Instructions to use Jonjew/Pokimane with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jonjew/Pokimane 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/Pokimane") prompt = "<lora:Pokimane_Flux:1> beautiful detailed photograph, brown hair cascading over her shoulders, wearing a n elegant dress, standing in cafe looking at the viewer, smile" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Pokimane

- Prompt
- <lora:Pokimane_Flux:1> beautiful detailed photograph, brown hair cascading over her shoulders, wearing a n elegant dress, standing in cafe looking at the viewer, smile
Model description
FROM https://civitai.com/models/920200/pokimane-flux?modelVersionId=1029987
Strength 1
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/Pokimane
Base model
black-forest-labs/FLUX.1-dev