Instructions to use jzli/DreamShaper-8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jzli/DreamShaper-8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jzli/DreamShaper-8", 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
Download text_encoder/pytorch_model.bin from jzli/DreamShaper-8: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/jzli/DreamShaper-8/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://jzli/DreamShaper-8/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jzli/DreamShaper-8/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 7b2bf840e31bf27ee519b4d6057a5818d2feda4d608300eee509d6f12e8f0bc3
- Size of remote file:
- 492 MB
- SHA256:
- 796270ff28100e85585f5726e4e5e7732e06a0e155c96afdb2484cfe1283b8af
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