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:
- 8455e0bd982e91ece14152fab9ece79baddb7a263fde9de14857deab356e146a
- Size of remote file:
- 1.38 MB
- SHA256:
- 7f299238017c52cf89de6debd23c121e75c3f7c43aa715c3b8e9304a2a63552b
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