Instructions to use LHRuig/ppasc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LHRuig/ppasc 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/ppasc") prompt = "suit" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
|
Download README.md from LHRuig/ppasc: direct link, hf CLI and curl.
- Browser
- Download file 558 Bytes
-
https://huggingface.co/LHRuig/ppasc/resolve/main/README.md
- Command line
-
hf download hf://LHRuig/ppasc/README.md
-
curl -L -o README.md https://huggingface.co/LHRuig/ppasc/resolve/main/README.md
558 Bytes
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: ppasc
ppasc

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