Update nightly.yml

may not cover all cases. will observet

Co-authored-by: dan <dan@nod-labs.com>

.github/workflows/gh-pages-releases.yml

@@ -23,7 +23,7 @@ jobs:
       - run: git fetch --all
       - run: git switch github-pages
       - run: git config --global user.email "none@none.com"
-      - run: git config --global user.name "nod-team"
+      - run: git config --global user.name "nod-ai"
       - run: mv /tmp/index.html package-index/index.html
       - run: git add package-index/index.html
 

.github/workflows/nightly.yml

@@ -9,13 +9,92 @@ on:
   workflow_dispatch:
 
 jobs:
-  build:
+  windows-build:
+    runs-on: 7950X
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11"]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Compute version
+      shell: powershell
+      run: |
+        $package_version = $(Get-Date -UFormat "%Y%m%d")+"."+${{ github.run_number }}
+        $package_version_ = $(Get-Date -UFormat "%Y%m%d")+"_"+${{ github.run_number }}
+        $tag_name=$package_version
+        echo "package_version=$package_version" | Out-File -FilePath $Env:GITHUB_ENV -Encoding utf8 -Append
+        echo "package_version_=$package_version_" | Out-File -FilePath $Env:GITHUB_ENV -Encoding utf8 -Append
+        echo "tag_name=$tag_name" | Out-File -FilePath $Env:GITHUB_ENV -Encoding utf8 -Append
+
+    - name: Create Release
+      id: create_release
+      uses: actions/create-release@v1
+      env:
+        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
+      with:
+        tag_name: ${{ env.tag_name }}
+        release_name: nod.ai SHARK ${{ env.tag_name }}
+        body: |
+          Automatic snapshot release of nod.ai SHARK.
+        draft: true
+        prerelease: false
+
+    - name: Build Package 
+      shell: powershell
+      run: |
+        ./setup_venv.ps1
+        python process_skipfiles.py
+        pyinstaller .\apps\stable_diffusion\shark_sd.spec
+        mv ./dist/shark_sd.exe ./dist/shark_sd_${{ env.package_version_ }}.exe
+        signtool sign /f c:\g\shark_02152023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/shark_sd_${{ env.package_version_ }}.exe
+        pyinstaller .\apps\stable_diffusion\shark_sd_cli.spec
+        python process_skipfiles.py
+        mv ./dist/shark_sd_cli.exe ./dist/shark_sd_cli_${{ env.package_version_ }}.exe
+        signtool sign /f c:\g\shark_02152023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/shark_sd_cli_${{ env.package_version_ }}.exe
+
+        
+    # GHA windows VM OOMs so disable for now
+    #- name: Build and validate the SHARK Runtime package
+    #  shell: powershell
+    #  run: |
+    #    $env:SHARK_PACKAGE_VERSION=${{ env.package_version }}
+    #    pip wheel -v -w dist . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
+
+    - uses: actions/upload-artifact@v2
+      with:
+        path: dist/*
+    
+    - name: Upload Release Assets
+      id: upload-release-assets
+      uses: dwenegar/upload-release-assets@v1
+      env:
+        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
+      with:
+        release_id: ${{ steps.create_release.outputs.id }}
+        assets_path: ./dist/*
+
+    - name: Publish Release
+      id: publish_release
+      uses: eregon/publish-release@v1
+      env:
+        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
+      with:
+        release_id: ${{ steps.create_release.outputs.id }}
+
+  linux-build:
 
     runs-on: a100
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.11"]
         backend: [IREE, SHARK]
 
     steps:
@@ -32,40 +111,13 @@ jobs:
         key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
         restore-keys: |
           ${{ runner.os }}-pip-
-    
-    - name: Compute version
-      run: |
-        package_version="$(printf '%(%Y%m%d)T.${{ github.run_number }}')"
-        tag_name="${package_version}"
-        echo "package_version=${package_version}" >> $GITHUB_ENV
-        echo "tag_name=${tag_name}" >> $GITHUB_ENV    
-    - name: Set Environment Variables
-      run: |
-        echo "SHORT_SHA=`git rev-parse --short=4 HEAD`" >> $GITHUB_ENV
-        echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
-    - name: Create Release
-      id: create_release
-      uses: actions/create-release@v1
-      env:
-        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
-      with:
-        tag_name: ${{ env.tag_name }}
-        release_name: nod.ai SHARK ${{ env.tag_name }}
-        body: |
-          Automatic snapshot release of nod.ai SHARK.
-        draft: true
-        prerelease: false
-    - name: Find Torch-MLIR Release
-      run: |
-        TM_HTML_URL="$(python3 -c "import urllib.request, json, sys; u=json.loads(urllib.request.urlopen('https://api.github.com/repos/llvm/torch-mlir/releases/latest').read().decode()).get('html_url', False); print(u) if u else sys.exit(1);")"
-        TM_RELEASE_DIR=${TM_HTML_URL/"tag"/"expanded_assets"}
-        echo "TM_RELEASE_DIR=${TM_RELEASE_DIR}" >> $GITHUB_ENV
+
     - name: Install dependencies
       run: |
-        echo "Torch-MLIR Release DIR is ${{ env.TM_RELEASE_DIR }}"
+        echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest toml
-        if [ -f requirements.txt ]; then pip install -r requirements.txt -f ${{ env.TM_RELEASE_DIR }} -f https://github.com/nod-ai/SHARK-Runtime/releases; fi
+        if [ -f requirements.txt ]; then pip install -r requirements.txt -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html; fi
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
@@ -74,25 +126,26 @@ jobs:
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude shark.venv,lit.cfg.py 
     - name: Build and validate the IREE package
       if: ${{ matrix.backend == 'IREE' }}
+      continue-on-error: true
       run: |
         cd $GITHUB_WORKSPACE
         USE_IREE=1 VENV_DIR=iree.venv ./setup_venv.sh
         source iree.venv/bin/activate
         package_version="$(printf '%(%Y%m%d)T.${{ github.run_number }}')"
         SHARK_PACKAGE_VERSION=${package_version} \
-        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f ${{ env.TM_RELEASE_DIR }} -f https://github.com/iree-org/iree/releases
+        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://iree-org.github.io/iree/pip-release-links.html
         # Install the built wheel
         pip install ./wheelhouse/nodai*
         # Validate the Models
         /bin/bash "$GITHUB_WORKSPACE/build_tools/populate_sharktank_ci.sh"
-        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="./gen_shark_tank/" tank/test_models.py |
+        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="./gen_shark_tank/" -k "not metal" |
           tail -n 1 |
           tee -a pytest_results.txt
         if !(grep -Fxq " failed" pytest_results.txt) 
           then 
             export SHA=$(git log -1 --format='%h')
-            gsutil -m cp -r $GITHUB_WORKSPACE/gen_shark_tank/* gs://shark_tank/$SHA
-            gsutil -m cp -r gs://shark_tank/$SHA/* gs://shark_tank/latest/
+            gsutil -m cp -r $GITHUB_WORKSPACE/gen_shark_tank/* gs://shark_tank/${DATE}_$SHA
+            gsutil -m cp -r gs://shark_tank/${DATE}_$SHA/* gs://shark_tank/nightly/
         fi
         rm -rf ./wheelhouse/nodai*
 
@@ -104,29 +157,10 @@ jobs:
         source shark.venv/bin/activate
         package_version="$(printf '%(%Y%m%d)T.${{ github.run_number }}')"
         SHARK_PACKAGE_VERSION=${package_version} \
-        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f ${{ env.TM_RELEASE_DIR }} -f https://github.com/nod-ai/SHARK-Runtime/releases
+        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
         # Install the built wheel
         pip install ./wheelhouse/nodai*
         # Validate the Models
-        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="./gen_shark_tank/" tank/test_models.py |
+        pytest --ci --ci_sha=${SHORT_SHA} -k "not metal" |
           tail -n 1 |
           tee -a pytest_results.txt
-    
-    - name: Upload Release Assets
-      if: ${{ matrix.backend == 'SHARK' }}
-      id: upload-release-assets
-      uses: dwenegar/upload-release-assets@v1
-      env:
-        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
-      with:
-        release_id: ${{ steps.create_release.outputs.id }}
-        assets_path: ${GITHUB_WORKSPACE}/wheelhouse/nodai_*.whl
-
-    - name: Publish Release
-      if: ${{ matrix.backend == 'SHARK' }}
-      id: publish_release
-      uses: eregon/publish-release@v1
-      env:
-        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
-      with:
-        release_id: ${{ steps.create_release.outputs.id }}

.github/workflows/test-models.yml

@@ -6,18 +6,32 @@ name: Validate Models on Shark Runtime
 on:
   push:
     branches: [ main ]
+    paths-ignore:
+      - '**.md'
+      - 'shark/examples/**'
   pull_request:
     branches: [ main ]
+    paths-ignore:
+      - '**.md'
+      - 'shark/examples/**'
   workflow_dispatch:
 
+# Ensure that only a single job or workflow using the same
+# concurrency group will run at a time. This would cancel
+# any in-progress jobs in the same github workflow and github
+# ref (e.g. refs/heads/main or refs/pull/<pr_number>/merge).
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   build-validate:
     strategy:
       fail-fast: true
       matrix:
-        os: [icelake, a100, MacStudio, ubuntu-latest]
+        os: [7950x, icelake, a100, MacStudio, ubuntu-latest]
         suite: [cpu,cuda,vulkan]
-        python-version: ["3.10"]
+        python-version: ["3.11"]
         include:
           - os: ubuntu-latest
             suite: lint
@@ -32,21 +46,25 @@ jobs:
             suite: cuda
           - os: MacStudio
             suite: cpu
-          - os: MacStudio
-            suite: vulkan
           - os: icelake
             suite: vulkan
           - os: icelake
             suite: cuda
           - os: a100
             suite: cpu
+          - os: 7950x
+            suite: cpu
+          - os: 7950x
+            suite: cuda
 
     runs-on: ${{ matrix.os }}
 
     steps:
     - uses: actions/checkout@v3
+      if: matrix.os != '7950x'
     
     - name: Set Environment Variables
+      if: matrix.os != '7950x'
       run: |
         echo "SHORT_SHA=`git rev-parse --short=4 HEAD`" >> $GITHUB_ENV
         echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
@@ -66,6 +84,9 @@ jobs:
         #cache-dependency-path: |
         #  **/requirements-importer.txt
         #  **/requirements.txt
+    
+    - uses: actions/checkout@v2
+      if: matrix.os == '7950x'
           
     - name: Install dependencies
       if: matrix.suite == 'lint'
@@ -88,9 +109,9 @@ jobs:
       if: matrix.suite == 'cpu'
       run: |
         cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
+        PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
         source shark.venv/bin/activate
-        pytest --benchmark --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/data/anush" tank/test_models.py -k cpu
+        pytest --forked --benchmark --ci --ci_sha=${SHORT_SHA} --update_tank -k cpu
         gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${DATE}/bench_results_cpu_${SHORT_SHA}.csv
         gsutil cp gs://shark-public/builder/bench_results/${DATE}/bench_results_cpu_${SHORT_SHA}.csv gs://shark-public/builder/bench_results/latest/bench_results_cpu_latest.csv
 
@@ -100,14 +121,42 @@ jobs:
         cd $GITHUB_WORKSPACE
         PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
         source shark.venv/bin/activate
-        pytest --benchmark --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/data/anush" tank/test_models.py -k cuda
+        pytest --forked --benchmark --ci --ci_sha=${SHORT_SHA} --update_tank -k cuda
         gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${DATE}/bench_results_cuda_${SHORT_SHA}.csv
         gsutil cp gs://shark-public/builder/bench_results/${DATE}/bench_results_cuda_${SHORT_SHA}.csv gs://shark-public/builder/bench_results/latest/bench_results_cuda_latest.csv
+        # Disabled due to black image bug
+        # python build_tools/stable_diffusion_testing.py --device=cuda 
 
-    - name: Validate Vulkan Models
-      if: matrix.suite == 'vulkan'
+    - name: Validate Vulkan Models (MacOS)
+      if: matrix.suite == 'vulkan' && matrix.os == 'MacStudio'
       run: |
         cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
+        PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
         source shark.venv/bin/activate
-        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/data/anush" tank/test_models.py -k vulkan
+        export DYLD_LIBRARY_PATH=/usr/local/lib/
+        echo $PATH
+        pip list | grep -E "torch|iree"
+        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/Volumes/builder/anush/shark_cache" -k vulkan --update_tank
+
+    - name: Validate Vulkan Models (a100)
+      if: matrix.suite == 'vulkan' && matrix.os == 'a100'
+      run: |
+        cd $GITHUB_WORKSPACE
+        PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
+        source shark.venv/bin/activate
+        pytest --forked --benchmark --ci --ci_sha=${SHORT_SHA} --update_tank -k vulkan
+        python build_tools/stable_diffusion_testing.py --device=vulkan
+
+    - name: Validate Vulkan Models (Windows)
+      if: matrix.suite == 'vulkan' && matrix.os == '7950x'
+      run: |
+        ./setup_venv.ps1
+        pytest --benchmark -k vulkan -s
+        type bench_results.csv
+
+    - name: Validate Stable Diffusion Models (Windows)
+      if: matrix.suite == 'vulkan' && matrix.os == '7950x'
+      run: |
+        ./setup_venv.ps1
+        ./shark.venv/Scripts/activate
+        python build_tools/stable_diffusion_testing.py --device=vulkan

.gitignore

@@ -31,7 +31,6 @@ MANIFEST
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 *.manifest
-*.spec
 
 # Installer logs
 pip-log.txt
@@ -160,10 +159,26 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
+# vscode related
+.vscode
+
 # Shark related artefacts
 *venv/
 shark_tmp/
+*.vmfb
+.use-iree
+tank/dict_configs.py
 
 # ORT related artefacts
 cache_models/
 onnx_models/
+
+# Generated images
+generated_imgs/
+
+# Custom model related artefacts
+variants.json
+models/
+
+# models folder
+apps/stable_diffusion/web/models/

README.md

@@ -1,29 +1,147 @@
 # SHARK
 
-High Performance Machine Learning and Data Analytics for CPUs, GPUs, Accelerators and Heterogeneous Clusters
+High Performance Machine Learning Distribution
 
 [![Nightly Release](https://github.com/nod-ai/SHARK/actions/workflows/nightly.yml/badge.svg)](https://github.com/nod-ai/SHARK/actions/workflows/nightly.yml)
 [![Validate torch-models on Shark Runtime](https://github.com/nod-ai/SHARK/actions/workflows/test-models.yml/badge.svg)](https://github.com/nod-ai/SHARK/actions/workflows/test-models.yml)
 
-## Communication Channels
-
-*   [SHARK Discord server](https://discord.gg/RUqY2h2s9u): Real time discussions with the SHARK team and other users
-*   [GitHub issues](https://github.com/nod-ai/SHARK/issues): Feature requests, bugs etc
-
-
-## Installation
 
