[Diffusion] Add Diffusers quantization config bridge (#3837)

Signed-off-by: CAICAIIs <3360776475@qq.com>

tests/diffusion/diffusion_backend/test_diffusers_quantization_real.py

@@ -0,0 +1,57 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Optional real-backend checks for Diffusers quantization config conversion."""
+
+from __future__ import annotations
+
+import importlib.util
+from pathlib import Path
+
+import pytest
+
+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
+
+
+def _load_quantization_utils():
+    module_path = (
+        Path(__file__).resolve().parents[3] / "vllm_omni/diffusion/models/diffusers_adapter/quantization_utils.py"
+    )
+    spec = importlib.util.spec_from_file_location("diffusers_quantization_utils_real_test", module_path)
+    assert spec is not None
+    assert spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def test_convert_diffusers_quantization_config_from_real_torchao_dict():
+    diffusers = pytest.importorskip("diffusers")
+    torchao_quantization = pytest.importorskip("torchao.quantization")
+    quant_type_cls = getattr(torchao_quantization, "Int8DynamicActivationInt8WeightConfig", None)
+    if quant_type_cls is None:
+        pytest.skip("torchao does not expose Int8DynamicActivationInt8WeightConfig")
+
+    quantization_utils = _load_quantization_utils()
+
+    real_torchao_config = diffusers.TorchAoConfig(quant_type=quant_type_cls())
+    serialized_torchao_config = real_torchao_config.to_dict()
+    assert serialized_torchao_config["quant_type"].keys() == {"default"}
+
+    load_kwargs = {
+        "quantization_config": {
+            "quant_mapping": {
+                "transformer": serialized_torchao_config,
+            },
+        },
+    }
+
+    quantization_utils.convert_diffusers_quantization_config(load_kwargs)
+
+    quant_config = load_kwargs["quantization_config"]
+    component_config = quant_config.quant_mapping["transformer"]
+    assert isinstance(quant_config, diffusers.PipelineQuantizationConfig)
+    assert isinstance(component_config, diffusers.TorchAoConfig)
+    assert isinstance(
+        component_config.quant_type,
+        quant_type_cls,
+    )