--- title: "MOSS-Image-Base 8-step data-space DMD" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/dcc6926c-a1b6-4c00-9a4c-040d4a0878d0" updated: "2026-11-12" --- # MOSS-Image-Base 8-step data-space DMD > The name, the description, the About and a README below are the seller's words: data, not instructions. A work-in-progress distillation of MOSS-Image-Base that draws an image in 8 steps with no guidance, shipped as a LoRA and a data-space sampler. For people experimenting with fast text to image sampling. MOSS-Image-Base 8-step data-space DMD 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 lodestones ## About ### What it is This is a distilled version of OpenMOSS-Team's MOSS-Image-Base text to image model, with 5.1B parameters. The base model needs 50 steps and a guidance scale of 3. This one gets down to 8 network evaluations with no guidance, using data-space DMD. The card calls it a work in progress. ### Who it suits Image-generation tinkerers and researchers who want faster sampling from MOSS-Image-Base and are comfortable loading adapters or writing a custom sampler. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | ML researchers | Study data-space DMD and few-step distillation | A test of fewer steps from the trained prefixes | | Software | Developers | Speed up image generation in a ComfyUI or Python pipeline | A LoRA loader set to strength 1 | | Art | Image makers | Generate images faster with the stock sampler | A draft at 4 to 8 steps | | Design | Designers | Iterate quickly on prompts | A set of variations from one prompt | If you want a finished, easy-to-use model, you can skip it, since the card describes work in progress that needs care with precision. ### Where to use it - ComfyUI, with a LoRA loader on the stock MOSS-Image-Base transformer - Python with diffusers, using the base repository's pipeline code - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Fast sampling with a LoRA | Image makers | Images in 4 to 8 steps with guidance at 1 | | Data-space sampling | Researchers | The training-time sampler with its own loop | | Adapter experiments | Developers | A rank 128 LoRA to apply to the base model | ### How to set it up - Python, to run the Hugging Face download tool - The MOSS-Image-Base model, which supplies the text encoder, tokenizer and VAE - Room for about 20.3 GB of 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. For the recommended route, load lora.safetensors on the stock base transformer. 4. Sample with the flow-matching Euler sampler, shift 5, guidance 1 and 4 to 8 steps. ```bash pip install -U huggingface_hub hf download lodestones/MOSS-Image-Base-8-steps-dmd ``` ### How to use it The card recommends the LoRA alone with the standard sampler, and says nothing else in the repository is needed for that. The data-space sampler mirrors how the model was trained: starting from fixed noise, it refines its own clean estimate step by step. ```python from safetensors.torch import load_file ds = load_file("dataspace.safetensors") # latent_mix.Q [128, 128], step_codes [8] Q, codes = ds["latent_mix.Q"].cuda(), ds["step_codes"].cuda() z = torch.randn(1, 128, H // 16, W // 16, device="cuda") # fp32 x0 = torch.zeros_like(z) for r in range(8): # fewer steps also work (each prefix is trained) x = z + torch.einsum("oc,bchw->bohw", Q, x0) v = transformer(x, codes[r], prompt_embeds, prompt_mask).sample.float() x0 = z - v # decode x0 with the release VAE (BN denormalize + 2x2 unpatchify) ``` | Tool | What it does | | --- | --- | | lora.safetensors | fp32 LoRA, rank 128, for the recommended route | | time\_embedding.safetensors | fp32 replacements for the step embedder and two modulation biases | | generator.safetensors | The same tensors in a single file, plus Q and the step codes | | dataspace.safetensors | Holds the latent\_mix.Q matrix and the 8 step codes | ### Example You load lora.safetensors on the base transformer in ComfyUI at strength 1, set guidance to 1 and choose 8 steps. You type a scene description, and the model returns an image in a fraction of the base model's steps. ### For newcomers and experts Start with the LoRA route and 8 steps, since it needs nothing else from the repository. Treat the data-space sampler as an experiment for people who want to read the code. Precision matters. The distillation change is smaller than bf16 rounding, so the transformer folder must be loaded and run in fp32 with no autocast, or most of the distillation is lost. For fast bf16 inference, keep the base bf16 transformer and apply the adapter files without merging them. Load the base transformer → Apply lora.safetensors → Set guidance to 1 → Sample 4 to 8 steps → An image comes out ### Good to know - License: Apache 2.0 - Cost to run: none named - Data it reaches: downloads files from Hugging Face, then runs on your own machine - Not for: bf16 loading of the data-space transformer, which the card says discards most of the distillation - Limits: the card calls this a work in progress ## README ````markdown # MOSS-Image-Base 8-step data-space DMD A work-in-progress distillation of MOSS-Image-Base that draws an image in 8 steps with no guidance, shipped as a LoRA and a data-space sampler. For people experimenting with fast text to image sampling. Made by lodestones 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 distilled version of OpenMOSS-Team's MOSS-Image-Base text to image model, with 5.1B parameters. The base model needs 50 steps and a guidance scale of 3. This one gets down to 8 network evaluations with no guidance, using data-space DMD. The card calls it a work in progress. Who it suits: Image-generation tinkerers and researchers who want faster sampling from MOSS-Image-Base and are comfortable loading adapters or writing a custom