 <details>
-  <summary>Installation (Linux and macOS)</summary>
+  <summary>Prerequisites - Drivers </summary>
+  
+#### Install your Windows hardware drivers
+* [AMD RDNA Users] Download the latest driver [here](https://www.amd.com/en/support/kb/release-notes/rn-rad-win-23-2-1).
+* [macOS Users] Download and install the 1.3.216 Vulkan SDK from [here](https://sdk.lunarg.com/sdk/download/1.3.216.0/mac/vulkansdk-macos-1.3.216.0.dmg). Newer versions of the SDK will not work. 
+* [Nvidia Users] Download and install the latest CUDA / Vulkan drivers from [here](https://developer.nvidia.com/cuda-downloads)
+  
+#### Linux Drivers
+* MESA / RADV drivers wont work with FP16. Please use the latest AMGPU-PRO drivers (non-pro OSS drivers also wont work) or the latest NVidia Linux Drivers.
+
+Other users please ensure you have your latest vendor drivers and Vulkan SDK from [here](https://vulkan.lunarg.com/sdk/home) and if you are using vulkan check `vulkaninfo` works in a terminal window
+
+</details>
+
+
+ 
+### Quick Start for SHARK Stable Diffusion for Windows 10/11 Users
+
+Install Driver from [Prerequisites](https://github.com/nod-ai/SHARK#install-your-hardware-drivers) above 
+
+Download the latest .exe https://github.com/nod-ai/SHARK/releases. 
+
+Double click the .exe and you should have the [UI]( http://localhost:8080/?__theme=dark) in the browser. 
+
+If you have custom models (ckpt, safetensors) put in a `models/` directory where the .exe is. 
+
+Enjoy. 
+
+Some known AMD Driver quirks and are documented [here](https://github.com/nod-ai/SHARK/blob/main/apps/stable_diffusion/stable_diffusion_amd.md ).
+
+
+<details>
+  <summary>Advanced Installation (Only for developers)</summary>
+  
+## Advanced Installation (Windows, Linux and macOS) for developers
+
+## Check out the code
+
+```shell
+git clone https://github.com/nod-ai/SHARK.git
+cd SHARK
+```
+
+## Setup your Python VirtualEnvironment and Dependencies
+
+### Windows 10/11 Users
+
+* Install the latest Python 3.11.x version from [here](https://www.python.org/downloads/windows/)
+
+* Install Git for Windows from [here](https://git-scm.com/download/win)
+
+#### Allow the install script to run in Powershell
+```powershell
+set-executionpolicy remotesigned
+```
+
+#### Setup venv and install necessary packages (torch-mlir, nodLabs/Shark, ...)
+```powershell
+./setup_venv.ps1 #You can re-run this script to get the latest version
+```
+
+### Linux / macOS Users
+
+```shell
+./setup_venv.sh
+source shark.venv/bin/activate
+```
+
+
+### Run Stable Diffusion on your device - WebUI
+
+#### Windows 10/11 Users
+```powershell
+(shark.venv) PS C:\g\shark> cd .\apps\stable_diffusion\web\
+(shark.venv) PS C:\g\shark\apps\stable_diffusion\web> python .\index.py
+```
+#### Linux / macOS Users
+```shell
+(shark.venv) > cd apps/stable_diffusion/web
+(shark.venv) > python index.py
+```
+
+#### Access Stable Diffusion on http://localhost:8080/?__theme=dark
+
+
+<img width="1607" alt="webui" src="https://user-images.githubusercontent.com/74956/204939260-b8308bc2-8dc4-47f6-9ac0-f60b66edab99.png">
+
+
+
+### Run Stable Diffusion on your device - Commandline
+
+#### Windows 10/11 Users
+```powershell
+(shark.venv) PS C:\g\shark> python .\apps\stable_diffusion\scripts\txt2img.py --precision="fp16" --prompt="tajmahal, snow, sunflowers, oil on canvas" --device="vulkan"
+```
+
+#### Linux / macOS Users
+```shell
+python3.11 apps/stable_diffusion/scripts/txt2img.py --precision=fp16 --device=vulkan --prompt="tajmahal, oil on canvas, sunflowers, 4k, uhd"
+```
+
+You can replace `vulkan` with `cpu` to run on your CPU or with `cuda` to run on CUDA devices. If you have multiple vulkan devices you can address them with `--device=vulkan://1` etc
+</details>
+
+The output on a AMD 7900XTX would look something like:
+
+```shell
+Average step time: 47.19188690185547ms/it
+Clip Inference time (ms) = 109.531
+VAE Inference time (ms): 78.590
+
+Total image generation time: 2.5788655281066895sec
+```
+
+Here are some samples generated:
+
+![tajmahal, snow, sunflowers, oil on canvas_0](https://user-images.githubusercontent.com/74956/204934186-141f7e43-6eb2-4e89-a99c-4704d20444b3.jpg)
+
+![a photo of a crab playing a trumpet](https://user-images.githubusercontent.com/74956/204933258-252e7240-8548-45f7-8253-97647d38313d.jpg)
+
+
+Find us on [SHARK Discord server](https://discord.gg/RUqY2h2s9u) if you have any trouble with running it on your hardware. 
+
+
+<details>
+  <summary>Binary Installation</summary>
 
 ### Setup a new pip Virtual Environment
 
 This step sets up a new VirtualEnv for Python
 
 ```shell
-python --version #Check you have 3.7->3.10 on Linux or 3.10 on macOS
+python --version #Check you have 3.11 on Linux, macOS or Windows Powershell
 python -m venv shark_venv
-source shark_venv/bin/activate
+source shark_venv/bin/activate   # Use shark_venv/Scripts/activate on Windows
 
 # If you are using conda create and activate a new conda env
 
@@ -35,12 +153,17 @@ python -m pip install --upgrade pip
 
 ### Install SHARK
 
-This step pip installs SHARK and related packages on Linux Python 3.7, 3.8, 3.9, 3.10 and macOS Python 3.10
+This step pip installs SHARK and related packages on Linux Python 3.8, 3.10 and 3.11 and macOS / Windows Python 3.11
 
 ```shell
-pip install nodai-shark -f https://nod-ai.github.io/SHARK/package-index/ -f https://llvm.github.io/torch-mlir/package-index/ -f https://github.com/nod-ai/shark-runtime/releases --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+pip install nodai-shark -f https://nod-ai.github.io/SHARK/package-index/ -f https://llvm.github.io/torch-mlir/package-index/ -f  https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
 ```
-If you are on an Intel macOS machine you need this [workaround](https://github.com/nod-ai/SHARK/issues/102) for an upstream issue.
+
+### Run shark tank model tests.
+```shell
+pytest tank/test_models.py
+```
+See tank/README.md for a more detailed walkthrough of our pytest suite and CLI.
 
 ### Download and run Resnet50 sample
 
@@ -61,29 +184,27 @@ python ./minilm_jit.py --device="cpu"  #use cuda or vulkan or metal
 </details>
 
 
+
 <details>
-  <summary>Source Installation</summary>
+  <summary>Development, Testing and Benchmarks</summary>
 
-## Check out the code
+If you want to use Python3.11 and with TF Import tools you can use the environment variables like:
+Set `USE_IREE=1` to use upstream IREE
+```
+# PYTHON=python3.11 VENV_DIR=0617_venv IMPORTER=1 ./setup_venv.sh 
+```
 
+### Run any of the hundreds of SHARK tank models via the test framework
 ```shell
-git clone https://github.com/nod-ai/SHARK.git
-```
-
-## Setup your Python VirtualEnvironment and Dependencies
-```shell
-# Setup venv and install necessary packages (torch-mlir, nodLabs/Shark, ...).
-./setup_venv.sh
-source shark.venv/bin/activate
-```
-For example if you want to use Python3.10 and upstream IREE with TF Import tools you can use the environment variables like:
-```
-# PYTHON=python3.10 VENV_DIR=0617_venv IMPORTER=1 USE_IREE=1 ./setup_venv.sh 
+python -m  shark.examples.shark_inference.resnet50_script --device="cpu" # Use gpu | vulkan
+# Or a pytest
+pytest tank/test_models.py -k "MiniLM"
 ```
+  
 
 If you are a *Torch-mlir developer or an IREE developer* and want to test local changes you can uninstall
 the provided packages with `pip uninstall torch-mlir` and / or `pip uninstall iree-compiler iree-runtime` and build locally
-with Python bindings and set your PYTHONPATH as mentioned [here](https://google.github.io/iree/bindings/python/)
+with Python bindings and set your PYTHONPATH as mentioned [here](https://github.com/iree-org/iree/tree/main/docs/api_docs/python#install-iree-binaries)
 for IREE and [here](https://github.com/llvm/torch-mlir/blob/main/development.md#setup-python-environment-to-export-the-built-python-packages)
 for Torch-MLIR.
 
@@ -102,82 +223,39 @@ for Torch-MLIR.
 ```
 Now the SHARK will use your locally build Torch-MLIR repo.
 
-### Run a demo script
-```shell
-python -m  shark.examples.shark_inference.resnet50_script --device="cpu" # Use gpu | vulkan
-# Or a pytest
-pytest tank/test_models.py -k "MiniLM"
+
+## Benchmarking Dispatches
+
+To produce benchmarks of individual dispatches, you can add `--dispatch_benchmarks=All --dispatch_benchmarks_dir=<output_dir>` to your command line argument.  
+If you only want to compile specific dispatches, you can specify them with a space seperated string instead of `"All"`.  E.G. `--dispatch_benchmarks="0 1 2 10"`
+
+if you want to instead incorporate this into a python script, you can pass the `dispatch_benchmarks` and `dispatch_benchmarks_dir` commands when initializing `SharkInference`, and the benchmarks will be generated when compiled.  E.G:
+
 ```
+shark_module = SharkInference(
+        mlir_model,
+        func_name,
+        device=args.device,
+        mlir_dialect="tm_tensor",
+        dispatch_benchmarks="all",
+        dispatch_benchmarks_dir="results"
+    )
+```
+
+Output will include:
+- An ordered list ordered-dispatches.txt of all the dispatches with their runtime
+- Inside the specified directory, there will be a directory for each dispatch (there will be mlir files for all dispatches, but only compiled binaries and benchmark data for the specified dispatches)
+- An .mlir file containing the dispatch benchmark 
+- A compiled .vmfb file containing the dispatch benchmark
+- An .mlir file containing just the hal executable
+- A compiled .vmfb file of the hal executable
+- A .txt file containing benchmark output
+
+
+See tank/README.md for instructions on how to run model tests and benchmarks from the SHARK tank.
 
 </details>
 
-<details>
-  <summary>Testing and Benchmarks</summary>
-
-### Run all model tests on CPU/GPU/VULKAN/Metal
-```shell
-pytest tank/test_models.py
-
-# If on Linux for multithreading on CPU (faster results):
-pytest tank/test_models.py -n auto
-```
-
-### Running specific tests
-```shell
-
-# Search for test cases by including a keyword that matches all or part of the test case's name;
-pytest tank/test_models.py -k "keyword" 
-
-# Test cases are named uniformly by format test_module_<model_name_underscores_only>_<torch/tf>_<static/dynamic>_<device>.
-
-# Example: Test all models on nvidia gpu:
-pytest tank/test_models.py -k "cuda"
-
-# Example: Test all tensorflow resnet models on Vulkan backend:
-pytest tank/test_models.py -k "resnet and tf and vulkan"
-
-# Exclude a test case:
-pytest tank/test_models.py -k "not ..."
-
-### Run benchmarks on SHARK tank pytests and generate bench_results.csv with results.
-
-(the following requires source installation with `IMPORTER=1 ./setup_venv.sh`)
-
-```shell
-pytest --benchmark tank/test_models.py
-  
-# Just do static GPU benchmarks for PyTorch tests:
-pytest --benchmark tank/test_models.py -k "pytorch and static and cuda"
-
-```
-  
-### Benchmark Resnet50, MiniLM on CPU
-
-(requires source installation with `IMPORTER=1 ./setup_venv.sh`)  
-  
-```shell
-# We suggest running the following commands as root before running benchmarks on CPU:
-  
-cat /sys/devices/system/cpu/cpu*/topology/thread_siblings_list | awk -F, '{print $2}' | sort -n | uniq | ( while read X ; do echo $X ; echo 0 > /sys/devices/system/cpu/cpu$X/online ; done )
-echo 1 > /sys/devices/system/cpu/intel_pstate/no_turbo
-
-# Benchmark canonical Resnet50 on CPU via pytest
-pytest --benchmark tank/test_models -k "resnet50 and tf_static_cpu"
-
-# Benchmark canonical MiniLM on CPU via pytest
-pytest --benchmark tank/test_models -k "MiniLM and cpu"
-
-# Benchmark MiniLM on CPU via transformer-benchmarks:
-git clone --recursive https://github.com/nod-ai/transformer-benchmarks.git
-cd transformer-benchmarks
-./perf-ci.sh -n
-# Check detail.csv for MLIR/IREE results.
-
-```
-
-</details>
-
-
 <details>
   <summary>API Reference</summary>
 
@@ -228,160 +306,26 @@ result = shark_module.forward((arg0, arg1))
 ```
 </details>
 
-
 ## Supported and Validated Models
 
-<details>
-  <summary>PyTorch Models</summary>
+SHARK is maintained to support the latest innovations in ML Models: 
 
-### Huggingface PyTorch Models
+| TF HuggingFace Models | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|---------------------|----------|----------|-------------|
+| BERT                | :green_heart:         | :green_heart:         | :green_heart:            |
+| DistilBERT         | :green_heart:         | :green_heart:         | :green_heart:            |
+| GPT2         | :green_heart:         | :green_heart:         | :green_heart:            |
+| BLOOM         | :green_heart:         | :green_heart:         | :green_heart:            |
+| Stable Diffusion         | :green_heart:         | :green_heart:         | :green_heart:            |
+| Vision Transformer       | :green_heart:         | :green_heart:         | :green_heart:            |
+| ResNet50         | :green_heart:         | :green_heart:         | :green_heart:            |
 
-| Hugging Face Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|---------------------|----------------------|----------|----------|-------------|
-| BERT                | :green_heart: (JIT)          | :green_heart:         | :green_heart:         | :green_heart:            |
-| Albert              | :green_heart: (JIT)            | :green_heart:         | :green_heart:         | :green_heart:            |
-| BigBird             | :green_heart: (AOT)            |          |          |             |
-| DistilBERT          | :green_heart: (JIT)            | :green_heart:         | :green_heart:         | :green_heart:            |
-| GPT2                | :broken_heart: (AOT)            |          |          |             |
-| MobileBert          | :green_heart: (JIT)            | :green_heart:         | :green_heart:         | :green_heart:            |
+For a complete list of the models supported in SHARK, please refer to [tank/README.md](https://github.com/nod-ai/SHARK/blob/main/tank/README.md).
 
