Instructions to use huangdffs/dreamlitee128model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use huangdffs/dreamlitee128model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("huangdffs/dreamlitee128model", 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
Download traindata.pt from huangdffs/dreamlitee128model: direct link, hf CLI and curl.
- Browser
- Download file 767 MB
-
https://huggingface.co/huangdffs/dreamlitee128model/resolve/main/traindata.pt
- Command line
-
hf download hf://huangdffs/dreamlitee128model/traindata.pt
-
curl -L -o traindata.pt https://huggingface.co/huangdffs/dreamlitee128model/resolve/main/traindata.pt
767 MB
- Xet hash:
- b7e095f361c473fbc822e67f3b95076b8d59c53be2ff209d8a40e9b3add623fe
- Size of remote file:
- 767 MB
- SHA256:
- 27d4f8e1a8105ed48080cc327116af9dd0a54f14121b218c28850dc15990e21e
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