From b91d8cc9e977cd912d828a8b439f775acaeba3e9 Mon Sep 17 00:00:00 2001
From: Guo Ziyuan <ziyuanguo@microsoft.com>
Date: Mon, 26 Jan 2026 11:03:28 +0800
Subject: [PATCH 01/11] init sd in aitk

---
 .aitk/configs/model_list.json                 |  13 +
 .../aitk/.gitignore                           |   8 +
 .../aitk/README.md                            |  35 +++
 .../aitk/inference_sample.ipynb               |  22 ++
 sd-legacy-stable-diffusion-v1-5/aitk/info.yml |  12 +
 .../aitk/model_project.config                 |  12 +
 .../aitk/qnn_workflow.json                    |  38 +++
 .../aitk/qnn_workflow.json.config             | 232 ++++++++++++++++++
 .../aitk/qnn_workflow.py                      |  10 +
 sd-legacy-stable-diffusion-v1-5/aitk/winml.py |  21 ++
 10 files changed, 403 insertions(+)
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/.gitignore
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/README.md
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/info.yml
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/model_project.config
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json.config
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.py
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/winml.py

diff --git a/.aitk/configs/model_list.json b/.aitk/configs/model_list.json
index 5091df7b..4c16c4e9 100644
--- a/.aitk/configs/model_list.json
+++ b/.aitk/configs/model_list.json
@@ -636,6 +636,19 @@
             "status": "Ready",
             "relativePath": "Qwen-Qwen2.5-Coder-14B-Instruct/aitk",
             "version": 3
+        },
+        {
+            "displayName": "stable-diffusion-v1-5/stable-diffusion-v1-5",
+            "icon": "HuggingFace",
+            "modelLink": "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5",
+            "id": "huggingface/stable-diffusion-v1-5/stable-diffusion-v1-5",
+            "runtimes": [
+                "QNN"
+            ],
+            "architecture": "Transformer",
+            "status": "Hide",
+            "relativePath": "sd-legacy-stable-diffusion-v1-5/aitk",
+            "version": 1
         }
     ],
     "template_models": [
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/.gitignore b/sd-legacy-stable-diffusion-v1-5/aitk/.gitignore
new file mode 100644
index 00000000..ce174cca
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/.gitignore
@@ -0,0 +1,8 @@
+__pycache__
+/cache
+/history/*/*
+!/history/*/history.config
+!/history/*/olive_config.json
+/footprints/
+/result_*.png
+/quantize_data*/
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/README.md b/sd-legacy-stable-diffusion-v1-5/aitk/README.md
new file mode 100644
index 00000000..5acd4f91
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/README.md
@@ -0,0 +1,35 @@
+## Stable Diffusion Optimization with ONNX Runtime QNN EP
+
+### Prerequisites
+```bash
+python -m pip install -r requirements_qnn.txt
+```
+### Generate data for static quantization
+
+To get better result, we need to generate real data from original model instead of using random data for static quantization.
+
+First generate onnx unoptimized model:
+
+`python stable_diffusion.py --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --provider cpu --format qdq --optimize --only_conversion`
+
+Then generate data:
+
+`python .\evaluation.py --save_data --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --num_inference_steps 25 --seed 0 --num_data 100 --guidance_scale 7.5`
+
+### Optimize
+
+Optimize the onnx models for performance improvements. vae_decoder and unet are per-channel quantized and text_encoder runs in fp16 precision.
+
+`python stable_diffusion.py --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --provider qnn --format qdq --optimize`
+
+### Test and evaluate
+
+`python .\evaluation.py --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --num_inference_steps 25 --seed 0 --num_data 100 --guidance_scale 7.5 --provider QNNExecutionProvider --model_dir optimized-qnn_qdq`
+
+To generate one image:
+
+`python stable_diffusion.py --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --provider qnn --format qdq --guidance_scale 7.5 --seed 0 --num_inference_steps 25 --prompt "A baby is laying down with a teddy bear"`
+
+### References
+
+[stable-diffusion-v1-4](https://github.com/microsoft/olive-recipes/tree/main/compvis-stable-diffusion-v1-4/olive#readme)
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb b/sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb
new file mode 100644
index 00000000..41105388
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb
@@ -0,0 +1,22 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21cb1e5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO\n",
+    "ExecutionProvider=\"QNNExecutionProvider\""
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/info.yml b/sd-legacy-stable-diffusion-v1-5/aitk/info.yml
new file mode 100644
index 00000000..bc041b01
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/info.yml
@@ -0,0 +1,12 @@
+keywords:
+  aitk
+arch: sd
+recipes:
+  - file: "qnn_workflow.json"
+    device: npu
+    ep: QNNExecutionProvider
+aitk:
+  modelInfo:
+    id: "huggingface/stable-diffusion-v1-5/stable-diffusion-v1-5"
+    version: 1
+    status: Hide
\ No newline at end of file
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/model_project.config b/sd-legacy-stable-diffusion-v1-5/aitk/model_project.config
new file mode 100644
index 00000000..3e130ee9
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/model_project.config
@@ -0,0 +1,12 @@
+{
+    "workflows": [
+        {
+            "file": "qnn_workflow.json",
+            "templateName": "qnn_workflow"
+        }
+    ],
+    "modelInfo": {
+        "id": "huggingface/stable-diffusion-v1-5/stable-diffusion-v1-5"
+        "version": 1
+    }
+}
\ No newline at end of file
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json b/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json
new file mode 100644
index 00000000..89170b4d
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json
@@ -0,0 +1,38 @@
+{
+    "input_model": {
+        "model_path": "stable-diffusion-v1-5/stable-diffusion-v1-5"
+    },
+    "systems": {
+        "target_system": {
+            "type": "LocalSystem",
+            "accelerators": [
+                {
+                    "device": "npu",
+                    "execution_providers": [
+                        "QNNExecutionProvider"
+                    ]
+                }
+            ]
+        }
+    },
+    "evaluators": {
+        "common_evaluator": {
+        }
+    },
+    "passes": {
+        "aitkpython": {
+            "type": "AitkPython",
+            "activation_type": "uint16",
+            "precision": "uint8",
+            "split": "test",
+            "length": 100,
+            "user_script": "qnn_workflow.py"
+        }
+    },
+    "evaluator": "common_evaluator",
+    "evaluate_input_model": false,
+    "target": "target_system",
+    "clean_cache": false,
+    "output_dir": "model/sd_qnn",
+    "cache_dir": "cache"
+}
\ No newline at end of file
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json.config b/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json.config
new file mode 100644
index 00000000..d1b24f37
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json.config
@@ -0,0 +1,232 @@
+{
+    "name": "Convert to Qualcomm NPU",
+    "evalMetrics": {
+        "encoder-latency-avg": "encoder-latency-avg",
+        "decoder-latency-avg": "decoder-latency-avg"
+    },
+    "memoryGbSuggested": 30,
+    "executeRuntimeFeatures": [
+        "QAI"
+    ],
+    "evaluationRuntimeFeatures": [
+        "QAI"
+    ],
+    "addCpu": true,
+    "runtime": {
+        "autoGenerated": true,
+        "name": "Evaluate on",
+        "type": "enum",
+        "displayNames": [
+            "Qualcomm NPU",
+            "CPU"
+        ],
+        "path": "systems.target_system.accelerators.0.execution_providers.0",
+        "values": [
+            "QNNExecutionProvider",
+            "CPUExecutionProvider"
+        ],
+        "actions": [
+            [
+                {
+                    "type": "update",
+                    "path": "systems.target_system.accelerators.0.execution_providers.0",
+                    "value": "QNNExecutionProvider"
+                },
+                {
+                    "type": "update",
+                    "path": "systems.target_system.accelerators.0.device",
+                    "value": "npu"
+                }
+            ],
+            [
+                {
+                    "type": "update",
+                    "path": "systems.target_system.accelerators.0.execution_providers.0",
+                    "value": "CPUExecutionProvider"
+                },
+                {
+                    "type": "update",
+                    "path": "systems.target_system.accelerators.0.device",
+                    "value": "cpu"
+                }
+            ]
+        ],
+        "readOnly": false
+    },
+    "optimizationPaths": [
+        {
+            "path": "passes.aitkpython.precision",
+            "name": "WeightType"
+        },
+        {
+            "path": "passes.aitkpython.activation_type",
+            "name": "ActivationType"
+        }
+    ],
+    "optimizationDefault": "w8a16",
+    "aitkPython": "qnn_workflow.py",
+    "sections": [
+        {
+            "autoGenerated": true,
+            "name": "Convert",
+            "phase": "Conversion",
+            "parameters": [],
+            "toggle": {
+                "autoGenerated": true,
+                "name": "Convert to ONNX format",
+                "type": "bool",
+                "path": "passes.aitkpython",
+                "actions": [
+                    [],
+                    []
+                ],
+                "readOnly": true
+            }
+        },
+        {
+            "name": "Quantize",
+            "phase": "Quantization",
+            "parameters": [
+                {
+                    "name": "Activation Type",
+                    "tags": [
+                        "ActivationType"
+                    ],
+                    "description": "Quantization data type of activation. ‘Int8’ for signed 8-bit integer, ‘UInt8’ for unsigned 8-bit integer etc.",
+                    "descriptionLink": "https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html",
+                    "type": "enum",
+                    "displayNames": [
+                        "Int8",
+                        "UInt8",
+                        "Int16",
+                        "UInt16"
+                    ],
+                    "displayType": "RadioGroup",
+                    "path": "passes.aitkpython.activation_type",
+                    "values": [
+                        "int8",
+                        "uint8",
+                        "int16",
+                        "uint16"
+                    ],
+                    "template": {
+                        "path": "passes.aitkpython.activation_type",
+                        "template": "ActivationType"
+                    }
+                },
+                {
+                    "name": "Weight Type",
+                    "tags": [
+                        "WeightType"
+                    ],
+                    "description": "Data type for quantizing weights. ‘Int8’ for signed 8-bit integer, ‘UInt8’ for unsigned 8-bit integer etc.",
+                    "descriptionLink": "https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html",
+                    "type": "enum",
+                    "displayNames": [
+                        "Int8",
+                        "UInt8",
+                        "Int16",
+                        "UInt16"
+                    ],
+                    "displayType": "RadioGroup",
+                    "path": "passes.aitkpython.precision",
+                    "values": [
+                        "int8",
+                        "uint8",
+                        "int16",
+                        "uint16"
+                    ],
+                    "template": {
+                        "path": "passes.aitkpython.precision",
+                        "template": "WeightType"
+                    }
+                },
+                {
+                    "name": "Quantization Dataset",
+                    "tags": [
+                        "QuantizationDataset"
+                    ],
+                    "type": "enum",
+                    "path": "passes.aitkpython.dataset_name",
+                    "values": [
+                        "librispeech_asr"
+                    ],
+                    "template": {
+                        "path": "passes.aitkpython.dataset_name",
+                        "values": [
+                            "librispeech_asr"
+                        ],
+                        "template": "QuantizationDataset"
+                    }
+                },
+                {
+                    "name": "Quantization Dataset Split",
+                    "tags": [
+                        "QuantizationDatasetSplit",
+                        "DependsOnDataset"
+                    ],
+                    "type": "enum",
+                    "path": "passes.aitkpython.split",
+                    "values": [
+                        "test",
+                        "validation",
+                        "train.100",
+                        "train.360"
+                    ],
+                    "template": {
+                        "path": "passes.aitkpython.split",
+                        "values": [
+                            "test",
+                            "validation",
+                            "train.100",
+                            "train.360"
+                        ],
+                        "template": "QuantizationDatasetSplit"
+                    }
+                },
+                {
+                    "name": "Quantization Dataset Size",
+                    "description": "ATTENTION! Using 100 samples of librispeech dataset to generate the required real data requires around 164 GB of disk space.",
+                    "type": "int",
+                    "path": "passes.aitkpython.length",
+                    "template": {
+                        "description": "ATTENTION! Using 100 samples of librispeech dataset to generate the required real data requires around 164 GB of disk space.",
+                        "path": "passes.aitkpython.length",
+                        "template": "QuantizationDatasetSize"
+                    }
+                }
+            ],
+            "disableToggleGeneration": true,
+            "toggle": {
+                "name": "Quantize model",
+                "type": "bool",
+                "path": "passes.aitkpython",
+                "actions": [
+                    [],
+                    []
+                ],
+                "readOnly": true
+            }
+        },
+        {
+            "name": "Evaluate",
+            "phase": "Evaluation",
+            "parameters": [],
+            "toggle": {
+                "autoGenerated": true,
+                "name": "Evaluate model performance",
+                "type": "bool",
+                "path": "evaluator",
+                "actions": [
+                    [],
+                    [
+                        {
+                            "type": "delete",
+                            "path": "evaluator"
+                        }
+                    ]
+                ]
+            }
+        }
+    ]
+}
\ No newline at end of file
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.py b/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.py
new file mode 100644
index 00000000..a6a2be60
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.py
@@ -0,0 +1,10 @@
+import argparse
+
+def main():
+    parser = argparse.ArgumentParser()
+    args = parser.parse_args()
+
+    print(args)
+
+if __name__ == "__main__":
+    main()
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/winml.py b/sd-legacy-stable-diffusion-v1-5/aitk/winml.py
new file mode 100644
index 00000000..74a12c53
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/winml.py
@@ -0,0 +1,21 @@
+import json
+
+def _get_ep_paths() -> dict[str, str]:
+    from winui3.microsoft.windows.applicationmodel.dynamicdependency.bootstrap import (
+        InitializeOptions,
+        initialize
+    )
+    import winui3.microsoft.windows.ai.machinelearning as winml
+    eps = {}
+    with initialize(options = InitializeOptions.ON_NO_MATCH_SHOW_UI):
+        catalog = winml.ExecutionProviderCatalog.get_default()
+        providers = catalog.find_all_providers()
+        for provider in providers:
+            provider.ensure_ready_async().get()
+            eps[provider.name] = provider.library_path
+            # DO NOT call provider.try_register in python. That will register to the native env.
+    return eps
+
+if __name__ == "__main__":
+    eps = _get_ep_paths()
+    print(json.dumps(eps))

From 755821d1918e03d80b0192f24b5f3a4a984bbc4e Mon Sep 17 00:00:00 2001
From: ziyuanguo <ziyuanguo@microsoft.com>
Date: Wed, 28 Jan 2026 02:46:16 +0800
Subject: [PATCH 02/11] add basic arch for sd conv

---
 .aitk/configs/model_list.json                 |  3 +-
 sd-legacy-stable-diffusion-v1-5/aitk/info.yml |  2 +-
 .../aitk/model_project.config                 |  4 +--
 ...qnn_workflow.json => sd_qnn_workflow.json} |  5 +--
 ...son.config => sd_qnn_workflow.json.config} | 32 ++++++-------------
 .../{qnn_workflow.py => sd_qnn_workflow.py}   |  0
 6 files changed, 17 insertions(+), 29 deletions(-)
 rename sd-legacy-stable-diffusion-v1-5/aitk/{qnn_workflow.json => sd_qnn_workflow.json} (87%)
 rename sd-legacy-stable-diffusion-v1-5/aitk/{qnn_workflow.json.config => sd_qnn_workflow.json.config} (89%)
 rename sd-legacy-stable-diffusion-v1-5/aitk/{qnn_workflow.py => sd_qnn_workflow.py} (100%)

diff --git a/.aitk/configs/model_list.json b/.aitk/configs/model_list.json
index 4c16c4e9..1d7764b8 100644
--- a/.aitk/configs/model_list.json
+++ b/.aitk/configs/model_list.json
@@ -693,7 +693,8 @@
         "wikipedia": "https://huggingface.co/datasets/wikimedia/wikipedia",
         "google-research-datasets/conceptual_captions": "https://huggingface.co/datasets/google-research-datasets/conceptual_captions",
         "AIMClab-RUC/COCO-CN": "https://huggingface.co/datasets/AIMClab-RUC/COCO-CN",
-        "librispeech_asr": "https://huggingface.co/datasets/openslr/librispeech_asr"
+        "librispeech_asr": "https://huggingface.co/datasets/openslr/librispeech_asr",
+        "phiyodr/coco2017": "https://huggingface.co/datasets/phiyodr/coco2017"
     },
     "LoginRequiredDatasets": [
         "imagenet-1k"
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/info.yml b/sd-legacy-stable-diffusion-v1-5/aitk/info.yml
index bc041b01..c4e27750 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/info.yml
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/info.yml
@@ -2,7 +2,7 @@ keywords:
   aitk
 arch: sd
 recipes:
-  - file: "qnn_workflow.json"
+  - file: "sd_qnn_workflow.json"
     device: npu
     ep: QNNExecutionProvider
 aitk:
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/model_project.config b/sd-legacy-stable-diffusion-v1-5/aitk/model_project.config
index 3e130ee9..df77afbc 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/model_project.config
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/model_project.config
@@ -1,8 +1,8 @@
 {
     "workflows": [
         {
-            "file": "qnn_workflow.json",
-            "templateName": "qnn_workflow"
+            "file": "sd_qnn_workflow.json",
+            "templateName": "sd_qnn_workflow"
         }
     ],
     "modelInfo": {
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json
similarity index 87%
rename from sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json
rename to sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json
index 89170b4d..a089cf0b 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json
@@ -24,9 +24,10 @@
             "type": "AitkPython",
             "activation_type": "uint16",
             "precision": "uint8",
-            "split": "test",
+            "dataset_name": "phiyodr/coco2017",
+            "split": "train",
             "length": 100,
-            "user_script": "qnn_workflow.py"
+            "user_script": "sd_qnn_workflow.py"
         }
     },
     "evaluator": "common_evaluator",
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json.config b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
similarity index 89%
rename from sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json.config
rename to sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
index d1b24f37..9c7feb20 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.json.config
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
@@ -1,16 +1,6 @@
 {
     "name": "Convert to Qualcomm NPU",
-    "evalMetrics": {
-        "encoder-latency-avg": "encoder-latency-avg",
-        "decoder-latency-avg": "decoder-latency-avg"
-    },
     "memoryGbSuggested": 30,
-    "executeRuntimeFeatures": [
-        "QAI"
-    ],
-    "evaluationRuntimeFeatures": [
-        "QAI"
-    ],
     "addCpu": true,
     "runtime": {
         "autoGenerated": true,
@@ -64,7 +54,7 @@
         }
     ],
     "optimizationDefault": "w8a16",
-    "aitkPython": "qnn_workflow.py",
+    "aitkPython": "sd_qnn_workflow.py",
     "sections": [
         {
             "autoGenerated": true,
@@ -149,12 +139,12 @@
                     "type": "enum",
                     "path": "passes.aitkpython.dataset_name",
                     "values": [
-                        "librispeech_asr"
+                        "phiyodr/coco2017"
                     ],
                     "template": {
                         "path": "passes.aitkpython.dataset_name",
                         "values": [
-                            "librispeech_asr"
+                            "phiyodr/coco2017"
                         ],
                         "template": "QuantizationDataset"
                     }
@@ -168,29 +158,25 @@
                     "type": "enum",
                     "path": "passes.aitkpython.split",
                     "values": [
-                        "test",
-                        "validation",
-                        "train.100",
-                        "train.360"
+                        "train",
+                        "validation"
                     ],
                     "template": {
                         "path": "passes.aitkpython.split",
                         "values": [
-                            "test",
-                            "validation",
-                            "train.100",
-                            "train.360"
+                            "train",
+                            "validation"
                         ],
                         "template": "QuantizationDatasetSplit"
                     }
                 },
                 {
                     "name": "Quantization Dataset Size",
-                    "description": "ATTENTION! Using 100 samples of librispeech dataset to generate the required real data requires around 164 GB of disk space.",
+                    "description": "ATTENTION! Using 100 samples of coco2017 dataset to generate the required real data requires large space of disk space.",
                     "type": "int",
                     "path": "passes.aitkpython.length",
                     "template": {
-                        "description": "ATTENTION! Using 100 samples of librispeech dataset to generate the required real data requires around 164 GB of disk space.",
+                        "description": "ATTENTION! Using 100 samples of coco2017 dataset to generate the required real data requires large space of disk space.",
                         "path": "passes.aitkpython.length",
                         "template": "QuantizationDatasetSize"
                     }
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py
similarity index 100%
rename from sd-legacy-stable-diffusion-v1-5/aitk/qnn_workflow.py
rename to sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py

From 1eb84b3bb35981d68bc5f3790a92731d090d5e3b Mon Sep 17 00:00:00 2001
From: ziyuanguo <ziyuanguo@microsoft.com>
Date: Wed, 28 Jan 2026 03:00:23 +0800
Subject: [PATCH 03/11] fix footprint typo

---
 sd-legacy-stable-diffusion-v1-5/olive/sd_utils/ort.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sd-legacy-stable-diffusion-v1-5/olive/sd_utils/ort.py b/sd-legacy-stable-diffusion-v1-5/olive/sd_utils/ort.py
index 69b1502b..4b20ee4a 100644
--- a/sd-legacy-stable-diffusion-v1-5/olive/sd_utils/ort.py
+++ b/sd-legacy-stable-diffusion-v1-5/olive/sd_utils/ort.py
@@ -53,7 +53,7 @@ def validate_ort_version(provider: str):
 
 
 def save_optimized_onnx_submodel(submodel_name, provider, model_info):
-    footprints_file_path = Path(__file__).resolve().parents[1] / "footprints" / submodel_name / "footprints.json"
+    footprints_file_path = Path(__file__).resolve().parents[1] / "footprints" / submodel_name / "footprint.json"
     with footprints_file_path.open("r") as footprint_file:
         footprints = json.load(footprint_file)
 

From 85e935ec2f84214b40ed0634e6fd58386145bb69 Mon Sep 17 00:00:00 2001
From: ziyuanguo <ziyuanguo@microsoft.com>
Date: Wed, 28 Jan 2026 03:30:22 +0800
Subject: [PATCH 04/11] add sanitize check

---
 .aitk/configs/checks.json                        | 16 ++++++++--------
 .aitk/requirements/requirements-WCR-SD.txt       |  0
 .aitk/scripts/project_processor.py               |  1 +
 .../aitk/model_project.config                    |  4 ++--
 .../aitk/sd_qnn_workflow.json.config             |  8 +++++++-
 5 files changed, 18 insertions(+), 11 deletions(-)
 create mode 100644 .aitk/requirements/requirements-WCR-SD.txt

diff --git a/.aitk/configs/checks.json b/.aitk/configs/checks.json
index a3026389..1a7a7ade 100644
--- a/.aitk/configs/checks.json
+++ b/.aitk/configs/checks.json
@@ -1,16 +1,16 @@
 {
-    "configCheck": 128,
+    "configCheck": 129,
     "copyCheck": 182,
     "extensionCheck": 1,
-    "gitignoreCheck": 38,
+    "gitignoreCheck": 39,
     "inferenceModelCheck": 25,
-    "ipynbCheck": 38,
-    "licenseCheck": 37,
-    "modelProjectCheck": 39,
+    "ipynbCheck": 39,
+    "licenseCheck": 38,
+    "modelProjectCheck": 40,
     "oliveCheck": 36,
-    "oliveJsonCheck": 128,
-    "pathCheck": 1105,
+    "oliveJsonCheck": 129,
+    "pathCheck": 1122,
     "requirementsCheck": 37,
     "templateCheck": 1,
-    "venvRequirementsCheck": 11
+    "venvRequirementsCheck": 12
 }
diff --git a/.aitk/requirements/requirements-WCR-SD.txt b/.aitk/requirements/requirements-WCR-SD.txt
new file mode 100644
index 00000000..e69de29b
diff --git a/.aitk/scripts/project_processor.py b/.aitk/scripts/project_processor.py
index d36c3f82..fb04a69f 100644
--- a/.aitk/scripts/project_processor.py
+++ b/.aitk/scripts/project_processor.py
@@ -28,6 +28,7 @@
     "mistralai": IconEnum.mistralai,
     # TODO add
     "OFA-Sys": IconEnum.HuggingFace,
+    "stable-diffusion-v1-5": IconEnum.HuggingFace,
 }
 
 
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/model_project.config b/sd-legacy-stable-diffusion-v1-5/aitk/model_project.config
index df77afbc..516e503b 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/model_project.config
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/model_project.config
@@ -6,7 +6,7 @@
         }
     ],
     "modelInfo": {
-        "id": "huggingface/stable-diffusion-v1-5/stable-diffusion-v1-5"
+        "id": "huggingface/stable-diffusion-v1-5/stable-diffusion-v1-5",
         "version": 1
     }
-}
\ No newline at end of file
+}
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
index 9c7feb20..1c14e4dd 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
@@ -1,6 +1,12 @@
 {
     "name": "Convert to Qualcomm NPU",
     "memoryGbSuggested": 30,
+    "executeRuntimeFeatures": [
+        "SD"
+    ],
+    "evaluationRuntimeFeatures": [
+        "SD"
+    ],
     "addCpu": true,
     "runtime": {
         "autoGenerated": true,
@@ -215,4 +221,4 @@
             }
         }
     ]
-}
\ No newline at end of file
+}

From 049c3b2e27b7ad31643bb8c86b33659b827ec8d4 Mon Sep 17 00:00:00 2001
From: ziyuanguo <ziyuanguo@microsoft.com>
Date: Wed, 28 Jan 2026 08:15:11 +0800
Subject: [PATCH 05/11] add sd requirements

---
 .aitk/requirements/requirements-WCR-SD.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.aitk/requirements/requirements-WCR-SD.txt b/.aitk/requirements/requirements-WCR-SD.txt
index e69de29b..0b601bcd 100644
--- a/.aitk/requirements/requirements-WCR-SD.txt
+++ b/.aitk/requirements/requirements-WCR-SD.txt
@@ -0,0 +1,4 @@
+accelerate==1.12.0
+diffusers==0.35.0
+onnxruntime-qnn==1.23.2
+torch-fidelity==0.3.0

From 5325325833f91ae74aefefec34f61469a609e058 Mon Sep 17 00:00:00 2001
From: ziyuanguo <ziyuanguo@microsoft.com>
Date: Wed, 28 Jan 2026 08:48:30 +0800
Subject: [PATCH 06/11] add conv script

