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license: apache-2.0
tags:
- ruslanmv
- avatar-renderer-mcp
- VideoGenie
---
# Avatar‑Renderer Checkpoints
This repository bundles all pretrained model checkpoints required by the [Avatar Renderer MCP](https://github.com/ruslanmv/avatar-renderer-mcp) pipeline.
**VideoGenie Avatar Generator** is a single‑image → talking‑head engine that ships an MCP‑native stdio server (`render_avatar` tool) and a FastAPI REST façade in one CUDA container. Drop it into any GPU pool and your MCP Gateway auto‑discovers it on boot.
This model‑hub repo allows you to fetch **all** necessary checkpoints from a **single source** via Git LFS or the Hugging Face Hub API.
---
## Directory structure
```
├── diff2lip
│ └── Diff2Lip.pth # Audio‑to‑lip Diffusion model
├── fomm
│ └── vox-cpk.pth # First‑Order‑Motion vox‑cpk checkpoint
├── gfpgan
│ └── GFPGANv1.3.pth # GFPGAN v1.3 face enhancement model
├── sadtalker
│ ├── SadTalker_V0.0.2_256.safetensors # Safetensors release bundle
│ ├── epoch_20.pth # Training checkpoint (epoch 20)
│ └── sadtalker.pth # Legacy binary checkpoint
└── wav2lip
└── wav2lip_gan.pth # Wav2Lip GAN audio-to-lip model
```
Each subfolder contains one or more formats of the same model, ensuring compatibility with different inference pipelines.
---
## Usage
### 1. Clone via Git LFS
```bash
# Ensure Git LFS is installed:
# https://git-lfs.github.com/
git clone https://huggingface.co/ruslanmv/avatar-renderer
cd avatar-renderer
# You'll now have a `models/` tree matching the structure above.
```
### 2. Download via Python (Hugging Face Hub API)
```python
from huggingface_hub import snapshot_download
# Download all files into ./models-cache
models_dir = snapshot_download(
repo_id="ruslanmv/avatar-renderer",
cache_dir="./models-cache",
)
print("Checkpoints downloaded to:", models_dir)
```
### 3. Integrate with Avatar Renderer MCP
In your **Avatar Renderer MCP** project, configure the checkpoint environment variables to point at the local `models` directory:
```bash
export FOMM_CKPT_DIR=/path/to/avatar-renderer/fomm
export DIFF2LIP_CKPT=/path/to/avatar-renderer/diff2lip/Diff2Lip.pth
export SADTALKER_CKPT_DIR=/path/to/avatar-renderer/sadtalker
export WAV2LIP_CKPT=/path/to/avatar-renderer/wav2lip/wav2lip_gan.pth
export GFPGAN_CKPT=/path/to/avatar-renderer/gfpgan/GFPGANv1.3.pth
```
Alternatively, mount the entire repo into `/models` inside a Docker container:
```dockerfile
FROM ruslanmv/avatar-renderer-mcp:latest
COPY --from=ruslanmv/avatar-renderer /models /models
CMD ["uvicorn", "app.api:app", "--host", "0.0.0.0", "--port", "8000"]
```
---
## License
This repository collects checkpoints that were released under their respective open licenses:
* **FOMM**: [Apache‑2.0](https://github.com/AliaksandrSiarohin/first-order-model/blob/master/LICENSE)
* **Diff2Lip**: [MIT](https://github.com/YuanGary/DiffusionLi/blob/main/LICENSE)
* **SadTalker**: [Apache‑2.0](https://github.com/Winfredy/SadTalker/blob/main/LICENSE)
* **Wav2Lip**: [MIT](https://github.com/Rudrabha/Wav2Lip/blob/master/LICENSE)
* **GFPGAN**: [MIT](https://github.com/TencentARC/GFPGAN/blob/main/LICENSE)
Please refer to each upstream project for full license details.
---
> Maintained by [ruslanmv](https://github.com/ruslanmv).
> Part of the [Avatar Renderer MCP](https://github.com/ruslanmv/avatar-renderer-mcp) ecosystem.
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