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:
- 0f6f0b17e23cd259265301e13c1a92562e525c6b83c813cbeece3c717ed0b8ce
- Size of remote file:
- 3.29 MB
- SHA256:
- b2ad3766f07e8148eac2005ca9b8c6ae68a4a20f5ec2ae70e8cd9f21af0bbdae
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