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Add Vivid; Remove enhancr (unmaintained) from FAQ #500

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Add Vivid; Remove enhancr (unmaintained) from FAQ #500

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Add Vivid; Remove enhancr (unmaintained) from FAQ
adhityanadooli Apr 1, 2026
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Fix Vivid link in FAQ
adhityanadooli Apr 1, 2026
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9 changes: 5 additions & 4 deletions docs/faq.md
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Expand Up @@ -26,14 +26,15 @@ There are many programs that can perform AI upscaling, though not all are able t
- [Download](https://chainner.app/download) | [GitHub](https://github.com/chaiNNer-org/chaiNNer) | [Website](https://chainner.app/)
- chaiNNer is a node-based image processing GUI that can also be used for image upscaling. It has the most support for models listed on OpenModelDB and can be run with PyTorch (CUDA), ONNX (CUDA), and NCNN (AMD/Intel). Being node based, it allows you to process your images with a lot more control, at the cost of being a bit more complex. Besides upscaling, chaiNNer also has a variety of other use cases and is a very versatile program.

- **Vivid** (closed-source | paid)
- [Website](https://vividenhance.com/) | [GitHub (Extension SDK)](https://github.com/dusklaboratory/vivid-core)
- Desktop AI video processing platform focused on upscaling, frame interpolation, and restoration. It supports multiple runtimes including TensorRT, NCNN, DirectML, CoreML, and ONNX Runtime, enabling efficient processing across NVIDIA, AMD, Intel, and Apple Silicon hardware. Built-in model hub and custom model support!
- Unlike traditional tools, Vivid is built around pipelines and queue-based workflows, allowing users to chain operations like upscaling, interpolation, and restoration into a single process. It also supports optional cloud acceleration for significantly faster processing when local hardware is insufficient.
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Copilot AI Apr 1, 2026

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The Vivid entry is marked closed-source/paid but only links to the GitHub “Extension SDK”. This makes it hard for readers to find the actual product/download, and may imply the app itself is open-source. Consider adding an official website/download link (and keeping the SDK link as a secondary reference), and briefly clarifying whether/how Vivid can load OpenModelDB model formats (e.g., ONNX/PyTorch) so users know if it works with models from this site.

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- Unlike traditional tools, Vivid is built around pipelines and queue-based workflows, allowing users to chain operations like upscaling, interpolation, and restoration into a single process. It also supports optional cloud acceleration for significantly faster processing when local hardware is insufficient.
- Unlike traditional tools, Vivid is built around pipelines and queue-based workflows, allowing users to chain operations like upscaling, interpolation, and restoration into a single process. It also supports optional cloud acceleration for significantly faster processing when local hardware is insufficient. Vivid can load ONNX (`.onnx`) models from OpenModelDB via its ONNX Runtime/TensorRT backends; PyTorch (`.pth`) models can be converted to ONNX for use in Vivid, while NCNN (`.bin`/`.param`) models are not supported directly.

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- **AnimeJaNaiConverterGui** (open-source | free)
- [GitHub](https://github.com/the-database/AnimeJaNaiConverterGui)
- Allows for fast video upscaling utilizing TensorRT (On Nvidia cards), DirectML, or NCNN within a clean GUI. Only supports ONNX. Use chaiNNer to convert PyTorch models to ONNX for usage.

- **enhancr** (open-source | paid)
- [GitHub](https://github.com/mafiosnik777/enhancr)
- Meant for fast video frame upscaling and interpolation, enhancr takes advantage of Nvidia's TensorRT to provide fast processing on video. It supports a variety of models, including many of the custom ESRGAN ones from OpenModelDB.

- **VSGAN-TensorRT-docker** (open-source | free)
- [GitHub](https://github.com/styler00dollar/VSGAN-tensorrt-docker)
- A more complicated but free alternative for fast video upscaling with TensorRT. Supports an insane amount of models, but requires manual parameter selection.
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