Instructions to use jonibishop/jb23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jonibishop/jb23 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("jonibishop/jb23") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from jonibishop/jb23: direct link, hf CLI and curl.
- Browser
- Download file 544 Bytes
-
https://huggingface.co/jonibishop/jb23/resolve/main/README.md
- Command line
-
hf download hf://jonibishop/jb23/README.md
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curl -L -o README.md https://huggingface.co/jonibishop/jb23/resolve/main/README.md
544 Bytes
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/ca5da57c-b4a9-4747-b56a-c47b438b22df.jpeg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: jb23
jb23

- Prompt
- -
Model description
Model 1.0 based on Joni Bishop 2024
Trigger words
You should use jb23 to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.