Instructions to use LHRuig/randoging with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LHRuig/randoging 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/randoging") prompt = "suit" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from LHRuig/randoging: direct link, hf CLI and curl.
- Browser
- Download file 507 Bytes
-
https://huggingface.co/LHRuig/randoging/resolve/main/README.md
- Command line
-
hf download hf://LHRuig/randoging/README.md
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curl -L -o README.md https://huggingface.co/LHRuig/randoging/resolve/main/README.md
507 Bytes
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: randoging
randoging

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