Instructions to use LHRuig/cvcu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LHRuig/cvcu 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/cvcu") 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/michael-kors-blue-performance-stretch-slim-fit-wedding-suit-coat.webp
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: cvicu
cvicu

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