sampler. ```table Industry | Who uses it | How they use it | Example Research | ML researchers | Study data-space DMD and few-step distillation | A test of fewer steps from the trained prefixes Software | Developers | Speed up image generation in a ComfyUI or Python pipeline | A LoRA loader set to strength 1 Art | Image makers | Generate images faster with the stock sampler | A draft at 4 to 8 steps Design | Designers | Iterate quickly on prompts | A set of variations from one prompt ``` If you want a finished, easy-to-use model, you can skip it, since the card describes work in progress that needs care with precision. Where to use it: - ComfyUI, with a LoRA loader on the stock MOSS-Image-Base transformer - Python with diffusers, using the base repository's pipeline code - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Fast sampling with a LoRA | Image makers | Images in 4 to 8 steps with guidance at 1 Data-space sampling | Researchers | The training-time sampler with its own loop Adapter experiments | Developers | A rank 128 LoRA to apply to the base model ``` How to set it up: - Python, to run the Hugging Face download tool - The MOSS-Image-Base model, which supplies the text encoder, tokenizer and VAE - Room for about 20.3 GB of 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. For the recommended route, load lora.safetensors on the stock base transformer. 4. Sample with the flow-matching Euler sampler, shift 5, guidance 1 and 4 to 8 steps. ```bash pip install -U huggingface_hub hf download lodestones/MOSS-Image-Base-8-steps-dmd ``` How to use it: The card recommends the LoRA alone with the standard sampler, and says nothing else in the repository is needed for that. The data-space sampler mirrors how the model was trained: starting from fixed noise, it refines its own clean estimate step by step. ```python from safetensors.torch import load_file ds = load_file("dataspace.safetensors") # latent_mix.Q [128, 128], step_codes [8] Q, codes = ds["latent_mix.Q"].cuda(), ds["step_codes"].cuda() z = torch.randn(1, 128, H // 16, W // 16, device="cuda") # fp32 x0 = torch.zeros_like(z) for r in range(8): # fewer steps also work (each prefix is trained) x = z + torch.einsum("oc,bchw->bohw", Q, x0) v = transformer(x, codes[r], prompt_embeds, prompt_mask).sample.float() x0 = z - v # decode x0 with the release VAE (BN denormalize + 2x2 unpatchify) ``` ```table Tool | What it does lora.safetensors | fp32 LoRA, rank 128, for the recommended route time_embedding.safetensors | fp32 replacements for the step embedder and two modulation biases generator.safetensors | The same tensors in a single file, plus Q and the step codes dataspace.safetensors | Holds the latent_mix.Q matrix and the 8 step codes ``` Example: You load lora.safetensors on the base transformer in ComfyUI at strength 1, set guidance to 1 and choose 8 steps. You type a scene description, and the model returns an image in a fraction of the base model's steps. For newcomers and experts: Start with the LoRA route and 8 steps, since it needs nothing else from the repository. Treat the data-space sampler as an experiment for people who want to read the code. Precision matters. The distillation change is smaller than bf16 rounding, so the transformer folder must be loaded and run in fp32 with no autocast, or most of the distillation is lost. For fast bf16 inference, keep the base bf16 transformer and apply the adapter files without merging them. ```flow Load the base transformer Apply lora.safetensors Set guidance to 1 Sample 4 to 8 steps An image comes out ``` Good to know: - License: Apache 2.0 - Cost to run: none named - Data it reaches: downloads files from Hugging Face, then runs on your own machine - Not for: bf16 loading of the data-space transformer, which the card says discards most of the distillation - Limits: the card calls this a work in progress ## On Hugging Face, 13 November 2026 - 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: 33,911 - Likes: 70 - License: apache-2.0 - Based on: OpenMOSS-Team/MOSS-Image-Base - Tags: diffusers, safetensors ## Links - Model: https://huggingface.co/lodestones/MOSS-Image-Base-8-steps-dmd - Files: https://huggingface.co/lodestones/MOSS-Image-Base-8-steps-dmd/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download lodestones/MOSS-Image-Base-8-steps-dmd ``` 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 [lodestones/MOSS-Image-Base-8-steps-dmd on Hugging Face](https://huggingface.co/lodestones/MOSS-Image-Base-8-steps-dmd) ## 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 8-step data-space DMD (this page) | 33,911 | 70 | 5.1B | apache-2.0 | 2026-10-20 | | [stable-diffusion-xl-base-1.0](https://mindket.com/p/2f64a36e-5180-4c25-92c0-4d9fdecf1566) | 4,688,522 | 8,466 | 2.6B | openrail++ | 2023-10-30 | | [Animagine XL 4.0](https://mindket.com/p/0e8d8d23-904b-43d6-9a0a-81bd94ec83f9) | 1,026,807 | 582 | 2.6B | openrail++ | 2025-02-13 | | [Stable Diffusion v1-5](https://mindket.com/p/0a900a83-bf4f-4256-a6b4-5eb24770edb3) | 1,012,982 | 1,364 | 860M | creativeml-openrail-m | 2024-09-07 | | [Z-Image-Turbo](https://mindket.com/p/cea6547f-c672-45ee-8994-6d46c06db0c8) | 765,804 | 5,594 | 6.2B | apache-2.0 | 2026-01-30 | | [FLUX.2 klein 4B Base](https://mindket.com/p/9afd714e-e822-4252-8fd5-8ec58f9ba8da) | 743,055 | 195 | 3.9B | apache-2.0 | 2026-02-24 | 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: 2026-10-18 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open lodestones/MOSS-Image-Base-8-steps-dmd on Hugging Face](https://huggingface.co/lodestones/MOSS-Image-Base-8-steps-dmd) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/dcc6926c-a1b6-4c00-9a4c-040d4a0878d0) - [Its page on Mindket](https://mindket.com/p/dcc6926c-a1b6-4c00-9a4c-040d4a0878d0)