Instructions to use AML-group10/lora-output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AML-group10/lora-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("segmind/tiny-sd", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("AML-group10/lora-output") 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:
- 7a6f67e7d9d056ae61ce3b7753d031f7d49f510e3e432b5bebc75a6ee89b1b12
- Size of remote file:
- 885 kB
- SHA256:
- a518fadff21a3d23851cb26266d76cedb0ac90cd99ab328642bddfaa8342eb46
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