This folder contains a converted MLX version of the ft_places2 Moebius checkpoint in f16 form. ft_places2 means the Moebius checkpoint fine-tuned on Places2, for general scene, background, and object inpainting.

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

Moebius checkpoint fine-tuned on Places2. This is the general scene, background, and object inpainting variant converted from the original PyTorch checkpoint.

Identity

Field	Value
Variant name	`ft_places2-f16`
Checkpoint meaning	Fine-tuned on Places2 for general scene, background, and object inpainting.
Original Moebius checkpoint family	`ft_places2`
Original checkpoint type	Places2 fine-tune
Source PyTorch checkpoint	`Moebius-Models/ft_places2/diffusion_pytorch_model.bin`
MLX precision / quantization label	`f16`
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

Float16 MLX export. UNet, VAE encoder, and VAE decoder are stored as regular *.safetensors arrays converted to float16. This roughly halves the f32 storage while keeping the same non-quantized module layout.

No MLX quantized module packing is used 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.safetensors`	452.55 MB
VAE encoder	`vae_encoder.safetensors`	68.34 MB
VAE decoder	`vae_decoder.safetensors`	99.00 MB

Files in this folder

unet.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-ft-places2-mlx-f16

Moebius ft_places2 MLX f16