---
 .../aitk/assets/dog.png                       | Bin 0 -> 928958 bytes
 .../aitk/config_safety_checker.json           |  98 +++
 .../aitk/config_text_encoder.json             | 147 +++++
 .../aitk/config_unet.json                     | 170 +++++
 .../aitk/config_vae_decoder.json              | 127 ++++
 .../aitk/config_vae_encoder.json              | 125 ++++
 .../aitk/evaluation.py                        | 285 +++++++++
 .../aitk/model_adaptations.py                 | 605 ++++++++++++++++++
 .../aitk/sd_utils/config.py                   |  10 +
 .../aitk/sd_utils/ort.py                      | 181 ++++++
 .../aitk/sd_utils/ov.py                       | 531 +++++++++++++++
 .../aitk/sd_utils/qdq.py                      | 287 +++++++++
 .../aitk/sd_utils/qdq_xl.py                   | 238 +++++++
 .../aitk/stable_diffusion.py                  | 491 ++++++++++++++
 .../aitk/user_script.py                       | 382 +++++++++++
 15 files changed, 3677 insertions(+)
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/assets/dog.png
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/config_safety_checker.json
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/config_text_encoder.json
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/config_unet.json
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/config_vae_decoder.json
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/config_vae_encoder.json
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/evaluation.py
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/model_adaptations.py
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/config.py
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ort.py
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ov.py
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq_xl.py
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/stable_diffusion.py
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/user_script.py

diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/assets/dog.png b/sd-legacy-stable-diffusion-v1-5/aitk/assets/dog.png
new file mode 100644
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diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/config_safety_checker.json b/sd-legacy-stable-diffusion-v1-5/aitk/config_safety_checker.json
new file mode 100644
index 00000000..acd009a9
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/config_safety_checker.json
@@ -0,0 +1,98 @@
+{
+    "input_model": {
+        "type": "PyTorchModel",
+        "model_path": "sd-legacy/stable-diffusion-v1-5",
+        "model_loader": "safety_checker_load",
+        "model_script": "user_script.py",
+        "io_config": {
+            "input_names": [ "clip_input", "images" ],
+            "output_names": [ "out_images", "has_nsfw_concepts" ],
+            "dynamic_axes": {
+                "clip_input": { "0": "batch", "1": "channels", "2": "height", "3": "width" },
+                "images": { "0": "batch", "1": "height", "2": "width", "3": "channels" }
+            }
+        },
+        "dummy_inputs_func": "safety_checker_conversion_inputs"
+    },
+    "systems": {
+        "local_system": {
+            "type": "LocalSystem",
+            "accelerators": [ { "device": "gpu", "execution_providers": [ "CUDAExecutionProvider" ] } ]
+        }
+    },
+    "data_configs": [
+        {
+            "name": "latency_data_config",
+            "user_script": "user_script.py",
+            "load_dataset_config": { "type": "local_dataset" },
+            "dataloader_config": { "type": "safety_checker_data_loader", "batch_size": 1 }
+        }
+    ],
+    "evaluators": {
+        "common_evaluator": {
+            "metrics": [
+                {
+                    "name": "latency",
+                    "type": "latency",
+                    "data_config": "latency_data_config",
+                    "sub_types": [ { "name": "avg" } ]
+                }
+            ]
+        }
+    },
+    "passes": {
+        "convert": { "type": "OnnxConversion", "target_opset": 14 },
+        "ov_convert": {
+            "type": "OpenVINOConversion",
+            "user_script": "user_script.py",
+            "example_input_func": "safety_checker_conversion_inputs",
+            "output_model": "safety_checker"
+        },
+        "optimize": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "unet",
+            "opt_level": 0,
+            "float16": true,
+            "use_gpu": true,
+            "keep_io_types": false,
+            "optimization_options": {
+                "enable_gelu": true,
+                "enable_layer_norm": true,
+                "enable_attention": true,
+                "use_multi_head_attention": true,
+                "enable_skip_layer_norm": false,
+                "enable_embed_layer_norm": true,
+                "enable_bias_skip_layer_norm": false,
+                "enable_bias_gelu": true,
+                "enable_gelu_approximation": false,
+                "enable_qordered_matmul": false,
+                "enable_shape_inference": true,
+                "enable_gemm_fast_gelu": false,
+                "enable_nhwc_conv": false,
+                "enable_group_norm": true,
+                "enable_bias_splitgelu": false,
+                "enable_packed_qkv": true,
+                "enable_packed_kv": true,
+                "enable_bias_add": false,
+                "group_norm_channels_last": false
+            },
+            "force_fp32_ops": [ "RandomNormalLike" ],
+            "force_fp16_inputs": { "GroupNorm": [ 0, 1, 2 ] }
+        },
+        "optimize_cuda": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "unet",
+            "opt_level": 0,
+            "float16": true,
+            "use_gpu": true,
+            "keep_io_types": false
+        }
+    },
+    "log_severity_level": 0,
+    "evaluator": "common_evaluator",
+    "evaluate_input_model": false,
+    "host": "local_system",
+    "target": "local_system",
+    "cache_dir": "cache",
+    "output_dir": "footprints/safety_checker"
+}
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/config_text_encoder.json b/sd-legacy-stable-diffusion-v1-5/aitk/config_text_encoder.json
new file mode 100644
index 00000000..271b2c37
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/config_text_encoder.json
@@ -0,0 +1,147 @@
+{
+    "input_model": {
+        "type": "PyTorchModel",
+        "model_path": "sd-legacy/stable-diffusion-v1-5",
+        "model_loader": "text_encoder_load",
+        "model_script": "user_script.py",
+        "io_config": {
+            "input_names": [ "input_ids" ],
+            "output_names": [ "last_hidden_state", "pooler_output" ],
+            "dynamic_axes": { "input_ids": { "0": "batch", "1": "sequence" } }
+        },
+        "dummy_inputs_func": "text_encoder_conversion_inputs"
+    },
+    "systems": {
+        "local_system": {
+            "type": "LocalSystem",
+            "accelerators": [ { "device": "gpu", "execution_providers": [ "CUDAExecutionProvider" ] } ]
+        }
+    },
+    "data_configs": [
+        {
+            "name": "latency_data_config",
+            "user_script": "user_script.py",
+            "load_dataset_config": { "type": "local_dataset" },
+            "dataloader_config": { "type": "text_encoder_data_loader", "batch_size": 1 }
+        },
+        {
+            "name": "quantize_data_config",
+            "user_script": "user_script.py",
+            "load_dataset_config": { "type": "local_dataset" },
+            "dataloader_config": { "type": "text_encoder_quantize_data_loader", "data_num": 100 }
+        }
+    ],
+    "evaluators": {
+        "common_evaluator": {
+            "metrics": [
+                {
+                    "name": "latency",
+                    "type": "latency",
+                    "data_config": "latency_data_config",
+                    "sub_types": [ { "name": "avg" } ]
+                }
+            ]
+        }
+    },
+    "passes": {
+        "convert": { "type": "OnnxConversion", "target_opset": 17 },
+        "ov_convert": {
+            "type": "OpenVINOConversion",
+            "user_script": "user_script.py",
+            "example_input_func": "text_encoder_conversion_inputs",
+            "output_model": "text_encoder"
+        },
+        "optimize": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "clip",
+            "opt_level": 0,
+            "float16": true,
+            "use_gpu": true,
+            "keep_io_types": false,
+            "optimization_options": {
+                "enable_gelu": true,
+                "enable_layer_norm": true,
+                "enable_attention": true,
+                "use_multi_head_attention": true,
+                "enable_skip_layer_norm": false,
+                "enable_embed_layer_norm": true,
+                "enable_bias_skip_layer_norm": false,
+                "enable_bias_gelu": true,
+                "enable_gelu_approximation": false,
+                "enable_qordered_matmul": false,
+                "enable_shape_inference": true,
+                "enable_gemm_fast_gelu": false,
+                "enable_nhwc_conv": false,
+                "enable_group_norm": true,
+                "enable_bias_splitgelu": false,
+                "enable_packed_qkv": true,
+                "enable_packed_kv": true,
+                "enable_bias_add": false,
+                "group_norm_channels_last": false
+            },
+            "force_fp32_ops": [ "RandomNormalLike" ],
+            "force_fp16_inputs": { "GroupNorm": [ 0, 1, 2 ] }
+        },
+        "optimize_cuda": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "clip",
+            "opt_level": 0,
+            "float16": true,
+            "use_gpu": true,
+            "keep_io_types": false
+        },
+        "dynamic_shape_to_fixed": {
+            "type": "DynamicToFixedShape",
+            "dim_param": [ "batch", "sequence" ],
+            "dim_value": [ 1, 77 ]
+        },
+        "surgery": { "type": "GraphSurgeries", "surgeries": [ { "surgeon": "ReplaceAttentionMaskValue", "replacement": -200.0 } ] },
+        "optimize_qdq": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "clip",
+            "opt_level": 0,
+            "optimization_options": {
+                "enable_gelu": true,
+                "enable_layer_norm": true,
+                "enable_attention": false,
+                "use_multi_head_attention": false,
+                "enable_skip_layer_norm": false,
+                "enable_embed_layer_norm": false,
+                "enable_bias_skip_layer_norm": false,
+                "enable_bias_gelu": false,
+                "enable_gelu_approximation": false,
+                "enable_qordered_matmul": false,
+                "enable_shape_inference": false,
+                "enable_gemm_fast_gelu": false,
+                "enable_nhwc_conv": false,
+                "enable_group_norm": false,
+                "enable_bias_splitgelu": false,
+                "enable_packed_qkv": false,
+                "enable_packed_kv": false,
+                "enable_bias_add": false,
+                "group_norm_channels_last": false
+            }
+        },
+        "quantization": {
+            "type": "OnnxStaticQuantization",
+            "data_config": "quantize_data_config",
+            "activation_type": "uint16",
+            "precision": "uint8",
+            "calibrate_method": "MinMax",
+            "quant_preprocess": true
+        },
+        "cb": {
+            "type": "EPContextBinaryGenerator",
+            "provider_options": {
+                "htp_graph_finalization_optimization_mode": "3"
+            }
+        }
+    },
+    "log_severity_level": 0,
+    "evaluator": "common_evaluator",
+    "evaluate_input_model": false,
+    "host": "local_system",
+    "target": "local_system",
+    "cache_dir": "cache",
+    "output_dir": "footprints/text_encoder"
+}
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/config_unet.json b/sd-legacy-stable-diffusion-v1-5/aitk/config_unet.json
new file mode 100644
index 00000000..654bb619
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/config_unet.json
@@ -0,0 +1,170 @@
+{
+    "input_model": {
+        "type": "PyTorchModel",
+        "model_path": "sd-legacy/stable-diffusion-v1-5",
+        "model_loader": "unet_load",
+        "model_script": "user_script.py",
+        "io_config": {
+            "input_names": [ "sample", "timestep", "encoder_hidden_states", "return_dict" ],
+            "output_names": [ "out_sample" ],
+            "dynamic_axes": {
+                "sample": {
+                    "0": "unet_sample_batch",
+                    "1": "unet_sample_channels",
+                    "2": "unet_sample_height",
+                    "3": "unet_sample_width"
+                },
+                "timestep": { "0": "unet_time_batch" },
+                "encoder_hidden_states": { "0": "unet_hidden_batch", "1": "unet_hidden_sequence" }
+            }
+        },
+        "dummy_inputs_func": "unet_conversion_inputs"
+    },
+    "systems": {
+        "local_system": {
+            "type": "LocalSystem",
+            "accelerators": [ { "device": "gpu", "execution_providers": [ "CUDAExecutionProvider" ] } ]
+        }
+    },
+    "data_configs": [
+        {
+            "name": "latency_data_config",
+            "user_script": "user_script.py",
+            "load_dataset_config": { "type": "local_dataset" },
+            "dataloader_config": { "type": "unet_data_loader", "batch_size": 1 }
+        },
+        {
+            "name": "quantize_data_config",
+            "user_script": "user_script.py",
+            "load_dataset_config": { "type": "local_dataset" },
+            "dataloader_config": { "type": "unet_quantize_data_loader", "data_num": 200 }
+        }
+    ],
+    "evaluators": {
+        "common_evaluator": {
+            "metrics": [
+                {
+                    "name": "latency",
+                    "type": "latency",
+                    "data_config": "latency_data_config",
+                    "sub_types": [ { "name": "avg" } ]
+                }
+            ]
+        }
+    },
+    "passes": {
+        "convert": {
+            "type": "OnnxConversion",
+            "target_opset": 17,
+            "save_as_external_data": true,
+            "all_tensors_to_one_file": true,
+            "external_data_name": "weights.pb"
+        },
+        "ov_convert": {
+            "type": "OpenVINOConversion",
+            "user_script": "user_script.py",
+            "example_input_func": "get_unet_ov_example_input",
+            "output_model": "unet"
+        },
+        "optimize": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "unet",
+            "opt_level": 0,
+            "float16": true,
+            "use_gpu": true,
+            "keep_io_types": false,
+            "optimization_options": {
+                "enable_gelu": true,
+                "enable_layer_norm": true,
+                "enable_attention": true,
+                "use_multi_head_attention": true,
+                "enable_skip_layer_norm": false,
+                "enable_embed_layer_norm": true,
+                "enable_bias_skip_layer_norm": false,
+                "enable_bias_gelu": true,
+                "enable_gelu_approximation": false,
+                "enable_qordered_matmul": false,
+                "enable_shape_inference": true,
+                "enable_gemm_fast_gelu": false,
+                "enable_nhwc_conv": false,
+                "enable_group_norm": true,
+                "enable_bias_splitgelu": false,
+                "enable_packed_qkv": true,
+                "enable_packed_kv": true,
+                "enable_bias_add": false,
+                "group_norm_channels_last": false
+            },
+            "force_fp32_ops": [ "RandomNormalLike" ],
+            "force_fp16_inputs": { "GroupNorm": [ 0, 1, 2 ] }
+        },
+        "optimize_cuda": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "unet",
+            "opt_level": 0,
+            "float16": true,
+            "use_gpu": true,
+            "keep_io_types": false
+        },
+        "dynamic_shape_to_fixed": {
+            "type": "DynamicToFixedShape",
+            "dim_param": [
+                "unet_sample_batch",
+                "unet_sample_channels",
+                "unet_sample_height",
+                "unet_sample_width",
+                "unet_time_batch",
+                "unet_hidden_batch",
+                "unet_hidden_sequence"
+            ],
+            "dim_value": [ 1, 4, 64, 64, 1, 1, 77 ],
+            "save_as_external_data": true
+        },
+        "optimize_qdq": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "unet",
+            "opt_level": 0,
+            "optimization_options": {
+                "enable_gelu": true,
+                "enable_layer_norm": true,
+                "enable_attention": false,
+                "use_multi_head_attention": false,
+                "enable_skip_layer_norm": false,
+                "enable_embed_layer_norm": false,
+                "enable_bias_skip_layer_norm": false,
+                "enable_bias_gelu": false,
+                "enable_gelu_approximation": false,
+                "enable_qordered_matmul": false,
+                "enable_shape_inference": true,
+                "enable_gemm_fast_gelu": false,
+                "enable_nhwc_conv": false,
+                "enable_group_norm": false,
+                "enable_bias_splitgelu": false,
+                "enable_packed_qkv": false,
+                "enable_packed_kv": false,
+                "enable_bias_add": false,
+                "group_norm_channels_last": false
+            }
+        },
+        "quantization": {
+            "type": "OnnxStaticQuantization",
+            "data_config": "quantize_data_config",
+            "activation_type": "uint16",
+            "precision": "uint8",
+            "calibrate_method": "MinMax",
+            "quant_preprocess": true
+        },
+        "cb": {
+            "type": "EPContextBinaryGenerator",
+            "provider_options": {
+                "htp_graph_finalization_optimization_mode": "3"
+            }
+        }
+    },
+    "log_severity_level": 0,
+    "evaluator": "common_evaluator",
+    "evaluate_input_model": false,
+    "host": "local_system",
+    "target": "local_system",
+    "cache_dir": "cache",
+    "output_dir": "footprints/unet"
+}
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_decoder.json b/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_decoder.json
new file mode 100644
index 00000000..31766c6a
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_decoder.json
@@ -0,0 +1,127 @@
+{
+    "input_model": {
+        "type": "PyTorchModel",
+        "model_path": "sd-legacy/stable-diffusion-v1-5",
+        "model_loader": "vae_decoder_load",
+        "model_script": "user_script.py",
+        "io_config": {
+            "input_names": [ "latent_sample", "return_dict" ],
+            "output_names": [ "sample" ],
+            "dynamic_axes": {
+                "latent_sample": {
+                    "0": "decoder_batch",
+                    "1": "decoder_channels",
+                    "2": "decoder_height",
+                    "3": "decoder_width"
+                }
+            }
+        },
+        "dummy_inputs_func": "vae_decoder_conversion_inputs"
+    },
+    "systems": {
+        "local_system": {
+            "type": "LocalSystem",
+            "accelerators": [ { "device": "gpu", "execution_providers": [ "CUDAExecutionProvider" ] } ]
+        }
+    },
+    "data_configs": [
+        {
+            "name": "latency_data_config",
+            "user_script": "user_script.py",
+            "load_dataset_config": { "type": "local_dataset" },
+            "dataloader_config": { "type": "vae_decoder_data_loader", "batch_size": 1 }
+        },
+        {
+            "name": "quantize_data_config",
+            "user_script": "user_script.py",
+            "load_dataset_config": { "type": "local_dataset" },
+            "dataloader_config": { "type": "vae_decoder_quantize_data_loader", "data_num": 100 }
+        }
+    ],
+    "evaluators": {
+        "common_evaluator": {
+            "metrics": [
+                {
+                    "name": "latency",
+                    "type": "latency",
+                    "data_config": "latency_data_config",
+                    "sub_types": [ { "name": "avg" } ]
+                }
+            ]
+        }
+    },
+    "passes": {
+        "convert": { "type": "OnnxConversion", "target_opset": 17 },
+        "ov_convert": {
+            "type": "OpenVINOConversion",
+            "user_script": "user_script.py",
+            "example_input_func": "vae_decoder_conversion_inputs",
+            "output_model": "vae_decoder"
+        },
+        "optimize": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "vae",
+            "opt_level": 0,
+            "float16": true,
+            "use_gpu": true,
+            "keep_io_types": false,
+            "optimization_options": {
+                "enable_gelu": true,
+                "enable_layer_norm": true,
+                "enable_attention": true,
+                "use_multi_head_attention": true,
+                "enable_skip_layer_norm": false,
+                "enable_embed_layer_norm": true,
+                "enable_bias_skip_layer_norm": false,
+                "enable_bias_gelu": true,
+                "enable_gelu_approximation": false,
+                "enable_qordered_matmul": false,
+                "enable_shape_inference": true,
+                "enable_gemm_fast_gelu": false,
+                "enable_nhwc_conv": false,
+                "enable_group_norm": true,
+                "enable_bias_splitgelu": false,
+                "enable_packed_qkv": true,
+                "enable_packed_kv": true,
+                "enable_bias_add": false,
+                "group_norm_channels_last": false
+            },
+            "force_fp32_ops": [ "RandomNormalLike" ],
+            "force_fp16_inputs": { "GroupNorm": [ 0, 1, 2 ] }
+        },
+        "optimize_cuda": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "vae",
+            "opt_level": 0,
+            "float16": true,
+            "use_gpu": true,
+            "keep_io_types": false
+        },
+        "dynamic_shape_to_fixed": {
+            "type": "DynamicToFixedShape",
+            "dim_param": [ "decoder_batch", "decoder_channels", "decoder_height", "decoder_width" ],
+            "dim_value": [ 1, 4, 64, 64 ]
+        },
+        "quantization": {
+            "type": "OnnxStaticQuantization",
+            "data_config": "quantize_data_config",
+            "activation_type": "uint16",
+            "precision": "uint8",
+            "calibrate_method": "MinMax",
+            "quant_preprocess": true
+        },
+        "cb": {
+            "type": "EPContextBinaryGenerator",
+            "provider_options": {
+                "htp_graph_finalization_optimization_mode": "3"
+            }
+        }
+    },
+    "log_severity_level": 0,
+    "evaluator": "common_evaluator",
+    "evaluate_input_model": false,
+    "host": "local_system",
+    "target": "local_system",
+    "cache_dir": "cache",
+    "output_dir": "footprints/vae_decoder"
+}
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_encoder.json b/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_encoder.json
new file mode 100644
index 00000000..3a283bbb
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_encoder.json
@@ -0,0 +1,125 @@
+{
+    "input_model": {
+        "type": "PyTorchModel",
+        "model_path": "sd-legacy/stable-diffusion-v1-5",
+        "model_loader": "vae_encoder_load",
+        "model_script": "user_script.py",
+        "io_config": {
+            "input_names": [ "sample", "return_dict" ],
+            "output_names": [ "latent_sample" ],
+            "dynamic_axes": {
+                "sample": { "0": "encoder_batch", "1": "encoder_channels", "2": "encoder_height", "3": "encoder_width" }
+            }
+        },
+        "dummy_inputs_func": "vae_encoder_conversion_inputs"
+    },
+    "systems": {
+        "local_system": {
+            "type": "LocalSystem",
+            "accelerators": [ { "device": "gpu", "execution_providers": [ "CUDAExecutionProvider" ] } ]
+        }
+    },
+    "data_configs": [
+        {
+            "name": "latency_data_config",
+            "user_script": "user_script.py",
+            "load_dataset_config": { "type": "local_dataset" },
+            "dataloader_config": { "type": "vae_encoder_data_loader", "batch_size": 1 }
+        },
+        {
+            "name": "quantize_data_config",
+            "user_script": "user_script.py",
+            "load_dataset_config": { "type": "local_dataset" },
+            "dataloader_config": { "type": "vae_encoder_quantize_data_loader", "data_num": 100 }
+        }
+    ],
+    "evaluators": {
+        "common_evaluator": {
+            "metrics": [
+                {
+                    "name": "latency",
+                    "type": "latency",
+                    "data_config": "latency_data_config",
+                    "sub_types": [ { "name": "avg" } ]
+                }
+            ]
+        }
+    },
+    "passes": {
+        "convert": { "type": "OnnxConversion", "target_opset": 17 },
+        "ov_convert": {
+            "type": "OpenVINOConversion",
+            "user_script": "user_script.py",
+            "example_input_func": "vae_encoder_conversion_inputs",
+            "output_model": "vae_encoder"
+        },
+        "optimize": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "vae",
+            "opt_level": 0,
+            "float16": true,
+            "use_gpu": true,
+            "keep_io_types": false,
+            "optimization_options": {
+                "enable_gelu": true,
+                "enable_layer_norm": true,
+                "enable_attention": true,
+                "use_multi_head_attention": true,
+                "enable_skip_layer_norm": false,
+                "enable_embed_layer_norm": true,
+                "enable_bias_skip_layer_norm": false,
+                "enable_bias_gelu": true,
+                "enable_gelu_approximation": false,
+                "enable_qordered_matmul": false,
+                "enable_shape_inference": true,
+                "enable_gemm_fast_gelu": false,
+                "enable_nhwc_conv": false,
+                "enable_group_norm": true,
+                "enable_bias_splitgelu": false,
+                "enable_packed_qkv": true,
+                "enable_packed_kv": true,
+                "enable_bias_add": false,
+                "group_norm_channels_last": false
+            },
+            "force_fp32_ops": [ "RandomNormalLike" ],
+            "force_fp16_inputs": { "GroupNorm": [ 0, 1, 2 ] }
+        },
+        "optimize_cuda": {
+            "type": "OrtTransformersOptimization",
+            "model_type": "vae",
+            "opt_level": 0,
+            "float16": true,
+            "use_gpu": true,
+            "keep_io_types": false
+        },
+        "dynamic_shape_to_fixed": {
+            "type": "DynamicToFixedShape",
+            "dim_param": [
+                "encoder_batch",
+                "encoder_channels",
+                "encoder_height",
+                "encoder_width",
+                "Addlatent_sample_dim_0",
+                "Addlatent_sample_dim_1",
+                "Addlatent_sample_dim_2",
+                "Addlatent_sample_dim_3"
+            ],
+            "dim_value": [ 1, 3, 512, 512, 1, 4, 64, 64 ]
+        },
+        "quantization": {
+            "type": "OnnxStaticQuantization",
+            "data_config": "quantize_data_config",
+            "activation_type": "uint16",
+            "precision": "uint8",
+            "calibrate_method": "MinMax",
+            "quant_preprocess": true
+        }
+    },
+    "log_severity_level": 0,
+    "evaluator": "common_evaluator",
+    "evaluate_input_model": false,
+    "host": "local_system",
+    "target": "local_system",
+    "cache_dir": "cache",
+    "output_dir": "footprints/vae_encoder"
+}
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/evaluation.py b/sd-legacy-stable-diffusion-v1-5/aitk/evaluation.py
new file mode 100644
index 00000000..c87fc8db
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/evaluation.py
@@ -0,0 +1,285 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
+import logging
+import math
+import os
+import re
+import sys
+from functools import partial
+from pathlib import Path
+from typing import Callable
+
+import numpy as np
+import requests
+import torch
+from datasets import load_dataset
+from PIL import Image
+from sd_utils.qdq import OnnxStableDiffusionPipelineWithSave
+from torchmetrics.functional.multimodal import clip_score
+from torchmetrics.image.fid import FrechetInceptionDistance
+from torchvision.transforms import functional as F
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+logger.addHandler(logging.StreamHandler(sys.stdout))
+
+# Clip score
+
+
+def get_clip_score(prompt: str, path: Path, clip_score_fn):
+    with torch.no_grad():
+        image = Image.open(path / f"{prompt}.png")
+        image = torch.tensor(np.array(image), dtype=torch.uint8).permute(2, 0, 1)
+        score = clip_score_fn(image, prompt)
+        return score.detach()
+
+
+def get_clip_scores(prompts: list[str], path: Path, clip_score_fn):
+    scores = []
+    with open(path / "clip_scores.txt", "w") as f:
+        f.write("| Prompt | Score |\n")
+        for prompt in prompts:
+            score = get_clip_score(prompt, path, clip_score_fn)
+            scores.append(score)
+            f.write(f"| {prompt} | {score} |\n")
+
+        logger.info("CLIP Scores avg: %s", np.mean(np.array(scores)))
+        f.write(f"| Avg | {np.mean(np.array(scores))} |\n")
+
+
+# MSE score
+
+
+def calc_error(image1, image2):
+    image1 = Image.open(image1)
+    image2 = Image.open(image2)
+    image1 = np.array(image1, dtype=np.float32)
+    image2 = np.array(image2, dtype=np.float32)
+    return np.mean((image1 - image2) ** 2)
+
+
+def get_mse_scores(prompts: list[str], unoptimized_path: Path, optimized_path: Path, train_num: int):
+    train_error = []
+    test_error = []
+    with open(optimized_path / "mse_scores.txt", "w") as f:
+        f.write("| Prompt | Error |\n")
+        for i, prompt in enumerate(prompts):
+            error = calc_error(unoptimized_path / f"{prompt}.png", optimized_path / f"{prompt}.png")
+            f.write(f"| {prompt} | {error} |\n")
+            if i < train_num:
+                train_error.append(error)
+            else:
+                test_error.append(error)
+
+        train_error = np.mean(np.array(train_error))
+        test_error = np.mean(np.array(test_error))
+        logger.info("Average train error %f", train_error)
+        logger.info("Average test error %f", test_error)
+        f.write(f"| Avg Train | {train_error} |\n")
+        f.write(f"| Avg Test | {test_error} |\n")
+
+
+# FID score
+
+
+def get_fid_scores(prompts: list[str], path: Path, real_images):
+    with torch.no_grad(), open(path / "fid_scores.txt", "w") as f:
+        f.write("| Prompt | Score |\n")
+        images = []
+        for prompt in prompts:
+            image = Image.open(path / f"{prompt}.png")
+            image = torch.tensor(np.array(image), dtype=torch.uint8).permute(2, 0, 1).unsqueeze(0)
+            images.append(image)
+        images = torch.cat(images)
+
+        fid = FrechetInceptionDistance()
+        fid.update(real_images, real=True)
+        fid.update(images, real=False)
+
+        score = fid.compute()
+        logger.info("FID: %f", score)
+        f.write(f"| FID | {score} |\n")
+
+
+# hpsv2 score
+
+
+def get_hpsv2_scores(path: Path, generate_image: Callable[[str, str], None], prompt_style: str):
+    if prompt_style is None:
+        return
+    import hpsv2
+
+    # Get benchmark prompts (<style> = all, anime, concept-art, paintings, photo)
+    all_prompts = hpsv2.benchmark_prompts(prompt_style)
+    if prompt_style != "all":
+        all_prompts = {prompt_style: all_prompts}
+    for style, prompts in all_prompts.items():
+        os.makedirs(path / style, exist_ok=True)
+        for idx, prompt in enumerate(prompts):
+            logger.info("Generating %s for %s [%d/%d]", prompt, style, idx + 1, len(prompts))
+            output = path / style / f"{idx:05d}.jpg"
+            generate_image(prompt, output)
+    hpsv2.evaluate(path.as_posix(), hps_version="v2.1")
+
+
+# prepare data
+
+
+def sanitize_path(input_string):
+    return re.sub(r"[^\w\-, ]", "", input_string.strip())
+
+
+def download_file(url, save_path):
+    try:
+        # Send a GET request to the URL
+        response = requests.get(url, stream=True, timeout=10)
+        response.raise_for_status()  # Raise an error for bad status codes
+
+        # Write the content to the specified file
+        os.makedirs(os.path.dirname(save_path), exist_ok=True)
+        with open(save_path, "wb") as file:
+            for chunk in response.iter_content(chunk_size=8192):
+                file.write(chunk)
+
+        logger.info("File successfully downloaded and saved to %s", save_path)
+    except requests.exceptions.MissingSchema:
+        logger.info("Error: Invalid URL. Please provide a valid URL.")
+    except requests.exceptions.ConnectionError:
+        logger.info("Error: Network issue. Please check your internet connection.")
+    except requests.exceptions.HTTPError as http_err:
+        logger.info("HTTP error occurred: %s", http_err)
+    except Exception as e:
+        logger.info("An error occurred: %s", e)
+
+
+def get_real_images(train_data, num_data):
+    script_dir = Path(__file__).resolve().parent
+    data_dir = script_dir / "quantize_data_coco2017"
+    with torch.no_grad():
+        images = []
+        for i, example in enumerate(train_data):
+            if i >= num_data:
+                break
+            image_path = data_dir / example["file_name"]
+            if not image_path.exists():
+                download_file(example["coco_url"], image_path)
+            image = Image.open(image_path).convert("RGB")
+            image = torch.tensor(np.array(image), dtype=torch.uint8).permute(2, 0, 1).unsqueeze(0)
+            # TODO(anyone): use resize?
+            image = F.center_crop(image, (256, 256))
+            images.append(image)
+        return torch.cat(images)
+
+
+def run_inference(pipeline, args, prompt: str, output_path: Path, pathIsFile: bool = False):
+    output = output_path if pathIsFile else output_path / f"{prompt}.png"
+    if output.exists():
+        return
+    generator = None if args.seed is None else np.random.RandomState(seed=args.seed)
+    result = pipeline(
+        [prompt],
+        num_inference_steps=args.num_inference_steps,
+        height=args.image_size,
+        width=args.image_size,
+        guidance_scale=args.guidance_scale,
+        generator=generator,
+    )
+    result.images[0].save(output)
+
+
+def parse_args(raw_args):
+    import argparse
+
+    parser = argparse.ArgumentParser("Common arguments")
+    parser.add_argument("--save_data", action="store_true")
+    parser.add_argument("--model_dir", default="optimized-cpu_qdq", type=str, help="model_dir path")
+    parser.add_argument("--model_id", default="CompVis/stable-diffusion-v1-4", type=str)
+    parser.add_argument(
+        "--guidance_scale",
+        default=7.5,
+        type=float,
+        help="Guidance scale as defined in Classifier-Free Diffusion Guidance",
+    )
+    parser.add_argument("--num_inference_steps", default=50, type=int, help="Number of steps in diffusion process")
+    parser.add_argument(
+        "--seed",
+        default=None,
+        type=int,
+        help="The seed to give to the generator to generate deterministic results.",
+    )
+    parser.add_argument("--data_dir", default="quantize_data", type=str)
+    parser.add_argument("--provider", default="CPUExecutionProvider", type=str)
+    parser.add_argument(
+        "--sub_dir", default="optimized", type=str, help="Sub directory to save the data for optimized model test"
+    )
+    parser.add_argument("--num_data", default=10, type=int)
+    parser.add_argument("--train_ratio", default=0.5, type=float)
+    parser.add_argument("--image_size", default=512, type=int, help="Width and height of the images to generate")
+    parser.add_argument("--hpsv2_style", default=None, type=str, help="Style for hpsv2 benchmark")
+    parser.add_argument("--xl", action="store_true")
+    return parser.parse_args(raw_args)
+
+
+def main(raw_args=None):
+    args = parse_args(raw_args)
+    real_images = None
+    dataset = load_dataset("phiyodr/coco2017", streaming=True)
+    train_data = dataset["train"]
+    prompts = [sanitize_path(example["captions"][0]) for i, example in enumerate(train_data) if i < args.num_data]
+    real_images = get_real_images(train_data, args.num_data)
+
+    train_num = math.floor(len(prompts) * args.train_ratio)
+    clip_score_fn = partial(clip_score, model_name_or_path="openai/clip-vit-base-patch16")
+
+    script_dir = Path()  # Path(__file__).resolve().parent
+    unoptimized_path: Path = script_dir / args.data_dir / "unoptimized"
+    optimized_path: Path = script_dir / args.data_dir / args.sub_dir
+
+    unoptimized_model_dir = script_dir / "models" / "unoptimized" / args.model_id
+    optimized_dir_name = args.model_dir
+    optimized_model_dir = script_dir / "models" / optimized_dir_name / args.model_id
+
+    model_dir = unoptimized_model_dir if args.save_data else optimized_model_dir
+    if args.xl:
+        from sd_utils.qdq_xl import ORTStableDiffusionXLPipelineWithSave
+
+        pipeline = ORTStableDiffusionXLPipelineWithSave.from_pretrained(model_dir, provider=args.provider)
+    else:
+        pipeline = OnnxStableDiffusionPipelineWithSave.from_pretrained(model_dir, provider=args.provider)
+    pipeline.save_data_dir = None
+
+    if args.save_data:
+        os.makedirs(unoptimized_path, exist_ok=True)
+        for i, prompt in enumerate(prompts):
+            logger.info(prompt)
+            if i < train_num:
+                pipeline.save_data_dir = script_dir / args.data_dir / "data" / prompt
+                os.makedirs(pipeline.save_data_dir, exist_ok=True)
+            else:
+                pipeline.save_data_dir = None
+            run_inference(pipeline, args, prompt, unoptimized_path)
+        get_clip_scores(prompts, unoptimized_path, clip_score_fn)
+        get_fid_scores(prompts, unoptimized_path, real_images)
+        get_hpsv2_scores(
+            unoptimized_path / "hpsv2", partial(run_inference, pipeline, args, pathIsFile=True), args.hpsv2_style
+        )
+
+    else:
+        os.makedirs(optimized_path, exist_ok=True)
+        for prompt in prompts:
+            logger.info(prompt)
+            run_inference(pipeline, args, prompt, optimized_path)
+
+        get_clip_scores(prompts, optimized_path, clip_score_fn)
+        get_fid_scores(prompts, optimized_path, real_images)
+        get_mse_scores(prompts, unoptimized_path, optimized_path, train_num)
+        get_hpsv2_scores(
+            optimized_path / "hpsv2", partial(run_inference, pipeline, args, pathIsFile=True), args.hpsv2_style
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/model_adaptations.py b/sd-legacy-stable-diffusion-v1-5/aitk/model_adaptations.py
new file mode 100644
index 00000000..945237ad
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/model_adaptations.py
@@ -0,0 +1,605 @@
+# ---------------------------------------------------------------------
+# Copyright (c) 2024 Qualcomm Innovation Center, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+# ---------------------------------------------------------------------
+import math
+import types
+from typing import Callable, Optional
+
+import diffusers.models.attention_processor as attention_processor
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers import UNet2DConditionModel
+from diffusers.models.activations import GEGLU, GELU, ApproximateGELU
+from diffusers.models.attention import BasicTransformerBlock, FeedForward
+from diffusers.models.transformers.transformer_2d import Transformer2DModel
+
+
+class Conv2dLinear(torch.nn.Module):
+    """A class to convert a Linear layer to a Conv2D layer with a 1x1 kernel.
+    This allows the linear transformation to be applied to the channel dimension
+    at each spatial location in the input tensor.
+
+    Args:
+        linear (nn.Linear): The original linear layer to be converted.
+
+    """
+
+    def __init__(self, linear: torch.nn.Linear):
+        super().__init__()
+        self.in_features = linear.in_features
+        self.out_features = linear.out_features
+
+        # Initialize a Conv2D layer with a 1x1 kernel to mimic the Linear layer
+        self.conv = torch.nn.Conv2d(
+            in_channels=self.in_features,
+            out_channels=self.out_features,
+            kernel_size=1,
+            bias=(linear.bias is not None),
+        )
+
+        # Copy the weights from the Linear layer to the Conv2D layer
+        self.conv.weight.data.copy_(linear.weight.data.view(self.out_features, self.in_features, 1, 1))
+
+        # Copy the bias if it exists
+        if linear.bias is not None:
+            self.conv.bias.data.copy_(linear.bias.data)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward-pass routine for the Conv2D layer.
+
+        Args:
+            x (torch.Tensor): The input tensor in NCHW format.
+
+        Returns:
+            torch.Tensor: The output tensor after applying the Conv2D transformation.
+
+        """
+        return self.conv(x)
+
+
+class SHAAttention(nn.Module):
+    """Split-Head Attention with per-head Conv2D projections and a single output
+    Conv2D projection.  This implementation splits the attention heads into
+    separate Conv2D projection layers and applies a single output projection
+    after concatenating all heads.  Adjusted to handle spatial dimensions (H,
+    W) instead of sequence length.
+    """
+
+    def __init__(self, orig_attn: attention_processor.Attention):
+        """Initialize SHAAttention by copying weights from an existing Attention module.
+
+        Args:
+            orig_attn (attention_processor.Attention): The original Attention module to be replaced.
+
+        """
+        super().__init__()
+
+        for f in ["group_norm", "spatial_norm", "norm_q", "norm_k", "norm_cross"]:
+            if getattr(orig_attn, f) is not None:
+                raise NotImplementedError(f"{f} is not supported")
+
+        # Copy configuration from the original Attention module
+        self.heads = orig_attn.heads
+        self.kv_heads = int(orig_attn.inner_kv_dim / orig_attn.inner_dim * self.heads)
+
+        # Infer dim_head from to_q.out_features and heads
+        if orig_attn.to_q.out_features % self.heads != 0:
+            raise ValueError("to_q.out_features is not divisible by heads. Cannot infer dim_head.")
+        self.dim_head = orig_attn.to_q.out_features // self.heads
+        self.scale = 1 / math.sqrt(self.dim_head)
+        self.rescale_output_factor_inv = 1 / orig_attn.rescale_output_factor
+
+        self.residual_connection = orig_attn.residual_connection
+
+        # Verify to_k and to_v dimensions
+        expected_kv_out = self.kv_heads * self.dim_head
+        if orig_attn.to_k.out_features != expected_kv_out:
+            raise ValueError(
+                f"to_k.out_features ({orig_attn.to_k.out_features}) does not match expected {expected_kv_out}."
+            )
+        if orig_attn.to_v.out_features != expected_kv_out:
+            raise ValueError(
+                f"to_v.out_features ({orig_attn.to_v.out_features}) does not match expected {expected_kv_out}."
+            )
+
+        # Initialize separate Conv2D projection layers for each head
+        self.q_proj_sha = nn.ModuleList(
+            [
+                nn.Conv2d(
+                    orig_attn.to_q.in_features,
+                    self.dim_head,
+                    kernel_size=1,
+                    bias=(orig_attn.to_q.bias is not None),
+                )
+                for _ in range(self.heads)
+            ]
+        )
+        self.k_proj_sha = nn.ModuleList(
+            [
+                nn.Conv2d(
+                    orig_attn.to_k.in_features,
+                    self.dim_head,
+                    kernel_size=1,
+                    bias=(orig_attn.to_k.bias is not None),
+                )
+                for _ in range(self.kv_heads)
+            ]
+        )
+        self.v_proj_sha = nn.ModuleList(
+            [
+                nn.Conv2d(
+                    orig_attn.to_v.in_features,
+                    self.dim_head,
+                    kernel_size=1,
+                    bias=(orig_attn.to_v.bias is not None),
+                )
+                for _ in range(self.kv_heads)
+            ]
+        )
+
+        self.to_out = orig_attn.to_out
+
+        # Copy weights from the original shared Linear projections to the separate Conv2D projections
+        for i in range(self.heads):
+            # Query Projection
+            q_weight = orig_attn.to_q.weight.data[i * self.dim_head : (i + 1) * self.dim_head, :].clone()
+            q_weight = q_weight.unsqueeze(-1).unsqueeze(-1)  # Shape: (dim_head, in_features, 1, 1)
+            self.q_proj_sha[i].weight.data.copy_(q_weight)
+            if orig_attn.to_q.bias is not None:
+                self.q_proj_sha[i].bias.data.copy_(
+                    orig_attn.to_q.bias.data[i * self.dim_head : (i + 1) * self.dim_head].clone()
+                )
+
+        for i in range(self.kv_heads):
+            # Key Projection
+            k_weight = orig_attn.to_k.weight.data[i * self.dim_head : (i + 1) * self.dim_head, :].clone()
+            k_weight = k_weight.unsqueeze(-1).unsqueeze(-1)  # Shape: (dim_head, in_features, 1, 1)
+            self.k_proj_sha[i].weight.data.copy_(k_weight)
+            if orig_attn.to_k.bias is not None:
+                self.k_proj_sha[i].bias.data.copy_(
+                    orig_attn.to_k.bias.data[i * self.dim_head : (i + 1) * self.dim_head].clone()
+                )
+
+            # Value Projection
+            v_weight = orig_attn.to_v.weight.data[i * self.dim_head : (i + 1) * self.dim_head, :].clone()
+            v_weight = v_weight.unsqueeze(-1).unsqueeze(-1)  # Shape: (dim_head, in_features, 1, 1)
+            self.v_proj_sha[i].weight.data.copy_(v_weight)
+            if orig_attn.to_v.bias is not None:
+                self.v_proj_sha[i].bias.data.copy_(
+                    orig_attn.to_v.bias.data[i * self.dim_head : (i + 1) * self.dim_head].clone()
+                )
+
+        del orig_attn.to_q
+        del orig_attn.to_k
+        del orig_attn.to_v
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        """Forward pass for Split-Head Cross Attention.
+        Processes each head separately using head-specific Conv2D projection layers.
+
+        Args:
+            hidden_states (torch.Tensor): The hidden states (batch_size, hidden_size, H, W).
+            attention_mask (Optional[torch.Tensor]): The attention mask.
+            encoder_hidden_states (Optional[torch.Tensor]): The encoder hidden states for cross-attention.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            Tuple containing the attention output, attention weights, and past key-value.
+
+        """
+        bsz, hidden_size, H, W = hidden_states.size()
+        residual = hidden_states
+
+        if encoder_hidden_states is not None:
+            # (N, seq_len, inner_dim) to (N, inner_dim, 1, seq_len)
+            encoder_hidden_states = encoder_hidden_states.permute(0, 2, 1).unsqueeze(2)
+            # encoder_hidden_states: (N, inner_dim, 1, seq_len)
+        else:
+            encoder_hidden_states = hidden_states
+
+        query_states = [q_proj(hidden_states) for q_proj in self.q_proj_sha]
+        key_states = [k_proj(encoder_hidden_states) for k_proj in self.k_proj_sha]
+        value_states = [v_proj(encoder_hidden_states) for v_proj in self.v_proj_sha]
+        # query_states, key_states, value_states: List of (bsz, dim_head, H, W)
+
+        # Handle past_key_value for caching
+        past_key_value = kwargs.get("past_key_value")
+        if past_key_value is not None:
+            raise NotImplementedError("SHAAttention does not support kv cache yet")
+
+        # Prepare for attention computation
+        attn_outputs = []
+        for head_idx, (q, k, v) in enumerate(zip(query_states, key_states, value_states)):
+            q_flat = q.permute(0, 2, 3, 1)  # (bsz, H, W, dim_head)
+            k_flat = k.view(bsz, 1, self.dim_head, -1)  # (bsz, 1, dim_head, H_enc*W_enc)
+            v_flat = v.view(bsz, 1, self.dim_head, -1)  # (bsz, 1, dim_head, H_enc*W_enc)
+
+            attn_scores = torch.matmul(q_flat, k_flat) * self.scale
+            # attn_scores: (bsz, H, W, H_enc*W_enc)
+
+            if attention_mask is not None:
+                attn_scores = attn_scores + attention_mask
+
+            attn_probs = torch.nn.functional.softmax(attn_scores, dim=-1)
+            # attn_probs: (bsz, H, W, H_enc*W_enc)
+
+            # Compute attention output
+            v_perm = v_flat.permute(0, 1, 3, 2)  # (bsz, 1, H_enc*W_enc, dim_head)
+            attn_output = torch.matmul(attn_probs, v_perm)
+            # attn_output: (bsz, H, W, dim_head)
+
+            attn_outputs.append(attn_output)
+
+        # Concatenate all heads' outputs along the channel dimension
+        attn_output = torch.cat(attn_outputs, dim=-1)  # (bsz, H, W, heads * dim_head)
