[Model] FP8 Support For Boogu-Image (#6925)

Signed-off-by: Dmaner <2663515256@qq.com>

docs/user_guide/quantization/torchao.md

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+# TorchAO Quantization
+
+## Overview
+
+[TorchAO](https://github.com/pytorch/ao) provides quantization tools for
+PyTorch and supports both pre-quantized checkpoint loading and runtime
+quantization.
+
+## Hardware Support
+
+| Device | Support |
+| -------- | --------- |
+| NVIDIA Blackwell GPU (SM 100+) | ⭕ |
+| NVIDIA Ada GPU (SM 89) | ✅ |
+| NVIDIA Hopper GPU (SM 90) | ⭕ |
+| NVIDIA Ampere GPU (SM 80+) | ⭕ |
+| AMD ROCm | ⭕ |
+| Intel XPU | ⭕ |
+| Ascend NPU | ⭕ |
+
+Legend: `✅` supported, `❌` unsupported, `⭕` not verified in this
+guide.
+
+## Model Type Support
+
+### Diffusion Model (Boogu-Image)
+
+| Model                 | Checkpoint                                                         | Scope                      | Scheme                  |
+| --------------------- | ------------------------------------------------------------------ | -------------------------- | ----------------------- |
+| Boogu-Image Base/Edit | `Boogu/Boogu-Image-0.1-Base-fp8`, `Boogu/Boogu-Image-0.1-Edit-fp8` | Diffusion transformer only | FP8 weight-only (W8A16) |
+
+## Configuration
+
+Using Boogu-Image as an example:
+
+```bash
+vllm serve Boogu/Boogu-Image-0.1-Base-fp8 \
+  --omni \
+  --port 8091 \
+  --diffusion-quantization-config \
+  '{"transformer":{"method":"torchao_float8_weight_only"}}'
+```
+
+The equivalent command using the complete serialized TorchAO configuration is:
+
+```bash
+vllm serve Boogu/Boogu-Image-0.1-Base-fp8 \
+  --omni \
+  --port 8091 \
+  --diffusion-quantization-config \
+  '{
+    "transformer": {
+      "method": "torchao",
+      "quant_type": {
+        "default": {
+          "_type": "Float8WeightOnlyConfig",
+          "_version": 2,
+          "_data": {
+            "weight_dtype": {
+              "_type": "torch.dtype",
+              "_data": "float8_e4m3fn"
+            },
+            "set_inductor_config": false
+          }
+        }
+      }
+    }
+  }'
+```
+
+## Parameters
+
+| Parameter    | Type | Default | Description                                                                                                                         |
+| ------------ | ---- | ------- | ----------------------------------------------------------------------------------------------------------------------------------- |
+| `method`     | str  | -       | Use `torchao_float8_weight_only` for the serialized FP8 weight-only shorthand, or `torchao` with a full `quant_type` configuration. |
+| `quant_type` | dict | -       | Complete serialized TorchAO configuration used with `method: "torchao"`.                                                            |
+
+See TorchAO's [vLLM integration guide](https://docs.pytorch.org/ao/0.17/eager_tutorials/torchao_vllm_integration.html#configuration-system)
+for the serialized form and [workflow configs](https://docs.pytorch.org/ao/0.17/api_reference/api_ref_quantization.html#workflow-configs)
+for available configuration classes and parameters.
+
+## Validation and Notes
+
+At load time, vLLM-Omni builds a `TorchAOConfig` from
+`--diffusion-quantization-config`. For the Boogu-Image checkpoints documented
+here, indexed PyTorch `.bin` shards are loaded through `pt_weights_iterator`.
+
+When a component map is used, only the components included in the
+configuration use TorchAO. Other components keep their own checkpoint and
+runtime settings.
+
+Use either the `torchao_float8_weight_only` shorthand or the equivalent full
+`quant_type` configuration shown above. The checkpoint must already contain
+weights quantized with TorchAO.