Skip to content

v11 RIFE support
Compare
Choose a tag to compare
@github-actions github-actions released this 26 Oct 00:37
· 243 commits to master since this release
v11
afa5399

Added support for the RIFE video frame interpolation algorithm.

There are two APIs for RIFE:

  • vsmlrt.RIFE is a high-level API for interpolating a clip. set the multi argument to specify the fps factor. Just remember to perform scene detection on the input clip.
  • vsmlrt.RIFEMerge is a novel temporal std.MaskedMerge-like interface for RIFE. Use it if you want to precisely control the frames and/or time point for the interpolation.

Known issues

  • vstrt doesn't support RIFE for the moment1. The next release of TensorRT should include RIFE support and we will release v12 when that happens.

  • vstrt backend also doesn't yet support latest RTX 4000 series GPUs. This will be fixed after upgrading to the upcoming TensorRT 8.5 release. RTX 4000 series GPU owners please use other the other CUDA backends.

  • Users of the OV_GPU backend may experience errors like Exceeded max size of memory object allocation: Requested 11456040960 bytes but max alloc size is 4294959104 bytes. Please consider tiling for now.

    The reason is that the openvino library follows the opencl standard on memory object allocation restriction (CL_DEVICE_MAX_MEM_ALLOC_SIZE). For most existing intel gpus (gen9 and later), the driver imposes a maximum allocation size of ~4GiB2.

  1. It's missing grid_sample operator support, see https://github.com/onnx/onnx-tensorrt/blob/main/docs/operators.md. ↩

  2. this value is derived from here, which states that device not supporting sharedSystemMemCapabilities has a maximum allowed allocation size of 4294959104 bytes ↩

Assets 12
Loading