Instructions to use RunDiffusion/Juggernaut-Z-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RunDiffusion/Juggernaut-Z-Image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RunDiffusion/Juggernaut-Z-Image", 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
Title: same-size 'by RunDiffusion'
Browse files
README.md
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<img src="https://huggingface.co/RunDiffusion/Juggernaut-Z-Image/resolve/main/assets/RD_Mark.png" alt="RunDiffusion" width="110" />
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<h1>Juggernaut
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<p><i>A cinematic fine-tune of Z-Image Base — tuned for presentation-ready output.</i></p>
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<img src="https://huggingface.co/RunDiffusion/Juggernaut-Z-Image/resolve/main/assets/RD_Mark.png" alt="RunDiffusion" width="110" />
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</a>
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<h1>Juggernaut Z by RunDiffusion</h1>
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<p><i>A cinematic fine-tune of Z-Image Base — tuned for presentation-ready output.</i></p>
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