Instructions to use SearchingMan/Z-Image-Turbo-student-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SearchingMan/Z-Image-Turbo-student-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("SearchingMan/Z-Image-Turbo-student-adapter", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/__init__.py from SearchingMan/Z-Image-Turbo-student-adapter: direct link, hf CLI and curl.
- Browser
- Download file 269 Bytes
-
https://huggingface.co/SearchingMan/Z-Image-Turbo-student-adapter/resolve/main/text_encoder/__init__.py
- Command line
-
hf download hf://SearchingMan/Z-Image-Turbo-student-adapter/text_encoder/__init__.py
-
curl -L -o __init__.py https://huggingface.co/SearchingMan/Z-Image-Turbo-student-adapter/resolve/main/text_encoder/__init__.py
269 Bytes
| # Student Adapter Text Encoder — drop-in replacement for Qwen3 text encoder | |
| # Supports ZImagePipeline via trust_remote_code=True auto_map. | |
| from .configuration_student_adapter import StudentAdapterConfig | |
| from .modeling_student_adapter import StudentAdapterTextEncoder | |