Vox-adv-cpk.pth.tar -

# Initialize the model and load the checkpoint weights model = VoxAdvModel() model.load_state_dict(checkpoint['state_dict'])

: This version is the base model fine-tuned for an additional 50 epochs using an adversarial discriminator . This adversarial training typically improves the visual sharpness and realism of the generated animation. Vox-adv-cpk.pth.tar

To use this file, you generally need a Python environment with PyTorch installed. Most users interact with it via notebooks, which allow you to run the animation code in the cloud. You simply upload the .pth.tar file (or provide a link to it), select your image and video, and let the GPU process the frames. A Note on Ethics and Security # Initialize the model and load the checkpoint

, which enables the "driving" of a source image using a video stream. : This specific version ( vox-adv-cpk ) is a variation of the base model ( ). While the base model is trained for 100 epochs, the vox-adv-cpk version is fine-tuned for an additional 50 epochs using an adversarial discriminator to improve realism and detail. File Format : It is a compressed PyTorch checkpoint ( ) wrapped in a TAR archive. Despite being a file, the software is designed to read it directly; do not unpack it during installation. : Approximately Key Usage Instructions To use this file with Avatarify-Python , follow these critical placement steps: : Obtain the weights from official mirrors like : Place the file in the root directory of your local avatarify-python No Unpacking : The application expects the file exactly as it is. Unpacking it will lead to a FileNotFoundError when running the software. Performance & Requirements : For real-time performance, an NVIDIA GPU with CUDA support is highly recommended. GTX 1080 Ti : ~33 FPS. : ~15 FPS. CPU Fallback Most users interact with it via notebooks, which

The model enables , allowing a system to apply motion from a "driving" video (e.g., your own face on camera) to a static "source" image (e.g., a photo of a celebrity or a painting). It consists of two main parts:

: Uses the detected motion to warp the source image and generate a new, animated frame that matches the driver's expression. Common Use Cases and Implementation Questions about the pre-trained models of vox #127 - GitHub