Instructions to use Superstarimage/DGRSISE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Superstarimage/DGRSISE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Superstarimage/DGRSISE", 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 controlnet/model_hamlp_ep from Superstarimage/DGRSISE: direct link, hf CLI and curl.
- Browser
- Download file 6.69 MB
-
https://huggingface.co/Superstarimage/DGRSISE/resolve/main/controlnet/model_hamlp_ep
- Command line
-
hf download hf://Superstarimage/DGRSISE/controlnet/model_hamlp_ep
-
curl -L -o model_hamlp_ep https://huggingface.co/Superstarimage/DGRSISE/resolve/main/controlnet/model_hamlp_ep
6.69 MB
- Xet hash:
- 530ec0768faadc4d34347008eeab55f3ecc050b6ef21b0be1a64b61b6db790af
- Size of remote file:
- 6.69 MB
- SHA256:
- e4bf2a38b1a05de4af3a8d76a743c2c00aac125f63dfea081667820636bc93f4
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