Instructions to use rhendz/niji-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rhendz/niji-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("rhendz/niji-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Ctrl+K
- checkpoint-1000
- checkpoint-10000
- checkpoint-10250
- checkpoint-10500
- checkpoint-10750
- checkpoint-11000
- checkpoint-11250
- checkpoint-11500
- checkpoint-11750
- checkpoint-12000
- checkpoint-12250
- checkpoint-1250
- checkpoint-12500
- checkpoint-12750
- checkpoint-1500
- checkpoint-1750
- checkpoint-2000
- checkpoint-2250
- checkpoint-250
- checkpoint-2500
- checkpoint-2750
- checkpoint-3000
- checkpoint-3250
- checkpoint-3500
- checkpoint-3750
- checkpoint-4000
- checkpoint-4250
- checkpoint-4500
- checkpoint-4750
- checkpoint-500
- checkpoint-5000
- checkpoint-5250
- checkpoint-5500
- checkpoint-5750
- checkpoint-6000
- checkpoint-6250
- checkpoint-6500
- checkpoint-6750
- checkpoint-7000
- checkpoint-7250
- checkpoint-750
- checkpoint-7500
- checkpoint-7750
- checkpoint-8000
- checkpoint-8250
- checkpoint-8500
- checkpoint-8750
- checkpoint-9000
- checkpoint-9250
- checkpoint-9500