Instructions to use SidXXD/HAAD-kv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/HAAD-kv 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-kv", 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:
- d6e323572fefe51d26dfca8f9fb80df286a3657ff13a99f537d4edab0e80dbc3
- Size of remote file:
- 76.7 MB
- SHA256:
- c2401066b0d76c78849179aaaf57b77748882b8e45d648b9749e5e213f1d430c
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