Instructions to use Jonjew/DakotaFanning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jonjew/DakotaFanning 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/DakotaFanning") prompt = "dakotaf" 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/DakotaFanning")
prompt = "dakotaf"
image = pipe(prompt).images[0]Dakota Fanning

- Prompt
- dakotaf
Model description
FROM https://civitai.com/models/961258/dakota-fanning-flux?modelVersionId=1117650
Trigger dakotaf Strength 1
Trigger words
You should use dakotaf to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for Jonjew/DakotaFanning
Base model
black-forest-labs/FLUX.1-dev