Instructions to use LocalMuseAI/mlx-moss-image-sft-dmd-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use LocalMuseAI/mlx-moss-image-sft-dmd-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download LocalMuseAI/mlx-moss-image-sft-dmd-4bit --local-dir mlx-moss-image-sft-dmd-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
MOSS-Image Turbo β MLX INT4 for LocalMuse
On-device package for the LocalMuse iOS app (profile mossImage5B4Bit).
- Model: MOSS-Image-SFT
by the OpenMOSS Team, revision
fc5de0d56fc910cbca6f6625a70f5f582b26deb1(Apache-2.0): 5B single-stream DiT, Qwen3.5 text encoder, FLUX.2 small decoder. - Adapters (
adapters.json), unmerged and selected by step count:- 4 steps:
lora.safetensors(v2.0) from akhaliq/MOSS-Image-SFT-4-steps, revision2ded95c37a029360245926fe72e475e30039bbfb(Apache-2.0), Euler-native 4-step DMD; - 5β8 steps:
lora_dmd2.safetensorsfrom lodestones/MOSS-Image-Base-8-steps-dmd, revisionda05cb3b52899179e2bb5f7efe1058fefcbae21b(Apache-2.0), data-space DMD refined with DMD2's GAN stage; - every step count: UltraReal MOSS v2 by vizsumit (Civitai 2462105, version 3416438), a style LoRA against the smooth AI look (its license allows commercial use and derivatives).
- 4 steps:
This is a format conversion, not a new fine-tune. Every source file is
authenticated by source.json and adapters.json. Preserve LICENSE, NOTICE and the attribution.
Contents
| File | Content |
|---|---|
text_encoder/model.safetensors |
Qwen3.5 text model (visual tower dropped), all linears and the embedding table affine INT4, group 64 |
transformer/model.safetensors |
MOSS-Image DiT: attention/FFN INT4 group 64; embedders, time embedding, modulation, norms and output projection BF16 |
transformer/adapter_4step.safetensors |
akhaliq 4-step adapter, BF16 rank-128 factors, strength 1, used at 4 steps |
transformer/adapter_8step.safetensors |
lodestones DMD2 adapter, BF16 rank-128 factors, strength 1, used at 5β8 steps |
transformer/style_ultrareal.safetensors |
UltraReal v2, BF16 rank-32 factors (fused qkv split into q/k/v; block 22, absent from the LoRA, is zero), strength 1, always used |
vae.safetensors |
FP32 FLUX.2 small decoder and latent BatchNorm statistics |
tokenizer/ |
release tokenizer |
runtime_config.json, manifest.json |
runtime contract and SHA-256 manifest |
Adapters must not be merged into the weights: the distillation delta is smaller
than BF16 rounding of the merged matrices, so merging (and quantizing a merge)
loses it. LocalMuse concatenates the selected adapters along the rank and adds
up(down(x)) after each quantized block projection (runtime_config.json
version 2 lists each adapter's file, rank, alpha, strength and step range).
Inference contract
Text to Image, batch one, the release MOSSImageScheduler (flow-matching
Euler, shift 5, sigmas snapped to the 1000 training sigmas), 8 steps by
default or 4 steps for the faster adapter (5β7 use the 8-step adapter), no
classifier-free guidance and no negative prompt. Canvases:
1024 Γ 1024 and 896 Γ 1152. Prompts use the release Qwen3.5 chat template with
thinking enabled; natural language, tags and MOSS JSON layouts are accepted.
INT4 is not bit-identical to BF16: with the same seed the composition, subject
and lighting match while clothing, hair and brightness details can differ.
Conversion, independent references and measurements are in the LocalMuse
repository under Tools/MOSSImage.
Quantized
Model tree for LocalMuseAI/mlx-moss-image-sft-dmd-4bit
Base model
OpenMOSS-Team/MOSS-Image-Base