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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Flowframes Windows GUI for video interpolation using DAIN (NCNN) or RIFE (CUDA/NCNN)
| Date | Stars |
|---|---|
| 2026-07-24 | 2002 |
| 2026-07-25 | 2002 |
| 2026-07-28 | 2002 |
| 2026-07-30 | 2002 |
| 2026-08-06 | 2002 |
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# Flowframes - Windows GUI for Video Interpolation ## [itch.io (Free Old Builds)](https://nmkd.itch.io/flowframes) | [Patreon (Latest Builds)](https://www.patreon.com/n00mkrad) | [Discord](https://discord.com/invite/eJHD2NSJRe) Flowframes Windows GUI for video interpolation - Supports RIFE (Pytorch & NCNN), DAIN (NCNN), and FLAVR (Pytorch) implementations. Flowframes is **open-source donationware**. Builds are released for free on itch after an early-access period on Patreon. This repo's code is complete and does not "paywall" experienced users who want to compile the program themselves or want to contribute to the development. However, **I do not provide support for self-built versions** as I can't guarantee that the code of this repo is stable at any given moment.  ## Quick Installation * Download on [itch](https://nmkd.itch.io/flowframes) or, for the most recent beta versions, on [Patreon](https://www.patreon.com/n00mkrad). This repo does not provide builds. * Follow the instructions in the installer and wait for it to complete * Run Flowframes ## Using A Pytorch Implementation Flowframes comes with RIFE-NCNN which runs on Tencent's NCNN framework, which allows it to run on any modern (Vulkan-capable) GPU. However, the official RIFE implementation run best via its original Pytorch implementation. The requirements to run these are the following: * A **modern Nvidia GPU** (750 Ti, 900/1000/1600/2000/3000/4000 Series). * A **Python** installation including Pytorch (1.5 or later) as well as the packages `opencv-python`, `sk-video`, `imageio`. * The Flowframes Installer will automatically download all dependencies by default if these requirements are not fullfilled. [More Details On Python Dependencies](PythonDependencies.md) ## Configuration All Settings have reasonable defaults, so users do not need to do any configuration before using the program. Here is an explanation of some of the more important settings. ### Application * Processing Style: Either run all steps at once, or each step manually, in case you want to edit frames, or deduplicate manually. * Maximum Video Size: Frames are exported at this resolution if the video is larger. Lower resolutions speed up interpolation a lot. * Export Name Pattern: Customize the pattern of the filenames of outputs using variables. ### Interpolation * Input Media To Preserve: Toggle transfer of Audio, Subtitles and MKV Metadata. * Enable Transparency: Interpolate transparency. Only active if the input **and** output support transparency (PNG/GIF). * Import HQ JPEGs: Will extract JPEG instead of PNG frames from videos. Fast and lightweight, but with a tiny (invisible) quality loss. * Frame De-Duplication: This is meant for 2D animation. Removing duplicates makes a smooth interpolation possible. * You should disable this completely if you only use content without duplicates (e.g. camera footage, CG renders). * "During Extraction" works for most content. Use "Accurate (After Extraction)" for fine-tuning the sensitivity. * Loop Interpolation: This will make looped animations interpolate to a perfect loop by interpolating back to the first frame at the end. * Fix Scene Changes: This avoids interpolating scene changes (cuts) as this would produce weird a morphing effect. * Auto-Encode: Encode video while interpolating. Optionally delete the already encoded frames to minimize disk space usage. ### AI Specific Settings * RIFE - UHD Mode - This mode changes some scaling parameters and should improve results on high-resolution video. * GPU IDs: `0` is the default for setups with one dedicated GPU. Four dedicated GPUs would mean `0,1,2,3` for example. * NCNN Processing Threads: Increasing this number to 2, 3 or 4 can improve GPU utilization, but also slow things down. * RIFE CUDA Fast Mode: Utilizes Half-Precision (fp16) to speed things up and reduce VRAM usage, but can be unstable. ### Video Export * Encoding Options: Set o
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2c33128746197787, topic:pytorch