Instructions to use AML-group10/5e-4_30_hyperparameter_tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AML-group10/5e-4_30_hyperparameter_tuning with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("segmind/tiny-sd", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("AML-group10/5e-4_30_hyperparameter_tuning") 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:
- 283761467ce691c37c7da6373d2564d12e95b596317942b57044f02d8a5fdc11
- Size of remote file:
- 394 kB
- SHA256:
- 2fae6601a017406758debd2f9a5d78ff19b8da7620f2b91ec57b62993ec30749
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.