Instructions to use SidXXD/HAAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/HAAD with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SidXXD/HAAD", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a sks person" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 7d6fb4003d65346d63ace6ced8a0d8f7a87c41e64f17bfdf99b2f51c798063f9
- Size of remote file:
- 152 MB
- SHA256:
- 16c4cfc798711e8be7b6b0f96bb8bba4b40f43114e2ea7ddea60cd6028e7c3d7
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