Moebius Pretrained Mlx F16
This folder contains a converted MLX version of the pretrained Moebius checkpoint in f16 form. pretrained is the base Moebius checkpoint before dataset-specific fine-tuning.
Fuente del modelo
- Tipo
- UNet
- Tamaño del archivo
- 432 MB
- Modelo base
- hustvl/Moebius
- Versión
- vunet
- Actualizado
- 9 oct 2026
- Descargas
- 0
- Fuente
- Hugging Face
Descripción de la fuente
Moebius pretrained MLX f16
This folder contains a converted MLX version of the pretrained Moebius checkpoint in f16 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-f16 |
| 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 | 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.
Requisitos
Modelos
2- No resuelto
VAE · Hugging Face · iky1e/moebius-pretrained-mlx-f16
- No resuelto
VAE · Hugging Face · iky1e/moebius-pretrained-mlx-f16
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 9 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- license: apache-2.0 library_name: mlx pipeline_tag: image-to-image base_model: hustvl/Moebius tags: - mlx - image-inpainting - inpainting - diffusion - moebius - pretrained - f16 --- # Moebius pretrained MLX f16 This folder contains a converted MLX version of the **pretrained** Moebius checkpoint in **f16** form. `pretrained` is the base Moebius checkpoint before dataset-specific fine-tuning. Original upstream model: [hustvl/Moebius](https://huggingface.co/hustvl/Moebius) Original source repository: [hustvl/Moebius](https://github.com/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-f16` | | 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 | `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 n...
Source context: 0 downloads · 0 likes · Pipeline image-to-image · Library mlx · Repo iky1e/moebius-pretrained-mlx-f16
