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", 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:
- 927ce22c35fdfe611d92f3b7ab8b1e68b61f874c41d3c986d9f6d19a34afb25f
- Size of remote file:
- 889 kB
- SHA256:
- 0e336dc9664c5c97c4a9bbe856f5f2f7d5a8a17e98326aefc2abf1bb0b0c33fe
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