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
- Draw Things
- DiffusionBee
ckpt
Browse files- hasdx_emaonly.ckpt +3 -0
hasdx_emaonly.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:05274f5000deb85f9fd0300d3e32ebaa26526a447fc7b74a6dc337d395ae085a
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size 4265335100
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