metadata
license: other
license_name: bespoke-lora-trained-license
license_link: >-
https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=RentCivit&allowDerivatives=True&allowDifferentLicense=True
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- migrated
- character
- photorealistic
- sexy
- woman
- girls
- realistic
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Sexy girl
widget:
- text: ' '
parameters:
negative_prompt: text, watermark,Blurring, blurring,depth of field,anime
output:
url: 26655170.jpeg
- text: ' '
parameters:
negative_prompt: text, watermark,Blurring, blurring,depth of field,anime
output:
url: 26655193.jpeg
- text: ' '
parameters:
negative_prompt: text, watermark,Blurring, blurring,depth of field,anime
output:
url: 26655023.jpeg
- text: ' '
parameters:
negative_prompt: text, watermark,Blurring, blurring,depth of field,anime
output:
url: 26655122.jpeg
- text: ' '
parameters:
negative_prompt: text, watermark,Blurring, blurring,depth of field,anime
output:
url: 26655301.jpeg
Asian beauty - Flux Lora
Model description
Tag word is: Sexy girl,
A Flux Dev Lora of Sexy Asian beauty. Works best at 0.7~1.5 weight,About 0.9 is a good weight, will have a good generalization effect, sampler 40 steps will have a good effect. Recommended sampler: enlar a, scheduler: beta,cfg: 1~3, common size: 1024*1024*1536
Trigger words
You should use Sexy girl
to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device)
pipeline.load_lora_weights('Keltezaa/asian-beauty-flux-lora', weight_name='Sexy_Asian_beauty_lora.safetensors')
image = pipeline('`Sexy girl`').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers