Instructions to use Adminhuggingface/OUTPUTA_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Adminhuggingface/OUTPUTA_2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Adminhuggingface/OUTPUTA_2") 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:
- 7d5c0510dedc357a03e70ba173d49cf93fdc0ce53978b4189e7e737806b03057
- Size of remote file:
- 6.59 MB
- SHA256:
- d987588b8ffa9187e7a77645e27d719a292fbce8e562d2963b9d93137c49b095
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.