Instructions to use LHRuig/compmod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LHRuig/compmod 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/compmod") 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/compmod: direct link, hf CLI and curl.
- Browser
- Download file 497 Bytes
-
https://huggingface.co/LHRuig/compmod/resolve/main/README.md
- Command line
-
hf download hf://LHRuig/compmod/README.md
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curl -L -o README.md https://huggingface.co/LHRuig/compmod/resolve/main/README.md
497 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: compmod
compmod

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