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:
- f20904e67839be0457724cdacf4ca8cb32a59d0d10fce23f11dcc989761b5e12
- Size of remote file:
- 3.29 MB
- SHA256:
- 996a227d603b003e61b4179e87571ccf19f49cf01ad0c474e4e64424d0210951
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.