Instructions to use SimonJonsson1999/test_lora_decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SimonJonsson1999/test_lora_decoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("SimonJonsson1999/test_lora_decoder") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- cb77eaf8e71488b0493bf821f5085bc7e6ce4852487b59c48dd41db963157cad
- Size of remote file:
- 3.31 MB
- SHA256:
- 05791fa31e64f13e3c9a8c5247ff0dc8e370e68ba5944ae52e3bb3056ac15666
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.