Tween — Video Frame Interpolation for ComfyUI

Four video frame interpolation models in one package — BIM-VFI, EMA-VFI, SGM-VFI, and GIMM-VFI. Designed for long videos with thousands of frames without running out of VRAM.

Installation

Install from the ComfyUI Registry (recommended) or clone manually:

cd ComfyUI/custom_nodes
git clone https://github.com/Ethanfel/ComfyUI-Tween.git
pip install -r requirements.txt

All dependencies (gdown, timm, omegaconf, easydict, yacs, einops, huggingface_hub) are declared in pyproject.toml and requirements.txt, installed automatically by ComfyUI Manager or pip.

cupy (required for BIM-VFI, SGM-VFI, GIMM-VFI)

cupy provides GPU-accelerated optical flow warping. EMA-VFI works without it.

1. Find your CUDA version:

   python -c "import torch; print(torch.version.cuda)"
   

2. Install the matching package:

   CUDA	Command
   12.x	pip install cupy-cuda12x
   11.x	pip install cupy-cuda11x

Make sure to run pip in the same Python environment as ComfyUI. If cupy is missing, the Load node shows an error with your CUDA version and the exact install command.

cupy troubleshooting

Problem	Solution
ModuleNotFoundError: No module named 'cupy'	Install cupy using the steps above
cupy installed but ImportError at runtime	CUDA version mismatch — uninstall and reinstall the correct version
Install hangs or takes very long	cupy wheels are ~800 MB, be patient
Docker / no build tools	Use the prebuilt wheel: pip install cupy-cuda12x (not bare cupy which compiles from source)

Which model should I use?

	BIM-VFI	EMA-VFI	SGM-VFI	GIMM-VFI
Best for	General-purpose	Fast, low VRAM	Large motion	High multipliers (4x/8x)
Quality	Highest	Good	Best on large motion	Good
Speed	Moderate	Fastest	Slowest	Fast for 4x/8x
VRAM	~2 GB/pair	~1.5 GB/pair	~3 GB/pair	~2.5 GB/pair
Params	~17 M	~14–65 M	~15 M + GMFlow	~80 M (RAFT) / ~123 M (FlowFormer)
Arbitrary timestep	Yes	Yes (_t checkpoint)	No (fixed 0.5)	Yes (native)
4x/8x	Recursive passes	Recursive passes	Recursive passes	Single forward pass
Requires cupy	Yes	No	Yes	Yes
Paper	CVPR 2025	CVPR 2023	CVPR 2024	NeurIPS 2024

TL;DR: Start with BIM-VFI for best quality. Use EMA-VFI for speed or if you can't install cupy. Use SGM-VFI for large camera motion. Use GIMM-VFI for 4x/8x without recursive passes.

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

Nodes

All Interpolate nodes share a common set of controls:

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
keep_device	Keep model on GPU between pairs (faster, ~200 MB 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
source_fps	Input frame rate. Required when target_fps > 0
target_fps	Target output FPS. When > 0, overrides multiplier — auto-computes the optimal power-of-2 oversample then selects frames at exact target timestamps. 0 = use multiplier

Output	Description
images	Interpolated frames at the target FPS (or at the multiplied rate when target_fps = 0)
oversampled	Full power-of-2 oversampled frames before target FPS selection. Same as images when target_fps = 0

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 from models/bim-vfi/
auto_pyr_level	Auto pyramid level by resolution (<540p=3, 540p=5, 1080p=6, 4K=7)
pyr_level	Manual pyramid level (3–7), used when auto is off

BIM-VFI Interpolate

Common controls listed above.

BIM-VFI Segment Interpolate

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.

EMA-VFI

Load EMA-VFI Model

Auto-downloads from Google Drive to ComfyUI/models/ema-vfi/. Variant and timestep support are auto-detected from the filename.

Input	Description
model_path	Checkpoint from models/ema-vfi/
tta	Test-time augmentation (~2x slower, slightly better quality)

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

EMA-VFI Interpolate / Segment Interpolate

Same controls as above.

SGM-VFI

Load SGM-VFI Model

Auto-downloads from Google Drive to ComfyUI/models/sgm-vfi/. Requires cupy.

Input	Description
model_path	Checkpoint from models/sgm-vfi/
tta	Test-time augmentation (~2x slower, slightly better quality)
num_key_points	Global matching sparsity (0.0 = global everywhere, 0.5 = default, higher = faster)

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

SGM-VFI Interpolate / Segment Interpolate

Same controls as above.

GIMM-VFI

Load GIMM-VFI Model

Auto-downloads from HuggingFace to ComfyUI/models/gimm-vfi/. The matching flow estimator (RAFT or FlowFormer) is auto-detected and downloaded alongside.

Input	Description
model_path	Checkpoint from models/gimm-vfi/
ds_factor	Downscale factor for internal processing (1.0 = full, 0.5 = half). Try 0.5 for 4K inputs

Checkpoint	Variant	Params	Flow estimator (auto-downloaded)
gimmvfi_r_arb_lpips_fp32.safetensors	RAFT	~80 M	raft-things_fp32.safetensors
gimmvfi_f_arb_lpips_fp32.safetensors	FlowFormer	~123 M	flowformer_sintel_fp32.safetensors

GIMM-VFI Interpolate

Common controls plus:

Input	Description
single_pass	Generate all intermediate frames per pair in one forward pass (default on). No recursive 2x passes needed for 4x/8x. Disable to use the standard recursive approach

GIMM-VFI Segment Interpolate

Same pattern as other Segment nodes.

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 four models.

Output frame count

• Multiplier mode: 2x = 2N-1, 4x = 4N-3, 8x = 8N-7
• Target FPS mode: floor((N-1) / source_fps * target_fps) + 1 frames. Automatically oversamples to the nearest power-of-2 above the ratio, then selects frames at exact target timestamps. Downsampling (target < source) also works — frames are selected from the input with no model calls.

Acknowledgments

Model	Authors	Venue	Links
BIM-VFI	Seo, Oh, Kim (KAIST VIC Lab)	CVPR 2025	Paper · Code · Project
EMA-VFI	Zhang et al. (MCG-NJU)	CVPR 2023	Paper · Code
SGM-VFI	Zhang et al. (MCG-NJU)	CVPR 2024	Paper · Code
GIMM-VFI	Guo, Li, Loy (S-Lab NTU)	NeurIPS 2024	Paper · Code

GIMM-VFI adaptation from kijai/ComfyUI-GIMM-VFI with checkpoints from Kijai/GIMM-VFI_safetensors. Architecture files in bim_vfi_arch/, ema_vfi_arch/, sgm_vfi_arch/, and gimm_vfi_arch/ are vendored from their respective repositories with minimal modifications.

BibTeX citations

@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}
}

@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}
}

@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}
}

@inproceedings{guo2024gimmvfi,
  title={Generalizable Implicit Motion Modeling for Video Frame Interpolation},
  author={Guo, Zujin and Li, Wei and Loy, Chen Change},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2024}
}

License

BIM-VFI: Research and education only. Commercial use requires permission from Prof. Munchurl Kim (mkimee@kaist.ac.kr). See the original repository.

EMA-VFI, SGM-VFI, GIMM-VFI: Apache 2.0. GIMM-VFI ComfyUI adaptation based on kijai/ComfyUI-GIMM-VFI.

This wrapper code: Apache 2.0