Instructions to use HelioAI/Helio-Z-Image-Turbo-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HelioAI/Helio-Z-Image-Turbo-LoRA 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("HelioAI/Helio-Z-Image-Turbo-LoRA") 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:
- ece75fe87e06318cb6673af4aea93d321831e31658a76fb23fc26ab86ad85f2d
- Size of remote file:
- 122 kB
- SHA256:
- 1276d6d4e4688e31e3591aefbf4a3986276e26a440b842d5b5e4525aea3a0365
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.