+
+        attn_output = self.to_out[0](attn_output)  # (bsz, H, W, out_features)
+        attn_output = self.to_out[1](attn_output)  # (bsz, H, W, out_features)
+        attn_output = attn_output.permute(0, 3, 1, 2)  # (bsz,out_features, H, W)
+
+        if self.residual_connection:
+            hidden_states = hidden_states + residual
+
+        if kwargs.get("output_attentions", False):
+            raise NotImplementedError("output_attentions=True is not supported")
+
+        if self.rescale_output_factor_inv != 1:
+            hidden_states *= self.rescale_output_factor_inv
+
+        return attn_output
+
+
+class PermuteLayerNorm(nn.Module):
+    def __init__(self, original_norm):
+        super().__init__()
+        self.original_norm = original_norm
+
+    def forward(self, *args, **kwargs):
+        # Assuming the first argument is the tensor to be normalized
+        # Permute the tensor dimensions from (N, C, H, W) to (N, H, W, C)
+        permuted_args = list(args)
+        if len(permuted_args) > 0 and isinstance(permuted_args[0], torch.Tensor):
+            permuted_args[0] = permuted_args[0].permute(0, 2, 3, 1)
+
+        # Apply the original normalization
+        norm_output = self.original_norm(*permuted_args, **kwargs)
+
+        # If the output is a tuple (as in some custom norms), permute relevant tensors
+        if isinstance(norm_output, tuple):
+            # Permute the first tensor in the output tuple
+            norm_output = (norm_output[0].permute(0, 3, 1, 2),) + norm_output[1:]
+        elif isinstance(norm_output, torch.Tensor):
+            norm_output = norm_output.permute(0, 3, 1, 2)
+
+        return norm_output
+
+
+def traverse_and_replace(
+    model: nn.Module,
+    target_type: type[torch.nn.Module],
+    replacement_fn: Callable[[torch.nn.Module], torch.nn.Module],
+):
+    """Recursively traverses the model to find and replace modules of a specified type.
+
+    Args:
+        model (nn.Module): The model to traverse.
+        target_type (type): The type of modules to replace (e.g., Attention, GELU).
+        replacement_fn (callable): A function that takes a module instance and returns the replacement module.
+
+    """
+    for name, module in model.named_children():
+        if isinstance(module, target_type):
+            setattr(model, name, replacement_fn(module))
+
+        elif isinstance(module, nn.ModuleList):
+            for idx in range(len(module)):
+                child = module[idx]
+                if isinstance(child, target_type):
+                    module[idx] = replacement_fn(child)
+                else:
+                    # Recursively apply to child modules
+                    traverse_and_replace(child, target_type, replacement_fn)
+        else:
+            traverse_and_replace(module, target_type, replacement_fn)
+
+
+def replace_attention_modules(model: nn.Module):
+    """Recursively traverses the model to find and replace all instances of Attention with SHAAttention,
+    including those nested within ModuleList containers.
+
+    Args:
+        model (nn.Module): The model in which to replace Attention modules.
+
+    """
+    traverse_and_replace(model, attention_processor.Attention, lambda orig_attn: SHAAttention(orig_attn))
+
+
+def replace_gelu_and_approx_gelu_with_conv2d(activation_module: nn.Module) -> nn.Module:
+    """Replaces the projection layer in GELU and ApproximateGELU activation modules from Linear to Conv2D.
+
+    Args:
+        activation_module (nn.Module): The activation module to replace.
+
+    Returns:
+        nn.Module: The activation module with Conv2D projection.
+
+    """
+    assert isinstance(activation_module, GELU) or isinstance(activation_module, ApproximateGELU)
+    dim_in = activation_module.proj.in_features
+    dim_out = activation_module.proj.out_features
+    bias = activation_module.proj.bias is not None
+
+    # Define Conv2d projection
+    conv = nn.Conv2d(in_channels=dim_in, out_channels=dim_out, kernel_size=1, bias=bias)
+
+    # Copy weights from Linear to Conv2d
+    with torch.no_grad():
+        conv.weight.copy_(activation_module.proj.weight.view(dim_out, dim_in, 1, 1))
+        if bias:
+            conv.bias.copy_(activation_module.proj.bias)
+
+    # Replace the Linear layer with Conv2d
+    activation_module.proj = conv
+    return activation_module
+
+
+class QcGEGLU(nn.Module):
+    r"""A reimplemented version of the GEGLU activation function using two Conv2D layers.
+    This class replaces the original GEGLU's Linear projections with Conv2D projections
+    and eliminates the need for the chunk operation by directly computing the gate.
+
+    Parameters:
+        original_geglu (GEGLU): The original GEGLU module to be replaced.
+
+    """
+
+    def __init__(self, original_geglu: GEGLU):
+        super().__init__()
+        # Extract dimensions from the original GEGLU
+        dim_in = original_geglu.proj.in_features
+        dim_out = original_geglu.proj.out_features // 2  # GEGLU splits output into two parts
+        bias = original_geglu.proj.bias is not None
+
+        # Define separate Conv2D layers for hidden projection and gate projection
+        self.hidden_proj = nn.Conv2d(in_channels=dim_in, out_channels=dim_out, kernel_size=1, bias=bias)
+        self.gate_proj = nn.Conv2d(in_channels=dim_in, out_channels=dim_out, kernel_size=1, bias=bias)
+
+        # Initialize weights and biases from the original GEGLU's Linear layer
+        with torch.no_grad():
+            # Original Linear weights shape: [dim_out*2, dim_in]
+            linear_weight = original_geglu.proj.weight.data  # Shape: [dim_out*2, dim_in]
+            linear_bias = original_geglu.proj.bias.data if bias else None  # Shape: [dim_out*2]
+
+            # Assign weights to hidden_proj and gate_proj Conv2D layers
+            self.hidden_proj.weight.copy_(linear_weight[:dim_out, :].view(dim_out, dim_in, 1, 1))
+            if bias:
+                self.hidden_proj.bias.copy_(linear_bias[:dim_out])  # type: ignore
+
+            self.gate_proj.weight.copy_(linear_weight[dim_out:, :].view(dim_out, dim_in, 1, 1))
+            if bias:
+                self.gate_proj.bias.copy_(linear_bias[dim_out:])  # type: ignore
+
+    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
+        return F.gelu(gate)
+
+    def forward(self, hidden_states, *args, **kwargs):
+        # Project hidden states and compute gate
+        hidden_proj = self.hidden_proj(hidden_states)  # (N, dim_out, H, W)
+        gate = self.gate_proj(hidden_states)  # (N, dim_out, H, W)
+
+        # Apply GELU activation to the gate
+        gate = self.gelu(gate)
+
+        # Apply gating mechanism
+        return hidden_proj * gate
+
+
+def replace_geglu_with_conv2d(activation_module: nn.Module) -> nn.Module:
+    """Replaces the original GEGLU activation module with the QcGEGLU module,
+    which uses two Conv2D layers and eliminates the chunk operation.
+
+    Args:
+        activation_module (nn.Module): The GEGLU activation module to replace.
+
+    Returns:
+        nn.Module: The QcGEGLU activation module with two Conv2D projections.
+
+    """
+    if isinstance(activation_module, GEGLU):
+        # Instantiate QcGEGLU with the original GEGLU module
+        qc_geglu = QcGEGLU(activation_module)
+        return qc_geglu
+    else:
+        raise TypeError(f"Unsupported activation module type for GEGLU replacement: {type(activation_module)}")
+
+
+def replace_activations_with_conv2d(model: nn.Module):
+    """Recursively traverses the model to find and replace GELU, GEGLU, and ApproximateGELU activation projections
+    from Linear layers to Conv2D layers, ensuring compatibility with NCHW input shapes.
+    Also handles activations nested within ModuleList containers.
+
+    Args:
+        model (nn.Module): The model in which to perform the replacement.
+
+    """
+    # Replace GELU and ApproximateGELU
+    traverse_and_replace(model, GELU, replace_gelu_and_approx_gelu_with_conv2d)
+    traverse_and_replace(model, ApproximateGELU, replace_gelu_and_approx_gelu_with_conv2d)
+
+    # Replace GEGLU
+    traverse_and_replace(model, GEGLU, replace_geglu_with_conv2d)
+
+
+def replace_feedforward_with_conv2d(feedforward_module: nn.Module) -> nn.Module:
+    """Replaces the nn.Linear layer in the FeedForward module with a Conv2D layer
+    to handle hidden_states of shape (N, C, H, W).
+
+    Args:
+        feedforward_module (nn.Module): The FeedForward module to replace.
+
+    Returns:
+        nn.Module: The FeedForward module with Conv2D layers instead of Linear layers.
+
+    """
+    if isinstance(feedforward_module, FeedForward):
+        # Create a new ModuleList to hold the modified layers
+        new_net = nn.ModuleList()
+        for module in feedforward_module.net:
+            if isinstance(module, nn.Linear):
+                # Define Conv2d projection
+                conv = nn.Conv2d(
+                    in_channels=module.in_features,
+                    out_channels=module.out_features,
+                    kernel_size=1,
+                    bias=module.bias is not None,
+                )
+                # Copy weights from Linear to Conv2d
+                with torch.no_grad():
+                    conv.weight.copy_(module.weight.data.view(module.out_features, module.in_features, 1, 1))
+                    if module.bias is not None:
+                        conv.bias.copy_(module.bias.data)
+                # Append the Conv2d layer instead of Linear
+                new_net.append(conv)
+            else:
+                # Append other modules (e.g., activation functions, Dropout) unchanged
+                new_net.append(module)
+        # Replace the original ModuleList with the new one containing Conv2d layers
+        feedforward_module.net = new_net
+        return feedforward_module
+    else:
+        raise TypeError(f"Unsupported module type for FeedForward replacement: {type(feedforward_module)}")
+
+
+def replace_feedforward_modules(model: nn.Module):
+    # Replace FeedForward modules' Linear layers with Conv2D
+    traverse_and_replace(model, FeedForward, replace_feedforward_with_conv2d)
+
+
+def replace_layer_norm_modules(model: nn.Module):
+    """Recursively traverses the model to find and replace all instances of
+    LayerNorm within BasicTransformerBlock with PermuteLayerNorm to be
+    compatible with optimized_operate_on_continuous_inputs.
+
+    Args:
+        model (nn.Module): The model in which to replace LayerNorm modules.
+
+    """
+
+    def replace_layer_norm(block: BasicTransformerBlock) -> BasicTransformerBlock:
+        """Replaces norm1, norm2, and norm3 within a BasicTransformerBlock.
+
+        Args:
+            block (BasicTransformerBlock): The transformer block to modify.
+
+        Returns:
+            BasicTransformerBlock: The modified transformer block.
+
+        """
+        block.norm1 = PermuteLayerNorm(block.norm1)
+        block.norm2 = PermuteLayerNorm(block.norm2)
+        if hasattr(block, "norm3"):
+            block.norm3 = PermuteLayerNorm(block.norm3)
+        return block
+
+    traverse_and_replace(model, BasicTransformerBlock, replace_layer_norm)
+
+
+def replace_transformer2d_modules(model: nn.Module):
+    """Recursively traverses the model to find and replace all instances of
+    Transformer2DModel that use linear projection, patching only those
+    instances with the optimized continuous‐input methods and swapping in
+    Conv2dLinear for proj_in / proj_out.
+    """
+
+    def _patch_instance(m: Transformer2DModel):
+        # bind the two optimized routines onto this instance only
+        m._operate_on_continuous_inputs = types.MethodType(  # type: ignore
+            optimized_operate_on_continuous_inputs, m
+        )
+        m._get_output_for_continuous_inputs = types.MethodType(  # type: ignore
+            optimized_get_output_for_continuous_inputs, m
+        )
+
+    def _replace_transformer2d(m: Transformer2DModel) -> Transformer2DModel:
+        # 1) patch the instance methods
+        _patch_instance(m)
+        # 2) swap out the Linear‐based proj_in / proj_out for Conv2dLinear
+        if isinstance(m.proj_in, nn.Linear):
+            m.proj_in = Conv2dLinear(m.proj_in)
+        if isinstance(m.proj_out, nn.Linear):
+            m.proj_out = Conv2dLinear(m.proj_out)
+        return m
+
+    traverse_and_replace(model, Transformer2DModel, _replace_transformer2d)
+
+
+def optimized_operate_on_continuous_inputs(self, hidden_states):
+    """By using 4D NCHW hidden states, we can skip permutation and reshape
+    required in the HF implementation.
+    """
+    hidden_states = self.norm(hidden_states)
+    if not self.use_linear_projection:
+        hidden_states = self.proj_in(hidden_states)
+        inner_dim = hidden_states.shape[1]
+    else:
+        inner_dim = hidden_states.shape[1]
+        hidden_states = self.proj_in(hidden_states)
+    return hidden_states, inner_dim
+
+
+def optimized_get_output_for_continuous_inputs(self, hidden_states, residual, batch_size, height, width, inner_dim):
+    """Similar to optimized_operate_on_continuous_inputs"""
+    hidden_states = self.proj_out(hidden_states)
+    return hidden_states + residual
+
+
+def get_timestep_embedding(sample: torch.Tensor, timestep: torch.Tensor):
+    """Adapted from diffusers.models.get_timestep_embedding.
+    Removes parameters unused by our implementation and supports batching.
+    """
+    embedding_dim = 320  # TODO: Extract from last unet layers
+    MAX_PERIOD = 10000
+    half_dim = embedding_dim // 2
+    exponent = -math.log(MAX_PERIOD) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timestep.device)
+    exponent = exponent / half_dim
+
+    emb = torch.exp(exponent)
+    emb = timestep.float() * emb
+
+    # concat sine and cosine embeddings
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+    # flip sine and cosine embeddings
+    emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+    # zero pad
+    if embedding_dim % 2 == 1:
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+
+    return emb
+
+
+def monkey_patch_model(model: UNet2DConditionModel):
+    """1. Apply monkey patches
+    2. Apply module replacements for targeted modules whenever monkeypatch
+    code is too long
+
+    Note:
+    - This monkeypatch is verified against diffusers==0.31.0 on stable
+    diffusion 1.5
+
+    """
+    print("Monkeypatching Unet (replacing MHA with SHA attention etc)")
+    replace_attention_modules(model)
+    replace_activations_with_conv2d(model)
+    replace_layer_norm_modules(model)
+    replace_feedforward_modules(model)
+    replace_transformer2d_modules(model)
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/config.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/config.py
new file mode 100644
index 00000000..b4113cd8
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/config.py
@@ -0,0 +1,10 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
+vae_sample_size = 512
+unet_sample_size = 64
+cross_attention_dim = 768
+only_conversion = False
+data_dir = "quantize_data"
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ort.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ort.py
new file mode 100644
index 00000000..4b20ee4a
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ort.py
@@ -0,0 +1,181 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
+import json
+import shutil
+import sys
+from pathlib import Path
+
+import onnxruntime as ort
+from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline
+from olive.model import ONNXModelHandler
+from onnxruntime import __version__ as OrtVersion
+from packaging import version
+from sd_utils import config as sd_config
+
+# ruff: noqa: TID252, T201
+
+
+def update_cuda_config(config_cuda: dict):
+    if version.parse(OrtVersion) < version.parse("1.17.0"):
+        # disable skip_group_norm fusion since there is a shape inference bug which leads to invalid models
+        config_cuda["passes"]["optimize_cuda"]["optimization_options"] = {"enable_skip_group_norm": False}
+    used_passes = {"convert", "optimize_cuda"}
+    for pass_name in set(config_cuda["passes"].keys()):
+        if pass_name not in used_passes:
+            config_cuda["passes"].pop(pass_name, None)
+    config_cuda["systems"]["local_system"]["accelerators"][0]["execution_providers"] = ["CUDAExecutionProvider"]
+    return config_cuda
+
+
+def validate_args(args, provider):
+    ort.set_default_logger_severity(4)
+    if args.static_dims:
+        print(
+            "WARNING: the --static_dims option is deprecated, and static shape optimization is enabled by default. "
+            "Use --dynamic_dims to disable static shape optimization."
+        )
+
+    validate_ort_version(provider)
+
+
+def validate_ort_version(provider: str):
+    if provider == "cuda" and version.parse(OrtVersion) < version.parse("1.17.0"):
+        if version.parse(OrtVersion) < version.parse("1.16.2"):
+            print("This script requires onnxruntime-gpu 1.16.2 or newer")
+            sys.exit(1)
+        print(
+            f"WARNING: onnxruntime {OrtVersion} has known issues with shape inference for SkipGroupNorm. Will disable"
+            " skip_group_norm fusion. onnxruntime-gpu 1.17.0 or newer is strongly recommended!"
+        )
+
+
+def save_optimized_onnx_submodel(submodel_name, provider, model_info):
+    footprints_file_path = Path(__file__).resolve().parents[1] / "footprints" / submodel_name / "footprint.json"
+    with footprints_file_path.open("r") as footprint_file:
+        footprints = json.load(footprint_file)
+
+        conversion_footprint = None
+        optimizer_footprint = None
+        for footprint in footprints.values():
+            from_pass = footprint["from_pass"].lower() if footprint["from_pass"] else ""
+            if from_pass == "OnnxConversion".lower():
+                conversion_footprint = footprint
+                if sd_config.only_conversion:
+                    optimizer_footprint = footprint
+            elif (
+                from_pass == "OrtTransformersOptimization".lower()
+                or from_pass == "OnnxStaticQuantization".lower()
+                or from_pass == "EPContextBinaryGenerator".lower()
+                or from_pass == "DynamicToFixedShape".lower()
+            ):
+                optimizer_footprint = footprint
+
+        assert conversion_footprint
+        assert optimizer_footprint
+
+        unoptimized_olive_model = ONNXModelHandler(**conversion_footprint["model_config"]["config"])
+        optimized_olive_model = ONNXModelHandler(**optimizer_footprint["model_config"]["config"])
+
+        model_info[submodel_name] = {
+            "unoptimized": {
+                "path": Path(unoptimized_olive_model.model_path),
+            },
+            "optimized": {
+                "path": Path(optimized_olive_model.model_path),
+            },
+        }
+
+        print(f"Unoptimized Model : {model_info[submodel_name]['unoptimized']['path']}")
+        print(f"Optimized Model   : {model_info[submodel_name]['optimized']['path']}")
+
+
+def save_onnx_pipeline(
+    has_safety_checker, model_info, optimized_model_dir, unoptimized_model_dir, pipeline, submodel_names
+):
+    # Save the unoptimized models in a directory structure that the diffusers library can load and run.
+    # This is optional, and the optimized models can be used directly in a custom pipeline if desired.
+    print("\nCreating ONNX pipeline...")
+
+    if has_safety_checker:
+        safety_checker = OnnxRuntimeModel.from_pretrained(model_info["safety_checker"]["unoptimized"]["path"].parent)
+    else:
+        safety_checker = None
+
+    onnx_pipeline = OnnxStableDiffusionPipeline(
+        vae_encoder=OnnxRuntimeModel.from_pretrained(model_info["vae_encoder"]["unoptimized"]["path"].parent),
+        vae_decoder=OnnxRuntimeModel.from_pretrained(model_info["vae_decoder"]["unoptimized"]["path"].parent),
+        text_encoder=OnnxRuntimeModel.from_pretrained(model_info["text_encoder"]["unoptimized"]["path"].parent),
+        tokenizer=pipeline.tokenizer,
+        unet=OnnxRuntimeModel.from_pretrained(model_info["unet"]["unoptimized"]["path"].parent),
+        scheduler=pipeline.scheduler,
+        safety_checker=safety_checker,
+        feature_extractor=pipeline.feature_extractor,
+        requires_safety_checker=True,
+    )
+
+    print("Saving unoptimized models...")
+    onnx_pipeline.save_pretrained(unoptimized_model_dir)
+
+    # Create a copy of the unoptimized model directory, then overwrite with optimized models from the olive cache.
+    print("Copying optimized models...")
+    shutil.copytree(unoptimized_model_dir, optimized_model_dir, ignore=shutil.ignore_patterns("weights.pb"))
+    for submodel_name in submodel_names:
+        src_path = model_info[submodel_name]["optimized"]["path"]
+        dst_path = optimized_model_dir / submodel_name / "model.onnx"
+        shutil.copyfile(src_path, dst_path)
+
+        # Copy the QNN context bin if present
+        src_ctx_path = Path(str(src_path).replace(".onnx", "_qnn.bin"))
+        if src_ctx_path.exists():
+            dst_ctx_path = optimized_model_dir / submodel_name / "model_ctx_qnn.bin"
+            shutil.copyfile(src_ctx_path, dst_ctx_path)
+
+    print(f"The optimized pipeline is located here: {optimized_model_dir}")
+
+
+def get_ort_pipeline(model_dir, common_args, ort_args, guidance_scale):
+    ort.set_default_logger_severity(3)
+
+    print("Loading models into ORT session...")
+    sess_options = ort.SessionOptions()
+    sess_options.enable_mem_pattern = False
+
+    static_dims = not ort_args.dynamic_dims
+    batch_size = common_args.batch_size
+    image_size = common_args.image_size
+    provider = common_args.provider
+    vae_sample_size = sd_config.vae_sample_size
+    unet_sample_size = sd_config.unet_sample_size
+
+    if static_dims:
+        hidden_batch_size = batch_size if (guidance_scale <= 1.0) else batch_size * 2
+        # batch_size is doubled for sample & hidden state because of classifier free guidance:
+        # https://github.com/huggingface/diffusers/blob/46c52f9b9607e6ecb29c782c052aea313e6487b7/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L672
+        sess_options.add_free_dimension_override_by_name("unet_sample_batch", hidden_batch_size)
+        sess_options.add_free_dimension_override_by_name("unet_sample_channels", 4)
+        sess_options.add_free_dimension_override_by_name("unet_sample_height", image_size // 8)
+        sess_options.add_free_dimension_override_by_name("unet_sample_width", image_size // 8)
+        sess_options.add_free_dimension_override_by_name("unet_time_batch", 1)
+        sess_options.add_free_dimension_override_by_name("unet_hidden_batch", hidden_batch_size)
+        sess_options.add_free_dimension_override_by_name("unet_hidden_sequence", 77)
+
+        sess_options.add_free_dimension_override_by_name("decoder_batch", batch_size)
+        sess_options.add_free_dimension_override_by_name("decoder_channels", 4)
+        sess_options.add_free_dimension_override_by_name("decoder_height", unet_sample_size)
+        sess_options.add_free_dimension_override_by_name("decoder_width", unet_sample_size)
+
+        sess_options.add_free_dimension_override_by_name("encoder_batch", batch_size)
+        sess_options.add_free_dimension_override_by_name("encoder_channels", 3)
+        sess_options.add_free_dimension_override_by_name("encoder_height", vae_sample_size)
+        sess_options.add_free_dimension_override_by_name("encoder_width", vae_sample_size)
+
+    provider_map = {
+        "cuda": "CUDAExecutionProvider",
+    }
+    assert provider in provider_map, f"Unsupported provider: {provider}"
+    return OnnxStableDiffusionPipeline.from_pretrained(
+        model_dir, provider=provider_map[provider], sess_options=sess_options
+    )
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ov.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ov.py
new file mode 100644
index 00000000..aa784dc7
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ov.py
@@ -0,0 +1,531 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# Example modified from: https://docs.openvino.ai/2023.3/notebooks/225-stable-diffusion-text-to-image-with-output.html
+# --------------------------------------------------------------------------
+import inspect
+import json
+import shutil
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Callable, Optional, Union
+
+import cv2
+import numpy as np
+import openvino as ov
+import PIL
+import torch
+from diffusers import StableDiffusionPipeline
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from openvino.runtime import Model
+from transformers import CLIPTokenizer
+
+OV_OPTIMIZED_MODEL_INFO = "ov_optimized_model_info.json"
+
+# ruff: noqa: T201
+
+
+def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
+    """Preprocessing helper function for calculating image size.
+
+    Calculating image size for resize with peserving original aspect ratio and fitting image to specific window size.
+
+    Args:
+      dst_width (int): destination window width
+      dst_height (int): destination window height
+      image_width (int): source image width
+      image_height (int): source image height
+
+    Returns:
+      result_width (int): calculated width for resize
+      result_height (int): calculated height for resize
+
+    """
+    im_scale = min(dst_height / image_height, dst_width / image_width)
+    return int(im_scale * image_width), int(im_scale * image_height)
+
+
+def preprocess(image: PIL.Image.Image):
+    """Image preprocessing function.
+
+    Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
+    then converts it to np.ndarray and adds padding with zeros on right or
+    bottom side of image (depends from aspect ratio), after that
+    converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally,
+    converts data layout from planar NHWC to NCHW.
+    The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
+
+    Args:
+      image (PIL.Image.Image): input image
+    Returns:
+       image (np.ndarray): preprocessed image tensor
+       meta (Dict): dictionary with preprocessing metadata info
+
+    """
+    src_width, src_height = image.size
+    dst_width, dst_height = scale_fit_to_window(512, 512, src_width, src_height)
+    image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
+    pad_width = 512 - dst_width
+    pad_height = 512 - dst_height
+    pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
+    image = np.pad(image, pad, mode="constant")
+    image = image.astype(np.float32) / 255.0
+    image = 2.0 * image - 1.0
+    image = image.transpose(0, 3, 1, 2)
+    return image, {"padding": pad, "src_width": src_width, "src_height": src_height}
+
+
+@dataclass
+class OvStableDiffusionPipelineOutput:
+    """Output class for Stable Diffusion pipelines.
+
+    Args:
+        images (`List[PIL.Image.Image]` or `np.ndarray`)
+            List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
+            num_channels)`.
+
+    """
+
+    images: Union[list[PIL.Image.Image], np.ndarray]
+    nsfw_content_detected: Optional[list[bool]] = None
+
+
+class OVStableDiffusionPipeline(DiffusionPipeline):
+    def __init__(
+        self,
+        vae_decoder: Model,
+        text_encoder: Model,
+        tokenizer: CLIPTokenizer,
+        unet: Model,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        vae_encoder: Model = None,
+    ):
+        """Pipeline for text-to-image generation using Stable Diffusion.
+
+        Args:
+            vae_decoder (Model):
+                Variational Auto-Encoder (VAE) Model to decode images to and from latent representations.
+            text_encoder (Model):
+                Frozen text-encoder. Stable Diffusion uses the text portion of
+                [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+                the clip-vit-large-patch14(https://huggingface.co/openai/clip-vit-large-patch14) variant.
+            tokenizer (CLIPTokenizer):
+                Tokenizer of class CLIPTokenizer
+                (https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+            unet (Model): Conditional U-Net architecture to denoise the encoded image latents.
+            scheduler (SchedulerMixin):
+                A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of
+                DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
+            vae_encoder (Model):
+                Variational Auto-Encoder (VAE) Model to encode images to and from latent representations.
+
+        """
+        super().__init__()
+        self.scheduler = scheduler
+        self.vae_decoder = vae_decoder
+        self.vae_encoder = vae_encoder
+        self.text_encoder = text_encoder
+        self.unet = unet
+        self._text_encoder_output = text_encoder.output(0)
+        self._unet_output = unet.output(0)
+        self._vae_d_output = vae_decoder.output(0)
+        self._vae_e_output = vae_encoder.output(0) if vae_encoder is not None else None
+        self.height = 512
+        self.width = 512
+        self.tokenizer = tokenizer
+
+    def __call__(
+        self,
+        prompts: Union[str, list[str]],
+        image: PIL.Image.Image = None,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        num_inference_steps: Optional[int] = 50,
+        negative_prompt: Union[str, list[str]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        guidance_scale: Optional[float] = 7.5,
+        eta: Optional[float] = 0.0,
+        output_type: Optional[str] = "pil",
+        seed: Optional[int] = None,
+        strength: float = 0.5,
+        gif: Optional[bool] = False,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        callback_steps: int = 1,
+        **kwargs,
+    ):
+        """Invoke when calling the pipeline for generation.
+
+        Args:
+            prompts (str or List[str]):
+                The prompts to guide the image generation.
+            image (PIL.Image.Image, *optional*, None):
+                 Intinal image for generation.
+            height (int, *optional*, defaults to 512):
+                Height of the generated image.
+            width (int, *optional*, defaults to 512):
+                Width of the generated image.
+            num_inference_steps (int, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            negative_prompt (str or List[str]):
+                The negative prompt or prompts to guide the image generation.
+            num_images_per_prompt (int, *optional*, defaults to 1):
+                Number of images to generate per prompt.
+            guidance_scale (float, *optional*, defaults to 7.5):
+                Guidance scale as defined in Classifier-Free Diffusion Guidance(https://arxiv.org/abs/2207.12598).
+                guidance_scale is defined as `w` of equation 2.
+                Higher guidance scale encourages to generate images that are closely linked to the text prompt,
+                usually at the expense of lower image quality.
+            eta (float, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [DDIMScheduler], will be ignored for others.
+            output_type (`str`, *optional*, defaults to "pil"):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): PIL.Image.Image or np.array.
+            seed (int, *optional*, None):
+                Seed for random generator state initialization.
+            strength (float):
+                Controls the amount of noise that is added to the input image.
+            gif (bool, *optional*, False):
+                Flag for storing all steps results or not.
+            callback (Callable[[int, int, np.ndarray], None], *optional*, None):
+                Callback function for each step of generation.
+            callback_steps (int, *optional*, 1):
+                Number of steps between callback calls.
+            kwargs: Additional keyword arguments.
+
+        Returns:
+            Dictionary with keys:
+                sample - the last generated image PIL.Image.Image or np.array
+                iterations - *optional* (if gif=True) images for all diffusion steps,
+                List of PIL.Image.Image or np.array.
+
+        """
+        if seed is not None:
+            np.random.seed(seed)
+
+        batch_size = len(prompts) if isinstance(prompts, list) else 1
+
+        print(f"Start inference with prompt: {prompts}")
+        img_buffer = []
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # get prompt text embeddings
+        text_embeddings = self._encode_prompt(
+            prompts,
+            num_images_per_prompt,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+        )
+
+        # set timesteps
+        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
+        extra_set_kwargs = {}
+        if accepts_offset:
+            extra_set_kwargs["offset"] = 1
+
+        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
+        latent_timestep = timesteps[:1]
+
+        # get the initial random noise unless the user supplied it
+        latents, meta = self.prepare_latents(batch_size, num_images_per_prompt, height, width, image, latent_timestep)
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps)):
+            # expand the latents if you are doing classifier free guidance
+            latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet([latent_model_input, t, text_embeddings])[self._unet_output]
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(
+                torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
+            )["prev_sample"].numpy()
+            if gif:
+                image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
+                image = self.postprocess_image(image, meta, output_type)
+                img_buffer.extend(image)
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                step_idx = i // getattr(self.scheduler, "order", 1)
+                callback(step_idx, t, latents)
+
+        # scale and decode the image latents with vae
+        image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
+
+        image = self.postprocess_image(image, meta, output_type)
+
+        return OvStableDiffusionPipelineOutput(images=image)
+
+    def _encode_prompt(
+        self,
+        prompt: str,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Union[str, list[str]] = None,
+    ):
+        """Encode the prompt into text encoder hidden states.
+
+        Args:
+            prompt (str): prompt to be encoded
+            num_images_per_prompt (int): number of images to generate per prompt
+            do_classifier_free_guidance (bool): whether to use classifier free guidance or not
+            negative_prompt (str or list(str)): negative prompt to be encoded
+        Returns:
+            text_embeddings (np.ndarray): text encoder hidden states
+
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        # tokenize input prompts
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="np",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        text_embeddings = self.text_encoder(text_input_ids)[self._text_encoder_output]
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: list[str]
+            max_length = text_input_ids.shape[-1]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            else:
+                uncond_tokens = negative_prompt
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="np",
+            )
+
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids)[self._text_encoder_output]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
+            uncond_embeddings = np.reshape(uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_images_per_prompt,
+        height,
+        width,
+        image: PIL.Image.Image = None,
+        latent_timestep: torch.Tensor = None,
+    ):
+        """Get initial latents for starting generation.
+
+        Args:
+            batch_size (int):
+                Batch size for generation
+            num_images_per_prompt (int):
+                Number of images to generate per prompt
+            height (int):
+                Height of generated image
+            width (int):
+                Width of generated image
+            image (PIL.Image.Image, *optional*, None):
+                Input image for generation, if not provided randon noise will be used as starting point
+            latent_timestep (torch.Tensor, *optional*, None):
+                Predicted by scheduler initial step for image generation, required for latent image mixing with nosie
+        Returns:
+            latents (np.ndarray):
+                Image encoded in latent space
+
+        """
+        latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
+        noise = np.random.randn(*latents_shape).astype(np.float32)
+        if image is None and isinstance(self.scheduler, LMSDiscreteScheduler):
+            # if you use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
+            noise = noise * self.scheduler.sigmas[0].numpy()
+            return noise, {}
+        input_image, meta = preprocess(image)
+        latents = self.vae_encoder(input_image)[self._vae_e_output] * 0.18215
+        latents = self.scheduler.add_noise(torch.from_numpy(latents), torch.from_numpy(noise), latent_timestep).numpy()
+        return latents, meta
+
+    def postprocess_image(self, image: np.ndarray, meta: dict, output_type: str = "pil"):
+        """Postprocessing for decoded image.
+
+        Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
+        normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
+
+        Args:
+            image (np.ndarray):
+                Generated image
+            meta (Dict):
+                Metadata obtained on latents preparing step, can be empty
+            output_type (str, *optional*, pil):
+                Output format for result, can be pil or numpy
+        Returns:
+            image (List of np.ndarray or PIL.Image.Image):
+                Postprocessed images
+
+        """
+        if "padding" in meta:
+            pad = meta["padding"]
+            (_, end_h), (_, end_w) = pad[1:3]
+            h, w = image.shape[2:]
+            unpad_h = h - end_h
+            unpad_w = w - end_w
+            image = image[:, :, :unpad_h, :unpad_w]
+        image = np.clip(image / 2 + 0.5, 0, 1)
+        image = np.transpose(image, (0, 2, 3, 1))
+        # 9. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+            if "src_height" in meta:
+                orig_height, orig_width = meta["src_height"], meta["src_width"]
+                image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
+        else:
+            if "src_height" in meta:
+                orig_height, orig_width = meta["src_height"], meta["src_width"]
+                image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
+        return image
+
+    def get_timesteps(self, num_inference_steps: int, strength: float):
+        """Get scheduler timesteps for generation.
+
+        In case of image-to-image generation, it updates number of steps according to strength
+
+        Args:
+           num_inference_steps (int):
+              number of inference steps for generation
+           strength (float):
+               value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
+               Values that approach 1.0 enable lots of variations
+               but will also produce images that are not semantically consistent with the input.
+
+        """
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start:]
+
+        return timesteps, num_inference_steps - t_start
+
+
+def update_ov_config(config: dict):
+    config["passes"] = {"ov_convert": config["passes"]["ov_convert"]}
+    config["search_strategy"] = False
+    config["systems"]["local_system"]["accelerators"][0]["execution_providers"] = ["CPUExecutionProvider"]
+    del config["evaluators"]
+    del config["evaluator"]
+    return config
+
+
+def save_optimized_ov_submodel(workflow_output, submodel, optimized_model_dir, optimized_model_path_map):
+    output_model_dir = workflow_output.get_best_candidate().model_path
+    optimized_model_path = optimized_model_dir / submodel
+    shutil.copytree(output_model_dir, optimized_model_path)
+    model_path = (optimized_model_path / submodel).with_suffix(".xml")
+    optimized_model_path_map[submodel] = str(model_path)
+
+
+def get_ov_pipeline(common_args, ov_args, optimized_model_dir):
+    if common_args.test_unoptimized:
+        return StableDiffusionPipeline.from_pretrained(common_args.model_id)
+
+    with (optimized_model_dir / OV_OPTIMIZED_MODEL_INFO).open("r") as model_info_file:
+        optimized_model_path_map = json.load(model_info_file)
+
+    device = ov_args.device
+
+    core = ov.Core()
+    text_enc = core.compile_model(optimized_model_path_map["text_encoder"], device)
+    unet_model = core.compile_model(optimized_model_path_map["unet"], device)
+
+    ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device != "CPU" else {}
+
+    vae_decoder = core.compile_model(optimized_model_path_map["vae_decoder"], device, ov_config)
+    vae_encoder = core.compile_model(optimized_model_path_map["vae_encoder"], device, ov_config)
+
+    lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
+
+    return OVStableDiffusionPipeline(
+        tokenizer=tokenizer,
+        text_encoder=text_enc,
+        unet=unet_model,
+        vae_encoder=vae_encoder,
+        vae_decoder=vae_decoder,
+        scheduler=lms,
+    )
+
+
+def run_ov_image_inference(
+    pipe, image_path, prompt, strength, guidance_scale, image_size, num_inference_steps, common_args, generator=None
+):
+    image = None
+    if image_path:
+        img_path = Path(image_path)
+        print(f"Image path is {img_path}")
+        if not img_path.exists():
+            print("Image doesn't exist.")
+            sys.exit(1)
+        image = PIL.Image.open(img_path)
+
+    return pipe(
+        prompts=[prompt] * common_args.batch_size,
+        image=image,
+        num_inference_steps=num_inference_steps,
+        height=image_size,
+        width=image_size,
+        strength=strength,
+        guidance_scale=guidance_scale,
+        generator=generator,
+    )
+
+
+def run_ov_img_to_img_example(pipe, guidance_scale, common_args):
+    prompt = "amazing watercolor painting"
+    strength = 0.5
+    image_path = Path("./assets/dog.png")
+    image_size = 512
+    num_inference_steps = 10
+
+    return run_ov_image_inference(
+        pipe, image_path, prompt, strength, guidance_scale, image_size, num_inference_steps, common_args
+    )
+
+
+def save_ov_model_info(model_info, optimized_model_dir):
+    model_info_path = optimized_model_dir / OV_OPTIMIZED_MODEL_INFO
+    with model_info_path.open("w") as model_info_file:
+        json.dump(model_info, model_info_file, indent=4)
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py
new file mode 100644
index 00000000..3caa6032
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py
@@ -0,0 +1,287 @@
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
+import inspect
+import os
+from typing import Callable, Optional, Union
+
+import numpy as np
+import onnxruntime as ort
+import sd_utils
+import sd_utils.config
+import torch
+from diffusers import OnnxStableDiffusionPipeline
+from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+
+# ruff: noqa: T201
+
+
+def update_qdq_config(config: dict, provider: str, submodel_name: str):
+    used_passes = {}
+    if sd_utils.config.only_conversion:
+        used_passes = {"convert"}
+        config["evaluator"] = None
+    elif submodel_name == "text_encoder":
+        if provider == "qnn":
+            used_passes = {"convert", "dynamic_shape_to_fixed", "surgery", "cb"}
+        else:
+            used_passes = {"convert", "dynamic_shape_to_fixed", "surgery", "optimize_qdq", "quantization"}
+    elif submodel_name == "unet":
+        if provider == "qnn":
+            used_passes = {"convert", "dynamic_shape_to_fixed", "optimize_qdq", "quantization", "cb"}
+        else:
+            used_passes = {"convert", "dynamic_shape_to_fixed", "optimize_qdq", "quantization"}
+    elif submodel_name == "vae_encoder":
+        if provider == "qnn":
+            used_passes = {"convert", "dynamic_shape_to_fixed"}
+        else:
+            used_passes = {"convert", "dynamic_shape_to_fixed", "quantization"}
+    else:
+        if provider == "qnn":
+            used_passes = {"convert", "dynamic_shape_to_fixed", "quantization", "cb"}
+        else:
+            used_passes = {"convert", "dynamic_shape_to_fixed", "quantization"}
+
+    for pass_name in set(config["passes"].keys()):
+        if pass_name not in used_passes:
+            config["passes"].pop(pass_name, None)
+
+    if provider == "cuda":
+        config["systems"]["local_system"]["accelerators"][0]["execution_providers"] = ["CUDAExecutionProvider"]
+    elif provider == "qnn" and submodel_name not in ("vae_encoder"):
+        config["systems"]["local_system"]["accelerators"][0]["device"] = "npu"
+        config["systems"]["local_system"]["accelerators"][0]["execution_providers"] = ["QNNExecutionProvider"]
+        config["passes"]["convert"]["target_opset"] = 20
+
+        # Quantization params
+        if submodel_name not in ("text_encoder"):
+            config["passes"]["quantization"]["quant_preprocess"] = True
+            config["passes"]["quantization"]["per_channel"] = True
+            config["passes"]["quantization"]["weight_symmetric"] = True
+
+        if submodel_name == "unet":
+            config["input_model"]["model_loader"] = "unet_load_qnn"
+    else:
+        config["systems"]["local_system"]["accelerators"][0]["device"] = "cpu"
+        config["systems"]["local_system"]["accelerators"][0]["execution_providers"] = ["CPUExecutionProvider"]
+        # not meaningful to evaluate QDQ latency on CPU
+        config["evaluator"] = None
+    return config
+
+
+class OnnxStableDiffusionPipelineWithSave(OnnxStableDiffusionPipeline):
+    def __call__(
+        self,
+        prompt: Union[str, list[str]] = None,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        negative_prompt: Optional[Union[str, list[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[np.random.RandomState] = None,
+        latents: Optional[np.ndarray] = None,
+        prompt_embeds: Optional[np.ndarray] = None,
+        negative_prompt_embeds: Optional[np.ndarray] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        callback_steps: int = 1,
+    ):
+        # check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
+
+        # define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if generator is None:
+            generator = np.random
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        if self.save_data_dir:
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=77,
+                truncation=True,
+                return_tensors="np",
+            ).input_ids.astype(np.int32)
+            np.savez(self.save_data_dir / "text_inputs.npz", input_ids=text_inputs)
+
+            uncond_input = self.tokenizer(
+                negative_prompt if negative_prompt else "",
+                padding="max_length",
+                max_length=77,
+                truncation=True,
+                return_tensors="np",
+            ).input_ids.astype(np.int32)
+            np.savez(self.save_data_dir / "uncond_input.npz", input_ids=uncond_input)
+
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+        )
+
+        # get the initial random noise unless the user supplied it
+        latents_dtype = prompt_embeds.dtype
