Instructions to use erens/mikasalast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use erens/mikasalast with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("erens/mikasalast", 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 erens/mikasalast: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/erens/mikasalast/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://erens/mikasalast/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/erens/mikasalast/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- ac0ef9c37a80a0be8149dde8a45b7820803b1560cc2331eb173a82e6e0440aa7
- Size of remote file:
- 246 MB
- SHA256:
- 6df57d3f5cfb2a6c7becc28455194a8c9c3e084c496f5e2b3c3f72a2c4945c74
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