Instructions to use X-HighVoltage-X/cultures-Portait-FLUX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use X-HighVoltage-X/cultures-Portait-FLUX with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("X-HighVoltage-X/cultures-Portait-FLUX") prompt = "portrait photo of a nordic mature woman, 40yo, <lora:cultures_FLUX-sevenof9:0.5>" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
better faces cultures FLUX Portait
.jpeg)
- Prompt
- portrait photo of a nordic mature woman, 40yo, <lora:cultures_FLUX-sevenof9:0.5>
- Negative Prompt
- portrait photo of a nordic mature woman, 40yo, <lora:cultures_FLUX-sevenof9:0.5>
.jpeg)
- Prompt
- portrait photo of a nordic mature woman, 40yo,
- Negative Prompt
- portrait photo of a nordic mature woman, 40yo,
.jpeg)
- Prompt
- portrait photo of a japanese mature woman, 40yo,
- Negative Prompt
- portrait photo of a japanese mature woman, 40yo,
Model description
Include a diverse range of cultures and ethnicities—15 if I remember correctly—with both young and old subjects, currently focusing on women.
aborigines, african, arab, arctic, brazilian, chinese, egyptian, finish, german, havaiian, indian, japanese, mongolian, russian, western
my FLUX-lora responds to age prompts like __yo for 20yo, 30yo, 40yo. Weight: start with 0.3
Download model
Download them in the Files & versions tab.
- Downloads last month
- 3
Model tree for X-HighVoltage-X/cultures-Portait-FLUX
Base model
black-forest-labs/FLUX.1-dev