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 [email protected]: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.