Instructions to use Alunch/JunnyPortrait with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Alunch/JunnyPortrait 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("Alunch/JunnyPortrait") prompt = "JunnyLi" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 2eb8778ea7473f8b6e477d15f85230b4beaedc85788beadb11b75b0ee688bc3b
- Size of remote file:
- 173 MB
- SHA256:
- 9511f4b136c2753355c2f0fd31b0ad347fa3c8733ad316f36ab3bf4d973e8f27
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