Files

Ethanfel 3e8148b7e2 Add chunk_size for long video support, fix cache clearing, add README

- chunk_size input splits input into overlapping segments processed
  independently then stitched, bounding memory for 1000+ frame videos
  while producing identical results to processing all at once
- Fix cache clearing logic: use counter instead of modulo so it triggers
  regardless of batch_size value
- Replace inefficient torch.cat gather with direct tensor slicing
- Add README with usage guide, VRAM recommendations, and full
  attribution to BiM-VFI (Seo, Oh, Kim — CVPR 2025, KAIST VIC Lab)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-02-12 19:08:42 +01:00

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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.

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.

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