RIFE: Real-Time Video Frame Interpolation

RIFE (Real-Time Intermediate Flow Estimation) is the frame interpolation model from the ECCV 2022 paper "Real-Time Intermediate Flow Estimation for Video Frame Interpolation" by Huang et al. Given two consecutive video frames, it synthesizes new frames in between, so a clip can be converted to a higher frame rate for smoother motion or stretched into fluid slow motion. It is the most widely used open frame interpolation model, powering interpolation features in many video enhancement tools and generative video pipelines.

Overview

Classical frame interpolation methods first estimate bidirectional optical flow between the two input frames and then warp and blend them, which is slow and produces artifacts at motion boundaries. RIFE instead trains a dedicated network, IFNet, to estimate the intermediate flows directly - the flows from the in-between moment to each of the two input frames - in a single coarse-to-fine pass. A privileged distillation scheme, where a teacher that can peek at the ground-truth middle frame supervises the student, stabilizes training and sharpens results. The result is a model that runs in real time on ordinary GPUs while remaining competitive in quality with much heavier interpolation networks.

This template serves RIFE 4.9 from the practical 4.x line of RIFE checkpoints, which the author maintains as an actively improved successor to the original paper release, tuned for robustness on real-world footage and animation rather than only on benchmarks.

Key Features

• Arbitrary-timestep interpolation - The 4.x models synthesize a frame at any point between two inputs, enabling 2x, 4x, 8x, or non-integer frame rate conversion from a single model
• Real-time performance - The model is compact and interpolates HD frames in a fraction of a second, so full clips process quickly
• Strong motion handling - End-to-end intermediate flow estimation keeps motion boundaries clean where warp-and-blend approaches produce ghosting
• Works on real footage and animation - The practical checkpoint line is trained for robustness across live action, rendered content, and anime

Use Cases

• Frame rate upscaling: convert 24 or 30 fps footage to 60 fps or higher for smooth playback
• Slow motion: multiply frames while keeping the original frame rate to stretch a clip in time
• Smoothing AI-generated video: interpolate the low-fps output of text-to-video and image-to-video models into fluid clips
• Restoring choppy or low-frame-rate sources such as animation, screen recordings, and archival footage
• Batch video processing through the ComfyUI API for automated pipelines

Architecture

RIFE is built around IFNet, a lightweight fully convolutional network that estimates intermediate optical flow coarse-to-fine across three resolution scales, refining the flow field and a fusion mask at each stage. The two input frames are backward-warped by the estimated flows and blended by the mask, and a small refinement module cleans up the residual details. Because the flow is predicted directly for the target timestep rather than derived from bidirectional flow, the whole pipeline is a single feed-forward pass, which is what makes real-time operation possible.

On This Template

The template runs RIFE inside ComfyUI using the ComfyUI-Frame-Interpolation node pack, with the RIFE 4.9 checkpoint (rife49.pth) preloaded. A ready-made workflow is preloaded along with a sample clip: the video is split into frames, the RIFE VFI node doubles them, and the result is reassembled and saved at twice the original frame rate. Set the multiplier on the RIFE VFI node for 4x or higher interpolation, and set the frame rate on the Create Video node to match: source frame rate times multiplier for smooth motion, or the original frame rate for slow motion. Keep rife49.pth selected in the RIFE VFI node: the node pack's download of rife426.pth currently fails to load (a known issue in the node pack). Upload your own video in the Load Video node to process your footage, and use the bundled API wrapper payload for programmatic interpolation jobs.