Six video frame interpolation models in one package — BIM-VFI, EMA-VFI, SGM-VFI, GIMM-VFI, SPEED, and LDF-VFI. Pairwise models include chunked/segmented processing; LDF-VFI adds holistic long-sequence diffusion interpolation.
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.txtDependencies are declared in pyproject.toml and requirements.txt and are installed automatically by ComfyUI Manager or pip. There is intentionally no custom install.py, avoiding a second redundant dependency-install pass after Manager processes requirements.txt. LDF-VFI requires PyTorch 2.5+ plus a current diffusers/accelerate stack.
Import example_workflows/tween_speed_bim_model_lab.json for the recommended starter graph. It requires ComfyUI-VideoHelperSuite for video loading and encoding.
- The SPEED and BIM-VFI branches load the same 25-frame, 24 FPS sample, tune memory settings independently, interpolate to 48 FPS, preserve audio, and save separate comparison videos.
- Keep the loader's
force_rate, Tween'ssource_fps/target_fps, and Video Combine'sframe_ratesynchronized when changing cadence.
cupy provides GPU-accelerated optical flow warping. It is deliberately not installed automatically, because replacing or mixing CUDA-specific cupy wheels can disrupt other ComfyUI nodes. BIM-VFI, SGM-VFI, and GIMM-VFI work without it through their PyTorch fallback. EMA-VFI, SPEED, and LDF-VFI do not use it.
-
Find your CUDA version:
python -c "import torch; print(torch.version.cuda)" -
Install the matching package:
CUDA Command 13.x pip install cupy-cuda13x12.x pip install cupy-cuda12x11.x pip install cupy-cuda11x
Make sure to run pip in the same Python environment as ComfyUI, and uninstall any different cupy wheel variant first. If cupy is absent or incompatible, Tween safely uses its PyTorch fallback.
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 | Confirm pip selected a prebuilt wheel for your Python and CUDA versions |
| Docker / no build tools | Use the matching prebuilt cupy-cudaXXx wheel, not bare cupy which compiles from source |
| Model | Best for | Multiplier path | Typical VRAM | Trade-off |
|---|---|---|---|---|
| BIM-VFI | Strong general pairwise quality | Recursive 2x/4x/8x | ~2 GB/pair | Research/education license |
| EMA-VFI | Speed and lower VRAM | Recursive 2x/4x/8x | ~1.5 GB/pair | Less robust on extreme motion |
| SGM-VFI | Large motion | Recursive 2x/4x/8x | ~3 GB/pair | Slowest pairwise option |
| GIMM-VFI | Arbitrary timesteps, efficient 4x/8x | Native multi-frame per pair | ~2.5 GB/pair | Still frame-pair-centric |
| SPEED | New high-quality midpoint generation | One diffusion step at 2x; recursive 4x/8x | ~2.3–2.6 GB at benchmark resolutions | Stochastic, ~447 MB checkpoint |
| LDF-VFI | Long-range temporal coherence and 2x–16x | Native sequence diffusion | ~20 GB | ~6.4 GB weights; much slower |
TL;DR: Try SPEED as the modern pairwise default. Use EMA-VFI when latency matters, SGM-VFI for difficult large motion, GIMM-VFI for lightweight arbitrary timesteps, and LDF-VFI when sequence consistency matters more than speed or memory.
| 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 |
SPEED generally fits the 24 GB tier at HD resolutions. LDF-VFI is a separate workload: its official 8x quick start requires about 20 GB, and higher resolutions may require smaller VAE tiles or more VRAM.
The pairwise Interpolate nodes (BIM/EMA/SGM/GIMM/SPEED) share these 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 a power-of-2 oversample up to 8x, then selects the nearest generated frame for each target timestamp. 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
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 | Official automatic pyramid policy (below 1080p=5, 1080p=6, 4K=7) |
| pyr_level | Manual pyramid level (3–7), used when auto is off |
| artifact_safe_mode | Disables the RGB refinement residual to suppress wrong-edge/halo artifacts caused by flow misalignment in blurry or large-motion shots. Off preserves official behavior and can retain more detail on easy shots |
artifact_safe_mode implements the workaround recommended by the official BIM-VFI maintainer for wrong-edge artifacts caused by severely misaligned warped inputs. Enable it selectively for affected footage.
Common controls listed above.
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
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) |
Same controls as above.
SGM-VFI
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 |
Same controls as above.
GIMM-VFI
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 |
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 |
Same pattern as other Segment nodes.
