Ethanfel
6dd579dcc7
Add source_fps/target_fps inputs to all 4 Interpolate and Segment nodes (BIM, EMA, SGM, GIMM). When target_fps > 0, auto-computes optimal power-of-2 oversample, runs existing recursive t=0.5 interpolation, then selects frames at target timestamps. Handles downsampling (no model calls), same-fps passthrough, and high ratios (e.g. 3→30fps). Segment boundary logic uses global index computation for gap-free stitching. When target_fps=0, existing multiplier behavior is preserved. Remove video preview from TweenConcatVideos: drop preview input, delete web/js/tween_preview.js, remove WEB_DIRECTORY from __init__.py. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ComfyUI BIM-VFI + EMA-VFI + SGM-VFI + GIMM-VFI
ComfyUI custom nodes for video frame interpolation using BiM-VFI (CVPR 2025), EMA-VFI (CVPR 2023), SGM-VFI (CVPR 2024), and GIMM-VFI (NeurIPS 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 | GIMM-VFI | |
|---|---|---|---|---|
| Best for | General-purpose, non-uniform motion | Fast inference, light VRAM | Large motion, occlusion-heavy scenes | High multipliers (4x/8x) in a single pass |
| Quality | Highest overall | Good | Best on large motion | Good |
| Speed | Moderate | Fastest | Slowest | Fast for 4x/8x (single pass) |
| VRAM | ~2 GB/pair | ~1.5 GB/pair | ~3 GB/pair | ~2.5 GB/pair |
| Params | ~17M | ~14–65M | ~15M + GMFlow | ~80M (RAFT) / ~123M (FlowFormer) |
| Arbitrary timestep | Yes | Yes (with _t checkpoint) |
No (fixed 0.5) | Yes (native single-pass) |
| 4x/8x mode | Recursive 2x passes | Recursive 2x passes | Recursive 2x passes | Single forward pass (or recursive) |
| Paper | CVPR 2025 | CVPR 2023 | CVPR 2024 | NeurIPS 2024 |
| License | Research only | Apache 2.0 | 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. Use GIMM-VFI when you want 4x or 8x interpolation without recursive passes — it generates all intermediate frames in a single forward pass per pair.
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.
GIMM-VFI
Load GIMM-VFI Model
Loads a GIMM-VFI checkpoint. Auto-downloads from HuggingFace on first use to ComfyUI/models/gimm-vfi/. The matching flow estimator (RAFT or FlowFormer) is auto-detected and downloaded alongside the main model.
| Input | Description |
|---|---|
| model_path | Checkpoint file from models/gimm-vfi/ |
| ds_factor | Downscale factor for internal processing (1.0 = full res, 0.5 = half). Lower = less VRAM, faster, less quality. Try 0.5 for 4K inputs |
Available checkpoints:
| Checkpoint | Variant | Params | Flow estimator (auto-downloaded) |
|---|---|---|---|
gimmvfi_r_arb_lpips_fp32.safetensors |
RAFT | ~80M | raft-things_fp32.safetensors |
gimmvfi_f_arb_lpips_fp32.safetensors |
FlowFormer | ~123M | flowformer_sintel_fp32.safetensors |
GIMM-VFI Interpolate
Interpolates frames from an image batch. Same controls as BIM-VFI Interpolate, plus:
| Input | Description |
|---|---|
| single_pass | When enabled (default), generates all intermediate frames per pair in one forward pass using GIMM-VFI's arbitrary-timestep capability. No recursive 2x passes needed for 4x or 8x. Disable to use the standard recursive approach (same as BIM/EMA/SGM) |
GIMM-VFI Segment Interpolate
Same as GIMM-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, omegaconf, easydict, yacs, einops, huggingface_hub) 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-Tween
python install.py
Requirements
- PyTorch with CUDA
cupy(matching your CUDA version, for BIM-VFI, SGM-VFI, and GIMM-VFI)timm(for EMA-VFI and SGM-VFI)gdown(for BIM-VFI/EMA-VFI/SGM-VFI model auto-download)omegaconf,easydict,yacs,einops(for GIMM-VFI)huggingface_hub(for GIMM-VFI 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, the official SGM-VFI implementation by MCG-NJU, and the GIMM-VFI implementation by S-Lab (NTU). GIMM-VFI architecture files in gimm_vfi_arch/ are adapted from kijai/ComfyUI-GIMM-VFI with safetensors 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 (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}
}
GIMM-VFI:
Zujin Guo, Wei Li, and Chen Change Loy. "Generalizable Implicit Motion Modeling for Video Frame Interpolation." Advances in Neural Information Processing Systems (NeurIPS), 2024. [arXiv] [GitHub]
@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
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.
The GIMM-VFI model weights and architecture code are released under the Apache 2.0 License. See the original repository for details. ComfyUI adaptation based on kijai/ComfyUI-GIMM-VFI.