-### Torchvision  Models
+## Communication Channels
 
-| TORCHVISION Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|--------------------|----------------------|----------|----------|-------------|
-| AlexNet            | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| DenseNet121        | :green_heart: (Script)         |          |          |             |
-| MNasNet1_0         | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| MobileNetV2        | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| MobileNetV3        | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| Unet               | :broken_heart: (Script)         |          |          |             |
-| Resnet18           | :green_heart: (Script)         | :green_heart:         |  :green_heart:        | :green_heart:            |
-| Resnet50           | :green_heart: (Script)         | :green_heart:         |   :green_heart:       | :green_heart:            |
-| Resnet101           | :green_heart: (Script)         | :green_heart:         |   :green_heart:       | :green_heart:            |
-| Resnext50_32x4d    | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| ShuffleNet_v2      | :broken_heart: (Script)         |          |          |             |
-| SqueezeNet         | :green_heart: (Script)         | :green_heart:         |   :green_heart:       | :green_heart:            |
-| EfficientNet       | :green_heart: (Script)         |          |          |             |
-| Regnet             | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| Resnest            | :broken_heart: (Script)         |          |          |             |
-| Vision Transformer | :green_heart: (Script)         |          |          |             |
-| VGG 16             | :green_heart: (Script)         | :green_heart:         |   :green_heart:       |             |
-| Wide Resnet        | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| RAFT               | :broken_heart: (JIT)            |          |          |             |
-
-For more information refer to [MODEL TRACKING SHEET](https://docs.google.com/spreadsheets/d/15PcjKeHZIrB5LfDyuw7DGEEE8XnQEX2aX8lm8qbxV8A/edit#gid=0)
-
-### PyTorch Training Models
-
-| Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|---------------------|----------------------|----------|----------|-------------|
-| BERT                | :broken_heart:           | :broken_heart:         |          |             |
-| FullyConnected                | :green_heart:           | :green_heart:         |          |             |
-
-</details>
-
-<details>
-  <summary>JAX Models</summary>
-
-
-### JAX  Models
-
-| Models | JAX-MHLO lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|---------------------|----------------------|----------|----------|-------------|
-| DALL-E                | :broken_heart:           | :broken_heart:         |          |             |
-| FullyConnected                | :green_heart:           | :green_heart:         |          |             |
-
-</details>
-
-<details>
-  <summary>TFLite Models</summary>
-
-### TFLite Models
-
-| Models | TOSA/LinAlg  | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|---------------------|----------------------|----------|----------|-------------|
-| BERT                | :broken_heart:           | :broken_heart:         |          |             |
-| FullyConnected      | :green_heart:           | :green_heart:         |          |             |
-| albert | :green_heart:           | :green_heart:         |          |             |
-| asr_conformer | :green_heart:           | :green_heart:         |          |             |
-| bird_classifier | :green_heart:           | :green_heart:         |          |             |
-| cartoon_gan | :green_heart:           | :green_heart:         |          |             |
-| craft_text | :green_heart:           | :green_heart:         |          |             |
-| deeplab_v3 | :green_heart:           | :green_heart:         |          |             |
-| densenet | :green_heart:           | :green_heart:         |          |             |
-| east_text_detector | :green_heart:           | :green_heart:         |          |             |
-| efficientnet_lite0_int8 | :green_heart:           | :green_heart:         |          |             |
-| efficientnet | :green_heart:           | :green_heart:         |          |             |
-| gpt2 | :green_heart:           | :green_heart:         |          |             |
-| image_stylization | :green_heart:           | :green_heart:         |          |             |
-| inception_v4 | :green_heart:           | :green_heart:         |          |             |
-| inception_v4_uint8 | :green_heart:           | :green_heart:         |          |             |
-| lightning_fp16 | :green_heart:           | :green_heart:         |          |             |
-| lightning_i8 | :green_heart:           | :green_heart:         |          |             |
-| lightning | :green_heart:           | :green_heart:         |          |             |
-| magenta | :green_heart:           | :green_heart:         |          |             |
-| midas | :green_heart:           | :green_heart:         |          |             |
-| mirnet | :green_heart:           | :green_heart:         |          |             |
-| mnasnet | :green_heart:           | :green_heart:         |          |             |
-| mobilebert_edgetpu_s_float | :green_heart:           | :green_heart:         |          |             |
-| mobilebert_edgetpu_s_quant | :green_heart:           | :green_heart:         |          |             |
-| mobilebert | :green_heart:           | :green_heart:         |          |             |
-| mobilebert_tf2_float | :green_heart:           | :green_heart:         |          |             |
-| mobilebert_tf2_quant | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_ssd_quant | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v1 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v1_uint8 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v2_int8 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v2 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v2_uint8 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v3-large | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v3-large_uint8 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v35-int8 | :green_heart:           | :green_heart:         |          |             |
-| nasnet | :green_heart:           | :green_heart:         |          |             |
-| person_detect | :green_heart:           | :green_heart:         |          |             |
-| posenet | :green_heart:           | :green_heart:         |          |             |
-| resnet_50_int8 | :green_heart:           | :green_heart:         |          |             |
-| rosetta | :green_heart:           | :green_heart:         |          |             |
-| spice | :green_heart:           | :green_heart:         |          |             |
-| squeezenet | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v1 | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v1_uint8 | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v2_fpnlite | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v2_fpnlite_uint8 | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v2_int8 | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v2 | :green_heart:           | :green_heart:         |          |             |
-| ssd_spaghettinet_large | :green_heart:           | :green_heart:         |          |             |
-| ssd_spaghettinet_large_uint8 | :green_heart:           | :green_heart:         |          |             |
-| visual_wake_words_i8 | :green_heart:           | :green_heart:         |          |             |
-
-</details>
-
-<details>
-  <summary>TF Models</summary>
-
-### Tensorflow Models (Inference)
-
-| Hugging Face Models | tf-mhlo lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|---------------------|----------------------|----------|----------|-------------|
-| BERT                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
-| albert-base-v2              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| DistilBERT          | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| CamemBert                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
-| ConvBert              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| Deberta              |            |         |          |             |
-| electra          | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| funnel              |            |         |          |             |
-| layoutlm              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| longformer              |            |         |          |             |
-| mobile-bert                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
-| remembert              |            |         |          |             |
-| tapas              |            |         |          |             |
-| flaubert                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
-| roberta                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
-| xlm-roberta              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| mpnet              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-
-</details>
+*   [SHARK Discord server](https://discord.gg/RUqY2h2s9u): Real time discussions with the SHARK team and other users
+*   [GitHub issues](https://github.com/nod-ai/SHARK/issues): Feature requests, bugs etc
 
 ## Related Projects
 

apps/stable_diffusion/profiling_with_iree.md

@@ -0,0 +1,87 @@
+Compile / Run Instructions:
+
+To compile .vmfb for SD (vae, unet, CLIP), run the following commands with the .mlir in your local shark_tank cache (default location for Linux users is `~/.local/shark_tank`). These will be available once the script from [this README](https://github.com/nod-ai/SHARK/blob/main/shark/examples/shark_inference/stable_diffusion/README.md) is run once.
+Running the script mentioned above with the `--save_vmfb` flag will also save the .vmfb in your SHARK base directory if you want to skip straight to benchmarks.
+
+Compile Commands FP32/FP16: 
+
+```shell
+Vulkan AMD: 
+iree-compile --iree-input-type=none --iree-hal-target-backends=vulkan --iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 /path/to/input/mlir -o /path/to/output/vmfb
+
+#  add --mlir-print-debuginfo --mlir-print-op-on-diagnostic=true for debug
+#  use –iree-input-type=mhlo for tf models
+
+CUDA NVIDIA:
+iree-compile --iree-input-type=none --iree-hal-target-backends=cuda --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 /path/to/input/mlir -o /path/to/output/vmfb
+
+CPU:
+iree-compile --iree-input-type=none --iree-hal-target-backends=llvm-cpu  --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 /path/to/input/mlir -o /path/to/output/vmfb
+```
+
+
+
+Run / Benchmark Command (FP32 - NCHW):
+(NEED to use BS=2 since we do two forward passes to unet as a result of classifier free guidance.)
+
+```shell
+## Vulkan AMD:
+iree-benchmark-module --module=/path/to/output/vmfb --function=forward --device=vulkan --input=1x4x64x64xf32 --input=1xf32 --input=2x77x768xf32 --input=f32=1.0 --input=f32=1.0
+
+## CUDA:
+iree-benchmark-module --module=/path/to/vmfb --function=forward --device=cuda  --input=1x4x64x64xf32 --input=1xf32 --input=2x77x768xf32 --input=f32=1.0 --input=f32=1.0
+
+## CPU:
+iree-benchmark-module --module=/path/to/vmfb --function=forward --device=local-task  --input=1x4x64x64xf32 --input=1xf32 --input=2x77x768xf32 --input=f32=1.0 --input=f32=1.0
+
+```
+
+Run via vulkan_gui for RGP Profiling:
+
+To build the vulkan app for profiling UNet follow the instructions [here](https://github.com/nod-ai/SHARK/tree/main/cpp) and then run the following command from the cpp directory with your compiled stable_diff.vmfb
+```shell
+./build/vulkan_gui/iree-vulkan-gui --module=/path/to/unet.vmfb --input=1x4x64x64xf32 --input=1xf32 --input=2x77x768xf32 --input=f32=1.0 --input=f32=1.0
+```
+
+</details>
+  <details>
+  <summary>Debug Commands</summary>
+
+## Debug commands and other advanced usage follows.
+
+```shell
+python txt2img.py --precision="fp32"|"fp16" --device="cpu"|"cuda"|"vulkan" --import_mlir|--no-import_mlir --prompt "enter the text" 
+```
+
+## dump all dispatch .spv and isa using amdllpc
+
+```shell
+python txt2img.py --precision="fp16" --device="vulkan" --iree-vulkan-target-triple=rdna3-unknown-linux --no-load_vmfb --dispatch_benchmarks="all" --dispatch_benchmarks_dir="SD_dispatches" --dump_isa
+```
+
+## Compile and save the .vmfb (using vulkan fp16 as an example):
+
+```shell
+python txt2img.py --precision=fp16 --device=vulkan --steps=50 --save_vmfb
+```
+
+## Capture an RGP trace
+
+```shell
+python txt2img.py --precision=fp16 --device=vulkan --steps=50 --save_vmfb --enable_rgp
+```
+
+## Run the vae module with iree-benchmark-module (NCHW, fp16, vulkan, for example):
+
+```shell
+iree-benchmark-module --module=/path/to/output/vmfb --function=forward --device=vulkan --input=1x4x64x64xf16  
+```
+
+## Run the unet module with iree-benchmark-module (same config as above):
+```shell
+##if you want to use .npz inputs:
+unzip ~/.local/shark_tank/<your unet>/inputs.npz
+iree-benchmark-module --module=/path/to/output/vmfb --function=forward --input=@arr_0.npy --input=1xf16 --input=@arr_2.npy --input=@arr_3.npy --input=@arr_4.npy  
+```
+
+</details>

apps/stable_diffusion/scripts/__init__.py

@@ -0,0 +1,2 @@
+from apps.stable_diffusion.scripts.txt2img import txt2img_inf
+from apps.stable_diffusion.scripts.img2img import img2img_inf

apps/stable_diffusion/scripts/img2img.py

@@ -0,0 +1,265 @@
+import sys
+import torch
+import time
+from PIL import Image
+from dataclasses import dataclass
+from apps.stable_diffusion.src import (
+    args,
+    Image2ImagePipeline,
+    get_schedulers,
+    set_init_device_flags,
+    utils,
+    clear_all,
+    save_output_img,
+)
+
+
+@dataclass
+class Config:
+    model_id: str
+    ckpt_loc: str
+    precision: str
+    batch_size: int
+    max_length: int
+    height: int
+    width: int
+    device: str
+
+
+img2img_obj = None
+config_obj = None
+schedulers = None
+
+
+# Exposed to UI.
+def img2img_inf(
+    prompt: str,
+    negative_prompt: str,
+    init_image: str,
+    height: int,
+    width: int,
+    steps: int,
+    strength: float,
+    guidance_scale: float,
+    seed: int,
+    batch_count: int,
+    batch_size: int,
+    scheduler: str,
+    custom_model: str,
+    hf_model_id: str,
+    precision: str,
+    device: str,
+    max_length: int,
+    save_metadata_to_json: bool,
+    save_metadata_to_png: bool,
+):
+    global img2img_obj
+    global config_obj
+    global schedulers
+
+    args.prompts = [prompt]
+    args.negative_prompts = [negative_prompt]
+    args.guidance_scale = guidance_scale
+    args.seed = seed
+    args.steps = steps
+    args.strength = strength
+    args.scheduler = scheduler
+    args.img_path = init_image
+    image = Image.open(args.img_path)
+
+    # set ckpt_loc and hf_model_id.
+    types = (
+        ".ckpt",
+        ".safetensors",
+    )  # the tuple of file types
+    args.ckpt_loc = ""
+    args.hf_model_id = ""
+    if custom_model == "None":
+        if not hf_model_id:
+            return (
+                None,
+                "Please provide either custom model or huggingface model ID, both must not be empty",
+            )
+        args.hf_model_id = hf_model_id
+    elif ".ckpt" in custom_model or ".safetensors" in custom_model:
+        args.ckpt_loc = custom_model
+    else:
+        args.hf_model_id = custom_model
+
+    if image is None:
+        return None, "An Initial Image is required"
+
+    args.save_metadata_to_json = save_metadata_to_json
+    args.write_metadata_to_png = save_metadata_to_png
+
+    dtype = torch.float32 if precision == "fp32" else torch.half
+    cpu_scheduling = not scheduler.startswith("Shark")
+    new_config_obj = Config(
+        args.hf_model_id,
+        args.ckpt_loc,
+        precision,
+        batch_size,
+        max_length,
+        height,
+        width,
+        device,
+    )
+    if config_obj != new_config_obj:
+        config_obj = new_config_obj
+        args.precision = precision
+        args.batch_size = batch_size
+        args.max_length = max_length
+        args.height = height
+        args.width = width
+        args.device = device.split("=>", 1)[1].strip()
+        args.iree_vulkan_target_triple = ""
+        args.use_tuned = True
+        args.import_mlir = True
+        set_init_device_flags()
+        model_id = (
+            args.hf_model_id
+            if args.hf_model_id
+            else "runwayml/stable-diffusion-inpainting"
+        )
+        schedulers = get_schedulers(model_id)
+        scheduler_obj = schedulers[scheduler]
+        img2img_obj = Image2ImagePipeline.from_pretrained(
+            scheduler_obj,
+            args.import_mlir,
+            args.hf_model_id,
+            args.ckpt_loc,
+            args.custom_vae,
+            args.precision,
+            args.max_length,
+            args.batch_size,
+            args.height,
+            args.width,
+            args.use_base_vae,
+            args.use_tuned,
+        )
+
+    if not img2img_obj:
+        sys.exit("text to image pipeline must not return a null value")
+
+    img2img_obj.scheduler = schedulers[scheduler]
+
+    start_time = time.time()
+    img2img_obj.log = ""
+    generated_imgs = []
+    seeds = []
+    img_seed = utils.sanitize_seed(seed)
+    for current_batch in range(batch_count):
+        if current_batch > 0:
+            img_seed = utils.sanitize_seed(-1)
+        out_imgs = img2img_obj.generate_images(
+            prompt,
+            negative_prompt,
+            image,
+            batch_size,
+            height,
+            width,
+            steps,
+            strength,
+            guidance_scale,
+            img_seed,
+            args.max_length,
+            dtype,
+            args.use_base_vae,
+            cpu_scheduling,
+        )
+        save_output_img(out_imgs[0], img_seed)
+        generated_imgs.extend(out_imgs)
+        seeds.append(img_seed)
+        img2img_obj.log += "\n"
+
+    total_time = time.time() - start_time
+    text_output = f"prompt={args.prompts}"
+    text_output += f"\nnegative prompt={args.negative_prompts}"
+    text_output += f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+    text_output += f"\nscheduler={args.scheduler}, device={device}"
+    text_output += f"\nsteps={steps}, strength={args.strength}, guidance_scale={guidance_scale}, seed={seeds}"
+    text_output += f"\nsize={height}x{width}, batch_count={batch_count}, batch_size={batch_size}, max_length={args.max_length}"
+    text_output += img2img_obj.log
+    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+    return generated_imgs, text_output
+
+
+if __name__ == "__main__":
+    if args.clear_all:
+        clear_all()
+
+    if args.img_path is None:
+        print("Flag --img_path is required.")
+        exit()
+
+    # When the models get uploaded, it should be default to False.
+    args.import_mlir = True
+
+    dtype = torch.float32 if args.precision == "fp32" else torch.half
+    cpu_scheduling = not args.scheduler.startswith("Shark")
+    set_init_device_flags()
+    schedulers = get_schedulers(args.hf_model_id)
+    if args.scheduler != "PNDM":
+        if "Shark" in args.scheduler:
+            print(
+                f"SharkEulerDiscrete scheduler not supported. Switching to PNDM scheduler"
+            )
+            args.scheduler = "PNDM"
+        else:
+            sys.exit(
+                "Img2Img works best with PNDM scheduler. Other schedulers are not supported yet."
+            )
+
+    scheduler_obj = schedulers[args.scheduler]
+    image = Image.open(args.img_path)
+    seed = utils.sanitize_seed(args.seed)
+
+    # Adjust for height and width based on model
+
+    img2img_obj = Image2ImagePipeline.from_pretrained(
+        scheduler_obj,
+        args.import_mlir,
+        args.hf_model_id,
+        args.ckpt_loc,
+        args.custom_vae,
+        args.precision,
+        args.max_length,
+        args.batch_size,
+        args.height,
+        args.width,
+        args.use_base_vae,
+        args.use_tuned,
+    )
+
+    start_time = time.time()
+    generated_imgs = img2img_obj.generate_images(
+        args.prompts,
+        args.negative_prompts,
+        image,
+        args.batch_size,
+        args.height,
+        args.width,
+        args.steps,
+        args.strength,
+        args.guidance_scale,
+        seed,
+        args.max_length,
+        dtype,
+        args.use_base_vae,
+        cpu_scheduling,
+    )
+    total_time = time.time() - start_time
+    text_output = f"prompt={args.prompts}"
+    text_output += f"\nnegative prompt={args.negative_prompts}"
+    text_output += f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+    text_output += f"\nscheduler={args.scheduler}, device={args.device}"
+    text_output += f"\nsteps={args.steps}, strength={args.strength}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
+    text_output += (
+        f", batch size={args.batch_size}, max_length={args.max_length}"
+    )
+    text_output += img2img_obj.log
+    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+    save_output_img(generated_imgs[0], seed)
+    print(text_output)

