RIFE Interpolation

Increase video frame rate with RIFE AI interpolation.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Renting on CLORE.AI

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Select payment: CLORE, BTC, or USDT/USDC
Create order and wait for deployment

Access Your Server

Find connection details in My Orders
Web interfaces: Use the HTTP port URL
SSH: ssh -p <port> root@<proxy-address>

What is RIFE?

RIFE (Real-Time Intermediate Flow Estimation) can:

Increase FPS (24→60, 30→120)
Create smooth slow motion
Fix choppy footage
Real-time processing

Quick Deploy

Docker Image:

pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime

Ports:

22/tcp

Command:

pip install torch torchvision && \
git clone https://github.com/megvii-research/ECCV2022-RIFE.git && \
cd ECCV2022-RIFE && \
pip install -r requirements.txt

Installation

Option 1: Python Package

pip install rife-ncnn-vulkan-python

Option 2: From Source

git clone https://github.com/megvii-research/ECCV2022-RIFE.git
cd ECCV2022-RIFE
pip install -r requirements.txt

Basic Usage

Double Frame Rate

python inference_video.py --exp=1 --video=input.mp4

# Output: 2x FPS

4x Frame Rate

python inference_video.py --exp=2 --video=input.mp4

# Output: 4x FPS

8x Frame Rate

python inference_video.py --exp=3 --video=input.mp4

# Output: 8x FPS

Python API

Load Model

import torch
from model.RIFE import Model

device = torch.device("cuda")
model = Model()
model.load_model('./train_log', -1)
model.eval()
model.device()

Interpolate Single Frame

import cv2
import numpy as np
import torch

def interpolate_frames(frame1, frame2, model, num_frames=1):
    """Interpolate frames between two images"""
    # Prepare tensors
    img0 = torch.from_numpy(frame1).permute(2, 0, 1).float() / 255.0
    img1 = torch.from_numpy(frame2).permute(2, 0, 1).float() / 255.0

    img0 = img0.unsqueeze(0).cuda()
    img1 = img1.unsqueeze(0).cuda()

    # Interpolate
    with torch.no_grad():
        middle = model.inference(img0, img1)

    # Convert back
    middle = (middle[0] * 255).byte().cpu().numpy().transpose(1, 2, 0)
    return middle

# Load frames
frame1 = cv2.imread('frame1.png')
frame2 = cv2.imread('frame2.png')

# Get interpolated frame
middle_frame = interpolate_frames(frame1, frame2, model)
cv2.imwrite('interpolated.png', middle_frame)

Process Video

import cv2
import torch
from model.RIFE import Model

model = Model()
model.load_model('./train_log', -1)
model.eval()
model.device()

def process_video(input_path, output_path, multiplier=2):
    cap = cv2.VideoCapture(input_path)
    fps = cap.get(cv2.CAP_PROP_FPS)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    out = cv2.VideoWriter(
        output_path,
        cv2.VideoWriter_fourcc(*'mp4v'),
        fps * multiplier,
        (width, height)
    )

    ret, prev_frame = cap.read()
    if not ret:
        return

    out.write(prev_frame)

    while True:
        ret, curr_frame = cap.read()
        if not ret:
            break

        # Prepare tensors
        img0 = torch.from_numpy(prev_frame).permute(2, 0, 1).float() / 255.0
        img1 = torch.from_numpy(curr_frame).permute(2, 0, 1).float() / 255.0

        img0 = img0.unsqueeze(0).cuda()
        img1 = img1.unsqueeze(0).cuda()

        # Generate intermediate frames
        for i in range(multiplier - 1):
            t = (i + 1) / multiplier
            with torch.no_grad():
                middle = model.inference(img0, img1, timestep=t)
            middle = (middle[0] * 255).byte().cpu().numpy().transpose(1, 2, 0)
            out.write(middle)

        out.write(curr_frame)
        prev_frame = curr_frame

    cap.release()
    out.release()

process_video('input.mp4', 'output_60fps.mp4', multiplier=2)

Using rife-ncnn-vulkan

Faster NCNN implementation:

from rife_ncnn_vulkan import Rife

rife = Rife(gpu_id=0)

# Interpolate
frame1 = Image.open('frame1.png')
frame2 = Image.open('frame2.png')
middle = rife.process(frame1, frame2)
middle.save('interpolated.png')

Video Processing

from rife_ncnn_vulkan import Rife
import cv2
from PIL import Image

rife = Rife(gpu_id=0, num_threads=4)

def interpolate_video(input_path, output_path, factor=2):
    cap = cv2.VideoCapture(input_path)
    fps = cap.get(cv2.CAP_PROP_FPS)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    out = cv2.VideoWriter(
        output_path,
        cv2.VideoWriter_fourcc(*'mp4v'),
        fps * factor,
        (width, height)
    )

    ret, prev = cap.read()
    out.write(prev)

    while True:
        ret, curr = cap.read()
        if not ret:
            break

        # Convert to PIL
        prev_pil = Image.fromarray(cv2.cvtColor(prev, cv2.COLOR_BGR2RGB))
        curr_pil = Image.fromarray(cv2.cvtColor(curr, cv2.COLOR_BGR2RGB))

        # Interpolate
        for i in range(factor - 1):
            t = (i + 1) / factor
            mid = rife.process(prev_pil, curr_pil, timestep=t)
            mid_cv = cv2.cvtColor(np.array(mid), cv2.COLOR_RGB2BGR)
            out.write(mid_cv)

        out.write(curr)
        prev = curr

    cap.release()
    out.release()

Slow Motion

Create smooth slow motion:


