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    Light & Shadow — Photographic Style LoRA - v1.0
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    Light & Shadow — Photographic Style LoRA

    A different way to shape light, shadow, and tonal depth.

    This LoRA was trained on 220 high-resolution black-and-white photographs I took myself, featuring landscapes, buildings, and macro subjects, along with a few AI-generated portraits and full-body shots.

    It explores a photographic approach to light, tonal structure, and surface definition.

    Its influence goes beyond monochrome imagery and carries over into color generations, changing how highlights, shadows, and surfaces are rendered. At higher weights, it also changes the image composition.

    Examples

    The two examples show how the LoRA’s effect changes at different weights. Each series uses the same prompt and seed.

    What to explore

    • Tonal structure: A stronger visual separation between light and dark areas.

    • Sculpted forms: Light and shadow can give faces, objects, and architectural spaces a stronger sense of volume.

    • Surface presence: Wood grain, stone, fabric, and other materials can stand out more.

    • Photographic atmosphere: Explore how the treatment of light and shadow changes the mood and spatial feel of a scene.

    • Color and monochrome: Use the LoRA with both color and black-and-white prompts to explore its range.

    Usage notes

    The effect depends on the base model, prompt, and LoRA weight. Moderate weights are a good starting point. Higher weights can produce a stronger visual transformation and may also affect pose, exposure, color rendering, and composition.

    This is a photographic style LoRA—not a dedicated sharpening tool or a guaranteed realism enhancer. Its focus is the visual interplay of light, shadow, tone, and form.

    Description

    FAQ

    LORA
    Krea 2

    Details

    Downloads
    164
    Platform
    CivitAI
    Platform Status
    Available
    Created
    10/25/2026
    Updated
    10/30/2026
    Deleted
    -

    Files

    b_w_amateur_photography_krea2.safetensors