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:
- e7f9ee823fababd3af93430625ded6e56dc0f4c2701f6597978b129516850e25
- Size of remote file:
- 1 kB
- SHA256:
- 68d7b85c46d195e3cd6e587d94878d77f315e0a073acfab811251f39fbe891fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.