+        latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
+        if latents is None:
+            latents = generator.randn(*latents_shape).astype(latents_dtype)
+        elif latents.shape != latents_shape:
+            raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        latents = latents * np.float32(self.scheduler.init_noise_sigma)
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        timestep_dtype = next(
+            (unet_input.type for unet_input in self.unet.model.get_inputs() if unet_input.name == "timestep"),
+            "tensor(float)",
+        )
+        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
+
+        if do_classifier_free_guidance:
+            splits = np.split(prompt_embeds, 2)
+            neg_embeds, text_embeds = splits[0], splits[1]
+
+        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
+            latent_model_input = latents
+            latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
+            latent_model_input = latent_model_input.cpu().numpy()
+
+            # predict the noise residual
+            timestep = np.array([t], dtype=timestep_dtype)
+
+            if do_classifier_free_guidance:
+                # Note that in QDQ, we need to use static dimensions (batch is fixed to 1), so we need to split
+                unet_input = {"sample": latent_model_input, "timestep": timestep, "encoder_hidden_states": neg_embeds}
+                if self.save_data_dir:
+                    np.savez(self.save_data_dir / f"{i}_unet_input_neg.npz", **unet_input)
+                noise_pred_uncond = self.unet(**unet_input)
+                noise_pred_uncond = noise_pred_uncond[0]
+
+                unet_input = {"sample": latent_model_input, "timestep": timestep, "encoder_hidden_states": text_embeds}
+                if self.save_data_dir:
+                    np.savez(self.save_data_dir / f"{i}_unet_input.npz", **unet_input)
+                noise_pred_text = self.unet(**unet_input)
+                noise_pred_text = noise_pred_text[0]
+
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+            else:
+                unet_input = {
+                    "sample": latent_model_input,
+                    "timestep": timestep,
+                    "encoder_hidden_states": prompt_embeds,
+                }
+                if self.save_data_dir:
+                    np.savez(self.save_data_dir / f"{i}_unet_input.npz", **unet_input)
+                noise_pred = self.unet(**unet_input)
+                noise_pred = noise_pred[0]
+
+            # compute the previous noisy sample x_t -> x_t-1
+            scheduler_output = self.scheduler.step(
+                torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
+            )
+            latents = scheduler_output.prev_sample.numpy()
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                step_idx = i // getattr(self.scheduler, "order", 1)
+                callback(step_idx, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        # image = self.vae_decoder(latent_sample=latents)[0]
+        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
+        if self.save_data_dir:
+            np.savez(self.save_data_dir / "latent.npz", latent_sample=latents[0:1])
+        image = np.concatenate([self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])])
+        if self.save_data_dir:
+            np.savez(self.save_data_dir / "output_img.npz", sample=image)
+
+        image = np.clip(image / 2 + 0.5, 0, 1)
+        image = image.transpose((0, 2, 3, 1))
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(
+                self.numpy_to_pil(image), return_tensors="np"
+            ).pixel_values.astype(image.dtype)
+
+            images, has_nsfw_concept = [], []
+            for i in range(image.shape[0]):
+                image_i, has_nsfw_concept_i = self.safety_checker(
+                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
+                )
+                images.append(image_i)
+                has_nsfw_concept.append(has_nsfw_concept_i[0])
+            image = np.concatenate(images)
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+
+def get_qdq_pipeline(model_dir, common_args, qdq_args, script_dir):
+    ort.set_default_logger_severity(3)
+
+    print("Loading models into ORT session...")
+    sess_options = ort.SessionOptions()
+    provider_options = [{}]
+
+    provider = common_args.provider
+
+    provider_map = {
+        "cpu": "CPUExecutionProvider",
+        "cuda": "CUDAExecutionProvider",
+        "qnn": "QNNExecutionProvider",
+    }
+    assert provider in provider_map, f"Unsupported provider: {provider}"
+
+    if provider == "qnn":
+        provider_options[0]["backend_path"] = "QnnHtp.dll"
+        provider_options[0]["htp_performance_mode"] = "sustained_high_performance"
+        provider_options[0]["htp_graph_finalization_optimization_mode"] = "3"
+        provider_options[0]["offload_graph_io_quantization"] = "0"
+        provider_options[0]["enable_htp_fp16_precision"] = "1"
+
+    pipeline = OnnxStableDiffusionPipelineWithSave.from_pretrained(
+        model_dir, provider=provider_map[provider], sess_options=sess_options, provider_options=provider_options
+    )
+    if qdq_args.save_data:
+        pipeline.save_data_dir = script_dir / qdq_args.data_dir / common_args.prompt
+        os.makedirs(pipeline.save_data_dir, exist_ok=True)
+    else:
+        pipeline.save_data_dir = None
+    return pipeline
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq_xl.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq_xl.py
new file mode 100644
index 00000000..08ec41ad
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq_xl.py
@@ -0,0 +1,238 @@
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
+import inspect
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from typing import Any, Optional, Union
+
+import numpy as np
+import onnxruntime as ort
+import torch
+from diffusers import StableDiffusionXLPipeline
+from optimum.onnxruntime.modeling_diffusion import (
+    ORTDiffusionPipeline,
+    ORTModelTextEncoder,
+    ORTModelUnet,
+    ORTModelVaeDecoder,
+    ORTModelVaeEncoder,
+    ORTWrapperVae,
+)
+from transformers.modeling_outputs import ModelOutput
+
+# ruff: noqa: F821
+# ruff: noqa: SIM118
+
+
+class ORTDiffusionPipelineWithSave(ORTDiffusionPipeline):
+    def __init__(
+        self,
+        scheduler: "SchedulerMixin",
+        unet_session: ort.InferenceSession,
+        vae_decoder_session: ort.InferenceSession,
+        # optional pipeline models
+        vae_encoder_session: Optional[ort.InferenceSession] = None,
+        text_encoder_session: Optional[ort.InferenceSession] = None,
+        text_encoder_2_session: Optional[ort.InferenceSession] = None,
+        # optional pipeline submodels
+        tokenizer: Optional["CLIPTokenizer"] = None,
+        tokenizer_2: Optional["CLIPTokenizer"] = None,
+        feature_extractor: Optional["CLIPFeatureExtractor"] = None,
+        # stable diffusion xl specific arguments
+        force_zeros_for_empty_prompt: bool = True,
+        requires_aesthetics_score: bool = False,
+        add_watermarker: Optional[bool] = None,
+        # onnxruntime specific arguments
+        use_io_binding: Optional[bool] = None,
+        model_save_dir: Optional[Union[str, Path, TemporaryDirectory]] = None,
+        **kwargs,
+    ):
+        self.unet = ORTModelUnetWithSave(unet_session, self)
+        self.vae_decoder = ORTModelVaeDecoderWithSave(vae_decoder_session, self)
+        self.vae_encoder = ORTModelVaeEncoder(vae_encoder_session, self) if vae_encoder_session is not None else None
+        self.text_encoder = (
+            ORTModelTextEncoderWithSave(text_encoder_session, self) if text_encoder_session is not None else None
+        )
+        self.text_encoder_2 = (
+            ORTModelTextEncoderWithSave(text_encoder_2_session, self) if text_encoder_2_session is not None else None
+        )
+        # We wrap the VAE Decoder & Encoder in a single object to simulate diffusers API
+        self.vae = ORTWrapperVae(self.vae_encoder, self.vae_decoder)
+
+        # we allow passing these as torch models for now
+        self.image_encoder = kwargs.pop("image_encoder", None)  # TODO(anyone): maybe implement ORTModelImageEncoder
+        self.safety_checker = kwargs.pop("safety_checker", None)  # TODO(anyone): maybe implement ORTModelSafetyChecker
+
+        self.scheduler = scheduler
+        self.tokenizer = tokenizer
+        self.tokenizer_2 = tokenizer_2
+        self.feature_extractor = feature_extractor
+
+        all_pipeline_init_args = {
+            "vae": self.vae,
+            "unet": self.unet,
+            "text_encoder": self.text_encoder,
+            "text_encoder_2": self.text_encoder_2,
+            "safety_checker": self.safety_checker,
+            "image_encoder": self.image_encoder,
+            "scheduler": self.scheduler,
+            "tokenizer": self.tokenizer,
+            "tokenizer_2": self.tokenizer_2,
+            "feature_extractor": self.feature_extractor,
+            "requires_aesthetics_score": requires_aesthetics_score,
+            "force_zeros_for_empty_prompt": force_zeros_for_empty_prompt,
+            "add_watermarker": add_watermarker,
+        }
+
+        diffusers_pipeline_args = {}
+        for key in inspect.signature(self.auto_model_class).parameters.keys():
+            if key in all_pipeline_init_args:
+                diffusers_pipeline_args[key] = all_pipeline_init_args[key]
+        # inits diffusers pipeline specific attributes (registers modules and config)
+        self.auto_model_class.__init__(self, **diffusers_pipeline_args)
+
+        # inits ort specific attributes
+        self.shared_attributes_init(
+            model=unet_session, use_io_binding=use_io_binding, model_save_dir=model_save_dir, **kwargs
+        )
+
+    @property
+    def save_data_dir(self):
+        return self.text_encoder.save_data_dir
+
+    @save_data_dir.setter
+    def save_data_dir(self, save_dir: Path):
+        self.text_encoder.save_data_dir = save_dir
+        self.text_encoder.save_data_index = 10
+        self.text_encoder_2.save_data_dir = save_dir
+        self.text_encoder_2.save_data_index = 20
+        self.unet.save_data_dir = save_dir
+        self.unet.save_data_index = 0
+        self.vae_decoder.save_data_dir = save_dir
+
+
+class ORTStableDiffusionXLPipelineWithSave(ORTDiffusionPipelineWithSave, StableDiffusionXLPipeline):
+    main_input_name = "prompt"
+    export_feature = "text-to-image"
+    auto_model_class = StableDiffusionXLPipeline
+
+    def _get_add_time_ids(
+        self,
+        original_size,
+        crops_coords_top_left,
+        target_size,
+        dtype,
+        text_encoder_projection_dim=None,
+    ):
+        add_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+        return torch.tensor([add_time_ids], dtype=dtype)
+
+
+# Wrappers
+
+
+class ORTModelTextEncoderWithSave(ORTModelTextEncoder):
+    def forward(
+        self,
+        input_ids: Union[np.ndarray, torch.Tensor],
+        attention_mask: Optional[Union[np.ndarray, torch.Tensor]] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: bool = False,
+    ):
+        use_torch = isinstance(input_ids, torch.Tensor)
+
+        model_inputs = {"input_ids": input_ids}
+
+        onnx_inputs = self.prepare_onnx_inputs(use_torch, **model_inputs)
+        if self.save_data_dir:
+            np.savez(self.save_data_dir / f"text_encoder_{self.save_data_index}.npz", **onnx_inputs)
+            self.save_data_index += 1
+        onnx_outputs = self.session.run(None, onnx_inputs)
+        model_outputs = self.prepare_onnx_outputs(use_torch, *onnx_outputs)
+
+        if output_hidden_states:
+            model_outputs["hidden_states"] = []
+            for i in range(self.config.num_hidden_layers):
+                model_outputs["hidden_states"].append(model_outputs.pop(f"hidden_states.{i}"))
+            model_outputs["hidden_states"].append(model_outputs.get("last_hidden_state"))
+        else:
+            for i in range(self.config.num_hidden_layers):
+                model_outputs.pop(f"hidden_states.{i}", None)
+
+        if return_dict:
+            return model_outputs
+
+        return ModelOutput(**model_outputs)
+
+
+class ORTModelUnetWithSave(ORTModelUnet):
+    def forward(
+        self,
+        sample: Union[np.ndarray, torch.Tensor],
+        timestep: Union[np.ndarray, torch.Tensor],
+        encoder_hidden_states: Union[np.ndarray, torch.Tensor],
+        text_embeds: Optional[Union[np.ndarray, torch.Tensor]] = None,
+        time_ids: Optional[Union[np.ndarray, torch.Tensor]] = None,
+        timestep_cond: Optional[Union[np.ndarray, torch.Tensor]] = None,
+        cross_attention_kwargs: Optional[dict[str, Any]] = None,
+        added_cond_kwargs: Optional[dict[str, Any]] = None,
+        return_dict: bool = False,
+    ):
+        use_torch = isinstance(sample, torch.Tensor)
+
+        if len(timestep.shape) == 0:
+            timestep = timestep.unsqueeze(0)
+
+        model_inputs = {
+            "sample": sample,
+            "timestep": timestep,
+            "encoder_hidden_states": encoder_hidden_states,
+            "text_embeds": text_embeds,
+            "time_ids": time_ids,
+            "timestep_cond": timestep_cond,
+            **(cross_attention_kwargs or {}),
+            **(added_cond_kwargs or {}),
+        }
+
+        onnx_inputs = self.prepare_onnx_inputs(use_torch, **model_inputs)
+        if self.save_data_dir:
+            np.savez(self.save_data_dir / f"unet_{self.save_data_index}.npz", **onnx_inputs)
+            self.save_data_index += 1
+        onnx_outputs = self.session.run(None, onnx_inputs)
+        model_outputs = self.prepare_onnx_outputs(use_torch, *onnx_outputs)
+
+        if return_dict:
+            return model_outputs
+
+        return ModelOutput(**model_outputs)
+
+
+class ORTModelVaeDecoderWithSave(ORTModelVaeDecoder):
+    def forward(
+        self,
+        latent_sample: Union[np.ndarray, torch.Tensor],
+        generator: Optional[torch.Generator] = None,
+        return_dict: bool = False,
+    ):
+        use_torch = isinstance(latent_sample, torch.Tensor)
+
+        model_inputs = {"latent_sample": latent_sample}
+
+        onnx_inputs = self.prepare_onnx_inputs(use_torch, **model_inputs)
+        if self.save_data_dir:
+            np.savez(self.save_data_dir / "vae_decoder.npz", **onnx_inputs)
+        onnx_outputs = self.session.run(None, onnx_inputs)
+        if self.save_data_dir:
+            np.savez(self.save_data_dir / "vae_decoder_output.npz", sample=onnx_outputs[0])
+        model_outputs = self.prepare_onnx_outputs(use_torch, *onnx_outputs)
+
+        if "latent_sample" in model_outputs:
+            model_outputs["latents"] = model_outputs.pop("latent_sample")
+
+        if return_dict:
+            return model_outputs
+
+        return ModelOutput(**model_outputs)
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/stable_diffusion.py b/sd-legacy-stable-diffusion-v1-5/aitk/stable_diffusion.py
new file mode 100644
index 00000000..caa8e3df
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/stable_diffusion.py
@@ -0,0 +1,491 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+import argparse
+import json
+import shutil
+import sys
+import warnings
+from pathlib import Path
+
+import numpy as np
+import torch
+from diffusers import DiffusionPipeline
+from olive.common.utils import set_tempdir
+from olive.workflows import run as olive_run
+from sd_utils import config
+from user_script import get_base_model_name
+
+# pylint: disable=redefined-outer-name
+# ruff: noqa: TID252, T201
+
+
+def save_image(result, batch_size, provider, num_images, images_saved, image_callback=None):
+    passed_safety_checker = 0
+    for image_index in range(batch_size):
+        if result.nsfw_content_detected is None or not result.nsfw_content_detected[image_index]:
+            passed_safety_checker += 1
+            if images_saved < num_images:
+                output_path = f"result_{images_saved}.png"
+                result.images[image_index].save(output_path)
+                if image_callback:
+                    image_callback(images_saved, output_path)
+                images_saved += 1
+                print(f"Generated {output_path}")
+    print(f"Inference Batch End ({passed_safety_checker}/{batch_size} images).")
+    if provider == "openvino":
+        print("WARNING: Safety checker is not supported by OpenVINO. It will be disabled.")
+    else:
+        print("Images passed the safety checker.")
+    return images_saved
+
+
+def run_inference_loop(
+    pipeline,
+    prompt,
+    num_images,
+    batch_size,
+    image_size,
+    num_inference_steps,
+    guidance_scale,
+    strength: float,
+    provider: str,
+    generator=None,
+    image_callback=None,
+    step_callback=None,
+):
+    images_saved = 0
+
+    def update_steps(step, timestep, latents):
+        if step_callback:
+            step_callback((images_saved // batch_size) * num_inference_steps + step)
+
+    while images_saved < num_images:
+        print(f"\nInference Batch Start (batch size = {batch_size}).")
+
+        kwargs = {"strength": strength} if provider == "openvino" else {}
+
+        result = pipeline(
+            [prompt] * batch_size,
+            num_inference_steps=num_inference_steps,
+            callback=update_steps if step_callback else None,
+            height=image_size,
+            width=image_size,
+            guidance_scale=guidance_scale,
+            generator=generator,
+            **kwargs,
+        )
+
+        images_saved = save_image(result, batch_size, provider, num_images, images_saved, image_callback)
+
+
+def run_inference_gui(
+    pipeline,
+    prompt,
+    num_images,
+    batch_size,
+    image_size,
+    num_inference_steps,
+    guidance_scale,
+    strength,
+    provider,
+    generator,
+):
+    import threading
+    import tkinter as tk
+    import tkinter.ttk as ttk
+
+    from PIL import Image, ImageTk
+
+    def update_progress_bar(total_steps_completed):
+        progress_bar["value"] = total_steps_completed
+
+    def image_completed(index, path):
+        img = Image.open(path)
+        photo = ImageTk.PhotoImage(img)
+        gui_images[index].config(image=photo)
+        gui_images[index].image = photo
+        if index == num_images - 1:
+            generate_button["state"] = "normal"
+
+    def on_generate_click():
+        generate_button["state"] = "disabled"
+        progress_bar["value"] = 0
+        threading.Thread(
+            target=run_inference_loop,
+            args=(
+                pipeline,
+                prompt_textbox.get(),
+                num_images,
+                batch_size,
+                image_size,
+                num_inference_steps,
+                guidance_scale,
+                strength,
+                provider,
+                generator,
+                image_completed,
+                update_progress_bar,
+            ),
+        ).start()
+
+    if num_images > 9:
+        print("WARNING: interactive UI only supports displaying up to 9 images")
+        num_images = 9
+
+    image_rows = 1 + (num_images - 1) // 3
+    image_cols = 2 if num_images == 4 else min(num_images, 3)
+    min_batches_required = 1 + (num_images - 1) // batch_size
+
+    bar_height = 10
+    button_width = 80
+    button_height = 30
+    padding = 2
+    window_width = image_cols * image_size + (image_cols + 1) * padding
+    window_height = image_rows * image_size + (image_rows + 1) * padding + bar_height + button_height
+
+    window = tk.Tk()
+    window.title("Stable Diffusion")
+    window.resizable(width=False, height=False)
+    window.geometry(f"{window_width}x{window_height}")
+
+    gui_images = []
+    for row in range(image_rows):
+        for col in range(image_cols):
+            label = tk.Label(window, width=image_size, height=image_size, background="black")
+            gui_images.append(label)
+            label.place(x=col * image_size, y=row * image_size)
+
+    y = image_rows * image_size + (image_rows + 1) * padding
+
+    progress_bar = ttk.Progressbar(window, value=0, maximum=num_inference_steps * min_batches_required)
+    progress_bar.place(x=0, y=y, height=bar_height, width=window_width)
+
+    y += bar_height
+
+    prompt_textbox = tk.Entry(window)
+    prompt_textbox.insert(tk.END, prompt)
+    prompt_textbox.place(x=0, y=y, width=window_width - button_width, height=button_height)
+
+    generate_button = tk.Button(window, text="Generate", command=on_generate_click)
+    generate_button.place(x=window_width - button_width, y=y, width=button_width, height=button_height)
+
+    window.mainloop()
+
+
+def update_config_with_provider(config: dict, provider: str, model_format: str, submodel_name: str):
+    if provider == "cuda" and not model_format:
+        from sd_utils.ort import update_cuda_config
+
+        return update_cuda_config(config)
+    elif provider == "openvino":
+        from sd_utils.ov import update_ov_config
+
+        return update_ov_config(config)
+    elif provider in ("cpu", "cuda", "qnn") and model_format == "qdq":
+        from sd_utils.qdq import update_qdq_config
+
+        return update_qdq_config(config, provider, submodel_name)
+    else:
+        raise ValueError(f"Unsupported provider: {provider} with format: {model_format}")
+
+
+def optimize(
+    common_args,
+    unoptimized_model_dir: Path,
+    optimized_model_dir: Path,
+):
+    model_id = common_args.model_id
+    provider = common_args.provider
+    model_format = common_args.format
+
+    script_dir = Path(__file__).resolve().parent
+
+    # Clean up previously optimized models, if any.
+    shutil.rmtree(script_dir / "footprints", ignore_errors=True)
+    shutil.rmtree(unoptimized_model_dir, ignore_errors=True)
+    shutil.rmtree(optimized_model_dir, ignore_errors=True)
+
+    # The model_id and base_model_id are identical when optimizing a standard stable diffusion model like
+    # CompVis/stable-diffusion-v1-4. These variables are only different when optimizing a LoRA variant.
+    base_model_id = get_base_model_name(model_id)
+    print(f"\nModel {base_model_id}")
+
+    # Load the entire PyTorch pipeline to ensure all models and their configurations are downloaded and cached.
+    # This avoids an issue where the non-ONNX components (tokenizer, scheduler, and feature extractor) are not
+    # automatically cached correctly if individual models are fetched one at a time.
+    print("Download stable diffusion PyTorch pipeline...")
+    pipeline = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float32)
+    config.vae_sample_size = pipeline.vae.config.sample_size
+    config.cross_attention_dim = pipeline.unet.config.cross_attention_dim
+    config.unet_sample_size = pipeline.unet.config.sample_size
+    if model_format == "qdq":
+        config.vae_sample_size = common_args.image_size
+        # config.unet_sample_size = common_args.image_size // 8
+
+    model_info = {}
+
+    submodel_names = ["vae_encoder", "vae_decoder", "unet", "text_encoder"]
+
+    has_safety_checker = getattr(pipeline, "safety_checker", None) is not None
+
+    if has_safety_checker:
+        if provider == "openvino" or model_format == "qdq":
+            print(f"WARNING: Safety checker is not supported by {provider}. It will be disabled.")
+            has_safety_checker = False
+        else:
+            submodel_names.append("safety_checker")
+
+    for submodel_name in submodel_names:
+        print(f"\nOptimizing {submodel_name}")
+
+        olive_config = None
+        with (script_dir / f"config_{submodel_name}.json").open() as fin:
+            olive_config = json.load(fin)
+        olive_config = update_config_with_provider(olive_config, provider, model_format, submodel_name)
+
+        if submodel_name in ("unet", "text_encoder"):
+            olive_config["input_model"]["model_path"] = model_id
+        else:
+            # Only the unet & text encoder are affected by LoRA, so it's better to use the base model ID for
+            # other models: the Olive cache is based on the JSON config, and two LoRA variants with the same
+            # base model ID should be able to reuse previously optimized copies.
+            olive_config["input_model"]["model_path"] = base_model_id
+
+        workflow_output = olive_run(olive_config)
+
+        if provider == "openvino":
+            from sd_utils.ov import save_optimized_ov_submodel
+
+            save_optimized_ov_submodel(workflow_output, submodel_name, optimized_model_dir, model_info)
+        else:
+            from sd_utils.ort import save_optimized_onnx_submodel
+
+            save_optimized_onnx_submodel(submodel_name, provider, model_info)
+
+    if provider == "openvino":
+        from sd_utils.ov import save_ov_model_info
+
+        save_ov_model_info(model_info, optimized_model_dir)
+    else:
+        from sd_utils.ort import save_onnx_pipeline
+
+        save_onnx_pipeline(
+            has_safety_checker, model_info, optimized_model_dir, unoptimized_model_dir, pipeline, submodel_names
+        )
+
+    return model_info
+
+
+def parse_common_args(raw_args):
+    parser = argparse.ArgumentParser("Common arguments")
+
+    parser.add_argument("--model_id", default="CompVis/stable-diffusion-v1-4", type=str)
+    parser.add_argument(
+        "--provider",
+        default="cuda",
+        type=str,
+        choices=["cuda", "openvino", "cpu", "qnn"],
+        help="Execution provider to use",
+    )
+    parser.add_argument("--optimize", action="store_true", help="Runs the optimization step")
+    parser.add_argument("--clean_cache", action="store_true", help="Deletes the Olive cache")
+    parser.add_argument("--test_unoptimized", action="store_true", help="Use unoptimized model for inference")
+    parser.add_argument("--batch_size", default=1, type=int, help="Number of images to generate per batch")
+    parser.add_argument(
+        "--prompt",
+        default=(
+            "castle surrounded by water and nature, village, volumetric lighting, photorealistic, "
+            "detailed and intricate, fantasy, epic cinematic shot, mountains, 8k ultra hd"
+        ),
+        type=str,
+    )
+    parser.add_argument(
+        "--guidance_scale",
+        default=7.5,
+        type=float,
+        help="Guidance scale as defined in Classifier-Free Diffusion Guidance",
+    )
+    parser.add_argument("--num_images", default=1, type=int, help="Number of images to generate")
+    parser.add_argument("--num_inference_steps", default=50, type=int, help="Number of steps in diffusion process")
+    parser.add_argument("--interactive", action="store_true", help="Run with a GUI")
+    parser.add_argument("--tempdir", default=None, type=str, help="Root directory for tempfile directories and files")
+    parser.add_argument(
+        "--strength",
+        default=1.0,
+        type=float,
+        help=(
+            "Value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. "
+            "Values that approach 1.0 enable lots of variations but will also produce images "
+            "that are not semantically consistent with the input."
+        ),
+    )
+    parser.add_argument("--image_size", default=512, type=int, help="Width and height of the images to generate")
+    parser.add_argument(
+        "--seed",
+        default=None,
+        type=int,
+        help="The seed to give to the generator to generate deterministic results.",
+    )
+    parser.add_argument(
+        "--format", default=None, type=str, help="Currently only support qdq with provider cpu, cuda or qnn"
+    )
+
+    return parser.parse_known_args(raw_args)
+
+
+def parse_ort_args(raw_args):
+    parser = argparse.ArgumentParser("ONNX Runtime arguments")
+
+    parser.add_argument(
+        "--static_dims",
+        action="store_true",
+        help="DEPRECATED (now enabled by default). Use --dynamic_dims to disable static_dims.",
+    )
+    parser.add_argument("--dynamic_dims", action="store_true", help="Disable static shape optimization")
+
+    return parser.parse_known_args(raw_args)
+
+
+def parse_ov_args(raw_args):
+    parser = argparse.ArgumentParser("OpenVINO arguments")
+
+    parser.add_argument("--device", choices=["CPU", "GPU", "VPU"], default="CPU", type=str)
+    parser.add_argument("--image_path", default=None, type=str)
+    parser.add_argument("--img_to_img_example", action="store_true", help="Runs the image to image example")
+
+    return parser.parse_known_args(raw_args)
+
+
+def parse_qdq_args(raw_args):
+    parser = argparse.ArgumentParser("QDQ arguments")
+
+    parser.add_argument("--save_data", action="store_true", help="Save the input data for qdq")
+    parser.add_argument("--data_dir", default="quantize_data/data", type=str)
+    parser.add_argument(
+        "--only_conversion", action="store_true", help="Only generate unoptimized model to generate data for qdq"
+    )
+
+    return parser.parse_known_args(raw_args)
+
+
+def main(raw_args=None):
+    common_args, extra_args = parse_common_args(raw_args)
+
+    provider = common_args.provider
+    model_id = common_args.model_id
+
+    script_dir = Path(__file__).resolve().parent
+    unoptimized_model_dir = script_dir / "models" / "unoptimized" / model_id
+    optimized_dir_name = f"optimized-{provider}"
+    if common_args.format:
+        optimized_dir_name += f"_{common_args.format}"
+    optimized_model_dir = script_dir / "models" / optimized_dir_name / model_id
+
+    if common_args.clean_cache:
+        shutil.rmtree(script_dir / "cache", ignore_errors=True)
+
+    guidance_scale = common_args.guidance_scale
+
+    if model_id == "stabilityai/sd-turbo" and guidance_scale > 0:
+        guidance_scale = 0.0
+        print(f"WARNING: Classifier free guidance has been forcefully disabled since {model_id} doesn't support it.")
+
+    ov_args, qdq_args, ort_args = None, None, None
+    if provider == "openvino":
+        ov_args, extra_args = parse_ov_args(extra_args)
+    elif common_args.format == "qdq":
+        qdq_args, extra_args = parse_qdq_args(extra_args)
+        config.only_conversion = qdq_args.only_conversion
+        config.data_dir = script_dir / qdq_args.data_dir
+    else:
+        ort_args, extra_args = parse_ort_args(extra_args)
+
+    if common_args.optimize or not optimized_model_dir.exists():
+        set_tempdir(common_args.tempdir)
+
+        # TODO(jstoecker): clean up warning filter (mostly during conversion from torch to ONNX)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            if not (provider == "openvino" or common_args.format == "qdq"):
+                from sd_utils.ort import validate_args
+
+                validate_args(ort_args, common_args.provider)
+            optimize(
+                common_args,
+                unoptimized_model_dir,
+                optimized_model_dir,
+            )
+
+    generator = None if common_args.seed is None else np.random.RandomState(seed=common_args.seed)
+
+    if not common_args.optimize:
+        model_dir = unoptimized_model_dir if common_args.test_unoptimized else optimized_model_dir
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            if provider == "openvino":
+                from sd_utils.ov import get_ov_pipeline
+
+                pipeline = get_ov_pipeline(common_args, ov_args, optimized_model_dir)
+            elif common_args.format == "qdq":
+                from sd_utils.qdq import get_qdq_pipeline
+
+                pipeline = get_qdq_pipeline(model_dir, common_args, qdq_args, script_dir)
+            else:
+                from sd_utils.ort import get_ort_pipeline
+
+                pipeline = get_ort_pipeline(model_dir, common_args, ort_args, guidance_scale)
+            if provider == "openvino" and (ov_args.image_path or ov_args.img_to_img_example):
+                res = None
+                if ov_args.image_path:
+                    from sd_utils.ov import run_ov_image_inference
+
+                    res = run_ov_image_inference(
+                        pipeline,
+                        ov_args.image_path,
+                        common_args.prompt,
+                        common_args.strength,
+                        guidance_scale,
+                        common_args.image_size,
+                        common_args.num_inference_steps,
+                        common_args,
+                        generator=generator,
+                    )
+                if ov_args.img_to_img_example:
+                    from sd_utils.ov import run_ov_img_to_img_example
+
+                    res = run_ov_img_to_img_example(pipeline, guidance_scale, common_args)
+                save_image(res, common_args.batch_size, "openvino", common_args.num_images, 0)
+                sys.exit(0)
+
+            if common_args.interactive:
+                run_inference_gui(
+                    pipeline,
+                    common_args.prompt,
+                    common_args.num_images,
+                    common_args.batch_size,
+                    common_args.image_size,
+                    common_args.num_inference_steps,
+                    guidance_scale,
+                    common_args.strength,
+                    provider=provider,
+                    generator=generator,
+                )
+            else:
+                run_inference_loop(
+                    pipeline,
+                    common_args.prompt,
+                    common_args.num_images,
+                    common_args.batch_size,
+                    common_args.image_size,
+                    common_args.num_inference_steps,
+                    guidance_scale,
+                    common_args.strength,
+                    provider=provider,
+                    generator=generator,
+                )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/user_script.py b/sd-legacy-stable-diffusion-v1-5/aitk/user_script.py
new file mode 100644
index 00000000..fe647855
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/user_script.py
@@ -0,0 +1,382 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+import os
+import random
+
+import numpy as np
+import torch
+from diffusers import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from huggingface_hub import model_info
+from model_adaptations import monkey_patch_model
+from olive.data.registry import Registry
+from sd_utils import config
+from transformers.models.clip.modeling_clip import CLIPTextModel
+
+# ruff: noqa: T201
+
+# Generated data helpers
+
+
+class BaseDataLoader:
+    def __init__(self, total):
+        self.data = []
+        self.total = total
+        self.data_folders = [config.data_dir / f.name for f in os.scandir(config.data_dir) if f.is_dir()]
+        self.data_folders.sort()
+
+    def __getitem__(self, idx):
+        if idx >= len(self.data) or idx >= self.total:
+            raise StopIteration
+        # print(f"Process data {idx}")
+        return self.data[idx]
+
+    def load(self, file):
+        self.data.append({key: torch.from_numpy(value) for key, value in np.load(file).items()})
+
+    def finish_load(self):
+        if len(self.data) > self.total:
+            self.data = random.sample(self.data, self.total)
+
+
+class UnetGeneratedDataLoader(BaseDataLoader):
+    def __init__(self, total):
+        super().__init__(total)
+        for f in self.data_folders:
+            i = 0
+            while True:
+                file = f / f"{i}_unet_input.npz"
+                if not os.path.exists(file):
+                    break
+                self.load(file)
+                file = f / f"{i}_unet_input_neg.npz"
+                if os.path.exists(file):
+                    self.load(file)
+                i += 1
+        self.finish_load()
+
+
+class TextEncoderGeneratedDataLoader(BaseDataLoader):
+    def __init__(self, total):
+        super().__init__(total)
+        for f in self.data_folders:
+            self.load(f / "text_inputs.npz")
+            self.load(f / "uncond_input.npz")
+        self.finish_load()
+
+
+class VaeDecoderGeneratedDataLoader(BaseDataLoader):
+    def __init__(self, total):
+        super().__init__(total)
+        for f in self.data_folders:
+            self.load(f / "latent.npz")
+        self.finish_load()
+
+
+class VaeEncoderGeneratedDataLoader(BaseDataLoader):
+    def __init__(self, total):
+        super().__init__(total)
+        for f in self.data_folders:
+            self.load(f / "output_img.npz")
+        self.finish_load()
+
+
+# Helper latency-only dataloader that creates random tensors with no label
+class RandomDataLoader:
+    def __init__(self, create_inputs_func, batch_size, torch_dtype):
+        self.create_input_func = create_inputs_func
+        self.batch_size = batch_size
+        self.torch_dtype = torch_dtype
+
+    def __getitem__(self, idx):
+        label = None
+        return self.create_input_func(self.batch_size, self.torch_dtype), label
+
+
+def get_base_model_name(model_name):
+    return model_info(model_name).cardData.get("base_model") or model_name
+
+
+def is_lora_model(model_name):
+    # TODO(jstoecker): might be a better way to detect (e.g. presence of LORA weights file)
+    return model_name != get_base_model_name(model_name)
+
+
+# Merges LoRA weights into the layers of a base model
+def merge_lora_weights(base_model, lora_model_id, submodel_name="unet", scale=1.0):
+    import inspect
+    from collections import defaultdict
+    from functools import reduce
+
+    try:
+        from diffusers.loaders import LORA_WEIGHT_NAME
+    except ImportError:
+        # moved in version 0.24.0
+        from diffusers.loaders.lora import LORA_WEIGHT_NAME
+    from diffusers.models.attention_processor import LoRAAttnProcessor
+    from diffusers.utils.hub_utils import _get_model_file
+
+    parameters = inspect.signature(_get_model_file).parameters
+
+    kwargs = {}
+    if "use_auth_token" in parameters:
+        kwargs["use_auth_token"] = None
+    elif "token" in parameters:
+        kwargs["token"] = None
+
+    # Load LoRA weights
+    model_file = _get_model_file(
+        lora_model_id,
+        weights_name=LORA_WEIGHT_NAME,
+        cache_dir=None,
+        force_download=False,
+        resume_download=False,
+        proxies=None,
+        local_files_only=False,
+        revision=None,
+        subfolder=None,
+        user_agent={
+            "file_type": "attn_procs_weights",
+            "framework": "pytorch",
+        },
+        **kwargs,
+    )
+    lora_state_dict = torch.load(model_file, map_location="cpu")
+
+    # All keys in the LoRA state dictionary should have 'lora' somewhere in the string.
+    keys = list(lora_state_dict.keys())
+    assert all("lora" in k for k in keys)
+
+    if all(key.startswith(submodel_name) for key in keys):
+        # New format (https://github.com/huggingface/diffusers/pull/2918) supports LoRA weights in both the
+        # unet and text encoder where keys are prefixed with 'unet' or 'text_encoder', respectively.
+        submodel_state_dict = {k: v for k, v in lora_state_dict.items() if k.startswith(submodel_name)}
+    else:
+        # Old format. Keys will not have any prefix. This only applies to unet, so exit early if this is
+        # optimizing the text encoder.
+        if submodel_name != "unet":
+            return
+        submodel_state_dict = lora_state_dict
+
+    # Group LoRA weights into attention processors
+    attn_processors = {}
+    lora_grouped_dict = defaultdict(dict)
+    for key, value in submodel_state_dict.items():
+        attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:])
+        lora_grouped_dict[attn_processor_key][sub_key] = value
+
+    for key, value_dict in lora_grouped_dict.items():
+        rank = value_dict["to_k_lora.down.weight"].shape[0]
+        cross_attention_dim = value_dict["to_k_lora.down.weight"].shape[1]
+        hidden_size = value_dict["to_k_lora.up.weight"].shape[0]
+
+        attn_processors[key] = LoRAAttnProcessor(
+            hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=rank
+        )
+        attn_processors[key].load_state_dict(value_dict)
+
+    # Merge LoRA attention processor weights into existing Q/K/V/Out weights
+    for name, proc in attn_processors.items():
+        attention_name = name[: -len(".processor")]
+        attention = reduce(getattr, attention_name.split(sep="."), base_model)
+        attention.to_q.weight.data += scale * torch.mm(proc.to_q_lora.up.weight, proc.to_q_lora.down.weight)
+        attention.to_k.weight.data += scale * torch.mm(proc.to_k_lora.up.weight, proc.to_k_lora.down.weight)
+        attention.to_v.weight.data += scale * torch.mm(proc.to_v_lora.up.weight, proc.to_v_lora.down.weight)
+        attention.to_out[0].weight.data += scale * torch.mm(proc.to_out_lora.up.weight, proc.to_out_lora.down.weight)
+
+
+# -----------------------------------------------------------------------------
+# TEXT ENCODER
+# -----------------------------------------------------------------------------
+
+
+def text_encoder_inputs(batch_size, torch_dtype):
+    return torch.zeros((batch_size, 77), dtype=torch_dtype)
+
+
+def text_encoder_load(model_name):
+    base_model_id = get_base_model_name(model_name)
+    model = CLIPTextModel.from_pretrained(base_model_id, subfolder="text_encoder")
+    if is_lora_model(model_name):
+        merge_lora_weights(model, model_name, "text_encoder")
+    return model
+
+
+def text_encoder_conversion_inputs(model=None):
+    return text_encoder_inputs(1, torch.int32)
+
+
+@Registry.register_dataloader()
+def text_encoder_data_loader(dataset, batch_size, *args, **kwargs):
+    return RandomDataLoader(text_encoder_inputs, batch_size, torch.int32)
+
+
+@Registry.register_dataloader()
+def text_encoder_quantize_data_loader(dataset, data_num, *args, **kwargs):
+    return TextEncoderGeneratedDataLoader(data_num)
+
+
+# -----------------------------------------------------------------------------
+# UNET
+# -----------------------------------------------------------------------------
+
+
+def unet_inputs(batch_size, torch_dtype, is_conversion_inputs=False):
+    # TODO(jstoecker): Rename onnx::Concat_4 to text_embeds and onnx::Shape_5 to time_ids
+    inputs = {
+        "sample": torch.rand((batch_size, 4, config.unet_sample_size, config.unet_sample_size), dtype=torch_dtype),
+        "timestep": torch.rand((batch_size,), dtype=torch_dtype),
+        "encoder_hidden_states": torch.rand((batch_size, 77, config.cross_attention_dim), dtype=torch_dtype),
+    }
+
+    # use as kwargs since they won't be in the correct position if passed along with the tuple of inputs
+    kwargs = {
+        "return_dict": False,
+    }
+    if is_conversion_inputs:
+        inputs["additional_inputs"] = {
+            **kwargs,
+            "added_cond_kwargs": {
+                "text_embeds": torch.rand((1, 1280), dtype=torch_dtype),
+                "time_ids": torch.rand((1, 5), dtype=torch_dtype),
+            },
+        }
+    else:
+        inputs.update(kwargs)
+        inputs["onnx::Concat_4"] = torch.rand((1, 1280), dtype=torch_dtype)
+        inputs["onnx::Shape_5"] = torch.rand((1, 5), dtype=torch_dtype)
+
+    return inputs
+
+
+def get_unet_ov_example_input():
+    encoder_hidden_state = torch.ones((2, 77, 768))
+    latents_shape = (2, 4, 512 // 8, 512 // 8)
+    latents = torch.randn(latents_shape)
+    t = torch.from_numpy(np.array(1, dtype=float))
+    return (latents, t, encoder_hidden_state)
+
+
+def unet_load(model_name):
+    base_model_id = get_base_model_name(model_name)
+    model = UNet2DConditionModel.from_pretrained(base_model_id, subfolder="unet")
+    if is_lora_model(model_name):
+        merge_lora_weights(model, model_name, "unet")
+    return model
+
+
+def unet_load_qnn(model_name):
+    base_model_id = get_base_model_name(model_name)
+    model = UNet2DConditionModel.from_pretrained(base_model_id, subfolder="unet")
+    if is_lora_model(model_name):
+        merge_lora_weights(model, model_name, "unet")
+    monkey_patch_model(model)
+    return model
+
+
+def unet_conversion_inputs(model=None):
+    return tuple(unet_inputs(1, torch.float32, True).values())
+
+
+@Registry.register_dataloader()
+def unet_data_loader(dataset, batch_size, *args, **kwargs):
+    return RandomDataLoader(unet_inputs, batch_size, torch.float16)
+
+
+@Registry.register_dataloader()
+def unet_quantize_data_loader(dataset, data_num, *args, **kwargs):
+    return UnetGeneratedDataLoader(data_num)
+
+
+# -----------------------------------------------------------------------------
+# VAE ENCODER
+# -----------------------------------------------------------------------------
+
+
+def vae_encoder_inputs(batch_size, torch_dtype):
+    return {"sample": torch.rand((batch_size, 3, config.vae_sample_size, config.vae_sample_size), dtype=torch_dtype)}
+
+
+def vae_encoder_load(model_name):
+    base_model_id = get_base_model_name(model_name)
+    model = AutoencoderKL.from_pretrained(base_model_id, subfolder="vae")
+    model.forward = lambda sample: model.encode(sample)[0].sample()
+    return model
+
+
+def vae_encoder_conversion_inputs(model=None):
+    return tuple(vae_encoder_inputs(1, torch.float32).values())
+
+
+@Registry.register_dataloader()
+def vae_encoder_data_loader(dataset, batch_size, *args, **kwargs):
+    return RandomDataLoader(vae_encoder_inputs, batch_size, torch.float16)
+
+
+@Registry.register_dataloader()
+def vae_encoder_quantize_data_loader(dataset, data_num, *args, **kwargs):
+    return VaeEncoderGeneratedDataLoader(data_num)
+
+
+# -----------------------------------------------------------------------------
+# VAE DECODER
+# -----------------------------------------------------------------------------
+
+
+def vae_decoder_inputs(batch_size, torch_dtype):
+    return {
+        "latent_sample": torch.rand(
+            (batch_size, 4, config.unet_sample_size, config.unet_sample_size), dtype=torch_dtype
+        )
+    }
+
+
+def vae_decoder_load(model_name):
+    base_model_id = get_base_model_name(model_name)
+    model = AutoencoderKL.from_pretrained(base_model_id, subfolder="vae")
+    model.forward = model.decode
+    return model
+
+
+def vae_decoder_conversion_inputs(model=None):
+    return tuple(vae_decoder_inputs(1, torch.float32).values())
+
+
+@Registry.register_dataloader()
+def vae_decoder_data_loader(dataset, batch_size, *args, **kwargs):
+    return RandomDataLoader(vae_decoder_inputs, batch_size, torch.float16)
+
+
+@Registry.register_dataloader()
+def vae_decoder_quantize_data_loader(dataset, data_num, *args, **kwargs):
+    return VaeDecoderGeneratedDataLoader(data_num)
+
+
+# -----------------------------------------------------------------------------
+# SAFETY CHECKER
+# -----------------------------------------------------------------------------
+
+
+def safety_checker_inputs(batch_size, torch_dtype):
+    return {
+        "clip_input": torch.rand((batch_size, 3, 224, 224), dtype=torch_dtype),
+        "images": torch.rand((batch_size, config.vae_sample_size, config.vae_sample_size, 3), dtype=torch_dtype),
+    }
+
+
+def safety_checker_load(model_name):
+    base_model_id = get_base_model_name(model_name)
+    model = StableDiffusionSafetyChecker.from_pretrained(base_model_id, subfolder="safety_checker")
+    model.forward = model.forward_onnx
+    return model
+
+
+def safety_checker_conversion_inputs(model=None):
+    return tuple(safety_checker_inputs(1, torch.float32).values())
+
+
+@Registry.register_dataloader()
+def safety_checker_data_loader(dataset, batch_size, *args, **kwargs):
+    return RandomDataLoader(safety_checker_inputs, batch_size, torch.float16)

