Note

⚠️ This is an heavily vibe-coded proof of concept — do not expose it to the internet — use it at your own risk. Github repo is a mirror of https://git.djeex.fr/Djeex/insta-hdr-converter. You'll find full package, history and release note there.

Instagram HDR Assembler

A small web front end for assembling Instagram-compatible HDR JPEGs (gain map) from an SDR export and an HDR export out of Lightroom or Camera Raw.

The gain-map assembly logic (app/assembler.py) is adapted from kostis-kounadis/instagram-hdr-assembler, itself based on the reverse-engineering work of karachungen/instagram-hdr-converter. Original MIT license kept in LICENSE.

Only the upstream's Method 2 (starting from an HDR JPEG that already carries a gain map) is implemented here. Method 1's AVIF decoding and its quality/transfer/gamut settings were dropped, but the HDR side can still be a 32-bit float linear TIFF (Lightroom's HDR TIFF export) instead of a gain-map JPEG for a more precise result. Either way the gain map is recomputed rather than reused as-is (see below), since it needs to be correct against the SDR file you actually provide.

How it works

1. Upload an SDR file (.jpg/.jpeg) and an HDR file: either a gain-map JPEG (.jpg/.jpeg) or Lightroom's 32-bit float linear HDR TIFF export (.tif/.tiff).
2. Both files must have identical dimensions and match one of Instagram's feed resolutions exactly: 1080x1080 (1:1), 1080x1350 (4:5), or 1080x566 (1.91:1).
3. The real HDR pixel data is obtained from the HDR file: decoded back out of a gain-map JPEG (ultrahdr_app, undoing its own embedded base image and gain map), or read directly from a linear TIFF and given HLG's system-gamma OOTF so it's on the same display-referred scale a gain-map JPEG's HDR intent would be. A gain map is only correct against the exact base it was computed from, and that's rarely the SDR file you upload, so reusing an existing one as-is would quietly reconstruct the wrong HDR look. Your SDR file is cleaned up and re-encoded at 4:2:0 chroma subsampling (Pillow), then a fresh gain map is computed against it and packaged into the final UltraHDR JPEG (ultrahdr_app). The output's visible image is exactly your SDR file; only the gain map is recomputed. The TIFF route skips the gain-map JPEG's 8-bit-in-8-bit precision ceiling, giving a more accurate result.
4. The validation report is shown on the result page, including the "GainMapMin is negative" warning when present, with a download link for the finished JPEG.

Uploads and results don't stick around: SDR/HDR originals are deleted right after conversion, the result a few seconds after you leave the result page (heartbeat + background reaper), and jobs/ is wiped on every app startup.

Running locally, without Docker

pip install -r requirements.txt
# also install exiftool and compile libultrahdr, see docker/Dockerfile
# for the exact packages and commands (validated on Alpine)
PORT=5050 python3 main.py   # PORT is optional, defaults to 5000

Running with Docker

docker compose -f docker/compose.yaml up -d --build

Opens on http://<host>:5050 (port 5000 collides with AirPlay Receiver on macOS, hence 5050 by default; change it in docker/compose.yaml if needed).

Running this on the open internet

Don't do it.