[Feature]: FP8 Quantization Support for DiT (#1034)

Signed-off-by: lishunyang <lishunyang12@163.com>
Signed-off-by: SYLAR <125541396+lishunyang12@users.noreply.github.com>

docs/user_guide/diffusion/quantization/fp8.md

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+# FP8 Quantization
+
+## Overview
+
+FP8 quantization converts BF16/FP16 weights to FP8 at model load time. No calibration or pre-quantized checkpoint needed.
+
+Depending on the model, either all layers can be quantized, or some sensitive layers should stay in BF16. See the [per-model table](#supported-models) for which case applies.
+
+Common sensitive layers in DiT-based diffusion models include **image-stream MLPs** (`img_mlp`). These are particularly vulnerable to FP8 precision loss because they process denoising latents whose dynamic range shifts significantly across timesteps, and unlike attention projections (which benefit from QK-Norm stabilization), MLPs have no built-in normalization to absorb quantization error. In deep architectures (e.g., 60+ residual blocks), small per-layer errors compound and degrade output quality. Other layers such as **attention projections** (`to_qkv`, `to_out`) and **text-stream MLPs** (`txt_mlp`) are generally more robust due to normalization or more stable input statistics.
+
+## Configuration
+
+1. **Python API**: set `quantization="fp8"`. To skip sensitive layers, use `quantization_config` with `ignored_layers`.
+
+```python
+from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+# All layers quantized
+omni = Omni(model="<your-model>", quantization="fp8")
+
+# Skip sensitive layers
+omni = Omni(
+    model="<your-model>",
+    quantization_config={
+        "method": "fp8",
+        "ignored_layers": ["<layer-name>"],
+    },
+)
+
+outputs = omni.generate(
+    "A cat sitting on a windowsill",
+    OmniDiffusionSamplingParams(num_inference_steps=50),
+)
+```
+
+2. **CLI**: pass `--quantization fp8` and optionally `--ignored-layers`.
+
+```bash
+# All layers
+python text_to_image.py --model <your-model> --quantization fp8
+
+# Skip sensitive layers
+python text_to_image.py --model <your-model> --quantization fp8 --ignored-layers "img_mlp"
+
+# Online serving
+vllm serve <your-model> --omni --quantization fp8
+```
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `method` | str | — | Quantization method (`"fp8"`) |
+| `ignored_layers` | list[str] | `[]` | Layer name patterns to keep in BF16 |
+| `activation_scheme` | str | `"dynamic"` | `"dynamic"` (no calibration) or `"static"` |
+| `weight_block_size` | list[int] \| None | `None` | Block size for block-wise weight quantization |
+
+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.
+
+## Supported Models
+
+| Model | HF Models | Recommendation | `ignored_layers` |
+|-------|-----------|---------------|------------------|
+| Z-Image | `Tongyi-MAI/Z-Image-Turbo` | All layers | None |
+| Qwen-Image | `Qwen/Qwen-Image`, `Qwen/Qwen-Image-2512` | Skip sensitive layers | `img_mlp` |
+
+## Combining with Other Features
+
+FP8 quantization can be combined with cache acceleration:
+
+```python
+omni = Omni(
+    model="<your-model>",
+    quantization="fp8",
+    cache_backend="tea_cache",
+    cache_config={"rel_l1_thresh": 0.2},
+)
+```

docs/user_guide/diffusion/quantization/overview.md

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+# Quantization for Diffusion Transformers
+
+vLLM-Omni supports quantization of DiT linear layers to reduce memory usage and accelerate inference.
+
+## Supported Methods
+
+| Method | Guide |
+|--------|-------|
+| FP8 | [FP8](fp8.md) |
+
+## Device Compatibility
+
+| GPU Generation | Example GPUs | FP8 Mode |
+|---------------|-------------------|----------|
+| Ada/Hopper (SM 89+) | RTX 4090, H100, H200 | Full W8A8 with native hardware |
+
+Kernel selection is automatic.