From 645e2a97f105a4e664597d5b825d8e545b2f8c5c Mon Sep 17 00:00:00 2001
From: ziyuanguo <ziyuanguo@microsoft.com>
Date: Tue, 10 Feb 2026 10:22:42 +0800
Subject: [PATCH 07/11] implement workflow in aitk

---
 .aitk/requirements/requirements-WCR-SD.txt    |   1 -
 .../aitk/.gitignore                           |   4 +-
 .../aitk/README.md                            |  14 +-
 .../aitk/config_safety_checker.json           |   2 +-
 .../aitk/config_text_encoder.json             |   2 +-
 .../aitk/config_unet.json                     |   2 +-
 .../aitk/config_vae_decoder.json              |   2 +-
 .../aitk/config_vae_encoder.json              |   2 +-
 .../aitk/evaluation.py                        |  33 ++++-
 .../aitk/inference_sample.ipynb               |  83 +++++++++++-
 .../aitk/sd_qnn_workflow.json                 |   2 +-
 .../aitk/sd_qnn_workflow.py                   | 121 +++++++++++++++++-
 .../aitk/sd_utils/onnx_patch.py               |  92 +++++++++++++
 .../aitk/sd_utils/ort.py                      |  19 +--
 .../aitk/sd_utils/qdq.py                      |  34 ++++-
 .../aitk/stable_diffusion.py                  |  24 ++--
 .../olive/winml.py                            |  21 +++
 17 files changed, 403 insertions(+), 55 deletions(-)
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/onnx_patch.py
 create mode 100644 sd-legacy-stable-diffusion-v1-5/olive/winml.py

