ComfyUI BIM-VFI + EMA-VFI + SGM-VFI

ComfyUI custom nodes for video frame interpolation using BiM-VFI (CVPR 2025), EMA-VFI (CVPR 2023), and SGM-VFI (CVPR 2024). Designed for long videos with thousands of frames — processes them without running out of VRAM.

Which model should I use?

	BIM-VFI	EMA-VFI	SGM-VFI
Best for	General-purpose, non-uniform motion	Fast inference, light VRAM	Large motion, occlusion-heavy scenes
Quality	Highest overall	Good	Best on large motion
Speed	Moderate	Fastest	Slowest
VRAM	~2 GB/pair	~1.5 GB/pair	~3 GB/pair
Params	~17M	~14–65M	~15M + GMFlow
Arbitrary timestep	Yes	Yes (with _t checkpoint)	No (fixed 0.5)
Paper	CVPR 2025	CVPR 2023	CVPR 2024
License	Research only	Apache 2.0	Apache 2.0

TL;DR: Start with BIM-VFI for best quality. Use EMA-VFI if you need speed or lower VRAM. Use SGM-VFI if your video has large camera motion or fast-moving objects that the others struggle with.

Nodes

BIM-VFI

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 VRAM 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

BIM-VFI Segment Interpolate

Same as Interpolate but processes a single segment of the input. Chain multiple instances with Save nodes between them to bound peak RAM. The model pass-through output forces sequential execution.

Tween Concat Videos

Concatenates segment video files into a single video using ffmpeg. Connect from any Segment Interpolate's model output to ensure it runs after all segments are saved. Works with all three models.

EMA-VFI

Load EMA-VFI Model

Loads an EMA-VFI checkpoint. Auto-downloads from Google Drive on first use to ComfyUI/models/ema-vfi/. Variant (large/small) and timestep support are auto-detected from the filename.

Input	Description
model_path	Checkpoint file from models/ema-vfi/
tta	Test-time augmentation: flip input and average with unflipped result (~2x slower, slightly better quality)

Available checkpoints:

Checkpoint	Variant	Params	Arbitrary timestep
ours_t.pkl	Large	~65M	Yes
ours.pkl	Large	~65M	No (fixed 0.5)
ours_small_t.pkl	Small	~14M	Yes
ours_small.pkl	Small	~14M	No (fixed 0.5)

EMA-VFI Interpolate

Interpolates frames from an image batch. Same controls as BIM-VFI Interpolate.

EMA-VFI Segment Interpolate

Same as EMA-VFI Interpolate but processes a single segment. Same pattern as BIM-VFI Segment Interpolate.

SGM-VFI

Load SGM-VFI Model

Loads an SGM-VFI checkpoint. Auto-downloads from Google Drive on first use to ComfyUI/models/sgm-vfi/. Variant (base/small) is auto-detected from the filename (default is small).

Input	Description
model_path	Checkpoint file from models/sgm-vfi/
tta	Test-time augmentation: flip input and average with unflipped result (~2x slower, slightly better quality)
num_key_points	Sparsity of global matching (0.0 = global everywhere, 0.5 = default balance, higher = faster)

Available checkpoints:

Checkpoint	Variant	Params
ours-1-2-points.pkl	Small	~15M + GMFlow

SGM-VFI Interpolate

Interpolates frames from an image batch. Same controls as BIM-VFI Interpolate.

SGM-VFI Segment Interpolate

Same as SGM-VFI Interpolate but processes a single segment. Same pattern as BIM-VFI Segment Interpolate.

Output frame count (all models): 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-Tween.git

Dependencies (gdown, cupy, timm) are auto-installed on first load. The correct cupy variant is detected from your PyTorch CUDA version.

Warning: cupy is a large package (~800MB) and compilation/installation can take several minutes. The first ComfyUI startup after installing this node may appear to hang while cupy installs 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-Tween
python install.py

Requirements

• PyTorch with CUDA
• cupy (matching your CUDA version, for BIM-VFI and SGM-VFI)
• timm (for EMA-VFI and SGM-VFI)
• 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 official EMA-VFI implementation by MCG-NJU, and the official SGM-VFI implementation by MCG-NJU. Architecture files in bim_vfi_arch/, ema_vfi_arch/, and sgm_vfi_arch/ are vendored from their respective repositories with minimal modifications (relative imports, device-awareness fixes, inference-only paths).

BiM-VFI:

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}
}

EMA-VFI:

Guozhen Zhang, Yuhan Zhu, Haonan Wang, Youxin Chen, Gangshan Wu, and Limin Wang. "Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation." IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [arXiv] [GitHub]

@inproceedings{zhang2023emavfi,
  title={Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation},
  author={Zhang, Guozhen and Zhu, Yuhan and Wang, Haonan and Chen, Youxin and Wu, Gangshan and Wang, Limin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2023}
}

SGM-VFI:

Guozhen Zhang, Yuhan Zhu, Evan Zheran Liu, Haonan Wang, Mingzhen Sun, Gangshan Wu, and Limin Wang. "Sparse Global Matching for Video Frame Interpolation with Large Motion." IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [arXiv] [GitHub]

@inproceedings{zhang2024sgmvfi,
  title={Sparse Global Matching for Video Frame Interpolation with Large Motion},
  author={Zhang, Guozhen and Zhu, Yuhan and Liu, Evan Zheran and Wang, Haonan and Sun, Mingzhen and Wu, Gangshan and Wang, Limin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2024}
}

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.

The EMA-VFI model weights and architecture code are released under the Apache 2.0 License. See the original repository for details.

The SGM-VFI model weights and architecture code are released under the Apache 2.0 License. See the original repository for details.