Instructions to use LHRuig/jamdor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LHRuig/jamdor 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("LHRuig/jamdor") prompt = "a photo of Jdor wearing a pinstriped suit" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: a photo of Jdor wearing a pinstriped suit
output:
url: images/jdor001.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: jdor
license: unknown
jamdor

- Prompt
- a photo of Jdor wearing a pinstriped suit
Model description
Jam dor lora
Trigger words
You should use jdor to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.