diff --git a/.aitk/requirements/requirements-WCR-SD.txt b/.aitk/requirements/requirements-WCR-SD.txt
index 0b601bcd..74b5790f 100644
--- a/.aitk/requirements/requirements-WCR-SD.txt
+++ b/.aitk/requirements/requirements-WCR-SD.txt
@@ -1,4 +1,3 @@
 accelerate==1.12.0
 diffusers==0.35.0
-onnxruntime-qnn==1.23.2
 torch-fidelity==0.3.0
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/.gitignore b/sd-legacy-stable-diffusion-v1-5/aitk/.gitignore
index ce174cca..ccd91a69 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/.gitignore
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/.gitignore
@@ -3,6 +3,6 @@ __pycache__
 /history/*/*
 !/history/*/history.config
 !/history/*/olive_config.json
-/footprints/
+/footprints
 /result_*.png
-/quantize_data*/
+/*data*/
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/README.md b/sd-legacy-stable-diffusion-v1-5/aitk/README.md
index 5acd4f91..1a0a7045 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/README.md
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/README.md
@@ -1,34 +1,30 @@
 ## Stable Diffusion Optimization with ONNX Runtime QNN EP
 
-### Prerequisites
-```bash
-python -m pip install -r requirements_qnn.txt
-```
 ### Generate data for static quantization
 
 To get better result, we need to generate real data from original model instead of using random data for static quantization.
 
 First generate onnx unoptimized model:
 
-`python stable_diffusion.py --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --provider cpu --format qdq --optimize --only_conversion`
+`python stable_diffusion.py --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --script_dir .\ --provider cpu --format qdq --optimize --only_conversion`
 
 Then generate data:
 
-`python .\evaluation.py --save_data --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --num_inference_steps 25 --seed 0 --num_data 100 --guidance_scale 7.5`
+`python .\evaluation.py --save_data --script_dir .\ --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --num_inference_steps 25 --seed 0 --num_data 100 --guidance_scale 7.5`
 
 ### Optimize
 
 Optimize the onnx models for performance improvements. vae_decoder and unet are per-channel quantized and text_encoder runs in fp16 precision.
 
-`python stable_diffusion.py --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --provider qnn --format qdq --optimize`
+`python stable_diffusion.py --script_dir .\ --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --provider qnn --format qdq --optimize`
 
 ### Test and evaluate
 
-`python .\evaluation.py --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --num_inference_steps 25 --seed 0 --num_data 100 --guidance_scale 7.5 --provider QNNExecutionProvider --model_dir optimized-qnn_qdq`
+`python .\evaluation.py --script_dir .\ --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --num_inference_steps 25 --seed 0 --num_data 100 --guidance_scale 7.5 --provider QNNExecutionProvider --model_dir optimized-qnn_qdq`
 
 To generate one image:
 
-`python stable_diffusion.py --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --provider qnn --format qdq --guidance_scale 7.5 --seed 0 --num_inference_steps 25 --prompt "A baby is laying down with a teddy bear"`
+`python stable_diffusion.py --script_dir .\ --model_id stable-diffusion-v1-5/stable-diffusion-v1-5 --provider qnn --format qdq --guidance_scale 7.5 --seed 0 --num_inference_steps 25 --prompt "A baby is laying down with a teddy bear"`
 
 ### References
 
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/config_safety_checker.json b/sd-legacy-stable-diffusion-v1-5/aitk/config_safety_checker.json
index acd009a9..8b2d063c 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/config_safety_checker.json
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/config_safety_checker.json
@@ -1,7 +1,7 @@
 {
     "input_model": {
         "type": "PyTorchModel",
-        "model_path": "sd-legacy/stable-diffusion-v1-5",
+        "model_path": "stable-diffusion-v1-5/stable-diffusion-v1-5",
         "model_loader": "safety_checker_load",
         "model_script": "user_script.py",
         "io_config": {
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/config_text_encoder.json b/sd-legacy-stable-diffusion-v1-5/aitk/config_text_encoder.json
index 271b2c37..ce2559d2 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/config_text_encoder.json
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/config_text_encoder.json
@@ -1,7 +1,7 @@
 {
     "input_model": {
         "type": "PyTorchModel",
-        "model_path": "sd-legacy/stable-diffusion-v1-5",
+        "model_path": "stable-diffusion-v1-5/stable-diffusion-v1-5",
         "model_loader": "text_encoder_load",
         "model_script": "user_script.py",
         "io_config": {
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/config_unet.json b/sd-legacy-stable-diffusion-v1-5/aitk/config_unet.json
index 654bb619..1f631b3f 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/config_unet.json
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/config_unet.json
@@ -1,7 +1,7 @@
 {
     "input_model": {
         "type": "PyTorchModel",
-        "model_path": "sd-legacy/stable-diffusion-v1-5",
+        "model_path": "stable-diffusion-v1-5/stable-diffusion-v1-5",
         "model_loader": "unet_load",
         "model_script": "user_script.py",
         "io_config": {
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_decoder.json b/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_decoder.json
index 31766c6a..ab59bdd1 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_decoder.json
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_decoder.json
@@ -1,7 +1,7 @@
 {
     "input_model": {
         "type": "PyTorchModel",
-        "model_path": "sd-legacy/stable-diffusion-v1-5",
+        "model_path": "stable-diffusion-v1-5/stable-diffusion-v1-5",
         "model_loader": "vae_decoder_load",
         "model_script": "user_script.py",
         "io_config": {
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_encoder.json b/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_encoder.json
index 3a283bbb..a1b09a1c 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_encoder.json
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/config_vae_encoder.json
@@ -1,7 +1,7 @@
 {
     "input_model": {
         "type": "PyTorchModel",
-        "model_path": "sd-legacy/stable-diffusion-v1-5",
+        "model_path": "stable-diffusion-v1-5/stable-diffusion-v1-5",
         "model_loader": "vae_encoder_load",
         "model_script": "user_script.py",
         "io_config": {
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/evaluation.py b/sd-legacy-stable-diffusion-v1-5/aitk/evaluation.py
index c87fc8db..c224ddca 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/evaluation.py
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/evaluation.py
@@ -157,7 +157,7 @@ def download_file(url, save_path):
 
 def get_real_images(train_data, num_data):
     script_dir = Path(__file__).resolve().parent
-    data_dir = script_dir / "quantize_data_coco2017"
+    data_dir = script_dir / "train_data"
     with torch.no_grad():
         images = []
         for i, example in enumerate(train_data):
@@ -194,9 +194,11 @@ def parse_args(raw_args):
     import argparse
 
     parser = argparse.ArgumentParser("Common arguments")
+
+    parser.add_argument("--script_dir", required=True, type=str)
     parser.add_argument("--save_data", action="store_true")
-    parser.add_argument("--model_dir", default="optimized-cpu_qdq", type=str, help="model_dir path")
-    parser.add_argument("--model_id", default="CompVis/stable-diffusion-v1-4", type=str)
+    parser.add_argument("--model_dir", default="optimized", type=str, help="model_dir path")
+    parser.add_argument("--model_id", default="stable-diffusion-v1-5/stable-diffusion-v1-5", type=str)
     parser.add_argument(
         "--guidance_scale",
         default=7.5,
@@ -215,7 +217,24 @@ def parse_args(raw_args):
     parser.add_argument(
         "--sub_dir", default="optimized", type=str, help="Sub directory to save the data for optimized model test"
     )
-    parser.add_argument("--num_data", default=10, type=int)
+    parser.add_argument(
+        "--dataset_name",
+        default="phiyodr/coco2017",
+        type=str,
+        help="(Optional) dataset to download",
+    )
+    parser.add_argument(
+        "--dataset_split",
+        default="train",
+        type=str,
+        help="(Optional) dataset split to download",
+    )
+    parser.add_argument(
+        "--num_data",
+        default=10,
+        type=int,
+        help="Number of data samples to use for quantization",
+    )
     parser.add_argument("--train_ratio", default=0.5, type=float)
     parser.add_argument("--image_size", default=512, type=int, help="Width and height of the images to generate")
     parser.add_argument("--hpsv2_style", default=None, type=str, help="Style for hpsv2 benchmark")
@@ -226,15 +245,15 @@ def parse_args(raw_args):
 def main(raw_args=None):
     args = parse_args(raw_args)
     real_images = None
-    dataset = load_dataset("phiyodr/coco2017", streaming=True)
-    train_data = dataset["train"]
+    dataset = load_dataset(args.dataset_name, streaming=True)
+    train_data = dataset[args.dataset_split]
     prompts = [sanitize_path(example["captions"][0]) for i, example in enumerate(train_data) if i < args.num_data]
     real_images = get_real_images(train_data, args.num_data)
 
     train_num = math.floor(len(prompts) * args.train_ratio)
     clip_score_fn = partial(clip_score, model_name_or_path="openai/clip-vit-base-patch16")
 
-    script_dir = Path()  # Path(__file__).resolve().parent
+    script_dir = Path(args.script_dir)
     unoptimized_path: Path = script_dir / args.data_dir / "unoptimized"
     optimized_path: Path = script_dir / args.data_dir / args.sub_dir
 
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb b/sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb
index 41105388..994f5f01 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb
@@ -3,13 +3,92 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "21cb1e5c",
+   "id": "547a25de",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO\n",
+    "model_dir = \"./models/optimized/stable-diffusion-v1-5/stable-diffusion-v1-5\"\n",
+    "\n",
     "ExecutionProvider=\"QNNExecutionProvider\""
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eed9c231",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reference: https://learn.microsoft.com/en-us/windows/ai/new-windows-ml/tutorial?tabs=python#acquiring-the-model-and-preprocessing\n",
+    "import subprocess\n",
+    "import json\n",
+    "import sys\n",
+    "import os\n",
+    "import onnxruntime as ort\n",
+    "\n",
+    "def register_execution_providers():\n",
+    "    worker_script = os.path.abspath('winml.py')\n",
+    "    print(worker_script)\n",
+    "    result = subprocess.check_output([sys.executable, worker_script], text=True)\n",
+    "    paths = json.loads(result)\n",
+    "    for item in paths.items():\n",
+    "        try:\n",
+    "            ort.register_execution_provider_library(item[0], item[1])\n",
+    "        except Exception as e:\n",
+    "            print(f\"Failed to register execution provider {item[0]}: {e}\")\n",
+    "\n",
+    "register_execution_providers()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffcd22ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from pathlib import Path\n",
+    "\n",
+    "NOTEBOOK_DIR = Path(__file__).parent if \"__file__\" in globals() else Path.cwd()\n",
+    "PROJECT_ROOT = NOTEBOOK_DIR.parents[1]\n",
+    "sys.path.insert(0, str(PROJECT_ROOT))\n",
+    "\n",
+    "import numpy as np\n",
+    "from sd_utils.qdq import OnnxStableDiffusionPipelineWithSave\n",
+    "\n",
+    "def add_ep_for_device(session_options, ep_name, device_type, ep_options=None):\n",
+    "    ep_devices = ort.get_ep_devices()\n",
+    "    for ep_device in ep_devices:\n",
+    "        if ep_device.ep_name == ep_name and ep_device.device.type == device_type:\n",
+    "            print(f\"Adding {ep_name} for {device_type}\")\n",
+    "            session_options.add_provider_for_devices([ep_device], {} if ep_options is None else ep_options)\n",
+    "            break\n",
+    "\n",
+    "\n",
+    "session_options = ort.SessionOptions()\n",
+    "provider_options = [{}]\n",
+    "\n",
+    "add_ep_for_device(session_options, ExecutionProvider, ort.OrtHardwareDeviceType.NPU)\n",
+    "\n",
+    "pipeline = OnnxStableDiffusionPipelineWithSave.from_pretrained(\n",
+    "    model_dir, provider=ExecutionProvider, sess_options=session_options, provider_options=provider_options\n",
+    ")\n",
+    "pipeline.save_data_dir = None\n",
+    "\n",
+    "prompt = \"A beautiful landscape painting of mountains during sunrise\"\n",
+    "result = pipeline(\n",
+    "    [prompt],\n",
+    "    num_inference_steps=25,\n",
+    "    height=512,\n",
+    "    width=512,\n",
+    "    guidance_scale=7.5,\n",
+    "    generator=np.random.RandomState(seed=0)\n",
+    ")\n",
+    "\n",
+    "from IPython.display import display\n",
+    "display(result.images[0])\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json
index a089cf0b..258dfee9 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json
@@ -36,4 +36,4 @@
     "clean_cache": false,
     "output_dir": "model/sd_qnn",
     "cache_dir": "cache"
-}
\ No newline at end of file
+}
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py
index a6a2be60..349b6ac1 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py
@@ -1,10 +1,125 @@
 import argparse
+import json
+import os
+import subprocess
+import sys
+import logging
 
-def main():
+logger = logging.getLogger(os.path.basename(__file__))
+logging.basicConfig(level=logging.INFO)
+
+def parse_arguments():
     parser = argparse.ArgumentParser()
-    args = parser.parse_args()
+    parser.add_argument("--config", required=True, help="path to input config file")
+    parser.add_argument("--model_config", help="path to input model config file")
+    parser.add_argument("--runtime", required=True, help="runtime")
+    return parser.parse_args()
+
+def load_update_config(
+        config_path: str,
+        cache_dir: str,
+        output_dir: str,
+        activation_type: str | None = None,
+        precision: str | None = None) -> dict:
+    with open(config_path, 'r', encoding='utf-8') as file:
+        oliveJson = json.load(file)
+
+    oliveJson["cache_dir"] = cache_dir
+    oliveJson["output_dir"] = os.path.join(os.path.dirname(output_dir), oliveJson["output_dir"])
+
+    if "quantization" in oliveJson["passes"]:
+        if activation_type is not None:
+            oliveJson["passes"]["quantization"]["activation_type"] = activation_type
+        if precision is not None:
+            oliveJson["passes"]["quantization"]["precision"] = precision
+
+    return oliveJson
+
+def copy_olive_config(
+        history_folder: str,
+        config_path: str,
+        cache_dir: str,
+        output_dir: str,
+        activation_type: str | None = None,
+        precision: str | None = None):
+    logger.info(f"Copying {config_path} to {history_folder}...")
+    oliveJson = load_update_config(config_path, cache_dir, output_dir, activation_type, precision)
+    # save updated config for record
+    config_name = os.path.basename(config_path)
+    os.makedirs(history_folder, exist_ok=True)
+    with open(os.path.join(history_folder, config_name), 'w', encoding='utf-8') as file:
+        json.dump(oliveJson, file, indent=4)
+
+def main():
+    args = parse_arguments()
+
+    if args.model_config:
+        metrics = {
+            "latency-avg": 666.666
+        }
+        output_file = os.path.join(os.path.dirname(args.config), "metrics.json")
+        resultStr = json.dumps(metrics, indent=4)
+        with open(output_file, 'w') as file:
+            file.write(resultStr)
+        logger.info("Model lab succeeded for evaluation.\n%s", resultStr)
+        return
+
+    with open(args.config, 'r', encoding='utf-8') as file:
+        oliveJson = json.load(file)
+
+    # Get arguments
+    output_dir: str = oliveJson["output_dir"]
+    cache_dir: str = oliveJson["cache_dir"]
+    config_pass = oliveJson["passes"]["aitkpython"]
+    activation_type: str = config_pass["activation_type"]
+    precision: str = config_pass["precision"]
+
+    dataset_name: str = config_pass["dataset_name"]
+    dataset_split: str = config_pass["split"]
+    num_data: int = config_pass["length"]
+
+    history_folder = os.path.dirname(args.config)
+
+    logger.info(f"history dir: {history_folder}")
+    os.makedirs(os.path.join(history_folder, "model"), exist_ok=True)
+
+    submodel_names = ["vae_encoder", "vae_decoder", "unet", "text_encoder", "safety_checker"]
+
+    for submodel_name in submodel_names:
+        config_name = f"config_{submodel_name}.json"
+        copy_olive_config(history_folder, config_name, cache_dir, output_dir, activation_type, precision)
 
-    print(args)
+    # run stable_diffusion.py to generate onnx unoptimized model
+    subprocess.run([sys.executable, "stable_diffusion.py",
+                    "--script_dir", history_folder,
+                    "--model_id", "stable-diffusion-v1-5/stable-diffusion-v1-5",
+                    "--provider", "cpu",
+                    "--format", "qdq",
+                    "--optimize",
+                    "--only_conversion"],
+                   check=True)
+    
+    # # run evaluation.py to generate data
+    subprocess.run([sys.executable, "evaluation.py",
+                    "--script_dir", history_folder,
+                    "--save_data",
+                    "--model_id", "stable-diffusion-v1-5/stable-diffusion-v1-5",
+                    "--num_inference_steps", "25",
+                    "--seed", "0",
+                    "--dataset_name", dataset_name,
+                    "--dataset_split", dataset_split,
+                    "--num_data", str(num_data),
+                    "--guidance_scale", "7.5"],
+                   check=True)
+    
+    # run stable_diffusion.py to generate onnx quantized model
+    subprocess.run([sys.executable, "stable_diffusion.py",
+                    "--script_dir", history_folder,
+                    "--model_id", "stable-diffusion-v1-5/stable-diffusion-v1-5",
+                    "--provider", "cpu",
+                    "--format", "qdq",
+                    "--optimize"],
+                   check=True)
 
 if __name__ == "__main__":
     main()
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/onnx_patch.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/onnx_patch.py
new file mode 100644
index 00000000..f9716f27
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/onnx_patch.py
@@ -0,0 +1,92 @@
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
+import os
+import shutil
+from pathlib import Path
+from typing import Optional, Union
+
+from diffusers import OnnxRuntimeModel
+from diffusers.utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME
+
+import numpy as np
+import onnxruntime as ort
+
+
+class PatchedOnnxRuntimeModel(OnnxRuntimeModel):
+    def __init__(self, model=None, **kwargs):
+        self.model = model
+        self.model_save_dir = kwargs.get("model_save_dir", None)
+
+    def __call__(self, **kwargs):
+        inputs = {k: np.array(v) for k, v in kwargs.items()}
+        return self.model.run(None, inputs)
+
+    @staticmethod
+    def load_model(path: Union[str, Path], provider=None, sess_options=None, provider_options=None):
+        return ort.InferenceSession(path, sess_options=sess_options)
+
+    def _save_pretrained(self, save_directory: Union[str, Path], file_name: Optional[str] = None, **kwargs):
+        model_file_name = ONNX_WEIGHTS_NAME
+
+        src_path = self.model_save_dir.joinpath(model_file_name)
+        dst_path = Path(save_directory).joinpath(model_file_name)
+
+        try:
+            shutil.copyfile(src_path, dst_path)
+        except shutil.SameFileError:
+            pass
+
+        # copy external weights (for models >2GB)
+        src_path = self.model_save_dir.joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
+        if src_path.exists():
+            dst_path = Path(save_directory).joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
+            try:
+                shutil.copyfile(src_path, dst_path)
+            except shutil.SameFileError:
+                pass
+
+    def save_pretrained(
+        self,
+        save_directory: Union[str, os.PathLike],
+        **kwargs,
+    ):
+        if os.path.isfile(save_directory):
+            print(f"Provided path ({save_directory}) should be a directory, not a file")
+            return
+
+        os.makedirs(save_directory, exist_ok=True)
+
+        self._save_pretrained(save_directory, **kwargs)
+
+
+    @classmethod
+    def _from_pretrained(
+        cls,
+        model_id: Union[str, Path],
+        provider: Optional[str] = None,
+        sess_options: Optional["ort.SessionOptions"] = None,
+        **kwargs,
+    ):
+        model_file_name = ONNX_WEIGHTS_NAME
+        model_path = Path(model_id, model_file_name).as_posix()
+
+        model = PatchedOnnxRuntimeModel.load_model(
+            model_path,
+            provider=provider,
+            sess_options=sess_options,
+        )
+
+        kwargs["model_save_dir"] = Path(model_id)
+
+        return cls(model=model, **kwargs)
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        model_id: Union[str, Path],
+        **model_kwargs,
+    ):
+        return cls._from_pretrained(model_id=model_id, **model_kwargs)
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ort.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ort.py
index 4b20ee4a..3f9c8e9b 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ort.py
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/ort.py
@@ -9,7 +9,8 @@
 from pathlib import Path
 
 import onnxruntime as ort
-from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline
+
+from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline
 from olive.model import ONNXModelHandler
 from onnxruntime import __version__ as OrtVersion
 from packaging import version
@@ -52,8 +53,8 @@ def validate_ort_version(provider: str):
         )
 
 
-def save_optimized_onnx_submodel(submodel_name, provider, model_info):
-    footprints_file_path = Path(__file__).resolve().parents[1] / "footprints" / submodel_name / "footprint.json"
+def save_optimized_onnx_submodel(script_dir, submodel_name, provider, model_info):
+    footprints_file_path = Path(script_dir).resolve() / "footprints" / submodel_name / "footprint.json"
     with footprints_file_path.open("r") as footprint_file:
         footprints = json.load(footprint_file)
 
@@ -99,17 +100,19 @@ def save_onnx_pipeline(
     # This is optional, and the optimized models can be used directly in a custom pipeline if desired.
     print("\nCreating ONNX pipeline...")
 
