feat(server): transcoding hardware acceleration (#3171)

* added transcode configs for nvenc,qsv and vaapi

* updated dev docker compose

* added software fallback

* working vaapi

* minor fixes and added tests

* updated api

* compile libvips

* move hwaccel settings to `hwaccel.yml`

* changed default dockerfile, moved `readdir` call

* removed unused import

* minor cleanup

* fix for arm build

* added documentation, minor fixes

* added intel driver

* updated docs

styling

* uppercase codec and api names

* formatting

* added tests

* updated docs

* removed semicolons

* added link to `hwaccel.yml`

* added newlines

* added `hwaccel` section to docker-compose.prod.yml

* ensure mesa drivers are installed

* switch to mimalloc for sharp

* moved build version and sha256 to json

* let libmfx set the render device

* possible fix for vp9 on qsv

* updated tests

* formatting

* review suggestions

* semicolon

* moved `LD_PRELOAD` to start script

* switched to jellyfin's ffmpeg package

* fixed dockerfile

* use cqp instead of icq for qsv vp9

* updated dockerfile

* added sha256sum for other platforms

* fixtures

docs/docs/features/hardware-transcoding.md

@@ -0,0 +1,60 @@
+# Hardware Transcoding [Experimental]
+
+This feature allows you to use a GPU or Intel Quick Sync to accelerate transcoding and reduce CPU load.
+Note that hardware transcoding is much less efficient for file sizes.
+As this is a new feature, it is still experimental and may not work on all systems.
+
+## Supported APIs
+
+- NVENC
+  - NVIDIA GPUs
+- Quick Sync
+  - Intel CPUs
+- VAAPI
+  - GPUs
+
+## Limitations
+
+- The instructions and configurations here are specific to Docker Compose. Other container engines may require different configuration.
+- Only Linux and Windows (through WSL2) servers are supported.
+- WSL2 does not support Quick Sync.
+- Raspberry Pi is currently not supported.
+- Two-pass mode is only supported for NVENC. Other APIs will ignore this setting.
+- Only encoding is currently hardware accelerated, so the CPU is still used for software decoding.
+  - This is mainly because the original video may not be hardware-decodable.
+- Hardware dependent
+  - Codec support varies, but H.264 and HEVC are usually supported.
+    - Notably, NVIDIA and AMD GPUs do not support VP9 encoding.
+  - Newer devices tend to have higher transcoding quality.
+
+## Prerequisites
+
+#### NVENC
+
+- You must have the official NVIDIA driver installed on the server.
+- On Linux (except for WSL2), you also need to have [NVIDIA Container Runtime][nvcr] installed.
+
+#### QSV
+
+- For VP9 to work:
+  - You must have a 9th gen Intel CPU or newer
+  - If you have an 11th gen CPU or older, then you may need to follow [these][jellyfin-lp] instructions as Low-Power mode is required
+  - Additionally, if the server specifically has an 11th gen CPU and is running kernel 5.15 (shipped with Ubuntu 22.04 LTS), then you will need to upgrade this kernel (from [Jellyfin docs][jellyfin-kernel-bug])
+
+## Setup
+
+1. If you do not already have it, download the latest [`hwaccel.yml`][hw-file] file and ensure it's in the same folder as the `docker-compose.yml`.
+2. Uncomment the lines that apply to your system and desired usage.
+3. In the `docker-compose.yml` under `immich-microservices`, uncomment the lines relating to the `hwaccel.yml` file.
+4. Redeploy the `immich-microservices` container with these updated settings.
+5. In the Admin page under `FFmpeg settings`, change the hardware acceleration setting to the appropriate option and save.
+
+## Tips
+
+- You may want to choose a slower preset than for software transcoding to maintain quality and efficiency
+- While you can use VAAPI with Nvidia GPUs and Intel CPUs, prefer the more specific APIs since they're more optimized for their respective devices
+
+[hw-file]: https://github.com/immich-app/immich/releases/latest/download/hwaccel.yml
+[nvcr]: https://github.com/NVIDIA/nvidia-container-runtime/
+[jellyfin-lp]: https://jellyfin.org/docs/general/administration/hardware-acceleration/intel/#configure-and-verify-lp-mode-on-linux
+[jellyfin-kernel-bug]: https://jellyfin.org/docs/general/administration/hardware-acceleration/intel/#known-issues-and-limitations