This is early experimental LoRa for researching borders of training the concept of strict mirror.

I think it is possible because SDXL can draw mirror floors.

Status

IL V2: this LoRa version can increase probability (+24% for 8-20 steps) of getting strict reflections in a new generation. Increasing steps is refining the results.

IL V1: this LoRa version can slightly increase probability (+10% for 4 dmd2-steps, +12-16% for 8 dmd2-steps) to find strict reflections in a new generation. Increasing steps is refining the results.

I go to the next step of the experiment.

Changelog

IL V2

Added negative tokens:
- x_out_of_mirror (protects against a reflection outside mirror's bounds)
- x_differect_reflection (supress a wrong symmetry)
- different reflection (same the x_out_of_mirror but danbooru tag)
Added ai-generated images with negative tokens to dataset.
60 epochs instead of 10 epochs of version 1
Trained with cleaned dictionary: x_mirror_reflection, x_reflection_face, x_reflection_facing_aside, x_reflection_facing_away, reflection, mirror, hand on mirror, full-length mirror, broken mirror, reflection focus, looking at mirror.

Usage

Connect LoRa in your workflow and then activate by word x_mirror_reflection in your prompt. Next choose one of these controls for the position of the character's face relative to the mirror:

x_reflection_facing_away
x_reflection_facing_aside
x_reflection_face

Now you can use negative prompt (+8% to find good mirroring in IL V2): x_out_of_mirror, x_differect_reflection, different reflection

Trigger Words

x_mirror_reflection

x_reflection_facing_away

x_reflection_facing_aside

x_reflection_face

Strict mirroring - V1