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:
- dc19e282f20a536e1b16bb9d8f53e026141c7dc463d463fc9a91334a63e18a1d
- Size of remote file:
- 3.29 MB
- SHA256:
- 474bc52e6ce6cf475feeabb6810e9d5a73f73f1a4acb4a9cdb291024a614ec29
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.