apps/stable_diffusion/scripts/inpaint.py

@@ -0,0 +1,256 @@
+import sys
+import torch
+import time
+from PIL import Image
+from dataclasses import dataclass
+from apps.stable_diffusion.src import (
+    args,
+    InpaintPipeline,
+    get_schedulers,
+    set_init_device_flags,
+    utils,
+    clear_all,
+    save_output_img,
+)
+
+
+@dataclass
+class Config:
+    model_id: str
+    ckpt_loc: str
+    precision: str
+    batch_size: int
+    max_length: int
+    height: int
+    width: int
+    device: str
+
+
+inpaint_obj = None
+config_obj = None
+schedulers = None
+
+
+# Exposed to UI.
+def inpaint_inf(
+    prompt: str,
+    negative_prompt: str,
+    image: Image,
+    mask_image: Image,
+    height: int,
+    width: int,
+    steps: int,
+    guidance_scale: float,
+    seed: int,
+    batch_count: int,
+    batch_size: int,
+    scheduler: str,
+    custom_model: str,
+    hf_model_id: str,
+    precision: str,
+    device: str,
+    max_length: int,
+    save_metadata_to_json: bool,
+    save_metadata_to_png: bool,
+):
+    global inpaint_obj
+    global config_obj
+    global schedulers
+
+    args.prompts = [prompt]
+    args.negative_prompts = [negative_prompt]
+    args.guidance_scale = guidance_scale
+    args.steps = steps
+    args.scheduler = scheduler
+
+    # set ckpt_loc and hf_model_id.
+    types = (
+        ".ckpt",
+        ".safetensors",
+    )  # the tuple of file types
+    args.ckpt_loc = ""
+    args.hf_model_id = ""
+    if custom_model == "None":
+        if not hf_model_id:
+            return (
+                None,
+                "Please provide either custom model or huggingface model ID, both must not be empty",
+            )
+        args.hf_model_id = hf_model_id
+    elif ".ckpt" in custom_model or ".safetensors" in custom_model:
+        args.ckpt_loc = custom_model
+    else:
+        args.hf_model_id = custom_model
+
+    args.save_metadata_to_json = save_metadata_to_json
+    args.write_metadata_to_png = save_metadata_to_png
+
+    dtype = torch.float32 if precision == "fp32" else torch.half
+    cpu_scheduling = not scheduler.startswith("Shark")
+    new_config_obj = Config(
+        args.hf_model_id,
+        args.ckpt_loc,
+        precision,
+        batch_size,
+        max_length,
+        height,
+        width,
+        device,
+    )
+    if config_obj != new_config_obj:
+        config_obj = new_config_obj
+        args.precision = precision
+        args.batch_size = batch_size
+        args.max_length = max_length
+        args.height = height
+        args.width = width
+        args.device = device.split("=>", 1)[1].strip()
+        args.iree_vulkan_target_triple = ""
+        args.use_tuned = True
+        args.import_mlir = False
+        set_init_device_flags()
+        model_id = (
+            args.hf_model_id
+            if args.hf_model_id
+            else "stabilityai/stable-diffusion-2-inpainting"
+        )
+        schedulers = get_schedulers(model_id)
+        scheduler_obj = schedulers[scheduler]
+        inpaint_obj = InpaintPipeline.from_pretrained(
+            scheduler_obj,
+            args.import_mlir,
+            args.hf_model_id,
+            args.ckpt_loc,
+            args.custom_vae,
+            args.precision,
+            args.max_length,
+            args.batch_size,
+            args.height,
+            args.width,
+            args.use_base_vae,
+            args.use_tuned,
+        )
+
+    if not inpaint_obj:
+        sys.exit("text to image pipeline must not return a null value")
+
+    inpaint_obj.scheduler = schedulers[scheduler]
+
+    start_time = time.time()
+    inpaint_obj.log = ""
+    generated_imgs = []
+    seeds = []
+    img_seed = utils.sanitize_seed(seed)
+    for i in range(batch_count):
+        if i > 0:
+            img_seed = utils.sanitize_seed(-1)
+        out_imgs = inpaint_obj.generate_images(
+            prompt,
+            negative_prompt,
+            image,
+            mask_image,
+            batch_size,
+            height,
+            width,
+            steps,
+            guidance_scale,
+            img_seed,
+            args.max_length,
+            dtype,
+            args.use_base_vae,
+            cpu_scheduling,
+        )
+        save_output_img(out_imgs[0], img_seed)
+        generated_imgs.extend(out_imgs)
+        seeds.append(img_seed)
+        inpaint_obj.log += "\n"
+
+    total_time = time.time() - start_time
+    text_output = f"prompt={args.prompts}"
+    text_output += f"\nnegative prompt={args.negative_prompts}"
+    text_output += f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+    text_output += f"\nscheduler={args.scheduler}, device={device}"
+    text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seeds}"
+    text_output += f"\nsize={args.height}x{args.width}, batch-count={batch_count}, batch-size={args.batch_size}, max_length={args.max_length}"
+    text_output += inpaint_obj.log
+    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+    return generated_imgs, text_output
+
+
+if __name__ == "__main__":
+    if args.clear_all:
+        clear_all()
+
+    if args.img_path is None:
+        print("Flag --img_path is required.")
+        exit()
+    if args.mask_path is None:
+        print("Flag --mask_path is required.")
+        exit()
+    if "inpaint" not in args.hf_model_id:
+        print("Please use inpainting model with --hf_model_id.")
+        exit()
+
+    dtype = torch.float32 if args.precision == "fp32" else torch.half
+    cpu_scheduling = not args.scheduler.startswith("Shark")
+    set_init_device_flags()
+    schedulers = get_schedulers(args.hf_model_id)
+    scheduler_obj = schedulers[args.scheduler]
+    seed = args.seed
+    image = Image.open(args.img_path)
+    mask_image = Image.open(args.mask_path)
+
+    inpaint_obj = InpaintPipeline.from_pretrained(
+        scheduler_obj,
+        args.import_mlir,
+        args.hf_model_id,
+        args.ckpt_loc,
+        args.custom_vae,
+        args.precision,
+        args.max_length,
+        args.batch_size,
+        args.height,
+        args.width,
+        args.use_base_vae,
+        args.use_tuned,
+    )
+
+    for current_batch in range(args.batch_count):
+        if current_batch > 0:
+            seed = -1
+        seed = utils.sanitize_seed(seed)
+
+        start_time = time.time()
+        generated_imgs = inpaint_obj.generate_images(
+            args.prompts,
+            args.negative_prompts,
+            image,
+            mask_image,
+            args.batch_size,
+            args.height,
+            args.width,
+            args.steps,
+            args.guidance_scale,
+            seed,
+            args.max_length,
+            dtype,
+            args.use_base_vae,
+            cpu_scheduling,
+        )
+        total_time = time.time() - start_time
+        text_output = f"prompt={args.prompts}"
+        text_output += f"\nnegative prompt={args.negative_prompts}"
+        text_output += (
+            f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+        )
+        text_output += f"\nscheduler={args.scheduler}, device={args.device}"
+        text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
+        text_output += (
+            f", batch size={args.batch_size}, max_length={args.max_length}"
+        )
+        text_output += inpaint_obj.log
+        text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+        save_output_img(generated_imgs[0], seed)
+        print(text_output)

apps/stable_diffusion/scripts/telegram_bot.py

@@ -0,0 +1,240 @@
+import logging
+import os
+from models.stable_diffusion.main import stable_diff_inf
+from models.stable_diffusion.utils import get_available_devices
+from dotenv import load_dotenv
+from telegram import Update, InlineKeyboardButton, InlineKeyboardMarkup
+from telegram import BotCommand
+from telegram.ext import Application, ApplicationBuilder, CallbackQueryHandler
+from telegram.ext import ContextTypes, MessageHandler, CommandHandler, filters
+from io import BytesIO
+import random
+
+log = logging.getLogger("TG.Bot")
+logging.basicConfig()
+log.warning("Start")
+load_dotenv()
+os.environ["AMD_ENABLE_LLPC"] = "0"
+TG_TOKEN = os.getenv("TG_TOKEN")
+SELECTED_MODEL = "stablediffusion"
+SELECTED_SCHEDULER = "EulerAncestralDiscrete"
+STEPS = 30
+NEGATIVE_PROMPT = (
+    "Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra"
+    " limbs,Gross proportions,Missing arms,Mutated hands,Long"
+    " neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad"
+    " anatomy,Cloned face,Malformed limbs,Missing legs,Too many"
+    " fingers,blurry, lowres, text, error, cropped, worst quality, low"
+    " quality, jpeg artifacts, out of frame, extra fingers, mutated hands,"
+    " poorly drawn hands, poorly drawn face, bad anatomy, extra limbs, cloned"
+    " face, malformed limbs, missing arms, missing legs, extra arms, extra"
+    " legs, fused fingers, too many fingers"
+)
+GUIDANCE_SCALE = 6
+available_devices = get_available_devices()
+models_list = [
+    "stablediffusion",
+    "anythingv3",
+    "analogdiffusion",
+    "openjourney",
+    "dreamlike",
+]
+sheds_list = [
+    "DDIM",
+    "PNDM",
+    "LMSDiscrete",
+    "DPMSolverMultistep",
+    "EulerDiscrete",
+    "EulerAncestralDiscrete",
+    "SharkEulerDiscrete",
+]
+
+
+def image_to_bytes(image):
+    bio = BytesIO()
+    bio.name = "image.jpeg"
+    image.save(bio, "JPEG")
+    bio.seek(0)
+    return bio
+
+
+def get_try_again_markup():
+    keyboard = [[InlineKeyboardButton("Try again", callback_data="TRYAGAIN")]]
+    reply_markup = InlineKeyboardMarkup(keyboard)
+    return reply_markup
+
+
+def generate_image(prompt):
+    seed = random.randint(1, 10000)
+    log.warning(SELECTED_MODEL)
+    log.warning(STEPS)
+    image, text = stable_diff_inf(
+        prompt=prompt,
+        negative_prompt=NEGATIVE_PROMPT,
+        steps=STEPS,
+        guidance_scale=GUIDANCE_SCALE,
+        seed=seed,
+        scheduler_key=SELECTED_SCHEDULER,
+        variant=SELECTED_MODEL,
+        device_key=available_devices[0],
+    )
+
+    return image, seed
+
+
+async def generate_and_send_photo(
+    update: Update, context: ContextTypes.DEFAULT_TYPE
+) -> None:
+    progress_msg = await update.message.reply_text(
+        "Generating image...", reply_to_message_id=update.message.message_id
+    )
+    im, seed = generate_image(prompt=update.message.text)
+    await context.bot.delete_message(
+        chat_id=progress_msg.chat_id, message_id=progress_msg.message_id
+    )
+    await context.bot.send_photo(
+        update.effective_user.id,
+        image_to_bytes(im),
+        caption=f'"{update.message.text}" (Seed: {seed})',
+        reply_markup=get_try_again_markup(),
+        reply_to_message_id=update.message.message_id,
+    )
+
+
+async def button(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
+    query = update.callback_query
+    if query.data in models_list:
+        global SELECTED_MODEL
+        SELECTED_MODEL = query.data
+        await query.answer()
+        await query.edit_message_text(text=f"Selected model: {query.data}")
+        return
+    if query.data in sheds_list:
+        global SELECTED_SCHEDULER
+        SELECTED_SCHEDULER = query.data
+        await query.answer()
+        await query.edit_message_text(text=f"Selected scheduler: {query.data}")
+        return
+    replied_message = query.message.reply_to_message
+    await query.answer()
+    progress_msg = await query.message.reply_text(
+        "Generating image...", reply_to_message_id=replied_message.message_id
+    )
+
+    if query.data == "TRYAGAIN":
+        prompt = replied_message.text
+        im, seed = generate_image(prompt)
+
+    await context.bot.delete_message(
+        chat_id=progress_msg.chat_id, message_id=progress_msg.message_id
+    )
+    await context.bot.send_photo(
+        update.effective_user.id,
+        image_to_bytes(im),
+        caption=f'"{prompt}" (Seed: {seed})',
+        reply_markup=get_try_again_markup(),
+        reply_to_message_id=replied_message.message_id,
+    )
+
+
+async def select_model_handler(update, context):
+    text = "Select model"
+    keyboard = []
+    for model in models_list:
+        keyboard.append(
+            [
+                InlineKeyboardButton(text=model, callback_data=model),
+            ]
+        )
+    markup = InlineKeyboardMarkup(keyboard)
+    await update.message.reply_text(text=text, reply_markup=markup)
+
+
+async def select_scheduler_handler(update, context):
+    text = "Select schedule"
+    keyboard = []
+    for shed in sheds_list:
+        keyboard.append(
+            [
+                InlineKeyboardButton(text=shed, callback_data=shed),
+            ]
+        )
+    markup = InlineKeyboardMarkup(keyboard)
+    await update.message.reply_text(text=text, reply_markup=markup)
+
+
+async def set_steps_handler(update, context):
+    input_mex = update.message.text
+    log.warning(input_mex)
+    try:
+        input_args = input_mex.split("/set_steps ")[1]
+        global STEPS
+        STEPS = int(input_args)
+    except Exception:
+        input_args = (
+            "Invalid parameter for command. Correct command looks like\n"
+            " /set_steps 30"
+        )
+    await update.message.reply_text(input_args)
+
+
+async def set_negative_prompt_handler(update, context):
+    input_mex = update.message.text
+    log.warning(input_mex)
+    try:
+        input_args = input_mex.split("/set_negative_prompt ")[1]
+        global NEGATIVE_PROMPT
+        NEGATIVE_PROMPT = input_args
+    except Exception:
+        input_args = (
+            "Invalid parameter for command. Correct command looks like\n"
+            " /set_negative_prompt ugly, bad art, mutated"
+        )
+    await update.message.reply_text(input_args)
+
+
+async def set_guidance_scale_handler(update, context):
+    input_mex = update.message.text
+    log.warning(input_mex)
+    try:
+        input_args = input_mex.split("/set_guidance_scale ")[1]
+        global GUIDANCE_SCALE
+        GUIDANCE_SCALE = int(input_args)
+    except Exception:
+        input_args = (
+            "Invalid parameter for command. Correct command looks like\n"
+            " /set_guidance_scale 7"
+        )
+    await update.message.reply_text(input_args)
+
+
+async def setup_bot_commands(application: Application) -> None:
+    await application.bot.set_my_commands(
+        [
+            BotCommand("select_model", "to select model"),
+            BotCommand("select_scheduler", "to select scheduler"),
+            BotCommand("set_steps", "to set steps"),
+            BotCommand("set_guidance_scale", "to set guidance scale"),
+            BotCommand("set_negative_prompt", "to set negative prompt"),
+        ]
+    )
+
+
+app = (
+    ApplicationBuilder().token(TG_TOKEN).post_init(setup_bot_commands).build()
+)
+app.add_handler(CommandHandler("select_model", select_model_handler))
+app.add_handler(CommandHandler("select_scheduler", select_scheduler_handler))
+app.add_handler(CommandHandler("set_steps", set_steps_handler))
+app.add_handler(
+    CommandHandler("set_guidance_scale", set_guidance_scale_handler)
+)
+app.add_handler(
+    CommandHandler("set_negative_prompt", set_negative_prompt_handler)
+)
+app.add_handler(
+    MessageHandler(filters.TEXT & ~filters.COMMAND, generate_and_send_photo)
+)
+app.add_handler(CallbackQueryHandler(button))
+log.warning("Start bot")
+app.run_polling()

