Add Vivid; Remove enhancr (unmaintained) from FAQ#500
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adhityanadooli wants to merge 2 commits intoOpenModelDB:mainfrom
Open
Add Vivid; Remove enhancr (unmaintained) from FAQ#500adhityanadooli wants to merge 2 commits intoOpenModelDB:mainfrom
adhityanadooli wants to merge 2 commits intoOpenModelDB:mainfrom
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Removed enhancr entry from the FAQ section. Vivid successes Enhancr
Updated the Vivid entry with a new website link.
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Pull request overview
Updates the FAQ’s “Maintained Programs” list to replace the unmaintained enhancr entry with Vivid, keeping the recommended upscaling software list current.
Changes:
- Added a new Vivid entry (closed-source | paid) to the maintained programs list.
- Removed the enhancr entry from the FAQ.
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| - **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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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.
Suggested change
| - 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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Removed enhancr entry from the FAQ section. Vivid successes Enhancr