ComfyUI-TLBVFI

A node pack for ComfyUI that provides video frame interpolation using the TLB-VFI model.

This is a wrapper for the TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation project.

Features

• Zero-Dependency: All non-standard requirements (CuPy, PyTorch-Lightning, etc.) have been removed or replaced with native implementations.
• Efficient Batching: Supports processing multiple frame pairs simultaneously.

⚙️ Installation

Step 1: Install the Custom Node

Clone this repository into your ComfyUI/custom_nodes/ directory:

cd ComfyUI/custom_nodes/
git clone https://github.com/BobRandomNumber/ComfyUI-TLBVFI.git

Step 2: Download the Pre-trained Model

Download vimeo_unet.pth from the official repository:

• ucfzl/TLBVFI on Hugging Face

Step 3: Place Model in the interpolation Folder

Place the downloaded .pth file into ComfyUI/models/interpolation/.

ComfyUI/
└── models/
    └── interpolation/
        └── vimeo_unet.pth

🚀 Usage

1. Add the TLBVFI Frame Interpolation node from the frame_interpolation/TLBVFI category.
2. Select the correct model from the model_name dropdown.
3. times_to_interpolate: Sets how many new frames are generated between pairs (1 = double FPS, 2 = quadruple, etc.).
4. diffusion_steps: Controls the refinement quality. Higher values (e.g., 20-50) improve quality at the cost of speed.
5. batch_size: Number of pairs to process at once. Increase if you have sufficient VRAM for a speed boost.
6. flow_scale: Resolution for motion analysis. Use 0.5 for most videos; lower values handle fast motion better.

🧠 How It Works

1. VQGAN: Compresses input frames into a latent space.
2. UNet (Brownian Bridge): Operates in latent space to diffuse and generate the in-between representation using a reverse diffusion process.
3. VQGAN Decoder: Reconstructs the generated latent back into a full-resolution image.

🙏 Acknowledgements

All credit for the architecture and research goes to the original authors of TLB-VFI.

• Original GitHub: ZonglinL/TLBVFI
• Project Page: https://zonglinl.github.io/tlbvfi_page/

@article{lyu2025tlbvfitemporalawarelatentbrownian,
      title={TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation}, 
      author={Zonglin Lyu and Chen Chen},
      year={2025},
      eprint={2507.04984},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
}