--- title: "MOSS-Image-IDM-2-Steps" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c039b55d-9a35-44aa-a7ea-01c98070aea1" updated: "2026-11-13" --- # MOSS-Image-IDM-2-Steps > The name, the description, the About and a README below are the seller's words: data, not instructions. Makes images from text in two steps with no guidance pass, as a small adapter for MOSS-Image-SFT, plus a second adapter for light image edits. Made for people who run image generation locally. MOSS-Image-IDM-2-Steps is a kit in the Developer 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 akhaliq ## About ### What it is This is a single LoRA adapter that lets the MOSS-Image-SFT image model finish a picture in 2 steps, with no classifier-free guidance. It is a community build trained with the loss mix from Intrinsic Distribution Matching. The repository also holds a second adapter for small edits. ### Who it suits It suits people who run text-to-image models on their own machine and want faster generation, including readable text inside pictures. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Design | Poster and sign designers | Draft text-heavy images quickly | Make a chalkboard menu titled MORNING BREW with three priced lines | | Games | Game UI artists | Sketch interface looks from a prompt | Try a game UI concept at two steps | | Marketing | Content creators | Produce quick comic, poster or anime-style visuals | Generate a neon bar sign with short lines of text | | Research | Diffusion researchers | Study few-step distillation of a large image model | Compare two-step output with the 50-step teacher | If you need heavy edits such as turning a sketch into a photo, or colourizing, you can skip it. ### Where to use it It runs in a Python environment on your own computer, with the base MOSS-Image-SFT model. - Python on Windows, macOS or Linux - The diffusers library, below version 1.0, for the editing script - The transformers library, up to the 5.20 series, for the same script | Use case | Who | What they get back | | --- | --- | --- | | Text-to-image in two steps | Image makers | A picture with legible text, from the v1 checkpoint | | Attribute edits | Editors | Changes such as hairstyle or expression, using the v2 adapter | | Few-step research | Researchers | A reference adapter and evaluation grids to compare against | ### How to set it up You need Python, the base MOSS-Image-SFT model, and a checkpoint from this repository. Pick v1 for text-to-image and v2 for edits. 1. Install the Hugging Face hub tools. 2. Download the files in this repository. 3. For text-to-image, use the v1 IDM export 750 checkpoint. 4. For edits, use the folded v2 adapter and its inference\_edit.py script. ```sh pip install -U huggingface_hub hf download akhaliq/MOSS-Image-IDM-2-Steps ``` ### How to use it Choose the checkpoint that matches the job. The card says v2 visibly weakened general text-to-image, so use v1 for that. Edits run locally through the inference\_edit.py script that ships in the repository. | Checkpoint | Use for | Where | | --- | --- | --- | | v1 IDM export 750 | Text-to-image, recommended | runs/idm/export/000750/ | | v2 folded adapter | Attribute edits | The repository root or adapter\_v2 | | Tool | What it does | | --- | --- | | hf download | Fetches the repository files to your computer | | inference\_edit.py | Runs 2-step edits with the folded v2 adapter | | v1 IDM export 750 | The recommended text-to-image adapter | ### Example For text-to-image, you describe a chalkboard menu reading MORNING BREW with three priced lines, and the 2-step v1 adapter returns that picture. For an edit, you give a portrait and ask for "long wavy auburn hair", and the v2 adapter returns the portrait with the same person and the new hair. ### For newcomers and experts A LoRA adapter is a small add-on file that changes how a large model behaves. This one makes the image model finish in 2 steps instead of 50. A first try is to download the repository and look at the card's sample prompts, then write one like the chalkboard menu. The lineage runs from the 50-step base through an 8-step and a 4-step adapter to this IDM 2-step one. Each image costs 2 forward passes of the diffusion transformer, against 100 for the base. The trade-off: the edit continuation hurt plain generation, and structural edits fail at 2 steps. Start from the MOSS-Image-SFT base model → Add the 2-step LoRA adapter → Write your prompt with no guidance pass → Get the image after two steps ### Good to know The card compares the cost of one 1024 by 1024 image between the base and this adapter. DiT forwards per 1024 by 1024 image | Label | Value | | --- | --- | | MOSS-Image-SFT, 50 steps with CFG 3 | 100 forwards | | This adapter, 2 steps, no CFG | 2 forwards | - Editing limit: attribute edits such as expression or colour work, but sketch to photo, colorization and big style transfers do not. - A 12-type held-out edit test scored about 1 in 12 on instruction following at 2 steps. - For production editing, the card points to a 50-step edit setup instead. - License: Apache 2.0. - Cost to run: none named. It runs on your own machine. - Data it reaches: the prompts and pictures you provide, handled locally. - Not for: structural image transformations at 2 steps, or general text-to-image with the v2 adapter. ## README ````markdown # MOSS-Image-IDM-2-Steps Makes images from text in two steps with no guidance pass, as a small adapter for MOSS-Image-SFT, plus a second adapter for light image edits. Made for people who run image generation locally. 