Instructions to use Quantumbraid/amber with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Quantumbraid/amber with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Raw", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Quantumbraid/amber") prompt = "A hyper-realistic portrait of @mb3r as a futuristic cybernetic deity floating in a void of liquid gold and neon circuitry." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- a77cd8d763293c17f01180ebb1e2bb238fc0c9f25b6ee01dc0e2fe324c7d66f9
- Size of remote file:
- 1.65 MB
- SHA256:
- b5b891d9965e57275c8f5cc0dde7d197cfb6db331a31b9f6fa43b7562cb7ceaf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.