Instructions to use Prajeevan/akshyaid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Prajeevan/akshyaid with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Prajeevan/akshyaid", dtype=torch.bfloat16, device_map="cuda") prompt = "akshyaiD" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 6a1a25867b7d1e6f5bc8418f4dc01b7392ad582271c533d3e1486323f6d7b2b2
- Size of remote file:
- 3.44 GB
- SHA256:
- 948ec24ea30aa65bb83790650f9ef69a8930d08ba6e207b9dcb05f5b820aa876
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.