Instructions to use ostris/zimage_turbo_training_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ostris/zimage_turbo_training_adapter with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ostris/zimage_turbo_training_adapter") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 88b80238bdd788978738ae07e488c5b4b2878bb625d1576ea1154b3891e2ab0b
- Size of remote file:
- 340 MB
- SHA256:
- 20a541d3e016ab8de0da076321b48b6cd9b3ffd072d9df830a068220ab2265f6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.