75 lines
2.3 KiB
Markdown
75 lines
2.3 KiB
Markdown
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# Int8 Quantization
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## Overview
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Int8 quantization converts BF16/FP16 weights to Int8 at model load time. No calibration or pre-quantized checkpoint needed.
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Depending on the model, either all layers can be quantized, or some sensitive layers should stay in BF16/FP16. See the [per-model table](#supported-models) for which case applies.
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## Configuration
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1. **Python API**: set `quantization="int8"`. To skip sensitive layers, use `quantization_config` with `ignored_layers`.
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```python
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from vllm_omni import Omni
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams
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# All layers quantized
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omni = Omni(model="<your-model>", quantization="int8")
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# Skip sensitive layers
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omni = Omni(
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model="<your-model>",
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quantization_config={
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"method": "int8",
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"ignored_layers": ["<layer-name>"],
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},
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)
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outputs = omni.generate(
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"A cat sitting on a windowsill",
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OmniDiffusionSamplingParams(num_inference_steps=50),
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)
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```
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2. **CLI**: pass `--quantization int8` and optionally `--ignored-layers`.
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```bash
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# All layers
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python text_to_image.py --model <your-model> --quantization int8
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# Skip sensitive layers
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python text_to_image.py --model <your-model> --quantization int8 --ignored-layers "img_mlp"
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# Online serving
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vllm serve <your-model> --omni --quantization int8
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```
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `method` | str | — | Quantization method (`"int8"`) |
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| `ignored_layers` | list[str] | `[]` | Layer name patterns to keep in BF16/FP16 |
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| `activation_scheme` | str | `"dynamic"` | `"dynamic"` (no calibration) |
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The available `ignored_layers` names depend on the model architecture (e.g., `to_qkv`, `to_out`, `img_mlp`, `txt_mlp`). Consult the transformer source for your target model.
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## Supported Models
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| Model | HF Models | Recommendation | `ignored_layers` |
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|-------|-----------|---------------|------------------|
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| Z-Image | `Tongyi-MAI/Z-Image-Turbo` | All layers | None |
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| Qwen-Image | `Qwen/Qwen-Image`, `Qwen/Qwen-Image-2512` | All layers | None |
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## Combining with Other Features
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Int8 quantization can be combined with cache acceleration:
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```python
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omni = Omni(
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model="<your-model>",
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quantization="int8",
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cache_backend="tea_cache",
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cache_config={"rel_l1_thresh": 0.2},
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)
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```
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