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", torch_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:
- e830074b055b4b80660ad26089ad4d17d6cc0f8e8838373b703c975ca06cd67c
- Size of remote file:
- 335 MB
- SHA256:
- 4fde32116b4a2e9560c0911d5791019093e402ea97400136ec9946af62610f3d
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