Instructions to use fal/Z-Image-Turbo-Control-FlashPack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/Z-Image-Turbo-Control-FlashPack with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fal/Z-Image-Turbo-Control-FlashPack", torch_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
- Xet hash:
- 9885f63e1151fe45b115b90615ab6170b04b33f7424bae046efb3a03de724fa9
- Size of remote file:
- 15.4 GB
- SHA256:
- ce07e500baadb480bdc4319403fbe6589d7db3c972770d1ad46dec37d61b89a7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.