SPEED
Downloads the official speed.pt checkpoint from zhZ524/SPEED to ComfyUI/models/speed-vfi/. The loader also fetches a checksum-pinned snapshot of the official runtime on first use; Tween does not bundle that source.
| Input | Description |
|---|---|
| model_path | Checkpoint from models/speed-vfi/ (official default is ~447 MB) |
| precision | auto prefers BF16, then FP16; FP32 is available for comparison |
Uses the same batching, chunking, segment, and exact-target-FPS controls as BIM-VFI, plus a seed input for repeatable starting pixel noise. Keeping the seed on the interpolation node lets it change without reloading the model. SPEED is repeatable for the same seed and execution settings; changing batch, chunk, or segment boundaries can change how its stochastic noise is assigned. The released model predicts only the midpoint, so 4x and 8x are recursive passes. Inputs are padded to the model's 64-pixel divisor and cropped back automatically.
LDF-VFI
Downloads the official transformer and conditional VAE from onecat-ai/LDF-VFI to ComfyUI/models/ldf-vfi/ (~6.4 GB total). A checksum-pinned Apache-2.0 runtime snapshot is fetched on first use. Loading stays on CPU until the interpolation node executes.
| Input | Description |
|---|---|
| tile_size / tile_overlap | Spatial VAE tiling and seam blending; default 256/64 |
| vae_batch_size | Lower first if VAE encode/decode runs out of VRAM |
| attention_type | Official slide_chunk_all_block_2x1x1 sparse attention is recommended |
LDF-VFI is not a pairwise node. It processes the ordered source batch with the paper's skip-concat autoregressive sampler and internally chunks long sequences without breaking temporal context.
| Input | Description |
|---|---|
| temporal_factor | Any integer from 2x through 16x |
| sampling_steps | Diffusion steps per temporal block; official quick start uses 16 |
| t_shift / t_cond | Official defaults are 8.0 / 0.1 |
| seed | Repeatable VAE and diffusion sampling |
| offload_after | Return transformer and VAE to CPU after generation |
| source_fps / target_fps | Optional exact-FPS selection using the smallest sufficient native factor |
The second output, generated_sequence, is the full native-factor sequence before exact-FPS selection. LDF has no Segment node because externally splitting the sequence would discard the long-range context it is designed to preserve.
Concatenates segment video files into a single video using ffmpeg. Connect from any pairwise Segment Interpolate's model output to ensure it runs after all segments are saved.
- Pairwise multiplier mode: 2x = 2N-1, 4x = 4N-3, 8x = 8N-7
- LDF-VFI native factor: factor
F=F(N-1)+1, for any integerFfrom 2 through 16 - Target FPS mode:
floor((N-1) / source_fps * target_fps) + 1frames. Pairwise nodes oversample to the nearest power-of-2 above the ratio (up to 8x), then select the nearest generated frame for each target timestamp. Downsampling (target < source) also works — frames are selected from the input with no model calls. LDF-VFI supports native factors up to 16x.
In target-FPS Segment mode, a very small segment_size can cover less than one output-frame interval while downsampling. Increase segment_size if the node reports that the segment contains no target timestamps; returning a placeholder frame would make concatenated timing incorrect.
| 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 |
| SPEED | Zhang et al. | ACM MM 2026 | Paper · Code · Model |
| LDF-VFI | Peng et al. | CVPR 2026 | Paper · Code · Model |
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. SPEED and LDF-VFI use checksum-pinned official source snapshots downloaded into their model directories on demand.
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}
}
@misc{zhang2026speed,
title={SPEED: One-Step Pixel Diffusion for High-quality Video Frame Interpolation},
author={Zhang, Zihao and Zhao, Haoyu and Yang, Siqian and Wu, Yidi and Jiang, Yudong and Wu, Zuxuan},
year={2026},
eprint={2607.15585},
archivePrefix={arXiv}
}
@misc{peng2026holistic,
title={Towards Holistic Modeling for Video Frame Interpolation with Auto-regressive Diffusion Transformers},
author={Peng, Xinyu and Li, Han and Huang, Yuyang and Zheng, Ziyang and Wang, Yaoming and Chen, Xin and Dai, Wenrui and Li, Chenglin and Zou, Junni and Xiong, Hongkai},
year={2026},
eprint={2601.14959},
archivePrefix={arXiv}
}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, LDF-VFI: Apache 2.0. GIMM-VFI ComfyUI adaptation based on kijai/ComfyUI-GIMM-VFI.
SPEED: The official source repository did not include a license file when this integration was pinned. Tween does not redistribute that source; the loader downloads it directly from the official repository. Review the upstream terms before redistribution or commercial use. The checkpoint is likewise downloaded from its official Hugging Face repository.
This wrapper code: Apache 2.0