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:
- 21490607690d9c6a7683b9306491868b0d61e565e6693a72d178cf1bcac0ff90
- Size of remote file:
- 6.59 MB
- SHA256:
- c67bc7a2bc2299db6d9eba73ed02c5438e32ff090b923ca6eaed3cb981e1fdfa
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