Instructions to use jayparmr/icbinp_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jayparmr/icbinp_final with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jayparmr/icbinp_final", 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
- Draw Things
- DiffusionBee
- Xet hash:
- a2765c36f62891acd49c974f9ef510300b9b065f181de2aeed9a48a64c748b26
- Size of remote file:
- 335 MB
- SHA256:
- ae37525af6efd9295331a726decb386143be7734a4131ea21c35699bf5abb618
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.