Instructions to use grisha2000/yananet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use grisha2000/yananet 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/yananet", 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:
- 85203f0bf3912e453da6245de3fa6d23eb8ae00c7afd5892a97fb914503b0f3c
- Size of remote file:
- 246 MB
- SHA256:
- 1514dc5f3bc71041326204bf8221af71b633bd1a9df3ea081e9542f04a5ff499
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