Instructions to use iab/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use iab/output with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("iab/output") prompt = "a photo of sks style" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 64995ed4d0bc43767045de9b5e4be46fffa13505f02f293a204f3f18f02d26f0
- Size of remote file:
- 6.59 MB
- SHA256:
- c74a90301597c692473cd359b0d8d26b174c08efac9d1ecec7348deee113a2f9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.