This repository provides FlashPortrait custom nodes for ComfyUI. It allows you to generate infinite-length portrait animations driven by a video, directly within your ComfyUI workflow.

Original Paper: FlashPortrait: 6x Faster Infinite Portrait Animation with Adaptive Latent Prediction

✨ Features

Automatic Model Downloading: No manual weight placement required. The loader fetches Wan2.1 and FlashPortrait models from Hugging Face automatically.
Infinite Length Support: Uses FlashPortrait's sliding window mechanism to process long driving videos.
Flexible Inputs:
- Reference Image: Defines the identity (ID).
- Driving Video: Defines the motion and expression.
- Initial Video Frames (Optional): Allows pre-defining the canvas content if distinct from the reference tiling.

🔧 Installation

Clone the Repository: Go to your ComfyUI custom_nodes folder and run:

cd ComfyUI/custom_nodes
git clone https://github.com/Francis-Rings/FlashPortrait
cd FlashPortrait

Install Dependencies:
```
pip install -r requirements.txt
```
Note: Requires approx. 40GB VRAM for full model loading (BF16).
Restart ComfyUI.

🚀 Nodes Usage

💡 Example Workflow

You can find a basic ComfyUI workflow in examples/flash_portraits_example.json. Simply drag and drop this file into your ComfyUI window to load the graph.

1. FlashPortrait Loader

Loads the heavy models (Transformer, VAE) and face alignment tools.

precision: bf16 (recommended), fp16, fp32.
download_missing: Enable this to automatically download models to ComfyUI/models/flash_portrait/ on first run.

2. FlashPortrait Feature Extractor

Extracts motion and expression features from the driving video.

images: Connect your driving video frames here (use Load Video or similar nodes).
source_fps: Frame rate of the source video (default 25.0).
context_size / context_overlap: Controls the sliding window size for feature extraction.

3. FlashPortrait Sampler

The main generation node.

pipe: Connect from Loader.
head_emo_features: Connect from Feature Extractor.
image: The Reference Image (Identity). Only the first frame is used.
initial_video_frames (Optional): If provided, these frames define the starting canvas/background instead of simply tiling the reference image.
prompt / negative_prompt: Text guidance.
guidance_scale / text_cfg_scale / emo_cfg_scale: Control the influence of unconditional, text, and emotion guidance.
steps: Inference steps (default 30).
max_size: Output resolution height (e.g., 720).

🧱 Acknowledgments

This implementation is based on the amazing work by the original FlashPortrait team. Huge congratulations and thanks to: Shuyuan Tu, Yueming Pan, Yinming Huang, Xintong Han, Zhen Xing, Qi Dai, Kai Qiu, Chong Luo, Zuxuan Wu

And to the open-source projects that made this possible:

📄 Citation

@article{tu2025flashportrait,
  title={FlashPortrait: 6x Faster Infinite Portrait Animation with Adaptive Latent Prediction},
  author={Tu, Shuyuan and Pan, Yueming and Huang, Yinming and Han, Xintong and Xing, Zhen and Dai, Qi and Qiu, Kai and Luo, Chong and Wu, Zuxuan},
  journal={arXiv preprint arXiv:2512.16900},
  year={2025}
}