Instructions to use bodam/lora-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bodam/lora-trained 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("bodam/lora-trained") prompt = "a olis chair" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Ctrl+K
- checkpoint-100
- checkpoint-1000
- checkpoint-10000
- checkpoint-100000
- checkpoint-10200
- checkpoint-10400
- checkpoint-10600
- checkpoint-10800
- checkpoint-11000
- checkpoint-11200
- checkpoint-11400
- checkpoint-11600
- checkpoint-11800
- checkpoint-1200
- checkpoint-12000
- checkpoint-12200
- checkpoint-12400
- checkpoint-12600
- checkpoint-12800
- checkpoint-13000
- checkpoint-13200
- checkpoint-13400
- checkpoint-13600
- checkpoint-13800
- checkpoint-1400
- checkpoint-14000
- checkpoint-14200
- checkpoint-14400
- checkpoint-14600
- checkpoint-14800
- checkpoint-15000
- checkpoint-15200
- checkpoint-15400
- checkpoint-15600
- checkpoint-15800
- checkpoint-1600
- checkpoint-16000
- checkpoint-16200
- checkpoint-16400
- checkpoint-16600
- checkpoint-16800
- checkpoint-17000
- checkpoint-17200
- checkpoint-17400
- checkpoint-17600
- checkpoint-17800
- checkpoint-1800
- checkpoint-18000
- checkpoint-18200
- checkpoint-18400