Instructions to use ckpt/hasdx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ckpt/hasdx with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ckpt/hasdx", dtype=torch.bfloat16, device_map="cuda") 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:
- a1d9d57f29c4eafe93885e32b90295bc1889c1d8a14b02a423d5f5aa9df852b2
- Size of remote file:
- 492 MB
- SHA256:
- d5b54444b33ed67fb5ec49cec78f3596c7fb3e0a028336bc3d46ee2271cbdc76
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