Instructions to use lightx2v/Z-Image-Turbo-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Z-Image-Turbo-Quantized with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lightx2v/Z-Image-Turbo-Quantized", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use lightx2v/Z-Image-Turbo-Quantized with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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- LightX2V: [GitHub](https://github.com/ModelTC/LightX2V)
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- Qwen3-4B-GPTQ-Int4: [JunHowie/Qwen3-4B-GPTQ-Int4](https://huggingface.co/JunHowie/Qwen3-4B-GPTQ-Int4)
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##
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- LightX2V: [GitHub](https://github.com/ModelTC/LightX2V)
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- Qwen3-4B-GPTQ-Int4: [JunHowie/Qwen3-4B-GPTQ-Int4](https://huggingface.co/JunHowie/Qwen3-4B-GPTQ-Int4)
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## 🤝 Community
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**If you find this project helpful, please give us a ⭐ on [GitHub](https://github.com/ModelTC/LightX2V)**
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