Instructions to use AMead10/climbing-lora-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AMead10/climbing-lora-xl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("AMead10/climbing-lora-xl") prompt = "a female sport climber on a overhang by the ocean" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- 123360a5b1ea7b88331ce5d64c6cf21cf3fc6eecc8ff088ca25c847955f93b51
- Size of remote file:
- 1.41 MB
- SHA256:
- 027c6b65f1276b50d5d6364858211f8e205b1fe1aae6497409bc2a5c1e743acd
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