apps/stable_diffusion/scripts/txt2img.py

@@ -0,0 +1,240 @@
+import sys
+import torch
+import time
+from dataclasses import dataclass
+from apps.stable_diffusion.src import (
+    args,
+    Text2ImagePipeline,
+    get_schedulers,
+    set_init_device_flags,
+    utils,
+    clear_all,
+    save_output_img,
+)
+
+
+@dataclass
+class Config:
+    model_id: str
+    ckpt_loc: str
+    precision: str
+    batch_size: int
+    max_length: int
+    height: int
+    width: int
+    device: str
+
+
+txt2img_obj = None
+config_obj = None
+schedulers = None
+
+
+# Exposed to UI.
+def txt2img_inf(
+    prompt: str,
+    negative_prompt: str,
+    height: int,
+    width: int,
+    steps: int,
+    guidance_scale: float,
+    seed: int,
+    batch_count: int,
+    batch_size: int,
+    scheduler: str,
+    custom_model: str,
+    hf_model_id: str,
+    precision: str,
+    device: str,
+    max_length: int,
+    save_metadata_to_json: bool,
+    save_metadata_to_png: bool,
+):
+    global txt2img_obj
+    global config_obj
+    global schedulers
+
+    args.prompts = [prompt]
+    args.negative_prompts = [negative_prompt]
+    args.guidance_scale = guidance_scale
+    args.steps = steps
+    args.scheduler = scheduler
+
+    # set ckpt_loc and hf_model_id.
+    types = (
+        ".ckpt",
+        ".safetensors",
+    )  # the tuple of file types
+    args.ckpt_loc = ""
+    args.hf_model_id = ""
+    if custom_model == "None":
+        if not hf_model_id:
+            return (
+                None,
+                "Please provide either custom model or huggingface model ID, both must not be empty",
+            )
+        args.hf_model_id = hf_model_id
+    elif ".ckpt" in custom_model or ".safetensors" in custom_model:
+        args.ckpt_loc = custom_model
+    else:
+        args.hf_model_id = custom_model
+
+    args.save_metadata_to_json = save_metadata_to_json
+    args.write_metadata_to_png = save_metadata_to_png
+
+    dtype = torch.float32 if precision == "fp32" else torch.half
+    cpu_scheduling = not scheduler.startswith("Shark")
+    new_config_obj = Config(
+        args.hf_model_id,
+        args.ckpt_loc,
+        precision,
+        batch_size,
+        max_length,
+        height,
+        width,
+        device,
+    )
+    if config_obj != new_config_obj:
+        config_obj = new_config_obj
+        args.precision = precision
+        args.batch_size = batch_size
+        args.max_length = max_length
+        args.height = height
+        args.width = width
+        args.device = device.split("=>", 1)[1].strip()
+        args.iree_vulkan_target_triple = ""
+        args.use_tuned = True
+        args.import_mlir = False
+        set_init_device_flags()
+        model_id = (
+            args.hf_model_id
+            if args.hf_model_id
+            else "stabilityai/stable-diffusion-2-1-base"
+        )
+        schedulers = get_schedulers(model_id)
+        scheduler_obj = schedulers[scheduler]
+        txt2img_obj = Text2ImagePipeline.from_pretrained(
+            scheduler_obj,
+            args.import_mlir,
+            args.hf_model_id,
+            args.ckpt_loc,
+            args.custom_vae,
+            args.precision,
+            args.max_length,
+            args.batch_size,
+            args.height,
+            args.width,
+            args.use_base_vae,
+            args.use_tuned,
+        )
+
+    if not txt2img_obj:
+        sys.exit("text to image pipeline must not return a null value")
+
+    txt2img_obj.scheduler = schedulers[scheduler]
+
+    start_time = time.time()
+    txt2img_obj.log = ""
+    generated_imgs = []
+    seeds = []
+    img_seed = utils.sanitize_seed(seed)
+    for i in range(batch_count):
+        if i > 0:
+            img_seed = utils.sanitize_seed(-1)
+        out_imgs = txt2img_obj.generate_images(
+            prompt,
+            negative_prompt,
+            batch_size,
+            height,
+            width,
+            steps,
+            guidance_scale,
+            img_seed,
+            args.max_length,
+            dtype,
+            args.use_base_vae,
+            cpu_scheduling,
+        )
+        save_output_img(out_imgs[0], img_seed)
+        generated_imgs.extend(out_imgs)
+        seeds.append(img_seed)
+        txt2img_obj.log += "\n"
+
+    total_time = time.time() - start_time
+    text_output = f"prompt={args.prompts}"
+    text_output += f"\nnegative prompt={args.negative_prompts}"
+    text_output += f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+    text_output += f"\nscheduler={args.scheduler}, device={device}"
+    text_output += (
+        f"\nsteps={steps}, guidance_scale={guidance_scale}, seed={seeds}"
+    )
+    text_output += f"\nsize={height}x{width}, batch_count={batch_count}, batch_size={batch_size}, max_length={args.max_length}"
+    text_output += txt2img_obj.log
+    text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+    return generated_imgs, text_output
+
+
+if __name__ == "__main__":
+    if args.clear_all:
+        clear_all()
+
+    dtype = torch.float32 if args.precision == "fp32" else torch.half
+    cpu_scheduling = not args.scheduler.startswith("Shark")
+    set_init_device_flags()
+    schedulers = get_schedulers(args.hf_model_id)
+    scheduler_obj = schedulers[args.scheduler]
+    seed = args.seed
+
+    txt2img_obj = Text2ImagePipeline.from_pretrained(
+        scheduler_obj,
+        args.import_mlir,
+        args.hf_model_id,
+        args.ckpt_loc,
+        args.custom_vae,
+        args.precision,
+        args.max_length,
+        args.batch_size,
+        args.height,
+        args.width,
+        args.use_base_vae,
+        args.use_tuned,
+    )
+
+    for current_batch in range(args.batch_count):
+        if current_batch > 0:
+            seed = -1
+        seed = utils.sanitize_seed(seed)
+
+        start_time = time.time()
+        generated_imgs = txt2img_obj.generate_images(
+            args.prompts,
+            args.negative_prompts,
+            args.batch_size,
+            args.height,
+            args.width,
+            args.steps,
+            args.guidance_scale,
+            seed,
+            args.max_length,
+            dtype,
+            args.use_base_vae,
+            cpu_scheduling,
+        )
+        total_time = time.time() - start_time
+        text_output = f"prompt={args.prompts}"
+        text_output += f"\nnegative prompt={args.negative_prompts}"
+        text_output += (
+            f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
+        )
+        text_output += f"\nscheduler={args.scheduler}, device={args.device}"
+        text_output += f"\nsteps={args.steps}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
+        text_output += (
+            f", batch size={args.batch_size}, max_length={args.max_length}"
+        )
+        # TODO: if using --batch_count=x txt2img_obj.log will output on each display every iteration infos from the start
+        text_output += txt2img_obj.log
+        text_output += f"\nTotal image generation time: {total_time:.4f}sec"
+
+        save_output_img(generated_imgs[0], seed)
+        print(text_output)

apps/stable_diffusion/shark_sd.spec

@@ -0,0 +1,79 @@
+# -*- mode: python ; coding: utf-8 -*-
+from PyInstaller.utils.hooks import collect_data_files
+from PyInstaller.utils.hooks import copy_metadata
+
+import sys ; sys.setrecursionlimit(sys.getrecursionlimit() * 5)
+
+datas = []
+datas += collect_data_files('torch')
+datas += copy_metadata('torch')
+datas += copy_metadata('tqdm')
+datas += copy_metadata('regex')
+datas += copy_metadata('requests')
+datas += copy_metadata('packaging')
+datas += copy_metadata('filelock')
+datas += copy_metadata('numpy')
+datas += copy_metadata('tokenizers')
+datas += copy_metadata('importlib_metadata')
+datas += copy_metadata('torchvision')
+datas += copy_metadata('torch-mlir')
+datas += copy_metadata('diffusers')
+datas += copy_metadata('transformers')
+datas += copy_metadata('omegaconf')
+datas += copy_metadata('safetensors')
+datas += collect_data_files('gradio')
+datas += collect_data_files('iree')
+datas += collect_data_files('google-cloud-storage')
+datas += collect_data_files('shark')
+datas += [
+         ( 'src/utils/resources/prompts.json', 'resources' ),
+         ( 'src/utils/resources/model_db.json', 'resources' ),
+         ( 'src/utils/resources/opt_flags.json', 'resources' ),
+         ( 'src/utils/resources/base_model.json', 'resources' ),
+         ( 'web/ui/css/*', 'ui/css' ),
+         ( 'web/ui/logos/*', 'logos' )
+         ]
+
+binaries = []
+
+block_cipher = None
+
+
+a = Analysis(
+    ['web/index.py'],
+    pathex=['.'],
+    binaries=binaries,
+    datas=datas,
+    hiddenimports=['shark', 'shark.*', 'shark.shark_inference', 'shark_inference', 'iree.tools.core', 'gradio', 'apps'],
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    win_no_prefer_redirects=False,
+    win_private_assemblies=False,
+    cipher=block_cipher,
+    noarchive=False,
+)
+pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.zipfiles,
+    a.datas,
+    [],
+    name='shark_sd',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)

apps/stable_diffusion/shark_sd_cli.spec

@@ -0,0 +1,77 @@
+# -*- mode: python ; coding: utf-8 -*-
+from PyInstaller.utils.hooks import collect_data_files
+from PyInstaller.utils.hooks import copy_metadata
+
+import sys ; sys.setrecursionlimit(sys.getrecursionlimit() * 5)
+
+datas = []
+datas += collect_data_files('torch')
+datas += copy_metadata('torch')
+datas += copy_metadata('tqdm')
+datas += copy_metadata('regex')
+datas += copy_metadata('requests')
+datas += copy_metadata('packaging')
+datas += copy_metadata('filelock')
+datas += copy_metadata('numpy')
+datas += copy_metadata('tokenizers')
+datas += copy_metadata('importlib_metadata')
+datas += copy_metadata('torchvision')
+datas += copy_metadata('torch-mlir')
+datas += copy_metadata('diffusers')
+datas += copy_metadata('transformers')
+datas += copy_metadata('omegaconf')
+datas += copy_metadata('safetensors')
+datas += collect_data_files('gradio')
+datas += collect_data_files('iree')
+datas += collect_data_files('google-cloud-storage')
+datas += collect_data_files('shark')
+datas += [
+         ( 'src/utils/resources/prompts.json', 'resources' ),
+         ( 'src/utils/resources/model_db.json', 'resources' ),
+         ( 'src/utils/resources/opt_flags.json', 'resources' ),
+         ( 'src/utils/resources/base_model.json', 'resources' ),
+         ]
+
+binaries = []
+
+block_cipher = None
+
+
+a = Analysis(
+    ['scripts/txt2img.py'],
+    pathex=['.'],
+    binaries=binaries,
+    datas=datas,
+    hiddenimports=['shark', 'shark.*', 'shark.shark_inference', 'shark_inference', 'iree.tools.core', 'gradio', 'apps'],
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    win_no_prefer_redirects=False,
+    win_private_assemblies=False,
+    cipher=block_cipher,
+    noarchive=False,
+)
+pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.zipfiles,
+    a.datas,
+    [],
+    name='shark_sd_cli',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)

apps/stable_diffusion/src/__init__.py

@@ -0,0 +1,14 @@
+from apps.stable_diffusion.src.utils import (
+    args,
+    set_init_device_flags,
+    prompt_examples,
+    get_available_devices,
+    clear_all,
+    save_output_img,
+)
+from apps.stable_diffusion.src.pipelines import (
+    Text2ImagePipeline,
+    InpaintPipeline,
+    Image2ImagePipeline,
+)
+from apps.stable_diffusion.src.schedulers import get_schedulers

apps/stable_diffusion/src/models/__init__.py

@@ -0,0 +1,12 @@
+from apps.stable_diffusion.src.models.model_wrappers import (
+    SharkifyStableDiffusionModel,
+)
+from apps.stable_diffusion.src.models.opt_params import (
+    get_vae_encode,
+    get_vae,
+    get_unet,
+    get_clip,
+    get_tokenizer,
+    get_params,
+    get_variant_version,
+)

