_lora.py script in the repository - Windows, macOS or Linux with Python, for downloading the files - A browser, through the demo Space the card links | Use case | Who | What they get back | | --- | --- | --- | | Fast text to image | Anyone with a GPU | A finished picture from a prompt in 4 steps | | Bilingual text in images | Designers | Signs and menus with legible English and Chinese | | Large outputs | Artists | Images up to 2048 by 2048 with no tiling, per the card | ### How to set it up - Python, to run the Hugging Face download tool - A GPU, where the card's timing comes from one A100 - ComfyUI, or diffusers with the repository's own pipeline - About 20.3 GB for the F32 weights, or about 2.5 GB at 4-bit as an estimate 1. Install the Hugging Face hub tools with pip. 2. Download the repository with the hf command. 3. Load the LoRA file in your tool, with LoraLoaderModelOnly in ComfyUI or apply\_lora.py in diffusers. 4. Set 4 steps, a guidance scale of 1 and the stock Euler sampler. ```bash pip install -U huggingface_hub hf download akhaliq/MOSS-Image-SFT-4-steps ``` ### How to use it Write a prompt and generate with 4 steps and no guidance. The card says there is no negative prompt either. It also ships an inference.py command line script. | Tool | What it does | | --- | --- | | lora.safetensors | The single adapter file that gives the 4-step behavior | | apply\_lora.py | Applies the adapter in diffusers | | LoraLoaderModelOnly | Loads the adapter in ComfyUI | | inference.py | Command line script for generating images | ### Example You ask for a cafe menu board that reads "TODAY'S SPECIAL, Flat White $4.50". The model draws the board in 4 steps, and the card's showcase shows that text rendering correctly. ### For newcomers and experts Start in ComfyUI if you already use it, add the LoRA with the model-only loader and ask for a sign with a few words on it. Four steps with guidance at 1 is the whole recipe. Version 2 is trained directly on the release schedule, so it works with the stock sampler instead of a custom one. The card says it was distilled with data-space DMD and started from an 8-step distilled model. On one A100, it reports about 11 seconds per 1024 by 1024 image. Applied to MOSS-Image-Base, the output is slightly softer. Write a prompt → Load the one LoRA file → Sample 4 steps with the Euler sampler → A finished image comes back ### Good to know - License: Apache 2.0, and the card says commercial use is fine - Cost to run: none named, though you need a capable GPU - Data it reaches: downloads files from Hugging Face, then runs where you load it - Not for: older v1.x adapters, which keep their own sampler in a separate folder ## README ````markdown # MOSS-Image-SFT-4-steps Makes the MOSS-Image-SFT text to image model finish a picture in 4 steps with no guidance, using one LoRA file. Renders English and Chinese text, for ComfyUI and diffusers users. Made by akhaliq 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-4-steps is a distilled adapter for MOSS-Image-SFT, a text to image model with 5.1B parameters. It lets the model draw an image in 4 steps, with no classifier-free guidance, using one LoRA file and the standard flow-matching Euler sampler. The repository also holds the pipeline, text encoder and VAE. Who it suits: Image makers and developers who want fast generations from MOSS-Image, especially pictures that contain readable English or Chinese text. ```table Industry | Who uses it | How they use it | Example Design | Designers | Draft signs, menus and posters with text in the picture | A cafe menu board with prices Marketing | Content teams | Produce quick visuals in English and Chinese | A storefront image with Chinese lettering Software | Developers | Add fast image generation to an app through diffusers | A script that renders a prompt in four steps Research | ML researchers | Study few-step distillation of an image model | A comparison of 3-step and 4-step results ``` If you do not use MOSS-Image models, or want the slower full-quality sampling, you can skip it. Where to use it: - ComfyUI, through its LoraLoaderModelOnly node - Python with diffusers, through the apply_lora.py script in the repository - Windows, macOS or Linux with Python, for downloading the files - A browser, through the