# Original: 30 fps, 10 seconds = 300 frames

# 8x interpolation: 2400 frames

# Play at 30 fps: 80 seconds (8x slower)

python inference_video.py --exp=3 --video=input.mp4

# This creates 8x frames, play at original FPS for slow motion

Slow Motion Script

def create_slow_motion(input_path, output_path, slowdown_factor=4):
    """Create slow motion video"""
    cap = cv2.VideoCapture(input_path)
    original_fps = cap.get(cv2.CAP_PROP_FPS)

    # Interpolate to get more frames
    exp = int(np.log2(slowdown_factor))
    interpolate_video(input_path, 'temp_interpolated.mp4', factor=slowdown_factor)

    # Re-encode at original FPS
    cap2 = cv2.VideoCapture('temp_interpolated.mp4')
    width = int(cap2.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap2.get(cv2.CAP_PROP_FRAME_HEIGHT))

    out = cv2.VideoWriter(
        output_path,
        cv2.VideoWriter_fourcc(*'mp4v'),
        original_fps,  # Keep original FPS
        (width, height)
    )

    while True:
        ret, frame = cap2.read()
        if not ret:
            break
        out.write(frame)

    cap.release()
    cap2.release()
    out.release()

Batch Processing

import os
from concurrent.futures import ThreadPoolExecutor

def process_single(input_path, output_dir, factor=2):
    filename = os.path.basename(input_path)
    output_path = os.path.join(output_dir, f"interpolated_{filename}")
    interpolate_video(input_path, output_path, factor)
    return output_path

input_dir = './videos'
output_dir = './interpolated'
os.makedirs(output_dir, exist_ok=True)

videos = [os.path.join(input_dir, f) for f in os.listdir(input_dir)
          if f.endswith(('.mp4', '.mkv', '.avi'))]

for video in videos:
    result = process_single(video, output_dir, factor=2)
    print(f"Completed: {result}")

Quality Settings

Model Versions

Model

Quality

Speed

RIFE v4.6

Best

Slow

RIFE v4.0

Great

Medium

RIFE-NCNN

Good

Fastest

UHD Mode

For 4K+ videos:

python inference_video.py --exp=1 --video=input.mp4 --UHD

Memory Optimization

For Limited VRAM


# Process in tiles
from model.RIFE import Model

model = Model()
model.load_model('./train_log', -1)
model.eval()

# Set tile size for large frames

# model.inference handles tiling internally

Reduce Memory


# Use NCNN version (more memory efficient)
pip install rife-ncnn-vulkan-python

Performance

Resolution

GPU

2x Interp FPS

1080p

RTX 3090

~60 fps

1080p

RTX 4090

~100 fps

RTX 3090

~15 fps

RTX 4090

~30 fps

Troubleshooting

Artifacts/Ghosting

Use scene detection to skip cuts
Reduce interpolation factor
Check for fast motion

Out of Memory

Use NCNN version
Process at lower resolution, upscale after
Reduce batch size

Slow Processing

Use NCNN-Vulkan version
Enable GPU acceleration
Use smaller model

Scene Detection

Skip interpolation across scene cuts:

from scenedetect import detect, ContentDetector

scenes = detect('input.mp4', ContentDetector())

# Don't interpolate between scenes
for scene in scenes:
    print(f"Scene: {scene[0].get_frames()} - {scene[1].get_frames()}")

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

GPU

Hourly Rate

Daily Rate

4-Hour Session

RTX 3060

~$0.03

~$0.70

~$0.12

RTX 3090

~$0.06

~$1.50

~$0.25

RTX 4090

~$0.10

~$2.30

~$0.40

A100 40GB

~$0.17

~$4.00

~$0.70

A100 80GB

~$0.25

~$6.00

~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

Save money:

Use Spot market for flexible workloads (often 30-50% cheaper)
Pay with CLORE tokens
Compare prices across different providers

Next Steps

FFmpeg NVENC - Encode output
Real-ESRGAN - Upscale video
AI Video Generation - Generate videos

PreviousFFmpeg NVENC NextOverview

Last updated 18 days ago

Was this helpful?

hashtagRenting on CLORE.AI

hashtagAccess Your Server

hashtagWhat is RIFE?

hashtagQuick Deploy

hashtagInstallation

hashtagOption 1: Python Package

hashtagOption 2: From Source

hashtagBasic Usage

hashtagDouble Frame Rate

hashtag4x Frame Rate

hashtag8x Frame Rate

hashtagPython API

hashtagLoad Model

hashtagInterpolate Single Frame

hashtagProcess Video

hashtagUsing rife-ncnn-vulkan

hashtagVideo Processing

hashtagSlow Motion

hashtagSlow Motion Script

hashtagBatch Processing

hashtagQuality Settings

hashtagModel Versions

hashtagUHD Mode

hashtagMemory Optimization

hashtagFor Limited VRAM

hashtagReduce Memory

hashtagPerformance

hashtagTroubleshooting

hashtagArtifacts/Ghosting

hashtagOut of Memory

hashtagSlow Processing

hashtagScene Detection

hashtagCost Estimate

hashtagNext Steps

Renting on CLORE.AI

Access Your Server

What is RIFE?

Quick Deploy

Installation

Option 1: Python Package

Option 2: From Source

Basic Usage

Double Frame Rate

4x Frame Rate

8x Frame Rate

Python API

Load Model

Interpolate Single Frame

Process Video

Using rife-ncnn-vulkan

Video Processing

Slow Motion

Slow Motion Script

Batch Processing

Quality Settings

Model Versions

UHD Mode

Memory Optimization

For Limited VRAM

Reduce Memory

Performance

Troubleshooting

Artifacts/Ghosting

Out of Memory

Slow Processing

Scene Detection

Cost Estimate

Next Steps