Instructions to use grisha2000/grishanet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use grisha2000/grishanet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("grisha2000/grishanet", 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
- Xet hash:
- 3eab8348ace22c5c83a0ada44ed0ad5d04a73eb617a4efa6b39e8234a89b8b02
- Size of remote file:
- 246 MB
- SHA256:
- 8f9a142f9435678309a08c7074c7d780f3fb7d331b6ede219d95ed8189c793d5
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