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", torch_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:
- 71723bf72775196592ec8b38ec2229bb32c41bc13ad9fc2133efb8d6d69fbdf0
- Size of remote file:
- 6.59 MB
- SHA256:
- f13c8bfc490ef3ae7f7eafec09be15d3b442e50b2429c1b1077c7edd8393c5ac
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