Ethanfel
7cf7162143
Processes numbered segments of the input batch so users can chain multiple instances with Save nodes between them, freeing each segment's output before the next starts. Model pass-through output forces sequential execution via ComfyUI's dependency graph. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ComfyUI BIM-VFI
ComfyUI custom nodes for video frame interpolation using BiM-VFI (CVPR 2025). Designed for long videos with thousands of frames — processes them without running out of VRAM.
Nodes
Load BIM-VFI Model
Loads the BiM-VFI checkpoint. Auto-downloads from Google Drive on first use to ComfyUI/models/bim-vfi/.
| Input | Description |
|---|---|
| model_path | Checkpoint file from models/bim-vfi/ |
| auto_pyr_level | Auto-select pyramid level by resolution (<540p=3, 540p=5, 1080p=6, 4K=7) |
| pyr_level | Manual pyramid level (3-7), only used when auto is off |
BIM-VFI Interpolate
Interpolates frames from an image batch.
| Input | Description |
|---|---|
| images | Input image batch |
| model | Model from the loader node |
| multiplier | 2x, 4x, or 8x frame rate (recursive 2x passes) |
| batch_size | Frame pairs processed simultaneously (higher = faster, more VRAM) |
| chunk_size | Process in segments of N input frames (0 = disabled). Bounds memory for very long videos. Result is identical to processing all at once |
| keep_device | Keep model on GPU between pairs (faster, ~200MB constant VRAM) |
| all_on_gpu | Keep all intermediate frames on GPU (fast, needs large VRAM) |
| clear_cache_after_n_frames | Clear CUDA cache every N pairs to prevent VRAM buildup |
Output frame count: 2x = 2N-1, 4x = 4N-3, 8x = 8N-7
Installation
Clone into your ComfyUI custom_nodes/ directory:
cd ComfyUI/custom_nodes
git clone https://github.com/your-user/Comfyui-BIM-VFI.git
Dependencies (gdown, cupy) are auto-installed on first load. The correct cupy variant is detected from your PyTorch CUDA version.
Warning:
cupyis a large package (~800MB) and compilation/installation can take several minutes. The first ComfyUI startup after installing this node may appear to hang whilecupyinstalls in the background. Check the console log for progress. If auto-install fails (e.g. missing build tools in Docker), install manually with:pip install cupy-cuda12x # replace 12 with your CUDA major version
To install manually:
cd Comfyui-BIM-VFI
python install.py
Requirements
- PyTorch with CUDA
cupy(matching your CUDA version)gdown(for model auto-download)
VRAM Guide
| VRAM | Recommended settings |
|---|---|
| 8 GB | batch_size=1, chunk_size=500 |
| 24 GB | batch_size=2-4, chunk_size=1000 |
| 48 GB+ | batch_size=4-16, all_on_gpu=true |
| 96 GB+ | batch_size=8-16, all_on_gpu=true, chunk_size=0 |
Acknowledgments
This project wraps the official BiM-VFI implementation by the KAIST VIC Lab. The model architecture files in bim_vfi_arch/ are vendored from their repository with minimal modifications (relative imports, inference-only paths).
Paper:
Wonyong Seo, Jihyong Oh, and Munchurl Kim. "BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions." IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. [arXiv] [Project Page] [GitHub]
@inproceedings{seo2025bimvfi,
title={BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions},
author={Seo, Wonyong and Oh, Jihyong and Kim, Munchurl},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2025}
}
License
The BiM-VFI model weights and architecture code are provided by KAIST VIC Lab for research and education purposes only. Commercial use requires permission from the principal investigator (Prof. Munchurl Kim, mkimee@kaist.ac.kr). See the original repository for details.