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InfiniteTalk
Sparse-Frame Video Dubbing Framework with 4-Step Inference
Quick Start
pip install infinitetalk
# Download LoRAs
python -m infinitetalk.download_models
# Generate video
infinitetalk generate \
--image reference.jpg \
--audio speech.wav \
--output video.mp4 \
--config configs/720p_4step.yaml
Features
- Sparse-Frame Generation: Infinite length with identity consistency
- 4-Step Inference: 10x faster than standard diffusion
- Dual LoRA System: FusioniX (consistency) + LightX2V (speed)
- FP8 Quantization: 50% memory reduction
- Multi-GPU: Ulysses sequence parallelism
Architecture
See docs/ARCHITECTURE.md for technical details on Sparse Reference Anchor Points (SRAP).
Installation
From Source
git clone https://github.com/swordenkisk/infinitetalk.git
cd infinitetalk
bash scripts/setup_env.sh
Requirements
- Python >= 3.9
- PyTorch >= 2.1.0
- CUDA >= 11.8 (for GPU support)
Usage
Python API
from infinitetalk.pipelines import InfiniteTalkPipeline
pipeline = InfiniteTalkPipeline.from_pretrained(
"stabilityai/stable-video-diffusion-xl"
)
pipeline.load_loras("loras/fusionix.safetensors", "loras/lightx2v.safetensors")
video = pipeline(
image=reference_image,
audio=audio_features,
num_frames=120,
num_inference_steps=4
)
Command Line
infinitetalk generate \
--image face.jpg \
--audio speech.wav \
--output output.mp4 \
--num-frames 120 \
--resolution 720p
Web UI
python -m infinitetalk.api.gradio_app
API Server
python -m infinitetalk.api.server --host 0.0.0.0 --port 8000
Configuration
See configs/ for example configurations:
480p_4step.yaml: 480p resolution, 4-step inference720p_4step.yaml: 720p resolution, 4-step inferenceinfinite_length.yaml: For videos up to 55 minuteslora_config.yaml: LoRA settings
Benchmarks
python scripts/benchmark.py --resolution 720p --num-frames 120
License
MIT License - See LICENSE for details.
Citation
@software{infinitetalk2024,
author = {swordenkisk},
title = {InfiniteTalk: Sparse-Frame Video Dubbing},
year = {2024},
url = {https://github.com/swordenkisk/infinitetalk}
}
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