Instructions to use harshvardhan96/output-results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use harshvardhan96/output-results 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("harshvardhan96/output-results") 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:
- 0e99de4180fdbcf6cecd8cbe38c061ced18954279e244863e0306e46906decfe
- Size of remote file:
- 538 kB
- SHA256:
- 461f2bb4e4d85d69c6895a944e4737a5982722b1ec99ed1c675a44d7b7977049
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.