Instructions to use 9r4n4y/Z-Image-Turbo-Ghibli-Style-backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use 9r4n4y/Z-Image-Turbo-Ghibli-Style-backup 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("9r4n4y/Z-Image-Turbo-Ghibli-Style-backup") prompt = "an image of a young woman sitting outdoors on grass. She appears to be in her 20s with brown hair tied back in a ponytail. She is wearing a dark blue sports bra-style crop top that shows her midriff, paired with high-waisted olive green cargo pants. She has a white shirt or jacket draped over her shoulders. Around her neck is a silver chain necklace, and she is wearing small hoop earrings. " image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- 7762b9407008d295686b58646f5e7b7eab238fdfe00083a6c52e219e0c457acd
- Size of remote file:
- 1.42 MB
- SHA256:
- 9be1f2dc346f48aa247bfcb96388111cf668889cededb217fa16efee4ea2fecd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.