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:
- 3a93906d7ae3857975775410345931f62dd4b6d12e0c9f518df33dcb6c197f39
- Size of remote file:
- 170 MB
- SHA256:
- 21dc91596ed2159c3edc87c204403b94b9bdf28d6ab1ef2763badffd670eefb6
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