lumina-dsh

LIMITATIONS

Known limitations

- Once 8-bit screenshot highlights clip (e.g. a fully white sky), the original detail is irreversible; `hdrsynth.py` **infers/paints** rather than restores, and does not guarantee semantic correctness. - `ps_grade` supports Windows + Adobe Photoshop only; `ps_measure` / the vision tools and metric computation are cross-platform. - Style LUTs need the local Photoshop-bundled `Presets/3DLUTs`; a trimmed Photoshop install without LUT files will skip styles. - LUTs are not written into the PSD (the PSD is the pre-LUT version); the final PNG is the version with the LUT. - **Super-resolution is only preprocessing**: the built-in Lanczos does not add real detail; the quality/licence of external AI super-resolution depends on the tool you hook up — the plugin bundles no models and makes no semantic guarantee about super-resolution results. A missing/failing engine or a corrupt frame falls back automatically and never blocks output. - **Very large but soft images are not auto-upscaled** (only a "consider AI restore" hint), avoiding a pointless 12MP→48MP blow-up; only small/low-resolution images are auto-upscaled. - Phone-photo **EXIF orientation is auto-normalised** (portrait shots are no longer processed sideways), provided the file still carries the orientation tag; images with completely stripped metadata can only be processed as raw pixels. - Auto classification is heuristic: if a complex image is misclassified as a screenshot (or vice versa), override it explicitly with `crop` / `auto.scene` / `recipe`. - The `depth` mask only layers by frame height; near/far objects at the same height cannot be distinguished; use `polygon` / `alpha` for intricate scenes. - AI "Select Subject" may pull neighbouring objects into the subject in complex scenes (the result reports the AI selection area as a hint); narrow it with a hand-drawn polygon. - Auto mode is a first draft; when it is not satisfying, append versions with `params` + `project` and keep iterating. - Cold-starting Photoshop takes ~6–10 seconds; warm, each image is ~5 seconds (4K images take longer; super-resolution adds extra Python processing time and disk usage).