Instructions to use HyperX-Sentience/AstraX-Redefined with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HyperX-Sentience/AstraX-Redefined with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("HyperX-Sentience/AstraXL", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("HyperX-Sentience/AstraX-Redefined") prompt = "Digital drawing of a cute anime girl with light skin, blue eyes, and brown hair. She is wearing a blue off-the-shoulder cropped sweater, navel, and beige shorts with a black belt. She is posing with one hand on her forehead, making a peace sign with the other. Simple background, vibrant yellow background. The lighting is bright and even, with shadows highlights. The camera angle is straight-on. The image quality is very high, with a sharp focus on the girl and a solid background. The aesthetic quality is very high, highly detailed, intricate details." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee

- Xet hash:
- 58670e4e9e2a21ae849bb39f78781d4ea957c1e76cb2f04e6fbe08870b35a9b5
- Size of remote file:
- 1.6 MB
- SHA256:
- 9af7e4ef9a289d5aabc903cf4ccc87c2b2090a675c464e099589bc89e263e0d0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.