Text-to-Image
Diffusers
Safetensors
English
ZImagePipeline
z-image
juggernaut
openvino-export-candidate
Instructions to use Aminfri/juggernaut-z-fast-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aminfri/juggernaut-z-fast-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Aminfri/juggernaut-z-fast-diffusers", 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
Juggernaut Z Fast Diffusers Assembly
This repo is an assembled diffusers-style model intended for OpenVINO export.
It combines:
- Pipeline/config/text-encoder/tokenizer/VAE files copied from
RunDiffusion/Juggernaut-Z-Image - Fast transformer weights copied from
RunDiffusion/Juggernaut-Z-Image-Fast/Juggernaut_Z_V1_Fast_FP16.safetensors
The Fast weight is placed at:
transformer/diffusion_pytorch_model.safetensors
Destination repo: Aminfri/juggernaut-z-fast-diffusers
RunDiffusion's Fast model is CC BY-NC 4.0. Confirm licensing before commercial use.
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Model tree for Aminfri/juggernaut-z-fast-diffusers
Base model
Tongyi-MAI/Z-Image