Diffusers
Safetensors
OrbitQuantComponentArtifact
orbitquant
quantized
diffusion-transformer
8-bit precision
Instructions to use WaveCut/Z-Image-Turbo-OrbitQuant-W2A3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/Z-Image-Turbo-OrbitQuant-W2A3 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-W2A3", 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
Replace artifact with paper-correct codebook v2 and native proof
Browse files- README.md +2 -2
- SHA256SUMS +8 -8
- assets/image_generation_comparison_matrix.webp +2 -2
- benchmark/summary.json +9 -24
- model.safetensors +1 -1
- model_index.json +2 -1
- orbitquant_codebooks.safetensors +1 -1
- orbitquant_manifest.json +8 -8
- quantization_config.json +5 -4
README.md
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@@ -90,7 +90,7 @@ The compact benchmark summary records native BF16-vs-OrbitQuant evidence for the
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- Method: `orbitquant`
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- Bits: `W2A3`
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- Runtime mode: `auto_fused`
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- Activation kernel backend: `
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- Activation normalization epsilon: `1e-10`
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- Quantization device: `cuda`
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- Weight quantization backend: `triton_cuda`
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- Block size: `paper`
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- Block size policy: `largest_power_of_two_dividing_dim`
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- Codebook: `lloyd_max`
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- Codebook version: `
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- Quantized transformer modules: `238`
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- AdaLN INT4 modules: `32`
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- Skipped modules: `6`
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- Method: `orbitquant`
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- Bits: `W2A3`
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- Runtime mode: `auto_fused`
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- Activation kernel backend: `auto`
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- Activation normalization epsilon: `1e-10`
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- Quantization device: `cuda`
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- Weight quantization backend: `triton_cuda`
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- Block size: `paper`
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- Block size policy: `largest_power_of_two_dividing_dim`
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- Codebook: `lloyd_max`
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- Codebook version: `2`
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- Quantized transformer modules: `238`
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- AdaLN INT4 modules: `32`
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- Skipped modules: `6`
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SHA256SUMS
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65ec8afff39a6cf80feee2eac2f06fa66e51cd471a8382f4355a04fd74eda0af prompts.json
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assets/image_generation_comparison_matrix.webp
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Git LFS Details
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Git LFS Details
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benchmark/summary.json
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"native_smoke": {
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model_index.json
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orbitquant_codebooks.safetensors
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orbitquant_manifest.json
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quantization_config.json
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