Instructions to use Jonjew/KimBasinger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jonjew/KimBasinger 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("Jonjew/KimBasinger") prompt = "kim-basinger" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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("Jonjew/KimBasinger")
prompt = "kim-basinger"
image = pipe(prompt).images[0]Kim Basinger

- Prompt
- kim-basinger
Model description
FROM https://civitai.com/models/740214/kim-basinger
Trigger kim-basinger
Trigger words
You should use kim-basinger to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
- Downloads last month
- 93
Model tree for Jonjew/KimBasinger
Base model
black-forest-labs/FLUX.1-dev