Instructions to use g8nz/dreamlike with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use g8nz/dreamlike with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("g8nz/dreamlike", 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 vae/diffusion_pytorch_model.bin from g8nz/dreamlike: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/g8nz/dreamlike/resolve/main/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://g8nz/dreamlike/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/g8nz/dreamlike/resolve/main/vae/diffusion_pytorch_model.bin
167 MB
- Xet hash:
- 781fddb969dedb13b82d04b824aa49f182b89d3030734518f3e088e7f5e3abb4
- Size of remote file:
- 167 MB
- SHA256:
- 836fbcb39d6da1180395749b5e25f298bdc3acf22cc7532ae338372adb7f6d28
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.