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Dahlia

4

20

0

0

Updated: Aug 23, 2025

character

Verified:

SafeTensor

Type

LoRA

Stats

34

0

Reviews

Published

Aug 22, 2025

Base Model

Flux.1 D

Training

Steps: 3,510
Epochs: 12

Usage Tips

Clip Skip: 1
Strength: 1

Hash

AutoV2
C75430141E
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SO

solss_

Created on Civitai

The FLUX.1 [dev] Model is licensed by Black Forest Labs. Inc. under the FLUX.1 [dev] Non-Commercial License. Copyright Black Forest Labs. Inc.

IN NO EVENT SHALL BLACK FOREST LABS, INC. BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH USE OF THIS MODEL.

LoRA: Dahlia Custom Character (Flux-Dev)

Trigger Word: None required — prompts naturally.
This LoRA was trained on 39 images with natural-language captions, designed to provide consistent generations of a fit, athletic woman across a wide range of settings. Example images were generated with Flux-Krea -- Trained on Flux-Dev, so likeness will be more preserved and consistent on Dev.


📝 Training Details

  • Model: Flux Dev (SDXL-based)

  • Images: 39, auto-captioned

  • Resolution: 1024x1024

  • LoRA Type: Standard (character consistency)

  • File Size: ~142MB

  • Network Dim/Alpha: 32/32

The dataset focused on portraits with expressive emotions, varying fashion styles, and natural lighting, so the LoRA adapts well to most realistic scenarios. Dataset created with Qwen-Edit from a single image generation made with BigLovePonyXL to test the versatility of SOTA image editing models.


🎨 How to Use

  • Works best with portrait photography, cinematic realism, or fashion concepts.

  • No trigger word needed. Simply describe the subject in natural prompts (e.g., “athletic woman, portrait, natural lighting, expressive smile”).

  • Strong activation with tags like: athletic, portrait, realistic, expressive face, fashion.


  • Weight: 0.6–0.9 for subtle consistency, 1.0–1.2 for stronger adherence.

  • Model Base: Any Flux model tuned for realism or portrait photography.


⚠️ Notes

  • Trained without a dedicated token — this is intentional, making the LoRA easier to blend into prompts.

  • Fashion styles from 2000s college wear to formal and athletic outfits were included for variety.

  • Overuse at high weights may reduce variety in generated faces.