Diffusers
Safetensors
OrbitQuantComponentArtifact
orbitquant
quantized
diffusion-transformer
8-bit precision
Instructions to use WaveCut/Z-Image-Turbo-OrbitQuant-W4A4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/Z-Image-Turbo-OrbitQuant-W4A4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/Z-Image-Turbo-OrbitQuant-W4A4", 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
Document OrbitQuant 0.2.0 runtime
Browse files
README.md
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Install OrbitQuant and the Hugging Face runtime dependencies:
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```bash
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pip install "orbitquant[hf]"
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```
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Download this model repository as an OrbitQuant artifact, then load the source Diffusers pipeline with the quantized component patched in:
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image.save("z-image-orbitquant.png")
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```
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`runtime_mode="auto_fused"` is the default optimized runtime. On CUDA
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## Native Settings
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Install OrbitQuant and the Hugging Face runtime dependencies:
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```bash
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pip install "orbitquant[hf,kernels]>=0.2.0"
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```
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Download this model repository as an OrbitQuant artifact, then load the source Diffusers pipeline with the quantized component patched in:
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image.save("z-image-orbitquant.png")
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```
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`runtime_mode="auto_fused"` is the default optimized runtime. On CUDA, the `kernels` extra provides the Triton packed fallback; a locally built native CUDA package is preferred automatically when installed. On MPS, build and install the native Metal package from the OrbitQuant source tree. See the [OrbitQuant runtime instructions](https://github.com/iamwavecut/OrbitQuant#native-packed-kernels). Use `runtime_mode="dequant_bf16"` only as an explicit compatibility/debug reference path.
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## Native Settings
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