Instructions to use AINovice2005/Sana_600M_ControlNet_HED-smashed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AINovice2005/Sana_600M_ControlNet_HED-smashed with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AINovice2005/Sana_600M_ControlNet_HED-smashed", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Pruna AI
How to use AINovice2005/Sana_600M_ControlNet_HED-smashed with Pruna AI:
from pruna import PrunaModel pip install -U diffusers transformers accelerate
from pruna import PrunaModel import torch # switch to "mps" for apple devices pipe = PrunaModel.from_pretrained("AINovice2005/Sana_600M_ControlNet_HED-smashed", 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 tokenizer/tokenizer.json from AINovice2005/Sana_600M_ControlNet_HED-smashed: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/AINovice2005/Sana_600M_ControlNet_HED-smashed/resolve/main/tokenizer/tokenizer.json
- Command line
-
hf download hf://AINovice2005/Sana_600M_ControlNet_HED-smashed/tokenizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/AINovice2005/Sana_600M_ControlNet_HED-smashed/resolve/main/tokenizer/tokenizer.json
34.4 MB
- Xet hash:
- afa78551f6fdaff5671645d2394d4478e28255203cd7456448b0da82ae0a3ba8
- Size of remote file:
- 34.4 MB
- SHA256:
- 5f7eee611703c5ce5d1eee32d9cdcfe465647b8aff0c1dfb3bed7ad7dbb05060
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