demo Space the card links ```table Use case | Who | What they get back Fast text to image | Anyone with a GPU | A finished picture from a prompt in 4 steps Bilingual text in images | Designers | Signs and menus with legible English and Chinese Large outputs | Artists | Images up to 2048 by 2048 with no tiling, per the card ``` How to set it up: - Python, to run the Hugging Face download tool - A GPU, where the card's timing comes from one A100 - ComfyUI, or diffusers with the repository's own pipeline - About 20.3 GB for the F32 weights, or about 2.5 GB at 4-bit as an estimate 1. Install the Hugging Face hub tools with pip. 2. Download the repository with the hf command. 3. Load the LoRA file in your tool, with LoraLoaderModelOnly in ComfyUI or apply_lora.py in diffusers. 4. Set 4 steps, a guidance scale of 1 and the stock Euler sampler. ```bash pip install -U huggingface_hub hf download akhaliq/MOSS-Image-SFT-4-steps ``` How to use it: Write a prompt and generate with 4 steps and no guidance. The card says there is no negative prompt either. It also ships an inference.py command line script. ```table Tool | What it does lora.safetensors | The single adapter file that gives the 4-step behavior apply_lora.py | Applies the adapter in diffusers LoraLoaderModelOnly | Loads the adapter in ComfyUI inference.py | Command line script for generating images ``` Example: You ask for a cafe menu board that reads "TODAY'S SPECIAL, Flat White $4.50". The model draws the board in 4 steps, and the card's showcase shows that text rendering correctly. For newcomers and experts: Start in ComfyUI if you already use it, add the LoRA with the model-only loader and ask for a sign with a few words on it. Four steps with guidance at 1 is the whole recipe. Version 2 is trained directly on the release schedule, so it works with the stock sampler instead of a custom one. The card says it was distilled with data-space DMD and started from an 8-step distilled model. On one A100, it reports about 11 seconds per 1024 by 1024 image. Applied to MOSS-Image-Base, the output is slightly softer. ```flow Write a prompt Load the one LoRA file Sample 4 steps with the Euler sampler A finished image comes back ``` Good to know: - License: Apache 2.0, and the card says commercial use is fine - Cost to run: none named, though you need a capable GPU - Data it reaches: downloads files from Hugging Face, then runs where you load it - Not for: older v1.x adapters, which keep their own sampler in a separate folder ## On Hugging Face, 13 November 2026 - Task: text to image - Library: diffusers - Parameters: 5.1B - Weights: about 20.3 GB in F32; about 2.5 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 73,131 - Likes: 96 - License: apache-2.0 - Based on: OpenMOSS-Team/MOSS-Image-SFT - Quantized versions: 1 - Tags: diffusers, safetensors, moss-image, text-to-image, turbo, few-step, lora, ml-intern ## Links - Model: https://huggingface.co/akhaliq/MOSS-Image-SFT-4-steps - Files: https://huggingface.co/akhaliq/MOSS-Image-SFT-4-steps/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download akhaliq/MOSS-Image-SFT-4-steps ``` 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 [akhaliq/MOSS-Image-SFT-4-steps on Hugging Face](https://huggingface.co/akhaliq/MOSS-Image-SFT-4-steps) ## 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-4-steps (this page) | 73,131 | 96 | 5.1B | apache-2.0 | 2026-10-20 | | [MOSS-Image-IDM-2-Steps](https://mindket.com/p/c039b55d-9a35-44aa-a7ea-01c98070aea1) | 0 | 43 | — | apache-2.0 | 2026-11-01 | | [moss\_image\_base\_dmd2](https://mindket.com/p/6d790ff1-891e-4740-8278-0c0e6eee95cc) | 0 | 23 | — | apache-2.0 | 2026-11-10 | | [Idol Turbo by speridlabs](https://mindket.com/p/c73efd7c-3784-4054-87eb-c0058c43b4d9) | 565 | 57 | — | apache-2.0 | 2026-11-03 | | [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 | 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: 2026-10-20 - First published: 2026-10-16 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open akhaliq/MOSS-Image-SFT-4-steps on Hugging Face](https://huggingface.co/akhaliq/MOSS-Image-SFT-4-steps) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/93e7242c-c8ed-4b9c-b82c-c4feed6c356f) - [Its page on Mindket](https://mindket.com/p/93e7242c-c8ed-4b9c-b82c-c4feed6c356f)