apps/stable_diffusion/src/models/model_wrappers.py

@@ -0,0 +1,385 @@
+from diffusers import AutoencoderKL, UNet2DConditionModel
+from transformers import CLIPTextModel
+from collections import defaultdict
+import torch
+import safetensors.torch
+import traceback
+import sys
+from apps.stable_diffusion.src.utils import (
+    compile_through_fx,
+    get_opt_flags,
+    base_models,
+    args,
+    fetch_or_delete_vmfbs,
+    preprocessCKPT,
+    get_path_to_diffusers_checkpoint,
+    fetch_and_update_base_model_id,
+    get_path_stem,
+    get_extended_name,
+)
+
+
+# These shapes are parameter dependent.
+def replace_shape_str(shape, max_len, width, height, batch_size):
+    new_shape = []
+    for i in range(len(shape)):
+        if shape[i] == "max_len":
+            new_shape.append(max_len)
+        elif shape[i] == "height":
+            new_shape.append(height)
+        elif shape[i] == "width":
+            new_shape.append(width)
+        elif isinstance(shape[i], str):
+            mul_val = int(shape[i].split("*")[0])
+            if "batch_size" in shape[i]:
+                new_shape.append(batch_size * mul_val)
+            elif "height" in shape[i]:
+                new_shape.append(height * mul_val)
+            elif "width" in shape[i]:
+                new_shape.append(width * mul_val)
+        else:
+            new_shape.append(shape[i])
+    return new_shape
+
+
+# Get the input info for various models i.e. "unet", "clip", "vae", "vae_encode".
+def get_input_info(model_info, max_len, width, height, batch_size):
+    dtype_config = {"f32": torch.float32, "i64": torch.int64}
+    input_map = defaultdict(list)
+    for k in model_info:
+        for inp in model_info[k]:
+            shape = model_info[k][inp]["shape"]
+            dtype = dtype_config[model_info[k][inp]["dtype"]]
+            tensor = None
+            if isinstance(shape, list):
+                clean_shape = replace_shape_str(
+                    shape, max_len, width, height, batch_size
+                )
+                if dtype == torch.int64:
+                    tensor = torch.randint(1, 3, tuple(clean_shape))
+                else:
+                    tensor = torch.randn(*clean_shape).to(dtype)
+            elif isinstance(shape, int):
+                tensor = torch.tensor(shape).to(dtype)
+            else:
+                sys.exit("shape isn't specified correctly.")
+            input_map[k].append(tensor)
+    return input_map
+
+
+class SharkifyStableDiffusionModel:
+    def __init__(
+        self,
+        model_id: str,
+        custom_weights: str,
+        custom_vae: str,
+        precision: str,
+        max_len: int = 64,
+        width: int = 512,
+        height: int = 512,
+        batch_size: int = 1,
+        use_base_vae: bool = False,
+        use_tuned: bool = False,
+    ):
+        self.check_params(max_len, width, height)
+        self.max_len = max_len
+        self.height = height // 8
+        self.width = width // 8
+        self.batch_size = batch_size
+        self.custom_weights = custom_weights
+        if custom_weights != "":
+            assert custom_weights.lower().endswith(
+                (".ckpt", ".safetensors")
+            ), "checkpoint files supported can be any of [.ckpt, .safetensors] type"
+            custom_weights = get_path_to_diffusers_checkpoint(custom_weights)
+        self.model_id = model_id if custom_weights == "" else custom_weights
+        # TODO: remove the following line when stable-diffusion-2-1 works
+        if self.model_id == "stabilityai/stable-diffusion-2-1":
+            self.model_id = "stabilityai/stable-diffusion-2-1-base"
+        self.custom_vae = custom_vae
+        self.precision = precision
+        self.base_vae = use_base_vae
+        self.model_name = (
+            str(batch_size)
+            + "_"
+            + str(max_len)
+            + "_"
+            + str(height)
+            + "_"
+            + str(width)
+            + "_"
+            + precision
+        )
+        self.use_tuned = use_tuned
+        if use_tuned:
+            self.model_name = self.model_name + "_tuned"
+        self.model_name = self.model_name + "_" + get_path_stem(self.model_id)
+
+    def get_extended_name_for_all_model(self):
+        model_name = {}
+        sub_model_list = ["clip", "unet", "vae", "vae_encode"]
+        for model in sub_model_list:
+            sub_model = model
+            model_config = self.model_name
+            if "vae" == model:
+                if self.custom_vae != "":
+                    model_config = model_config + get_path_stem(self.custom_vae)
+                if self.base_vae:
+                    sub_model = "base_vae"
+            model_name[model] = get_extended_name(sub_model + model_config)
+        return model_name
+
+    def check_params(self, max_len, width, height):
+        if not (max_len >= 32 and max_len <= 77):
+            sys.exit("please specify max_len in the range [32, 77].")
+        if not (width % 8 == 0 and width >= 384):
+            sys.exit("width should be greater than 384 and multiple of 8")
+        if not (height % 8 == 0 and height >= 384):
+            sys.exit("height should be greater than 384 and multiple of 8")
+
+    def get_vae_encode(self):
+        class VaeEncodeModel(torch.nn.Module):
+            def __init__(self, model_id=self.model_id):
+                super().__init__()
+                self.vae = AutoencoderKL.from_pretrained(
+                    model_id,
+                    subfolder="vae",
+                )
+
+            def forward(self, input):
+                latents = self.vae.encode(input).latent_dist.sample()
+                return 0.18215 * latents
+
+        vae_encode = VaeEncodeModel()
+        inputs = tuple(self.inputs["vae_encode"])
+        is_f16 = True if self.precision == "fp16" else False
+        shark_vae_encode = compile_through_fx(
+            vae_encode,
+            inputs,
+            is_f16=is_f16,
+            use_tuned=self.use_tuned,
+            model_name=self.model_name["vae_encode"],
+            extra_args=get_opt_flags("vae", precision=self.precision),
+        )
+        return shark_vae_encode
+
+    def get_vae(self):
+        class VaeModel(torch.nn.Module):
+            def __init__(self, model_id=self.model_id, base_vae=self.base_vae, custom_vae=self.custom_vae):
+                super().__init__()
+                self.vae = None
+                if custom_vae == "":
+                    self.vae = AutoencoderKL.from_pretrained(
+                        model_id,
+                        subfolder="vae",
+                    )
+                elif not isinstance(custom_vae, dict):
+                    self.vae = AutoencoderKL.from_pretrained(
+                        custom_vae,
+                        subfolder="vae",
+                    )
+                else:
+                    self.vae = AutoencoderKL.from_pretrained(
+                        model_id,
+                        subfolder="vae",
+                    )
+                    self.vae.load_state_dict(custom_vae)
+                self.base_vae = base_vae
+
+            def forward(self, input):
+                if not self.base_vae:
+                    input = 1 / 0.18215 * input
+                x = self.vae.decode(input, return_dict=False)[0]
+                x = (x / 2 + 0.5).clamp(0, 1)
+                if self.base_vae:
+                    return x
+                x = x * 255.0
+                return x.round()
+
+        vae = VaeModel()
+        inputs = tuple(self.inputs["vae"])
+        is_f16 = True if self.precision == "fp16" else False
+        shark_vae = compile_through_fx(
+            vae,
+            inputs,
+            is_f16=is_f16,
+            use_tuned=self.use_tuned,
+            model_name=self.model_name["vae"],
+            extra_args=get_opt_flags("vae", precision=self.precision),
+        )
+        return shark_vae
+
+    def get_unet(self):
+        class UnetModel(torch.nn.Module):
+            def __init__(self, model_id=self.model_id):
+                super().__init__()
+                self.unet = UNet2DConditionModel.from_pretrained(
+                    model_id,
+                    subfolder="unet",
+                )
+                self.in_channels = self.unet.in_channels
+                self.train(False)
+
+            def forward(
+                self, latent, timestep, text_embedding, guidance_scale
+            ):
+                # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
+                latents = torch.cat([latent] * 2)
+                unet_out = self.unet.forward(
+                    latents, timestep, text_embedding, return_dict=False
+                )[0]
+                noise_pred_uncond, noise_pred_text = unet_out.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (
+                    noise_pred_text - noise_pred_uncond
+                )
+                return noise_pred
+
+        unet = UnetModel()
+        is_f16 = True if self.precision == "fp16" else False
+        inputs = tuple(self.inputs["unet"])
+        input_mask = [True, True, True, False]
+        shark_unet = compile_through_fx(
+            unet,
+            inputs,
+            model_name=self.model_name["unet"],
+            is_f16=is_f16,
+            f16_input_mask=input_mask,
+            use_tuned=self.use_tuned,
+            extra_args=get_opt_flags("unet", precision=self.precision),
+        )
+        return shark_unet
+
+    def get_clip(self):
+        class CLIPText(torch.nn.Module):
+            def __init__(self, model_id=self.model_id):
+                super().__init__()
+                self.text_encoder = CLIPTextModel.from_pretrained(
+                    model_id,
+                    subfolder="text_encoder",
+                )
+
+            def forward(self, input):
+                return self.text_encoder(input)[0]
+
+        clip_model = CLIPText()
+        shark_clip = compile_through_fx(
+            clip_model,
+            tuple(self.inputs["clip"]),
+            model_name=self.model_name["clip"],
+            extra_args=get_opt_flags("clip", precision="fp32"),
+        )
+        return shark_clip
+
+    def process_custom_vae(self):
+        custom_vae = self.custom_vae.lower()
+        if not custom_vae.endswith((".ckpt", ".safetensors")):
+            return self.custom_vae
+        try:
+            preprocessCKPT(self.custom_vae)
+            return get_path_to_diffusers_checkpoint(self.custom_vae)
+        except:
+            print("Processing standalone Vae checkpoint")
+            vae_checkpoint = None
+            vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+            if custom_vae.endswith(".ckpt"):
+                vae_checkpoint = torch.load(self.custom_vae, map_location="cpu")
+            else:
+                vae_checkpoint = safetensors.torch.load_file(self.custom_vae, device="cpu")
+            if "state_dict" in vae_checkpoint:
+                vae_checkpoint = vae_checkpoint["state_dict"]
+            vae_dict = {k: v for k, v in vae_checkpoint.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
+            return vae_dict
+        
+            
+    # Compiles Clip, Unet and Vae with `base_model_id` as defining their input
+    # configiration.
+    def compile_all(self, base_model_id, need_vae_encode):
+        self.inputs = get_input_info(
+            base_models[base_model_id],
+            self.max_len,
+            self.width,
+            self.height,
+            self.batch_size,
+        )
+        compiled_unet = self.get_unet()
+        if self.custom_vae != "":
+            print("Plugging in custom Vae")
+        compiled_vae = self.get_vae()
+        compiled_clip = self.get_clip()
+        if need_vae_encode:
+            compiled_vae_encode = self.get_vae_encode()
+            return compiled_clip, compiled_unet, compiled_vae, compiled_vae_encode
+
+        return compiled_clip, compiled_unet, compiled_vae
+
+    def __call__(self):
+        # Step 1:
+        # --  Fetch all vmfbs for the model, if present, else delete the lot.
+        need_vae_encode = args.img_path is not None
+        self.model_name = self.get_extended_name_for_all_model()
+        vmfbs = fetch_or_delete_vmfbs(self.model_name, need_vae_encode, self.precision)   
+        if vmfbs[0]:
+            # -- If all vmfbs are indeed present, we also try and fetch the base
+            #    model configuration for running SD with custom checkpoints.
+            if self.custom_weights != "":
+                args.hf_model_id = fetch_and_update_base_model_id(self.custom_weights)
+            if args.hf_model_id == "":
+                sys.exit("Base model configuration for the custom model is missing. Use `--clear_all` and re-run.")
+            print("Loaded vmfbs from cache and successfully fetched base model configuration.")
+            return vmfbs
+
+        # Step 2:
+        # -- If vmfbs weren't found, we try to see if the base model configuration
+        #    for the required SD run is known to us and bypass the retry mechanism.
+        model_to_run = ""
+        if self.custom_weights != "":
+            model_to_run = self.custom_weights
+            assert self.custom_weights.lower().endswith(
+                (".ckpt", ".safetensors")
+            ), "checkpoint files supported can be any of [.ckpt, .safetensors] type"
+            preprocessCKPT(self.custom_weights)
+        else:
+            model_to_run = args.hf_model_id
+        # For custom Vae user can provide either the repo-id or a checkpoint file,
+        # and for a checkpoint file we'd need to process it via Diffusers' script.
+        self.custom_vae = self.process_custom_vae()
+        base_model_fetched = fetch_and_update_base_model_id(model_to_run)
+        if base_model_fetched != "":
+            print("Compiling all the models with the fetched base model configuration.")
+            if args.ckpt_loc != "":
+                args.hf_model_id = base_model_fetched
+            return self.compile_all(base_model_fetched, need_vae_encode)
+
+        # Step 3:
+        # -- This is the retry mechanism where the base model's configuration is not
+        #    known to us and figure that out by trial and error.
+        print("Inferring base model configuration.")
+        for model_id in base_models:
+            try:
+                if need_vae_encode:
+                    compiled_clip, compiled_unet, compiled_vae, compiled_vae_encode = self.compile_all(model_id, need_vae_encode)
+                else:
+                    compiled_clip, compiled_unet, compiled_vae = self.compile_all(model_id, need_vae_encode)
+            except Exception as e:
+                print("Retrying with a different base model configuration")
+                continue
+            # -- Once a successful compilation has taken place we'd want to store
+            #    the base model's configuration inferred.
+            fetch_and_update_base_model_id(model_to_run, model_id)
+            # This is done just because in main.py we are basing the choice of tokenizer and scheduler
+            # on `args.hf_model_id`. Since now, we don't maintain 1:1 mapping of variants and the base
+            # model and rely on retrying method to find the input configuration, we should also update
+            # the knowledge of base model id accordingly into `args.hf_model_id`.
+            if args.ckpt_loc != "":
+                args.hf_model_id = model_id
+            if need_vae_encode:
+                return (
+                    compiled_clip,
+                    compiled_unet,
+                    compiled_vae,
+                    compiled_vae_encode,
+                )
+            return compiled_clip, compiled_unet, compiled_vae
+        sys.exit(
+            "Cannot compile the model. Please create an issue with the detailed log at https://github.com/nod-ai/SHARK/issues"
+        )

apps/stable_diffusion/src/models/opt_params.py

@@ -0,0 +1,108 @@
+import sys
+from transformers import CLIPTokenizer
+from apps.stable_diffusion.src.utils import (
+    models_db,
+    args,
+    get_shark_model,
+    get_opt_flags,
+)
+
+
+hf_model_variant_map = {
+    "Linaqruf/anything-v3.0": ["anythingv3", "v1_4"],
+    "dreamlike-art/dreamlike-diffusion-1.0": ["dreamlike", "v1_4"],
+    "prompthero/openjourney": ["openjourney", "v1_4"],
+    "wavymulder/Analog-Diffusion": ["analogdiffusion", "v1_4"],
+    "stabilityai/stable-diffusion-2-1": ["stablediffusion", "v2_1base"],
+    "stabilityai/stable-diffusion-2-1-base": ["stablediffusion", "v2_1base"],
+    "CompVis/stable-diffusion-v1-4": ["stablediffusion", "v1_4"],
+    "runwayml/stable-diffusion-inpainting": ["stablediffusion", "inpaint_v1"],
+    "stabilityai/stable-diffusion-2-inpainting": ["stablediffusion", "inpaint_v2"],
+}
+
+
+def get_variant_version(hf_model_id):
+    return hf_model_variant_map[hf_model_id]
+
+
+def get_params(bucket_key, model_key, model, is_tuned, precision):
+    try:
+        bucket = models_db[0][bucket_key]
+        model_name = models_db[1][model_key]
+    except KeyError:
+        raise Exception(
+            f"{bucket_key}/{model_key} is not present in the models database"
+        )
+    iree_flags = get_opt_flags(model, precision="fp16")
+    return bucket, model_name, iree_flags
+
+
+def get_unet():
+    variant, version = get_variant_version(args.hf_model_id)
+    # Tuned model is present only for `fp16` precision.
+    is_tuned = "tuned" if args.use_tuned else "untuned"
+    if "vulkan" not in args.device and args.use_tuned:
+        bucket_key = f"{variant}/{is_tuned}/{args.device}"
+        model_key = f"{variant}/{version}/unet/{args.precision}/length_{args.max_length}/{is_tuned}/{args.device}"
+    else:
+        bucket_key = f"{variant}/{is_tuned}"
+        model_key = f"{variant}/{version}/unet/{args.precision}/length_{args.max_length}/{is_tuned}"
+
+    bucket, model_name, iree_flags = get_params(
+        bucket_key, model_key, "unet", is_tuned, args.precision
+    )
+    return get_shark_model(bucket, model_name, iree_flags)
+
+
+def get_vae_encode():
+    variant, version = get_variant_version(args.hf_model_id)
+    # Tuned model is present only for `fp16` precision.
+    is_tuned = "tuned" if args.use_tuned else "untuned"
+    if "vulkan" not in args.device and args.use_tuned:
+        bucket_key = f"{variant}/{is_tuned}/{args.device}"
+        model_key = f"{variant}/{version}/vae_encode/{args.precision}/length_77/{is_tuned}/{args.device}"
+    else:
+        bucket_key = f"{variant}/{is_tuned}"
+        model_key = f"{variant}/{version}/vae_encode/{args.precision}/length_77/{is_tuned}"
+
+    bucket, model_name, iree_flags = get_params(
+        bucket_key, model_key, "vae", is_tuned, args.precision
+    )
+    return get_shark_model(bucket, model_name, iree_flags)
+
+
+def get_vae():
+    variant, version = get_variant_version(args.hf_model_id)
+    # Tuned model is present only for `fp16` precision.
+    is_tuned = "tuned" if args.use_tuned else "untuned"
+    is_base = "/base" if args.use_base_vae else ""
+    if "vulkan" not in args.device and args.use_tuned:
+        bucket_key = f"{variant}/{is_tuned}/{args.device}"
+        model_key = f"{variant}/{version}/vae/{args.precision}/length_77/{is_tuned}{is_base}/{args.device}"
+    else:
+        bucket_key = f"{variant}/{is_tuned}"
+        model_key = f"{variant}/{version}/vae/{args.precision}/length_77/{is_tuned}{is_base}"
+
+    bucket, model_name, iree_flags = get_params(
+        bucket_key, model_key, "vae", is_tuned, args.precision
+    )
+    return get_shark_model(bucket, model_name, iree_flags)
+
+
+def get_clip():
+    variant, version = get_variant_version(args.hf_model_id)
+    bucket_key = f"{variant}/untuned"
+    model_key = (
+        f"{variant}/{version}/clip/fp32/length_{args.max_length}/untuned"
+    )
+    bucket, model_name, iree_flags = get_params(
+        bucket_key, model_key, "clip", "untuned", "fp32"
+    )
+    return get_shark_model(bucket, model_name, iree_flags)
+
+
+def get_tokenizer():
+    tokenizer = CLIPTokenizer.from_pretrained(
+        args.hf_model_id, subfolder="tokenizer"
+    )
+    return tokenizer

apps/stable_diffusion/src/pipelines/__init__.py

@@ -0,0 +1,9 @@
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_txt2img import (
+    Text2ImagePipeline,
+)
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_inpaint import (
+    InpaintPipeline,
+)
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_img2img import (
+    Image2ImagePipeline,
+)

