ComfyUI-RIFE

English | [中文](README_zhgit .md)

RIFE video frame interpolation node for ComfyUI, based on Practical-RIFE implementation.

Features

• Real-time Video Frame Interpolation: Uses RIFE (Real-Time Intermediate Flow Estimation) algorithm to generate smooth intermediate frames between video frames
• Automatic Model Download: Automatically downloads required models from GitHub Releases on first use
• Model Caching: Automatically caches loaded models to avoid reloading
• Multiple Precision Support: Supports float32, float16, and bfloat16 precision for speed/quality trade-off
• Torch Compile: Optional PyTorch 2.0+ torch.compile() acceleration (slower first run, faster subsequent inference)
• Batch Inference: Supports batch processing of interpolation tasks for higher throughput

Supported Models

Model Name	Version	Description
rife47.pth	4.7	RIFE version 4.7
rife49.pth	4.7	RIFE version 4.9
rife417.pth	4.17	RIFE version 4.17
rife426.pth	4.26	RIFE version 4.26 (ensemble not supported)

Models are automatically downloaded to ComfyUI/models/rife/ directory.

Installation

1. Clone this repository into ComfyUI's custom_nodes directory:

   cd ComfyUI/custom_nodes
   git clone git@github.com:er1cw00/ComfyUI-RIFE.git ComfyUI-RIFE

2. Install dependencies:

   cd ComfyUI-RIFE
   pip install -r requirements.txt

Node Parameters

RIFE_VFI

Required Parameters:

Parameter	Type	Default	Description
ckpt_name	Dropdown	-	Select RIFE model (auto-downloads if not present)
frames	IMAGE	-	Input video frame sequence
clear_cache_after_n_frames	INT	10	Clear GPU cache every N frames (1-1000)
multiplier	INT	2	Interpolation multiplier, e.g., 2 generates 1 intermediate frame between each pair
fast_mode	BOOLEAN	True	Fast mode, trade some quality for speed
ensemble	BOOLEAN	True	Use ensemble mode for better quality (forced off for v4.26)
scale_factor	Dropdown	1.0	Optical flow scale factor [0.25, 0.5, 1.0, 2.0, 4.0]
dtype	Dropdown	float32	Inference precision: float32 (safest), float16 (fast/less VRAM), bfloat16 (requires RTX 30xx+)
torch_compile	BOOLEAN	False	Use torch.compile() for acceleration (requires PyTorch 2.0+)
batch_size	INT	1	Number of interpolation tasks per batch (higher = more throughput but more VRAM, 1-64)

Optional Parameters:

Parameter	Type	Description
optional_interpolation_states	INTERPOLATION_STATES	Interpolation state list for controlling frame skipping

Outputs:

Output	Type	Description
IMAGE	IMAGE	Interpolated video frame sequence

Usage Example

Basic Workflow

1. Load video frames using Load Video or Load Image Batch node
2. Connect to RIFE_VFI node
3. Select a model from the dropdown (e.g., rife426.pth)
4. Set multiplier (e.g., 2 means 30fps → 60fps)
5. Output using Save Video or Preview Image node

Parameter Tuning Suggestions

• Quality Priority: dtype=float32, ensemble=True, fast_mode=False
• Speed Priority: dtype=float16, fast_mode=True, torch_compile=True (PyTorch 2.0+)
• Low VRAM: Reduce batch_size to 1, use dtype=float16
• RTX 30xx/40xx: Can use dtype=bfloat16, more stable than float16 with similar speed

File Structure

ComfyUI-RIFE/
├── __init__.py          # Node registration entry
├── rife_node.py         # Main node implementation (model loading, inference)
├── rife_model.py        # RIFE IFNet model definition
├── requirements.txt     # Python dependencies
└── README.md           # This file (Chinese)
└── README_EN.md        # English version

Model Download Notes

Model files are automatically downloaded to ComfyUI/models/rife/:

• On first use of a model, the node automatically downloads from https://github.com/Fannovel16/ComfyUI-Frame-Interpolation/releases/download/models/
• If download fails, check network connection or manually download models to the above directory

Important Notes

1. RIFE 4.26: This version does not support ensemble mode, it will be automatically disabled
2. torch.compile(): First inference will be slow (compilation phase), subsequent runs are 10-30% faster
3. bfloat16: Only supported on Ampere architecture (RTX 30xx/40xx) or newer GPUs
4. Model Caching: Changing ckpt_name, dtype, or torch_compile triggers model reload

Dependencies

• torch >= 2.0.0 (recommended)
• einops
• packaging
• numpy

License

This project is based on Practical-RIFE implementation, following the corresponding open-source license.