Instructions to use pcoundia/coundia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pcoundia/coundia 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("pcoundia/coundia") prompt = "a man , LORACOUNDIA01, a minimalistic and stylized illustration of yourself. The focus is on clean lines and soft details, highlighting your sharp features and confident expression. Dressed in a casual yet sleek outfit, the portrait emphasizes approachability and authenticity. The background is simple and neutral, with subtle gradients or textures to enhance the overall composition without overpowering the subject. The style is light, modern, and expressive, capturing your essence in a straightforward yet visually appealing way." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Add generated example
#7
by pcoundia - opened
Generated example for model pcoundia/coundia.
Prompt: a man , LORACOUNDIA01, a minimalistic and stylized illustration of yourself. The focus is on clean lines and soft details, highlighting your sharp features and confident expression. Dressed in a casual yet sleek outfit, the portrait emphasizes approachability and authenticity. The background is simple and neutral, with subtle gradients or textures to enhance the overall composition without overpowering the subject. The style is light, modern, and expressive, capturing your essence in a straightforward yet visually appealing way.
pcoundia changed pull request status to merged