Text-to-Image
Diffusers
Safetensors
English
QuantFuncPipeline
custom_z_image
image-generation
diffusion
quantized
quantfunc
Instructions to use QuantFunc/Z-Image-Series with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QuantFunc/Z-Image-Series with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("QuantFunc/Z-Image-Series", 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
- Draw Things
- DiffusionBee
Add base_model metadata; add root model_index.json for download stats
Browse files- README.md +3 -0
- model_index.json +5 -0
README.md
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---
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language:
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- en
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license: other
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---
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base_model:
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- Tongyi-MAI/Z-Image-Turbo
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base_model_relation: quantized
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language:
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- en
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license: other
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model_index.json
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{
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"_class_name": "QuantFuncPipeline",
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"_diffusers_version": "0.30.0",
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"_comment": "Minimal index enabling HF Hub download statistics. Loading uses the QuantFunc toolkit; see README."
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}
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