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ComfyUI-VFI

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파일 탐색기

최종 버전 다운로드 (.zip)

- image.png
- interp.json
- - loss.py
  - warplayer.py
- - IFNet_HDv3.py
  - refine.py
  - RIFE_HDv3.py
- download_rife.py
- rife_comfyui_wrapper.py
.gitignore
__init__.py
nodes.py
pyproject.toml
README.md
requirements.txt

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/GACLove/ComfyUI-VFI

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd ComfyUI-VFI

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# ComfyUI-VFI

Video Frame Interpolation nodes for ComfyUI using RIFE (Real-Time Intermediate Flow Estimation).

![RIFE](https://raw.githubusercontent.com/GACLove/ComfyUI-VFI/main/docs/image.png)

## Features

- High-quality frame interpolation using RIFE
- Convert between different frame rates (e.g., 30fps to 60fps)
- Adjustable processing scale for performance/quality trade-off
- Model caching for efficient processing
- Progress tracking in ComfyUI

## Installation

- Clone this repository into your ComfyUI custom_nodes directory:

```bash
cd ComfyUI/custom_nodes
git clone https://github.com/your-username/ComfyUI-VFI.git
```

- Install required dependencies:

```bash
cd ComfyUI-VFI
pip install -r requirements.txt
```

- The RIFE model will be automatically downloaded on first use
  - Alternatively, you can manually place `flownet.pkl` in:
    - `ComfyUI-VFI/rife/train_log/`
    - Or `ComfyUI/models/rife/`

## Usage

The node will appear in the "image/animation" category as "RIFE Frame Interpolation".

### Inputs

- **images**: Image sequence tensor [N, H, W, C]
- **source_fps**: Original frame rate (default: 30.0)
- **target_fps**: Desired frame rate (default: 60.0)
- **scale**: Processing scale factor (default: 1.0)
  - Lower values (0.25-0.5) for faster processing
  - Higher values (1.0-4.0) for better quality

### Output

- **images**: Interpolated image sequence tensor

## Example Workflow

1. Load video frames using a video loader node
2. Connect to RIFE Frame Interpolation node
3. Set source and target FPS
4. Connect output to video encoder or preview

## Model Download

The RIFE model (`flownet.pkl`) can be downloaded from the official RIFE repository.