ComfyUI-Lee-RIFE

Fast, GPU-batched RIFE frame interpolation for ComfyUI — a self-contained node with the weights bundled, no extra dependencies.

What it does

• Runs RIFE (rife49.pth, architecture v4.7) on the GPU in batches instead of one frame-pair at a time.
• Enables RIFE's real bidirectional ensemble (a forward and a time-reversed pass, averaged) for cleaner motion.
• fp16 by default (visually identical to fp32 for interpolation, roughly 2× faster on modern NVIDIA cards); fp32 selectable.
• Automatically halves the batch on out-of-memory, so it scales from small cards to big ones.

Requirements

• ComfyUI
• An NVIDIA GPU with a CUDA build of PyTorch (the same one ComfyUI already uses).
• No extra pip installs — it only uses what ComfyUI provides.

Install

1. Copy the ComfyUI-Lee-RIFE folder into ComfyUI/custom_nodes/ (or git clone it there).
2. Make sure models/rife49.pth is present (it ships with this repo; see models/HOW-TO-ADD-WEIGHTS.txt if it is missing).
3. Restart ComfyUI completely (the process, not just the browser tab).

The node appears as “RIFE GPU Fast (Lee)” under the Lee/RIFE category.

Usage

Wire your frames into frames. For doubling 15 fps → 30 fps, set multiplier = 2 and set your video-output node to 30 fps.

|Parameter|Meaning| |-|-| |multiplier|Output frames per input frame (2 = double the frame rate).| |ensemble|Bidirectional pass (better quality, ~2× the work). On by default.| |scale\_factor|Internal flow scale; leave at 1.0 unless resolution is very high.| |precision|fp16 (fast) or fp32 (bit-faithful to the original node).| |batch\_size|Frame-pairs per GPU batch. Higher = better GPU use; lowers itself on OOM.|

Output count: for multiplier = m and N input frames you get m × N frames (e.g. 81 frames at 15 fps → 162 frames at 30 fps, same duration).

On the first run you will see a console line like \[ComfyUI-Lee-RIFE] loaded rife49.pth (arch 4.7) on cuda:0 / torch.float16, confirming it is on the GPU.

Credits & license

This node's own code is MIT (see LICENSE), © 2026 Smai-Lee.

It builds on:

• RIFE / Practical-RIFE (Zhewei Huang et al.) — the model and architecture (MIT).
• ComfyUI-Frame-Interpolation (Fannovel16) — rife_arch.py is vendored from it (MIT).

Full attribution and the upstream license texts are in THIRD-PARTY-NOTICES.md. Please cite the RIFE paper if you use this in research (citation in the notices file).