+    from sd_utils.onnx_patch import PatchedOnnxRuntimeModel
+
     if has_safety_checker:
-        safety_checker = OnnxRuntimeModel.from_pretrained(model_info["safety_checker"]["unoptimized"]["path"].parent)
+        safety_checker = PatchedOnnxRuntimeModel.from_pretrained(model_info["safety_checker"]["unoptimized"]["path"].parent)
     else:
         safety_checker = None
 
     onnx_pipeline = OnnxStableDiffusionPipeline(
-        vae_encoder=OnnxRuntimeModel.from_pretrained(model_info["vae_encoder"]["unoptimized"]["path"].parent),
-        vae_decoder=OnnxRuntimeModel.from_pretrained(model_info["vae_decoder"]["unoptimized"]["path"].parent),
-        text_encoder=OnnxRuntimeModel.from_pretrained(model_info["text_encoder"]["unoptimized"]["path"].parent),
+        vae_encoder=PatchedOnnxRuntimeModel.from_pretrained(model_info["vae_encoder"]["unoptimized"]["path"].parent),
+        vae_decoder=PatchedOnnxRuntimeModel.from_pretrained(model_info["vae_decoder"]["unoptimized"]["path"].parent),
+        text_encoder=PatchedOnnxRuntimeModel.from_pretrained(model_info["text_encoder"]["unoptimized"]["path"].parent),
         tokenizer=pipeline.tokenizer,
-        unet=OnnxRuntimeModel.from_pretrained(model_info["unet"]["unoptimized"]["path"].parent),
+        unet=PatchedOnnxRuntimeModel.from_pretrained(model_info["unet"]["unoptimized"]["path"].parent, provider_options=[]),
         scheduler=pipeline.scheduler,
         safety_checker=safety_checker,
         feature_extractor=pipeline.feature_extractor,
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py
index 3caa6032..9c03ddd9 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py
@@ -12,7 +12,8 @@
 import sd_utils
 import sd_utils.config
 import torch
-from diffusers import OnnxStableDiffusionPipeline
+
+from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline
 from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 
@@ -253,7 +254,31 @@ def __call__(
         return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
 
 
+def add_ep_for_device(session_options, ep_name, device_type, ep_options=None):
+    ep_devices = ort.get_ep_devices()
+    for ep_device in ep_devices:
+        if ep_device.ep_name == ep_name and ep_device.device.type == device_type:
+            print(f"Adding {ep_name} for {device_type}")
+            session_options.add_provider_for_devices([ep_device], {} if ep_options is None else ep_options)
+            break
+
+def register_execution_providers():
+    import json
+    import subprocess
+    import sys
+    import onnxruntime as ort
+
+    worker_script = os.path.abspath('winml.py')
+    result = subprocess.check_output([sys.executable, worker_script], text=True)
+    paths = json.loads(result)
+    for item in paths.items():
+        try:
+            ort.register_execution_provider_library(item[0], item[1])
+        except Exception as e:
+            print(f"Failed to register execution provider {item[0]}: {e}")
+
 def get_qdq_pipeline(model_dir, common_args, qdq_args, script_dir):
+    register_execution_providers()
     ort.set_default_logger_severity(3)
 
     print("Loading models into ORT session...")
@@ -269,12 +294,7 @@ def get_qdq_pipeline(model_dir, common_args, qdq_args, script_dir):
     }
     assert provider in provider_map, f"Unsupported provider: {provider}"
 
-    if provider == "qnn":
-        provider_options[0]["backend_path"] = "QnnHtp.dll"
-        provider_options[0]["htp_performance_mode"] = "sustained_high_performance"
-        provider_options[0]["htp_graph_finalization_optimization_mode"] = "3"
-        provider_options[0]["offload_graph_io_quantization"] = "0"
-        provider_options[0]["enable_htp_fp16_precision"] = "1"
+    add_ep_for_device(sess_options, provider_map[provider], ort.OrtHardwareDeviceType.NPU)
 
     pipeline = OnnxStableDiffusionPipelineWithSave.from_pretrained(
         model_dir, provider=provider_map[provider], sess_options=sess_options, provider_options=provider_options
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/stable_diffusion.py b/sd-legacy-stable-diffusion-v1-5/aitk/stable_diffusion.py
index caa8e3df..4567305d 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/stable_diffusion.py
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/stable_diffusion.py
@@ -200,7 +200,7 @@ def optimize(
     provider = common_args.provider
     model_format = common_args.format
 
-    script_dir = Path(__file__).resolve().parent
+    script_dir = Path(common_args.script_dir)
 
     # Clean up previously optimized models, if any.
     shutil.rmtree(script_dir / "footprints", ignore_errors=True)
@@ -208,7 +208,7 @@ def optimize(
     shutil.rmtree(optimized_model_dir, ignore_errors=True)
 
     # The model_id and base_model_id are identical when optimizing a standard stable diffusion model like
-    # CompVis/stable-diffusion-v1-4. These variables are only different when optimizing a LoRA variant.
+    # stable-diffusion-v1-5/stable-diffusion-v1-5. These variables are only different when optimizing a LoRA variant.
     base_model_id = get_base_model_name(model_id)
     print(f"\nModel {base_model_id}")
 
@@ -253,6 +253,11 @@ def optimize(
             # base model ID should be able to reuse previously optimized copies.
             olive_config["input_model"]["model_path"] = base_model_id
 
+        # Save the config before olive_run modifies it with non-serializable objects
+        suffix = f"{model_format}_{provider}" if model_format else provider
+        with (script_dir / f"config_{submodel_name}_{suffix}.json").open("w") as fout:
+            json.dump(olive_config, fout, indent=4)
+
         workflow_output = olive_run(olive_config)
 
         if provider == "openvino":
@@ -262,7 +267,7 @@ def optimize(
         else:
             from sd_utils.ort import save_optimized_onnx_submodel
 
-            save_optimized_onnx_submodel(submodel_name, provider, model_info)
+            save_optimized_onnx_submodel(script_dir, submodel_name, provider, model_info)
 
     if provider == "openvino":
         from sd_utils.ov import save_ov_model_info
@@ -281,7 +286,9 @@ def optimize(
 def parse_common_args(raw_args):
     parser = argparse.ArgumentParser("Common arguments")
 
-    parser.add_argument("--model_id", default="CompVis/stable-diffusion-v1-4", type=str)
+    parser.add_argument("--script_dir", required=True, type=str)
+    parser.add_argument("--cache_dir", type=str)
+    parser.add_argument("--model_id", default="stable-diffusion-v1-5/stable-diffusion-v1-5", type=str)
     parser.add_argument(
         "--provider",
         default="cuda",
@@ -376,15 +383,12 @@ def main(raw_args=None):
     provider = common_args.provider
     model_id = common_args.model_id
 
-    script_dir = Path(__file__).resolve().parent
+    script_dir = Path(common_args.script_dir)
     unoptimized_model_dir = script_dir / "models" / "unoptimized" / model_id
-    optimized_dir_name = f"optimized-{provider}"
-    if common_args.format:
-        optimized_dir_name += f"_{common_args.format}"
-    optimized_model_dir = script_dir / "models" / optimized_dir_name / model_id
+    optimized_model_dir = script_dir / "models" / "optimized" / model_id
 
     if common_args.clean_cache:
-        shutil.rmtree(script_dir / "cache", ignore_errors=True)
+        shutil.rmtree(common_args.cache_dir, ignore_errors=True)
 
     guidance_scale = common_args.guidance_scale
 
diff --git a/sd-legacy-stable-diffusion-v1-5/olive/winml.py b/sd-legacy-stable-diffusion-v1-5/olive/winml.py
new file mode 100644
index 00000000..74a12c53
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/olive/winml.py
@@ -0,0 +1,21 @@
+import json
+
+def _get_ep_paths() -> dict[str, str]:
+    from winui3.microsoft.windows.applicationmodel.dynamicdependency.bootstrap import (
+        InitializeOptions,
+        initialize
+    )
+    import winui3.microsoft.windows.ai.machinelearning as winml
+    eps = {}
+    with initialize(options = InitializeOptions.ON_NO_MATCH_SHOW_UI):
+        catalog = winml.ExecutionProviderCatalog.get_default()
+        providers = catalog.find_all_providers()
+        for provider in providers:
+            provider.ensure_ready_async().get()
+            eps[provider.name] = provider.library_path
+            # DO NOT call provider.try_register in python. That will register to the native env.
+    return eps
+
+if __name__ == "__main__":
+    eps = _get_ep_paths()
+    print(json.dumps(eps))

From f6a6c4760ffbcc77daf2e57236cdb4bd43194279 Mon Sep 17 00:00:00 2001
From: ziyuanguo <ziyuanguo@microsoft.com>
Date: Tue, 10 Feb 2026 10:34:05 +0800
Subject: [PATCH 08/11] fix whitespace

---
 .aitk/configs/checks.json                            | 12 ++++++------
 sd-legacy-stable-diffusion-v1-5/aitk/info.yml        |  2 +-
 .../aitk/sd_qnn_workflow.py                          |  4 ++--
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/.aitk/configs/checks.json b/.aitk/configs/checks.json
index 1a7a7ade..c80ae155 100644
--- a/.aitk/configs/checks.json
+++ b/.aitk/configs/checks.json
@@ -1,16 +1,16 @@
 {
-    "configCheck": 129,
-    "copyCheck": 182,
+    "configCheck": 140,
+    "copyCheck": 179,
     "extensionCheck": 1,
     "gitignoreCheck": 39,
     "inferenceModelCheck": 25,
     "ipynbCheck": 39,
     "licenseCheck": 38,
     "modelProjectCheck": 40,
-    "oliveCheck": 36,
-    "oliveJsonCheck": 129,
-    "pathCheck": 1122,
+    "oliveCheck": 45,
+    "oliveJsonCheck": 140,
+    "pathCheck": 1170,
     "requirementsCheck": 37,
     "templateCheck": 1,
-    "venvRequirementsCheck": 12
+    "venvRequirementsCheck": 14
 }
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/info.yml b/sd-legacy-stable-diffusion-v1-5/aitk/info.yml
index c4e27750..2c096503 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/info.yml
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/info.yml
@@ -9,4 +9,4 @@ aitk:
   modelInfo:
     id: "huggingface/stable-diffusion-v1-5/stable-diffusion-v1-5"
     version: 1
-    status: Hide
\ No newline at end of file
+    status: Hide
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py
index 349b6ac1..94ff7972 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py
@@ -98,7 +98,7 @@ def main():
                     "--optimize",
                     "--only_conversion"],
                    check=True)
-    
+
     # # run evaluation.py to generate data
     subprocess.run([sys.executable, "evaluation.py",
                     "--script_dir", history_folder,
@@ -111,7 +111,7 @@ def main():
                     "--num_data", str(num_data),
                     "--guidance_scale", "7.5"],
                    check=True)
-    
+
     # run stable_diffusion.py to generate onnx quantized model
     subprocess.run([sys.executable, "stable_diffusion.py",
                     "--script_dir", history_folder,

From 6013735edff6104c0acc3fea75da6ee1c8a80cc2 Mon Sep 17 00:00:00 2001
From: ziyuanguo <ziyuanguo@microsoft.com>
Date: Tue, 10 Feb 2026 12:29:37 +0800
Subject: [PATCH 09/11] fix eval no data config

---
 sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
index 1c14e4dd..37c22ec1 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
@@ -1,5 +1,6 @@
 {
     "name": "Convert to Qualcomm NPU",
+    "evalNoDataConfig": true,
     "memoryGbSuggested": 30,
     "executeRuntimeFeatures": [
         "SD"

From 895140fe0f98d1acd96c29fb3099840cf38248e7 Mon Sep 17 00:00:00 2001
From: ziyuanguo <ziyuanguo@microsoft.com>
Date: Tue, 10 Feb 2026 15:15:02 +0800
Subject: [PATCH 10/11] update evaluation

---
 .../aitk/sd_qnn_evaluation.py                 | 126 ++++++++++++++++++
 .../aitk/sd_qnn_workflow.py                   |  25 ++--
 .../aitk/sd_utils/qdq.py                      |   1 -
 3 files changed, 142 insertions(+), 10 deletions(-)
 create mode 100644 sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_evaluation.py

diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_evaluation.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_evaluation.py
new file mode 100644
index 00000000..b415cf1b
--- /dev/null
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_evaluation.py
@@ -0,0 +1,126 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
+import json
+import logging
+import os
+from pathlib import Path
+
+import numpy as np
+import onnxruntime as ort
+
+from sd_utils.qdq import OnnxStableDiffusionPipelineWithSave
+
+logger = logging.getLogger(os.path.basename(__file__))
+logging.basicConfig(level=logging.INFO)
+
+def parse_args(raw_args):
+    import argparse
+
+    parser = argparse.ArgumentParser("Common arguments")
+
+    parser.add_argument("--script_dir", required=True, type=str)
+    parser.add_argument("--model_dir", default="optimized", type=str, help="model_dir path")
+    parser.add_argument("--model_id", default="stable-diffusion-v1-5/stable-diffusion-v1-5", type=str)
+    parser.add_argument(
+        "--guidance_scale",
+        default=7.5,
+        type=float,
+        help="Guidance scale as defined in Classifier-Free Diffusion Guidance",
+    )
+    parser.add_argument("--num_inference_steps", default=50, type=int, help="Number of steps in diffusion process")
+    parser.add_argument(
+        "--seed",
+        default=None,
+        type=int,
+        help="The seed to give to the generator to generate deterministic results.",
+    )
+    parser.add_argument("--image_size", default=512, type=int, help="Width and height of the images to generate")
+    parser.add_argument(
+        "--execution_provider",
+        type=str,
+        default="CPUExecutionProvider",
+        help="ORT Execution provider",
+    )
+    parser.add_argument(
+        "--device_str",
+        type=str,
+        default="cpu",
+    )
+    parser.add_argument(
+        "--output_file",
+        type=str,
+        required=True,
+    )
+    return parser.parse_args(raw_args)
+
+def get_device_type(device_str):
+    if device_str.lower() == "gpu":
+        return ort.OrtHardwareDeviceType.GPU
+    elif device_str.lower() == "npu":
+        return ort.OrtHardwareDeviceType.NPU
+    else:
+        return ort.OrtHardwareDeviceType.CPU
+
+def add_ep_for_device(session_options, ep_name, device_type, ep_options=None):
+    ep_devices = ort.get_ep_devices()
+    for ep_device in ep_devices:
+        if ep_device.ep_name == ep_name and ep_device.device.type == device_type:
+            print(f"Adding {ep_name} for {device_type}")
+            session_options.add_provider_for_devices([ep_device], {} if ep_options is None else ep_options)
+            break
+
+def register_execution_providers():
+    import subprocess
+    import sys
+
+    worker_script = os.path.abspath('winml.py')
+    result = subprocess.check_output([sys.executable, worker_script], text=True)
+    paths = json.loads(result)
+    for item in paths.items():
+        try:
+            ort.register_execution_provider_library(item[0], item[1])
+        except Exception as e:
+            print(f"Failed to register execution provider {item[0]}: {e}")
+
+
+def main(raw_args=None):
+    args = parse_args(raw_args)
+
+    prompts = ["A baby is laying down with a teddy bear"]
+    model_dir = Path(args.script_dir) / "models" / args.model_dir / args.model_id
+
+    register_execution_providers()
+
+    session_options = ort.SessionOptions()
+    provider_options = [{}]
+
+    add_ep_for_device(session_options, args.execution_provider, get_device_type(args.device_str))
+
+    pipeline = OnnxStableDiffusionPipelineWithSave.from_pretrained(
+        model_dir, provider=args.execution_provider, session_options=session_options, provider_options=provider_options
+    )
+    pipeline.save_data_dir = None
+
+    generator = None if args.seed is None else np.random.RandomState(seed=args.seed)
+    pipeline(
+        prompts,
+        num_inference_steps=args.num_inference_steps,
+        height=args.image_size,
+        width=args.image_size,
+        guidance_scale=args.guidance_scale,
+        generator=generator,
+    )
+
+    metrics = {
+        "latency-avg": 666.667
+    }
+    resultStr = json.dumps(metrics, indent=4)
+    with open(args.output_file, 'w') as file:
+        file.write(resultStr)
+    logger.info("Model lab succeeded for evaluation.\n%s", resultStr)
+
+if __name__ == "__main__":
+    main()
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py
index 94ff7972..abe175ec 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.py
@@ -53,19 +53,26 @@ def copy_olive_config(
 def main():
     args = parse_arguments()
 
+    with open(args.config, 'r', encoding='utf-8') as file:
+        oliveJson = json.load(file)
+
     if args.model_config:
-        metrics = {
-            "latency-avg": 666.666
-        }
+        model_path: str = os.path.dirname(args.model_config)
+        execution_provider: str = oliveJson["systems"]["target_system"]["accelerators"][0]["execution_providers"][0]
+        device_str: str = oliveJson["systems"]["target_system"]["accelerators"][0]["device"]
         output_file = os.path.join(os.path.dirname(args.config), "metrics.json")
-        resultStr = json.dumps(metrics, indent=4)
-        with open(output_file, 'w') as file:
-            file.write(resultStr)
-        logger.info("Model lab succeeded for evaluation.\n%s", resultStr)
+
+        # Run evaluator
+        subprocess.run([sys.executable, "sd_qnn_evaluation.py",
+                        "--script_dir", os.path.dirname(model_path),
+                        "--model_dir", "optimized",
+                        "--model_id", "stable-diffusion-v1-5/stable-diffusion-v1-5",
+                        "--execution_provider", execution_provider,
+                        "--device_str", device_str,
+                        "--output_file", output_file],
+                        check=True)
         return
 
-    with open(args.config, 'r', encoding='utf-8') as file:
-        oliveJson = json.load(file)
 
     # Get arguments
     output_dir: str = oliveJson["output_dir"]
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py
index 9c03ddd9..b1275bc7 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/qdq.py
@@ -266,7 +266,6 @@ def register_execution_providers():
     import json
     import subprocess
     import sys
-    import onnxruntime as ort
 
     worker_script = os.path.abspath('winml.py')
     result = subprocess.check_output([sys.executable, worker_script], text=True)

From 597a2ef4313188b0bc0702b235fd5ed43fee50fd Mon Sep 17 00:00:00 2001
From: ziyuanguo <ziyuanguo@microsoft.com>
Date: Wed, 11 Feb 2026 14:48:26 +0800
Subject: [PATCH 11/11] add metrics calculation

---
 .../aitk/inference_sample.ipynb               |   6 +-
 .../aitk/sd_qnn_evaluation.py                 |  45 +++--
 .../aitk/sd_qnn_workflow.json.config          |   5 +
 .../aitk/sd_utils/onnx_patch.py               | 189 +++++++++---------
 4 files changed, 137 insertions(+), 108 deletions(-)

diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb b/sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb
index 994f5f01..0f14e814 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/inference_sample.ipynb
@@ -66,13 +66,13 @@
     "            break\n",
     "\n",
     "\n",
-    "session_options = ort.SessionOptions()\n",
+    "sess_options = ort.SessionOptions()\n",
     "provider_options = [{}]\n",
     "\n",
-    "add_ep_for_device(session_options, ExecutionProvider, ort.OrtHardwareDeviceType.NPU)\n",
+    "add_ep_for_device(sess_options, ExecutionProvider, ort.OrtHardwareDeviceType.NPU)\n",
     "\n",
     "pipeline = OnnxStableDiffusionPipelineWithSave.from_pretrained(\n",
-    "    model_dir, provider=ExecutionProvider, sess_options=session_options, provider_options=provider_options\n",
+    "    model_dir, provider=ExecutionProvider, sess_options=sess_options, provider_options=provider_options\n",
     ")\n",
     "pipeline.save_data_dir = None\n",
     "\n",
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_evaluation.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_evaluation.py
index b415cf1b..5e4c0418 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_evaluation.py
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_evaluation.py
@@ -30,7 +30,7 @@ def parse_args(raw_args):
         type=float,
         help="Guidance scale as defined in Classifier-Free Diffusion Guidance",
     )
-    parser.add_argument("--num_inference_steps", default=50, type=int, help="Number of steps in diffusion process")
+    parser.add_argument("--num_inference_steps", default=25, type=int, help="Number of steps in diffusion process")
     parser.add_argument(
         "--seed",
         default=None,
@@ -94,28 +94,47 @@ def main(raw_args=None):
 
     register_execution_providers()
 
-    session_options = ort.SessionOptions()
+    sess_options = ort.SessionOptions()
     provider_options = [{}]
 
-    add_ep_for_device(session_options, args.execution_provider, get_device_type(args.device_str))
+    add_ep_for_device(sess_options, args.execution_provider, get_device_type(args.device_str))
 
     pipeline = OnnxStableDiffusionPipelineWithSave.from_pretrained(
-        model_dir, provider=args.execution_provider, session_options=session_options, provider_options=provider_options
+        model_dir, provider=args.execution_provider, sess_options=sess_options, provider_options=provider_options
     )
     pipeline.save_data_dir = None
 
+    text_encoder_latencies = []
+    unet_latencies = []
+    vae_decoder_latencies = []
+
     generator = None if args.seed is None else np.random.RandomState(seed=args.seed)
-    pipeline(
-        prompts,
-        num_inference_steps=args.num_inference_steps,
-        height=args.image_size,
-        width=args.image_size,
-        guidance_scale=args.guidance_scale,
-        generator=generator,
-    )
+
+    # we know that PatchedOnnxRuntimeModel records latencies for each call
+    for _ in range(10):
+        pipeline.text_encoder.latencies = []
+        pipeline.unet.latencies = []
+        pipeline.vae_decoder.latencies = []
+        pipeline(
+            prompts,
+            num_inference_steps=args.num_inference_steps,
+            height=args.image_size,
+            width=args.image_size,
+            guidance_scale=args.guidance_scale,
+            generator=generator,
+        )
+        text_encoder_latencies.extend(pipeline.text_encoder.latencies)
+        unet_latencies.extend(pipeline.unet.latencies)
+        vae_decoder_latencies.extend(pipeline.vae_decoder.latencies)
+
+    text_encoder_latency_avg = round(sum(text_encoder_latencies) / len(text_encoder_latencies) * 1000, 5)
+    unet_latency_avg = round(sum(unet_latencies) / len(unet_latencies) * 1000, 5)
+    vae_decoder_latency_avg = round(sum(vae_decoder_latencies) / len(vae_decoder_latencies) * 1000, 5)
 
     metrics = {
-        "latency-avg": 666.667
+        "text-encoder-latency-avg": text_encoder_latency_avg,
+        "unet-latency-avg": unet_latency_avg,
+        "vae-decoder-latency-avg": vae_decoder_latency_avg
     }
     resultStr = json.dumps(metrics, indent=4)
     with open(args.output_file, 'w') as file:
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
index 37c22ec1..0d7d2cf3 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_qnn_workflow.json.config
@@ -1,5 +1,10 @@
 {
     "name": "Convert to Qualcomm NPU",
+    "evalMetrics": {
+        "text-encoder-latency-avg": "text-encoder-latency-avg",
+        "unet-latency-avg": "unet-latency-avg",
+        "vae-decoder-latency-avg": "vae-decoder-latency-avg"
+    },
     "evalNoDataConfig": true,
     "memoryGbSuggested": 30,
     "executeRuntimeFeatures": [
diff --git a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/onnx_patch.py b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/onnx_patch.py
index f9716f27..9855a31d 100644
--- a/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/onnx_patch.py
+++ b/sd-legacy-stable-diffusion-v1-5/aitk/sd_utils/onnx_patch.py
@@ -1,92 +1,97 @@
-# --------------------------------------------------------------------------
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License.
-# --------------------------------------------------------------------------
-
-import os
-import shutil
-from pathlib import Path
-from typing import Optional, Union
-
-from diffusers import OnnxRuntimeModel
-from diffusers.utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME
-
-import numpy as np
-import onnxruntime as ort
-
-
-class PatchedOnnxRuntimeModel(OnnxRuntimeModel):
-    def __init__(self, model=None, **kwargs):
-        self.model = model
-        self.model_save_dir = kwargs.get("model_save_dir", None)
-
-    def __call__(self, **kwargs):
-        inputs = {k: np.array(v) for k, v in kwargs.items()}
-        return self.model.run(None, inputs)
-
-    @staticmethod
-    def load_model(path: Union[str, Path], provider=None, sess_options=None, provider_options=None):
-        return ort.InferenceSession(path, sess_options=sess_options)
-
-    def _save_pretrained(self, save_directory: Union[str, Path], file_name: Optional[str] = None, **kwargs):
-        model_file_name = ONNX_WEIGHTS_NAME
-
-        src_path = self.model_save_dir.joinpath(model_file_name)
-        dst_path = Path(save_directory).joinpath(model_file_name)
-
-        try:
-            shutil.copyfile(src_path, dst_path)
-        except shutil.SameFileError:
-            pass
-
-        # copy external weights (for models >2GB)
-        src_path = self.model_save_dir.joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
-        if src_path.exists():
-            dst_path = Path(save_directory).joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
-            try:
-                shutil.copyfile(src_path, dst_path)
-            except shutil.SameFileError:
-                pass
-
-    def save_pretrained(
-        self,
-        save_directory: Union[str, os.PathLike],
-        **kwargs,
-    ):
-        if os.path.isfile(save_directory):
-            print(f"Provided path ({save_directory}) should be a directory, not a file")
-            return
-
-        os.makedirs(save_directory, exist_ok=True)
-
-        self._save_pretrained(save_directory, **kwargs)
-
-
-    @classmethod
-    def _from_pretrained(
-        cls,
-        model_id: Union[str, Path],
-        provider: Optional[str] = None,
-        sess_options: Optional["ort.SessionOptions"] = None,
-        **kwargs,
-    ):
-        model_file_name = ONNX_WEIGHTS_NAME
-        model_path = Path(model_id, model_file_name).as_posix()
-
-        model = PatchedOnnxRuntimeModel.load_model(
-            model_path,
-            provider=provider,
-            sess_options=sess_options,
-        )
-
-        kwargs["model_save_dir"] = Path(model_id)
-
-        return cls(model=model, **kwargs)
-
-    @classmethod
-    def from_pretrained(
-        cls,
-        model_id: Union[str, Path],
-        **model_kwargs,
-    ):
-        return cls._from_pretrained(model_id=model_id, **model_kwargs)
+# --------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
+import os
+import time
+import shutil
+from pathlib import Path
+from typing import Optional, Union
+
+from diffusers import OnnxRuntimeModel
+from diffusers.utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME
+
+import numpy as np
+import onnxruntime as ort
+
+
+class PatchedOnnxRuntimeModel(OnnxRuntimeModel):
+    def __init__(self, model=None, **kwargs):
+        self.model = model
+        self.model_save_dir = kwargs.get("model_save_dir", None)
+        self.latencies = []
+
+    def __call__(self, **kwargs):
+        inputs = {k: np.array(v) for k, v in kwargs.items()}
+        start_time = time.perf_counter()
+        outputs = self.model.run(None, inputs)
+        self.latencies.append(time.perf_counter() - start_time)
+        return outputs
+
+    @staticmethod
+    def load_model(path: Union[str, Path], provider=None, sess_options=None, provider_options=None):
+        return ort.InferenceSession(path, sess_options=sess_options)
+
+    def _save_pretrained(self, save_directory: Union[str, Path], file_name: Optional[str] = None, **kwargs):
+        model_file_name = ONNX_WEIGHTS_NAME
+
+        src_path = self.model_save_dir.joinpath(model_file_name)
+        dst_path = Path(save_directory).joinpath(model_file_name)
+
+        try:
+            shutil.copyfile(src_path, dst_path)
+        except shutil.SameFileError:
+            pass
+
+        # copy external weights (for models >2GB)
+        src_path = self.model_save_dir.joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
+        if src_path.exists():
+            dst_path = Path(save_directory).joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
+            try:
+                shutil.copyfile(src_path, dst_path)
+            except shutil.SameFileError:
+                pass
+
+    def save_pretrained(
+        self,
+        save_directory: Union[str, os.PathLike],
+        **kwargs,
+    ):
+        if os.path.isfile(save_directory):
+            print(f"Provided path ({save_directory}) should be a directory, not a file")
+            return
+
+        os.makedirs(save_directory, exist_ok=True)
+
+        self._save_pretrained(save_directory, **kwargs)
+
+
+    @classmethod
+    def _from_pretrained(
+        cls,
+        model_id: Union[str, Path],
+        provider: Optional[str] = None,
+        sess_options: Optional["ort.SessionOptions"] = None,
+        **kwargs,
+    ):
+        model_file_name = ONNX_WEIGHTS_NAME
+        model_path = Path(model_id, model_file_name).as_posix()
+
+        model = PatchedOnnxRuntimeModel.load_model(
+            model_path,
+            provider=provider,
+            sess_options=sess_options,
+        )
+
+        kwargs["model_save_dir"] = Path(model_id)
+
+        return cls(model=model, **kwargs)
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        model_id: Union[str, Path],
+        **model_kwargs,
+    ):
+        return cls._from_pretrained(model_id=model_id, **model_kwargs)