Instructions to use AML-group10/5e-4_20_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_20_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_20_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:
- 436e1b2d4ca3be227e78bad0708ce201f567adc091b271b227bf0aa0f437a373
- Size of remote file:
- 400 kB
- SHA256:
- a83ba34ea67c164798da80c2e2623bf0fc0590c0872c377c3757df5bd8b0f723
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