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
- Draw Things
- DiffusionBee
- Xet hash:
- 20deab8c5242b71d37ae47493b01525f9c95b4cb18e30832d28be5ca615c56f2
- Size of remote file:
- 3.29 MB
- SHA256:
- 6517e24d2aa30206276ecb8e986aa64cdbecb8432d9f9a1689c21760aac054c0
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