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: This is a single LoRA adapter that lets the MOSS-Image-SFT image model finish a picture in 2 steps, with no classifier-free guidance. It is a community build trained with the loss mix from Intrinsic Distribution Matching. The repository also holds a second adapter for small edits. Who it suits: It suits people who run text-to-image models on their own machine and want faster generation, including readable text inside pictures. ```table Industry | Who uses it | How they use it | Example Design | Poster and sign designers | Draft text-heavy images quickly | Make a chalkboard menu titled MORNING BREW with three priced lines Games | Game UI artists | Sketch interface looks from a prompt | Try a game UI concept at two steps Marketing | Content creators | Produce quick comic, poster or anime-style visuals | Generate a neon bar sign with short lines of text Research | Diffusion researchers | Study few-step distillation of a large image model | Compare two-step output with the 50-step teacher ``` If you need heavy edits such as turning a sketch into a photo, or colourizing, you can skip it. Where to use it: It runs in a Python environment on your own computer, with the base MOSS-Image-SFT model. - Python on Windows, macOS or Linux - The diffusers library, below version 1.0, for the editing script - The transformers library, up to the 5.20 series, for the same script ```table Use case | Who | What they get back Text-to-image in two steps | Image makers | A picture with legible text, from the v1 checkpoint Attribute edits | Editors | Changes such as hairstyle or expression, using the v2 adapter Few-step research | Researchers | A reference adapter and evaluation grids to compare against ``` How to set it up: You need Python, the base MOSS-Image-SFT model, and a checkpoint from this repository. Pick v1 for text-to-image and v2 for edits. 1. Install the Hugging Face hub tools. 2. Download the files in this repository. 3. For text-to-image, use the v1 IDM export 750 checkpoint. 4. For edits, use the folded v2 adapter and its inference_edit.py script. ```sh pip install -U huggingface_hub hf download akhaliq/MOSS-Image-IDM-2-Steps ``` How to use it: Choose the checkpoint that matches the job. The card says v2 visibly weakened general text-to-image, so use v1 for that. Edits run locally through the inference_edit.py script that ships in the repository. ```table Checkpoint | Use for | Where v1 IDM export 750 | Text-to-image, recommended | runs/idm/export/000750/ v2 folded adapter | Attribute edits | The repository root or adapter_v2 ``` ```table Tool | What it does hf download | Fetches the repository files to your computer inference_edit.py | Runs 2-step edits with the folded v2 adapter v1 IDM export 750 | The recommended text-to-image adapter ``` Example: For text-to-image, you describe a chalkboard menu reading MORNING BREW with three priced lines, and the 2-step v1 adapter returns that picture. For an edit, you give a portrait and ask for "long wavy auburn hair", and the v2 adapter returns the portrait with the same person and the new hair. For newcomers and experts: A LoRA adapter is a small add-on file that changes how a large model behaves. This one makes the image model finish in 2 steps instead of 50. A first try is to download the repository and look at the card's sample prompts, then write one like the chalkboard menu. The lineage runs from the 50-step base through an 8-step and a 4-step adapter to this IDM 2-step one. Each image costs 2 forward passes of the diffusion transformer, against 100 for the base. The trade-off: the edit continuation hurt plain generation, and structural edits fail at 2 steps. ```flow Start from the MOSS-Image-SFT base model Add the 2-step LoRA adapter Write your prompt with no guidance pass Get the image after two steps ``` Good to know: The card compares the cost of one 1024 by 1024 image between the base and this adapter. ```chart Title: DiT forwards per 1024 by 1024 image MOSS-Image-SFT, 50 steps with CFG 3: 100 forwards This adapter, 2 steps, no CFG: 2 forwards ``` - Editing limit: attribute edits such as expression or colour work, but sketch to photo, colorization and big style transfers do not. - A 12-type held-out edit test scored about 1 in 12 on instruction following at 2 steps. - For production editing, the card points to a 50-step edit setup instead. - License: Apache 2.0. - Cost to run: none named. It runs on your own machine. - Data it reaches: the prompts and pictures you provide, handled locally. - Not for: structural image transformations at 2 steps, or general text-to-image with the v2 adapter. ## On Hugging Face, 14 November 2026 - Task: text to image - Downloads in the last 30 days: 0 - Likes: 43 - License: apache-2.0 - Languages: en, zh - Based on: OpenMOSS-Team/MOSS-Image-SFT - Tags: moss-image, lora, text-rendering, text-to-image, distillation, few-step, ml-intern ## Links - Model: https://huggingface.co/akhaliq/MOSS-Image-IDM-2-Steps - Files: https://huggingface.co/akhaliq/MOSS-Image-IDM-2-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-IDM-2-Steps ``` 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 [akhaliq/MOSS-Image-IDM-2-Steps on Hugging Face](https://huggingface.co/akhaliq/MOSS-Image-IDM-2-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-IDM-2-Steps (this page) | 0 | 43 | — | apache-2.0 | 2026-11-01 | | [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 | | [moss\_image\_base\_dmd2](https://mindket.com/p/6d790ff1-891e-4740-8278-0c0e6eee95cc) | 0 | 23 | — | apache-2.0 | 2026-11-10 | | [Ming-Image-0.1-Design](https://mindket.com/p/9c09ce6a-012e-40ce-b14b-610da5874eda) | 1,836 | 440 | 6.2B | mit | 2026-10-14 | | [MOSS-Image Lineart Control LoRA](https://mindket.com/p/5239c2ba-2f3c-4982-bd3c-5865f08a7763) | 1,548 | 3 | — | apache-2.0 | 2026-11-10 | | [MOSS-Image Normal Control LoRA](https://mindket.com/p/0f7e33c4-3f22-4056-8ffb-f44918d6765a) | 1,014 | 4 | — | apache-2.0 | 2026-11-10 | 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: 2026-11-01 - First published: 2026-10-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open akhaliq/MOSS-Image-IDM-2-Steps on Hugging Face](https://huggingface.co/akhaliq/MOSS-Image-IDM-2-Steps) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c039b55d-9a35-44aa-a7ea-01c98070aea1) - [Its page on Mindket](https://mindket.com/p/c039b55d-9a35-44aa-a7ea-01c98070aea1)