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

- Prompt
- suit
Model description
jdvanc lora
Trigger words
You should use jdvanc to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.