Diffusers
Safetensors
OrbitQuantComponentArtifact
orbitquant
quantized
diffusion-transformer
8-bit precision
Instructions to use WaveCut/Z-Image-Turbo-OrbitQuant-W3A3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/Z-Image-Turbo-OrbitQuant-W3A3 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-W3A3", 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
Clarify OrbitQuant activation epsilon wording
Browse files- README.md +1 -1
- SHA256SUMS +1 -1
README.md
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@@ -81,7 +81,7 @@ Use these settings when comparing this artifact against the BF16 source model or
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- Bits: `W3A3`
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- Runtime mode: `dequant_bf16`
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- Activation kernel backend: `triton_cuda`
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- Activation
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- Quantization device: `cuda`
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- Weight quantization backend: `triton_cuda`
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- Target policy: `z_image`
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- Bits: `W3A3`
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- Runtime mode: `dequant_bf16`
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- Activation kernel backend: `triton_cuda`
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- Activation normalization epsilon: `1e-12`
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- Quantization device: `cuda`
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- Weight quantization backend: `triton_cuda`
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- Target policy: `z_image`
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356c7effb90c651254795c0444bc0466db9993425a7f89759b672a983ce536c6 assets/image_generation_comparison_matrix.webp
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354d3ea98a709aa05123f43c88f08077dab7eef28af66907c246094c2287f09d benchmark/summary.json
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53a4a4444b217130187c72ccc1ea23011c0b20576d4b88ae96a0448865b9c9e1 model.safetensors
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83c4ae8509d79e59a9c198dfd4e67a655192b85697effdd508b87f3bea910d1f README.md
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356c7effb90c651254795c0444bc0466db9993425a7f89759b672a983ce536c6 assets/image_generation_comparison_matrix.webp
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354d3ea98a709aa05123f43c88f08077dab7eef28af66907c246094c2287f09d benchmark/summary.json
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53a4a4444b217130187c72ccc1ea23011c0b20576d4b88ae96a0448865b9c9e1 model.safetensors
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