You can deploy this model and start earning money today!

iky1e/
moebius-pretrained-mlx-q8

Name: moebius-pretrained-mlx-q8
Availability: InStock

Views

No views yet

Moebius pretrained MLX q8

This folder contains a converted MLX version of the pretrained Moebius checkpoint in q8 form. pretrained is the base Moebius checkpoint before dataset-specific fine-tuning.

Original upstream model: hustvl/Moebius
Original source repository: hustvl/Moebius

Base Moebius checkpoint before the dataset-specific inpainting fine-tunes. Use this when you want the upstream base model rather than a Places2 or face-specialized fine-tune.

Identity

Field	Value
Variant name	`pretrained-q8`
Checkpoint meaning	Base Moebius checkpoint before dataset-specific fine-tuning.
Original Moebius checkpoint family	`pretrained`
Original checkpoint type	base / pretrained checkpoint
Source PyTorch checkpoint	`Moebius-Models/pretrained/diffusion_pytorch_model.bin`
MLX precision / quantization label	`q8`
Image size	512 x 512
Latent size	64 x 64
Latent channels	4
Mask channels	1
Conditioning IDs	20
VAE scaling factor	0.13025
Noise offset	0.0357

Quantization

8-bit MLX quantized export. The manifest selects unet_quantized.safetensors for the UNet; VAE encoder and decoder remain regular f16 safetensors unless a quantized VAE file is explicitly listed. Quantization uses MLX grouped quantization metadata and the runtime applies the matching quantized module layout.

UNet precision mode: q8.
MLX grouped quantization is used for supported linear layers; grouped quantized weights are loaded through the Moebius-MLX manifest/runtime.
Quantization config: 8 bits, group size 64, standard MLX quantized mode.
The VAE encoder and decoder remain regular f16 safetensors for this variant.

Manifest-selected deployment files

These are the files selected by manifest.json when the Moebius-MLX runtime loads this variant.

Component	File	Size
UNet	`unet_quantized.safetensors`	432.89 MB
VAE encoder	`vae_encoder.safetensors`	68.34 MB
VAE decoder	`vae_decoder.safetensors`	99.00 MB

Files in this folder

unet.safetensors
unet_quantized.safetensors (selected by manifest)
vae_decoder.safetensors (selected by manifest)
vae_encoder.safetensors (selected by manifest)
manifest.json (runtime metadata and file selection)

A minimal runtime package needs manifest.json and the manifest-selected files above. Extra source or fallback files are optional and are not required for inference.

Runtime expectations

This is not a Transformers or Diffusers-native checkpoint. It is intended for the Swift/MLX runtime in Moebius-MLX. The runtime reads manifest.json, loads the selected safetensors files, builds the Moebius UNet and VAE modules, and runs the DDIM inpainting pipeline.

Pipeline constants must match the manifest:

DDIM scheduler: scaled_linear, beta start 0.00085, beta end 0.012, 1000 train timesteps, clip sample false
512 x 512 image resolution and 64 x 64 latent resolution
9-channel UNet input: noisy latent, mask, and masked-image latent
VAE scaling factor 0.13025

Attribution

Moebius was released by the original authors as hustvl/Moebius. This folder is a format conversion and/or quantized MLX packaging of the original PyTorch weights, not a newly trained model.

iky1e/moebius-pretrained-mlx-q8