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_img2img.py

@@ -0,0 +1,170 @@
+import torch
+import time
+import numpy as np
+from tqdm.auto import tqdm
+from random import randint
+from PIL import Image
+from transformers import CLIPTokenizer
+from typing import Union
+from shark.shark_inference import SharkInference
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+)
+from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+    StableDiffusionPipeline,
+)
+
+
+class Image2ImagePipeline(StableDiffusionPipeline):
+    def __init__(
+        self,
+        vae_encode: SharkInference,
+        vae: SharkInference,
+        text_encoder: SharkInference,
+        tokenizer: CLIPTokenizer,
+        unet: SharkInference,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+        ],
+    ):
+        super().__init__(vae, text_encoder, tokenizer, unet, scheduler)
+        self.vae_encode = vae_encode
+
+    def prepare_image_latents(
+        self,
+        image,
+        batch_size,
+        height,
+        width,
+        generator,
+        num_inference_steps,
+        strength,
+        dtype,
+    ):
+        # Pre process image -> get image encoded -> process latents
+
+        # TODO: process with variable HxW combos
+
+        # Pre process image
+        image = image.resize((width, height))
+        image_arr = np.stack([np.array(i) for i in (image,)], axis=0)
+        image_arr = image_arr / 255.0
+        image_arr = torch.from_numpy(image_arr).permute(0, 3, 1, 2).to(dtype)
+        image_arr = 2 * (image_arr - 0.5)
+
+        # image encode
+        latents = self.encode_image((image_arr,))
+        latents = torch.from_numpy(latents).to(dtype)
+
+        # set scheduler steps
+        self.scheduler.set_timesteps(num_inference_steps)
+        init_timestep = min(
+            int(num_inference_steps * strength), num_inference_steps
+        )
+        t_start = max(num_inference_steps - init_timestep, 0)
+        # timesteps reduced as per strength
+        timesteps = self.scheduler.timesteps[t_start:]
+        # new number of steps to be used as per strength will be
+        # num_inference_steps = num_inference_steps - t_start
+
+        # add noise to data
+        noise = torch.randn(latents.shape, generator=generator, dtype=dtype)
+        latents = self.scheduler.add_noise(
+            latents, noise, timesteps[0].repeat(1)
+        )
+
+        return latents
+
+    def encode_image(self, input_image):
+        vae_encode_start = time.time()
+        latents = self.vae_encode("forward", input_image)
+        vae_inf_time = (time.time() - vae_encode_start) * 1000
+        self.log += f"\nVAE Encode Inference time (ms): {vae_inf_time:.3f}"
+
+        return latents
+
+    def generate_images(
+        self,
+        prompts,
+        neg_prompts,
+        image,
+        batch_size,
+        height,
+        width,
+        num_inference_steps,
+        strength,
+        guidance_scale,
+        seed,
+        max_length,
+        dtype,
+        use_base_vae,
+        cpu_scheduling,
+    ):
+        # prompts and negative prompts must be a list.
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        if isinstance(neg_prompts, str):
+            neg_prompts = [neg_prompts]
+
+        prompts = prompts * batch_size
+        neg_prompts = neg_prompts * batch_size
+
+        # seed generator to create the inital latent noise. Also handle out of range seeds.
+        uint32_info = np.iinfo(np.uint32)
+        uint32_min, uint32_max = uint32_info.min, uint32_info.max
+        if seed < uint32_min or seed >= uint32_max:
+            seed = randint(uint32_min, uint32_max)
+        generator = torch.manual_seed(seed)
+
+        # Get text embeddings from prompts
+        text_embeddings = self.encode_prompts(prompts, neg_prompts, max_length)
+
+        # guidance scale as a float32 tensor.
+        guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
+
+        # Prepare input image latent
+        image_latents = self.prepare_image_latents(
+            image=image,
+            batch_size=batch_size,
+            height=height,
+            width=width,
+            generator=generator,
+            num_inference_steps=num_inference_steps,
+            strength=strength,
+            dtype=dtype,
+        )
+
+        # Get Image latents
+        latents = self.produce_img_latents(
+            latents=image_latents,
+            text_embeddings=text_embeddings,
+            guidance_scale=guidance_scale,
+            total_timesteps=self.scheduler.timesteps,
+            dtype=dtype,
+            cpu_scheduling=cpu_scheduling,
+        )
+
+        # Img latents -> PIL images
+        all_imgs = []
+        for i in tqdm(range(0, latents.shape[0], batch_size)):
+            imgs = self.decode_latents(
+                latents=latents[i : i + batch_size],
+                use_base_vae=use_base_vae,
+                cpu_scheduling=cpu_scheduling,
+            )
+            all_imgs.extend(imgs)
+
+        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_inpaint.py

@@ -0,0 +1,229 @@
+import torch
+from tqdm.auto import tqdm
+import numpy as np
+from random import randint
+from PIL import Image
+from transformers import CLIPTokenizer
+from typing import Union
+from shark.shark_inference import SharkInference
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+)
+from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+    StableDiffusionPipeline,
+)
+
+
+class InpaintPipeline(StableDiffusionPipeline):
+    def __init__(
+        self,
+        vae_encode: SharkInference,
+        vae: SharkInference,
+        text_encoder: SharkInference,
+        tokenizer: CLIPTokenizer,
+        unet: SharkInference,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+        ],
+    ):
+        super().__init__(vae, text_encoder, tokenizer, unet, scheduler)
+        self.vae_encode = vae_encode
+
+    def prepare_mask_and_masked_image(self, image, mask):
+        # preprocess image
+        if isinstance(image, (Image.Image, np.ndarray)):
+            image = [image]
+
+        if isinstance(image, list) and isinstance(image[0], Image.Image):
+            image = [np.array(i.convert("RGB"))[None, :] for i in image]
+            image = np.concatenate(image, axis=0)
+        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
+            image = np.concatenate([i[None, :] for i in image], axis=0)
+
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+        # preprocess mask
+        if isinstance(mask, (Image.Image, np.ndarray)):
+            mask = [mask]
+
+        if isinstance(mask, list) and isinstance(mask[0], Image.Image):
+            mask = np.concatenate(
+                [np.array(m.convert("L"))[None, None, :] for m in mask], axis=0
+            )
+            mask = mask.astype(np.float32) / 255.0
+        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
+            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
+
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+        mask = torch.from_numpy(mask)
+
+        masked_image = image * (mask < 0.5)
+
+        return mask, masked_image
+
+    def prepare_latents(
+        self,
+        batch_size,
+        height,
+        width,
+        generator,
+        num_inference_steps,
+        dtype,
+    ):
+        latents = torch.randn(
+            (
+                batch_size,
+                4,
+                height // 8,
+                width // 8,
+            ),
+            generator=generator,
+            dtype=torch.float32,
+        ).to(dtype)
+
+        self.scheduler.set_timesteps(num_inference_steps)
+        self.scheduler.is_scale_input_called = True
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_mask_latents(
+        self,
+        mask,
+        masked_image,
+        batch_size,
+        height,
+        width,
+        dtype,
+    ):
+        mask = torch.nn.functional.interpolate(
+            mask, size=(height // 8, width // 8)
+        )
+        mask = mask.to(dtype)
+
+        masked_image = masked_image.to(dtype)
+        masked_image_latents = self.vae_encode("forward", (masked_image,))
+        masked_image_latents = torch.from_numpy(masked_image_latents)
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        if mask.shape[0] < batch_size:
+            if not batch_size % mask.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+        if masked_image_latents.shape[0] < batch_size:
+            if not batch_size % masked_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            masked_image_latents = masked_image_latents.repeat(
+                batch_size // masked_image_latents.shape[0], 1, 1, 1
+            )
+        return mask, masked_image_latents
+
+    def generate_images(
+        self,
+        prompts,
+        neg_prompts,
+        image,
+        mask_image,
+        batch_size,
+        height,
+        width,
+        num_inference_steps,
+        guidance_scale,
+        seed,
+        max_length,
+        dtype,
+        use_base_vae,
+        cpu_scheduling,
+    ):
+        # prompts and negative prompts must be a list.
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        if isinstance(neg_prompts, str):
+            neg_prompts = [neg_prompts]
+
+        prompts = prompts * batch_size
+        neg_prompts = neg_prompts * batch_size
+
+        # seed generator to create the inital latent noise. Also handle out of range seeds.
+        uint32_info = np.iinfo(np.uint32)
+        uint32_min, uint32_max = uint32_info.min, uint32_info.max
+        if seed < uint32_min or seed >= uint32_max:
+            seed = randint(uint32_min, uint32_max)
+        generator = torch.manual_seed(seed)
+
+        # Get initial latents
+        init_latents = self.prepare_latents(
+            batch_size=batch_size,
+            height=height,
+            width=width,
+            generator=generator,
+            num_inference_steps=num_inference_steps,
+            dtype=dtype,
+        )
+
+        # Get text embeddings from prompts
+        text_embeddings = self.encode_prompts(prompts, neg_prompts, max_length)
+
+        # guidance scale as a float32 tensor.
+        guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
+
+        # Preprocess mask and image
+        mask, masked_image = self.prepare_mask_and_masked_image(
+            image, mask_image
+        )
+
+        # Prepare mask latent variables
+        mask, masked_image_latents = self.prepare_mask_latents(
+            mask=mask,
+            masked_image=masked_image,
+            batch_size=batch_size,
+            height=height,
+            width=width,
+            dtype=dtype,
+        )
+
+        # Get Image latents
+        latents = self.produce_img_latents(
+            latents=init_latents,
+            text_embeddings=text_embeddings,
+            guidance_scale=guidance_scale,
+            total_timesteps=self.scheduler.timesteps,
+            dtype=dtype,
+            cpu_scheduling=cpu_scheduling,
+            mask=mask,
+            masked_image_latents=masked_image_latents,
+        )
+
+        # Img latents -> PIL images
+        all_imgs = []
+        for i in tqdm(range(0, latents.shape[0], batch_size)):
+            imgs = self.decode_latents(
+                latents=latents[i : i + batch_size],
+                use_base_vae=use_base_vae,
+                cpu_scheduling=cpu_scheduling,
+            )
+            all_imgs.extend(imgs)
+
+        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_txt2img.py

@@ -0,0 +1,135 @@
+import torch
+from tqdm.auto import tqdm
+import numpy as np
+from random import randint
+from transformers import CLIPTokenizer
+from typing import Union
+from shark.shark_inference import SharkInference
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+)
+from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
+from apps.stable_diffusion.src.pipelines.pipeline_shark_stable_diffusion_utils import (
+    StableDiffusionPipeline,
+)
+
+
+class Text2ImagePipeline(StableDiffusionPipeline):
+    def __init__(
+        self,
+        vae: SharkInference,
+        text_encoder: SharkInference,
+        tokenizer: CLIPTokenizer,
+        unet: SharkInference,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+        ],
+    ):
+        super().__init__(vae, text_encoder, tokenizer, unet, scheduler)
+
+    def prepare_latents(
+        self,
+        batch_size,
+        height,
+        width,
+        generator,
+        num_inference_steps,
+        dtype,
+    ):
+        latents = torch.randn(
+            (
+                batch_size,
+                4,
+                height // 8,
+                width // 8,
+            ),
+            generator=generator,
+            dtype=torch.float32,
+        ).to(dtype)
+
+        self.scheduler.set_timesteps(num_inference_steps)
+        self.scheduler.is_scale_input_called = True
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def generate_images(
+        self,
+        prompts,
+        neg_prompts,
+        batch_size,
+        height,
+        width,
+        num_inference_steps,
+        guidance_scale,
+        seed,
+        max_length,
+        dtype,
+        use_base_vae,
+        cpu_scheduling,
+    ):
+        # prompts and negative prompts must be a list.
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        if isinstance(neg_prompts, str):
+            neg_prompts = [neg_prompts]
+
+        prompts = prompts * batch_size
+        neg_prompts = neg_prompts * batch_size
+
+        # seed generator to create the inital latent noise. Also handle out of range seeds.
+        # TODO: Wouldn't it be preferable to just report an error instead of modifying the seed on the fly?
+        uint32_info = np.iinfo(np.uint32)
+        uint32_min, uint32_max = uint32_info.min, uint32_info.max
+        if seed < uint32_min or seed >= uint32_max:
+            seed = randint(uint32_min, uint32_max)
+        generator = torch.manual_seed(seed)
+
+        # Get initial latents
+        init_latents = self.prepare_latents(
+            batch_size=batch_size,
+            height=height,
+            width=width,
+            generator=generator,
+            num_inference_steps=num_inference_steps,
+            dtype=dtype,
+        )
+
+        # Get text embeddings from prompts
+        text_embeddings = self.encode_prompts(prompts, neg_prompts, max_length)
+
+        # guidance scale as a float32 tensor.
+        guidance_scale = torch.tensor(guidance_scale).to(torch.float32)
+
+        # Get Image latents
+        latents = self.produce_img_latents(
+            latents=init_latents,
+            text_embeddings=text_embeddings,
+            guidance_scale=guidance_scale,
+            total_timesteps=self.scheduler.timesteps,
+            dtype=dtype,
+            cpu_scheduling=cpu_scheduling,
+        )
+
+        # Img latents -> PIL images
+        all_imgs = []
+        for i in tqdm(range(0, latents.shape[0], batch_size)):
+            imgs = self.decode_latents(
+                latents=latents[i : i + batch_size],
+                use_base_vae=use_base_vae,
+                cpu_scheduling=cpu_scheduling,
+            )
+            all_imgs.extend(imgs)
+
+        return all_imgs

