---
license: apache-2.0
library_name: mlx
pipeline_tag: image-to-image
base_model: hustvl/Moebius
tags:
  - mlx
  - image-inpainting
  - inpainting
  - diffusion
  - moebius
  - ft_celebahq
  - f32
---

# Moebius ft_celebahq MLX f32

This folder contains a converted MLX version of the **ft_celebahq** Moebius checkpoint in **f32** form. `ft_celebahq` means the Moebius checkpoint fine-tuned on CelebA-HQ, for face and portrait inpainting.

Original upstream model: [hustvl/Moebius](https://huggingface.co/hustvl/Moebius)  
Original source repository: [hustvl/Moebius](https://github.com/hustvl/Moebius)

Moebius checkpoint fine-tuned on CelebA-HQ. This variant is intended for face and portrait inpainting distributions.

## Identity

| Field | Value |
|---|---|
| Variant name | `ft_celebahq-f32` |
| Checkpoint meaning | Fine-tuned on CelebA-HQ for face and portrait inpainting. |
| Original Moebius checkpoint family | `ft_celebahq` |
| Original checkpoint type | CelebA-HQ fine-tune |
| Source PyTorch checkpoint | `Moebius-Models/ft_celebahq/diffusion_pytorch_model.bin` |
| MLX precision / quantization label | `f32` |
| 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

Full float32 MLX export. UNet, VAE encoder, and VAE decoder are stored as regular `*.safetensors` arrays without quantized module packing. This is the highest-fidelity MLX artifact and the port-parity reference.

- 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` | 904.94 MB |
| VAE encoder | `vae_encoder.safetensors` | 136.67 MB |
| VAE decoder | `vae_decoder.safetensors` | 197.98 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](https://github.com/kylehowells/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](https://huggingface.co/hustvl/Moebius). This folder is a format conversion and/or quantized MLX packaging of the original PyTorch weights, not a newly trained model.