COLORIZE TRUER (Variant 6)

Higher Rank (64) Training

Low(ish) Rank Adapter (LoRA) for Flux Kontext

Towards Consistent + Promptless Kontext Photo Colorization

|||| By SilverAgePoets.com ||||

Our aim with this checkpoint, & other Colorize Truer/+ LoRAs, is to help restore Kontext Dev towards the far more impressive photo colorization capabilities of Kontext Pro/Max, whilst further reinforcing task-specific reliability & realism at the standards of professional manual colorization.
This adapter was trained with AI Toolkit by Ostris over roughly 600 grayscale/colorized pairings of real archival photos.

The colorized (output) portion of the custom dataset we consolidated to train these "Colorize Truer" LoRAs consists of:
Open use manual/professional colorizations of historical photos (roughly half of the data).
Colorized outputs from Flux Kontext Max & Pro (the rest of the data).

The source photos were sourced from public domain, and represent a broad scope of geographical, cultural, aesthetic, historical (from late 1830s thru today), & other paradigms/contexts.

Trigger words

No trigger words neccessary.
With that said, with some inputs, it may be neccessary to specify colorize or colorize and upscale this photo. Leave all else the same. as the instruction prompt.
One may also prompt in suggestions/corrections/directions, as far as specific choices of color.
To "modernize" outputs, one may try: Colorize and upscale the photo. Restore crisp detailing. Rich tonality. Make indistinguishable from top quality UHD 50MP+ or 12k+ pro 2025 DSLR camera colorized_02.CR2 photo. restore blurred details. Retain facial features with great fidelity source. Add fine detailing only where missing. Render real life like tones. Leave unchanged all else about photo., etc...

Further usage tips:

For many inputs, a somewhat lower LoRA scale measure (0.7 or 0.8) seems to work best. Other inputs, however, seem to benefit from scales at around 1.2/1.3.
Make sure to match-up input/output dimensions, primarily to ensure fidelity of details/identity.
With starkly differing dimensions, the model might also get confused in regards to its instruction.