Instructions to use PaulTran/AdDiff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PaulTran/AdDiff 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("PaulTran/AdDiff") 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:
- 7755aedabfd2ea7058eca3cba2e488987d74119f44ef42df2d87c6a3e24b2ef1
- Size of remote file:
- 6.59 MB
- SHA256:
- c1060f4c32193679601e3850a5b301bbe991a4ee4ad701d8a3089e42425b2d13
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