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# Flowframes - Windows GUI for Video Interpolation # Flowframes - Windows GUI for Video Interpolation
Flowframes Windows GUI for video interpolation - Supports RIFE, RIFE-NCNN, DAIN-NCNN, CAIN-NCNN networks. Flowframes Windows GUI for video interpolation - Supports RIFE, RIFE-NCNN, DAIN-NCNN networks.
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. 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.
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* 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. * 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.
* Run Flowframes.exe * Run Flowframes.exe
* Select the components you want to install (certain packages are required, cannot be unticked) * Pre-1.18: Select the components you want to install (certain packages are required, cannot be unticked)
Starting with 1.18, the installer has been removed, and Flowframes is instead distributed as an all-in-one archive. Download the "Full" file if you are using a Maxwell/Pascal/Turing GPU and want to use embedded Pytorch. Use "NoPython" if you run an AMD GPU or want to use your system Python/Pytorch installation.
## Using A Pytorch AI ## Using A Pytorch Implementation
Some of the AI networks run on Tencent's NCNN framework, which allows them to run on any modern (Vulkan-capable) GPU. Some of the AI networks run on Tencent's NCNN framework, which allows them to run on any modern (Vulkan-capable) GPU.
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## Running A Pytorch AI on Nvidia Ampere (RTX 3000) GPUs ## Running A Pytorch Implementation on Nvidia Ampere GPUs
I do not have an Ampere card yet, so I can't fully test Flowframes on an RTX 3000 series GPU. I do not have an Ampere card yet, so I can't fully test Flowframes on an RTX 3000 series GPU.
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* You can disable this completely if you only use content without duplicates (e.g. camera footage, CG renders). * You can disable this completely if you only use content without duplicates (e.g. camera footage, CG renders).
* Animation Loop: This will make looped animations interpolate to a perfect loop by copying the first frame to the end of the frames. * Animation Loop: This will make looped animations interpolate to a perfect loop by copying the first frame to the end of the frames.
* Don't Interpolate Scene Changes: This avoids interpolating scene changes (cuts) as this would produce weird a morphing effect. * Don't Interpolate 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.
* Save Output Frames As JPEG: Save interpolated frames as JPEG before encoding. Not recommended unless you have little disk space. * Save Output Frames As JPEG: Save interpolated frames as JPEG before encoding. Not recommended unless you have little disk space.
### AI Specific Settings ### AI Specific Settings
* RIFE - Use Fast Parallel Mode - Speeds up RIFE interpolation a lot if you have lots of VRAM. Not recommended with <8GB GPUs. * 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. * 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. * NCNN Processing Threads: Increasing this number to 2, 3 or 4 can improve GPU utilization, but also slow things down.