Instructions to use dainlee/output_ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dainlee/output_ with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dainlee/output_", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 9ec7e2245dccc1f7e094431df8fd33d9ee8718c21d8f1db14788bdcf672648b5
- Size of remote file:
- 3.44 GB
- SHA256:
- 14277f0011994deed87e945e070d8275e54cc7e5548bbed53d6925ba2eb6f2a4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.