Instructions to use Lorenzob/astra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lorenzob/astra with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lorenzob/astra", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "My name is Julien and I like to" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- .bin
- accepts
- after
- arraybuffer.slice
- async-limiter
- async
- backo2
- balanced-match
- base64-arraybuffer
- base64id
- better-assert
- blob
- brace-expansion
- bunyan-rotating-file-stream
- bunyan
- callsite
- component-bind
- component-emitter
- component-inherit
- concat-map
- cookie
- debug
- dtrace-provider
- engine.io-client
- engine.io-parser
- engine.io
- eventemitter3
- follow-redirects
- glob
- has-binary2
- has-cors
- http-proxy
- indexof
- inflight
- inherits
- isarray
- lodash
- mime-db
- mime-types
- minimatch
- minimist
- mkdirp
- moment
- ms
- mv
- nan
- ncp
- negotiator
- node-pty
- object-component