apps/stable_diffusion/src/pipelines/pipeline_shark_stable_diffusion_utils.py

@@ -0,0 +1,255 @@
+import torch
+import numpy as np
+from transformers import CLIPTokenizer
+from PIL import Image
+from tqdm.auto import tqdm
+import time
+from typing import Union
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+)
+from shark.shark_inference import SharkInference
+from apps.stable_diffusion.src.schedulers import SharkEulerDiscreteScheduler
+from apps.stable_diffusion.src.models import (
+    SharkifyStableDiffusionModel,
+    get_vae_encode,
+    get_vae,
+    get_clip,
+    get_unet,
+    get_tokenizer,
+)
+from apps.stable_diffusion.src.utils import (
+    start_profiling,
+    end_profiling,
+)
+
+
+class StableDiffusionPipeline:
+    def __init__(
+        self,
+        vae: SharkInference,
+        text_encoder: SharkInference,
+        tokenizer: CLIPTokenizer,
+        unet: SharkInference,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+        ],
+    ):
+        self.vae = vae
+        self.text_encoder = text_encoder
+        self.tokenizer = tokenizer
+        self.unet = unet
+        self.scheduler = scheduler
+        # TODO: Implement using logging python utility.
+        self.log = ""
+
+    def encode_prompts(self, prompts, neg_prompts, max_length):
+        # Tokenize text and get embeddings
+        text_input = self.tokenizer(
+            prompts,
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        # Get unconditional embeddings as well
+        uncond_input = self.tokenizer(
+            neg_prompts,
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input = torch.cat([uncond_input.input_ids, text_input.input_ids])
+
+        clip_inf_start = time.time()
+        text_embeddings = self.text_encoder("forward", (text_input,))
+        clip_inf_time = (time.time() - clip_inf_start) * 1000
+        self.log += f"\nClip Inference time (ms) = {clip_inf_time:.3f}"
+
+        return text_embeddings
+
+    def decode_latents(self, latents, use_base_vae, cpu_scheduling):
+        if use_base_vae:
+            latents = 1 / 0.18215 * latents
+
+        latents_numpy = latents
+        if cpu_scheduling:
+            latents_numpy = latents.detach().numpy()
+
+        profile_device = start_profiling(file_path="vae.rdc")
+        vae_start = time.time()
+        images = self.vae("forward", (latents_numpy,))
+        vae_inf_time = (time.time() - vae_start) * 1000
+        end_profiling(profile_device)
+        self.log += f"\nVAE Inference time (ms): {vae_inf_time:.3f}"
+
+        if use_base_vae:
+            images = torch.from_numpy(images)
+            images = (images.detach().cpu() * 255.0).numpy()
+            images = images.round()
+
+        images = torch.from_numpy(images).to(torch.uint8).permute(0, 2, 3, 1)
+        pil_images = [Image.fromarray(image) for image in images.numpy()]
+        return pil_images
+
+    def produce_img_latents(
+        self,
+        latents,
+        text_embeddings,
+        guidance_scale,
+        total_timesteps,
+        dtype,
+        cpu_scheduling,
+        mask=None,
+        masked_image_latents=None,
+        return_all_latents=False,
+    ):
+        step_time_sum = 0
+        latent_history = [latents]
+        text_embeddings = torch.from_numpy(text_embeddings).to(dtype)
+        text_embeddings_numpy = text_embeddings.detach().numpy()
+        for i, t in tqdm(enumerate(total_timesteps)):
+            step_start_time = time.time()
+            timestep = torch.tensor([t]).to(dtype).detach().numpy()
+            latent_model_input = self.scheduler.scale_model_input(latents, t)
+            if mask is not None and masked_image_latents is not None:
+                latent_model_input = torch.cat(
+                    [
+                        torch.from_numpy(np.asarray(latent_model_input)),
+                        mask,
+                        masked_image_latents,
+                    ],
+                    dim=1,
+                ).to(dtype)
+            if cpu_scheduling:
+                latent_model_input = latent_model_input.detach().numpy()
+
+            # Profiling Unet.
+            profile_device = start_profiling(file_path="unet.rdc")
+            noise_pred = self.unet(
+                "forward",
+                (
+                    latent_model_input,
+                    timestep,
+                    text_embeddings_numpy,
+                    guidance_scale,
+                ),
+                send_to_host=False,
+            )
+            end_profiling(profile_device)
+
+            if cpu_scheduling:
+                noise_pred = torch.from_numpy(noise_pred.to_host())
+                latents = self.scheduler.step(
+                    noise_pred, t, latents
+                ).prev_sample
+            else:
+                latents = self.scheduler.step(noise_pred, t, latents)
+
+            latent_history.append(latents)
+            step_time = (time.time() - step_start_time) * 1000
+            #  self.log += (
+            #      f"\nstep = {i} | timestep = {t} | time = {step_time:.2f}ms"
+            #  )
+            step_time_sum += step_time
+
+        avg_step_time = step_time_sum / len(total_timesteps)
+        self.log += f"\nAverage step time: {avg_step_time}ms/it"
+
+        if not return_all_latents:
+            return latents
+        all_latents = torch.cat(latent_history, dim=0)
+        return all_latents
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+            SharkEulerDiscreteScheduler,
+        ],
+        import_mlir: bool,
+        model_id: str,
+        ckpt_loc: str,
+        custom_vae: str,
+        precision: str,
+        max_length: int,
+        batch_size: int,
+        height: int,
+        width: int,
+        use_base_vae: bool,
+        use_tuned: bool,
+    ):
+        if import_mlir:
+            mlir_import = SharkifyStableDiffusionModel(
+                model_id,
+                ckpt_loc,
+                custom_vae,
+                precision,
+                max_len=max_length,
+                batch_size=batch_size,
+                height=height,
+                width=width,
+                use_base_vae=use_base_vae,
+                use_tuned=use_tuned,
+            )
+            if cls.__name__ in ["Image2ImagePipeline", "InpaintPipeline"]:
+                clip, unet, vae, vae_encode = mlir_import()
+                return cls(
+                    vae_encode, vae, clip, get_tokenizer(), unet, scheduler
+                )
+            clip, unet, vae = mlir_import()
+            return cls(vae, clip, get_tokenizer(), unet, scheduler)
+        try:
+            if cls.__name__ in ["Image2ImagePipeline", "InpaintPipeline"]:
+                return cls(
+                    get_vae_encode(),
+                    get_vae(),
+                    get_clip(),
+                    get_tokenizer(),
+                    get_unet(),
+                    scheduler,
+                )
+            return cls(
+                get_vae(), get_clip(), get_tokenizer(), get_unet(), scheduler
+            )
+        except:
+            print("download pipeline failed, falling back to import_mlir")
+            mlir_import = SharkifyStableDiffusionModel(
+                model_id,
+                ckpt_loc,
+                custom_vae,
+                precision,
+                max_len=max_length,
+                batch_size=batch_size,
+                height=height,
+                width=width,
+                use_base_vae=use_base_vae,
+                use_tuned=use_tuned,
+            )
+            if cls.__name__ in ["Image2ImagePipeline", "InpaintPipeline"]:
+                clip, unet, vae, vae_encode = mlir_import()
+                return cls(
+                    vae_encode, vae, clip, get_tokenizer(), unet, scheduler
+                )
+            clip, unet, vae = mlir_import()
+            return cls(vae, clip, get_tokenizer(), unet, scheduler)

apps/stable_diffusion/src/schedulers/__init__.py

@@ -0,0 +1,4 @@
+from apps.stable_diffusion.src.schedulers.sd_schedulers import get_schedulers
+from apps.stable_diffusion.src.schedulers.shark_eulerdiscrete import (
+    SharkEulerDiscreteScheduler,
+)

apps/stable_diffusion/src/schedulers/sd_schedulers.py

@@ -0,0 +1,51 @@
+from diffusers import (
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+)
+from apps.stable_diffusion.src.schedulers.shark_eulerdiscrete import (
+    SharkEulerDiscreteScheduler,
+)
+
+
+def get_schedulers(model_id):
+    schedulers = dict()
+    schedulers["PNDM"] = PNDMScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["LMSDiscrete"] = LMSDiscreteScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["DDIM"] = DDIMScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers[
+        "DPMSolverMultistep"
+    ] = DPMSolverMultistepScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["EulerDiscrete"] = EulerDiscreteScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers[
+        "EulerAncestralDiscrete"
+    ] = EulerAncestralDiscreteScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers[
+        "SharkEulerDiscrete"
+    ] = SharkEulerDiscreteScheduler.from_pretrained(
+        model_id,
+        subfolder="scheduler",
+    )
+    schedulers["SharkEulerDiscrete"].compile()
+    return schedulers

apps/stable_diffusion/src/schedulers/shark_eulerdiscrete.py

@@ -0,0 +1,156 @@
+import sys
+import numpy as np
+from typing import List, Optional, Tuple, Union
+from diffusers import (
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerDiscreteScheduler,
+)
+from diffusers.configuration_utils import register_to_config
+from apps.stable_diffusion.src.utils import (
+    compile_through_fx,
+    get_shark_model,
+    args,
+)
+import torch
+
+
+class SharkEulerDiscreteScheduler(EulerDiscreteScheduler):
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        beta_start: float = 0.0001,
+        beta_end: float = 0.02,
+        beta_schedule: str = "linear",
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
+        prediction_type: str = "epsilon",
+    ):
+        super().__init__(
+            num_train_timesteps,
+            beta_start,
+            beta_end,
+            beta_schedule,
+            trained_betas,
+            prediction_type,
+        )
+
+    def compile(self):
+        SCHEDULER_BUCKET = "gs://shark_tank/stable_diffusion/schedulers"
+        BATCH_SIZE = args.batch_size
+
+        model_input = {
+            "euler": {
+                "latent": torch.randn(
+                    BATCH_SIZE, 4, args.height // 8, args.width // 8
+                ),
+                "output": torch.randn(
+                    BATCH_SIZE, 4, args.height // 8, args.width // 8
+                ),
+                "sigma": torch.tensor(1).to(torch.float32),
+                "dt": torch.tensor(1).to(torch.float32),
+            },
+        }
+
+        example_latent = model_input["euler"]["latent"]
+        example_output = model_input["euler"]["output"]
+        if args.precision == "fp16":
+            example_latent = example_latent.half()
+            example_output = example_output.half()
+        example_sigma = model_input["euler"]["sigma"]
+        example_dt = model_input["euler"]["dt"]
+
+        class ScalingModel(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, latent, sigma):
+                return latent / ((sigma**2 + 1) ** 0.5)
+
+        class SchedulerStepModel(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, noise_pred, sigma, latent, dt):
+                pred_original_sample = latent - sigma * noise_pred
+                derivative = (latent - pred_original_sample) / sigma
+                return latent + derivative * dt
+
+        iree_flags = []
+        if len(args.iree_vulkan_target_triple) > 0:
+            iree_flags.append(
+                f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
+            )
+        # Disable bindings fusion to work with moltenVK.
+        if sys.platform == "darwin":
+            iree_flags.append("-iree-stream-fuse-binding=false")
+
+        def _import(self):
+            scaling_model = ScalingModel()
+            self.scaling_model = compile_through_fx(
+                scaling_model,
+                (example_latent, example_sigma),
+                model_name=f"euler_scale_model_input_{BATCH_SIZE}_{args.height}_{args.width}"
+                + args.precision,
+                extra_args=iree_flags,
+            )
+
+            step_model = SchedulerStepModel()
+            self.step_model = compile_through_fx(
+                step_model,
+                (example_output, example_sigma, example_latent, example_dt),
+                model_name=f"euler_step_{BATCH_SIZE}_{args.height}_{args.width}"
+                + args.precision,
+                extra_args=iree_flags,
+            )
+
+        if args.import_mlir:
+            _import(self)
+
+        else:
+            try:
+                self.scaling_model = get_shark_model(
+                    SCHEDULER_BUCKET,
+                    "euler_scale_model_input_" + args.precision,
+                    iree_flags,
+                )
+                self.step_model = get_shark_model(
+                    SCHEDULER_BUCKET,
+                    "euler_step_" + args.precision,
+                    iree_flags,
+                )
+            except:
+                print(
+                    "failed to download model, falling back and using import_mlir"
+                )
+                args.import_mlir = True
+                _import(self)
+
+    def scale_model_input(self, sample, timestep):
+        step_index = (self.timesteps == timestep).nonzero().item()
+        sigma = self.sigmas[step_index]
+        return self.scaling_model(
+            "forward",
+            (
+                sample,
+                sigma,
+            ),
+            send_to_host=False,
+        )
+
+    def step(self, noise_pred, timestep, latent):
+        step_index = (self.timesteps == timestep).nonzero().item()
+        sigma = self.sigmas[step_index]
+        dt = self.sigmas[step_index + 1] - sigma
+        return self.step_model(
+            "forward",
+            (
+                noise_pred,
+                sigma,
+                latent,
+                dt,
+            ),
+            send_to_host=False,
+        )

apps/stable_diffusion/src/utils/__init__.py

@@ -0,0 +1,31 @@
+from apps.stable_diffusion.src.utils.profiler import (
+    start_profiling,
+    end_profiling,
+)
+from apps.stable_diffusion.src.utils.resources import (
+    prompt_examples,
+    models_db,
+    base_models,
+    opt_flags,
+    resource_path,
+)
+from apps.stable_diffusion.src.utils.sd_annotation import sd_model_annotation
+from apps.stable_diffusion.src.utils.stable_args import args
+from apps.stable_diffusion.src.utils.utils import (
+    get_shark_model,
+    compile_through_fx,
+    set_iree_runtime_flags,
+    map_device_to_name_path,
+    set_init_device_flags,
+    get_available_devices,
+    get_opt_flags,
+    preprocessCKPT,
+    fetch_or_delete_vmfbs,
+    fetch_and_update_base_model_id,
+    get_path_to_diffusers_checkpoint,
+    sanitize_seed,
+    get_path_stem,
+    get_extended_name,
+    clear_all,
+    save_output_img,
+)

apps/stable_diffusion/src/utils/profiler.py

@@ -0,0 +1,18 @@
+from apps.stable_diffusion.src.utils.stable_args import args
+
+
+# Helper function to profile the vulkan device.
+def start_profiling(file_path="foo.rdc", profiling_mode="queue"):
+    if args.vulkan_debug_utils and "vulkan" in args.device:
+        import iree
+
+        print(f"Profiling and saving to {file_path}.")
+        vulkan_device = iree.runtime.get_device(args.device)
+        vulkan_device.begin_profiling(mode=profiling_mode, file_path=file_path)
+        return vulkan_device
+    return None
+
+
+def end_profiling(device):
+    if device:
+        return device.end_profiling()

apps/stable_diffusion/src/utils/resources.py

@@ -0,0 +1,37 @@
+import os
+import json
+import sys
+
+
+def resource_path(relative_path):
+    """Get absolute path to resource, works for dev and for PyInstaller"""
+    base_path = getattr(
+        sys, "_MEIPASS", os.path.dirname(os.path.abspath(__file__))
+    )
+    return os.path.join(base_path, relative_path)
+
+
+def get_json_file(path):
+    json_var = []
+    loc_json = resource_path(path)
+    if os.path.exists(loc_json):
+        with open(loc_json, encoding="utf-8") as fopen:
+            json_var = json.load(fopen)
+
+    if not json_var:
+        print(f"Unable to fetch {path}")
+
+    return json_var
+
+
+# TODO: This shouldn't be called from here, every time the file imports
+# it will run all the global vars.
+prompt_examples = get_json_file("resources/prompts.json")
+models_db = get_json_file("resources/model_db.json")
+
+# The base_model contains the input configuration for the different
+# models and also helps in providing information for the variants.
+base_models = get_json_file("resources/base_model.json")
+
+# Contains optimization flags for different models.
+opt_flags = get_json_file("resources/opt_flags.json")

apps/stable_diffusion/src/utils/resources/base_model.json

@@ -0,0 +1,226 @@
+{
+    "stabilityai/stable-diffusion-2-1": {
+        "unet": {
+            "latents": {
+                "shape": [
+                    "1*batch_size",
+                    4,
+                    "height",
+                    "width"
+                ],
+                "dtype": "f32"
+            },
+            "timesteps": {
+                "shape": [
+                    1
+                ],
+                "dtype": "f32"
+            },
+            "embedding": {
+                "shape": [
+                    "2*batch_size",
+                    "max_len",
+                    1024
+                ],
+                "dtype": "f32"
+            },
+            "guidance_scale": {
+                "shape": 2,
+                "dtype": "f32"
+            }
+        },
+        "vae_encode": {
+            "image" : {
+                "shape" : [
+                    "1*batch_size",3,"8*height","8*width"
+                ],
+                "dtype":"f32"
+            }
+        },
+        "vae": {
+            "latents" : {
+                "shape" : [
+                    "1*batch_size",4,"height","width"
+                ],
+                "dtype":"f32"
+            }
+        },
+        "clip": {
+            "token" : {
+                "shape" : [
+                    "2*batch_size",
+                    "max_len"
+                ],
+                "dtype":"i64"
+            }
+        }
+    },
+    "CompVis/stable-diffusion-v1-4": {
+        "unet": {
+            "latents": {
+                "shape": [
+                    "1*batch_size",
+                    4,
+                    "height",
+                    "width"
+                ],
+                "dtype": "f32"
+            },
+            "timesteps": {
+                "shape": [
+                    1
+                ],
+                "dtype": "f32"
+            },
+            "embedding": {
+                "shape": [
+                    "2*batch_size",
+                    "max_len",
+                    768
+                ],
+                "dtype": "f32"
+            },
+            "guidance_scale": {
+                "shape": 2,
+                "dtype": "f32"
+            }
+        },
+        "vae_encode": {
+            "image" : {
+                "shape" : [
+                    "1*batch_size",3,"8*height","8*width"
+                ],
+                "dtype":"f32"
+            }
+        },
+        "vae": {
+            "latents" : {
+                "shape" : [
+                    "1*batch_size",4,"height","width"
+                ],
+                "dtype":"f32"
+            }
+        },
+        "clip": {
+            "token" : {
+                "shape" : [
+                    "2*batch_size",
+                    "max_len"
+                ],
+                "dtype":"i64"
+            }
+        }
+    },
+    "runwayml/stable-diffusion-inpainting": {
+        "unet": {
+            "latents": {
+                "shape": [
+                    "1*batch_size",
+                    9,
+                    "height",
+                    "width"
+                ],
+                "dtype": "f32"
+            },
+            "timesteps": {
+                "shape": [
+                    1
+                ],
+                "dtype": "f32"
+            },
+            "embedding": {
+                "shape": [
+                    "2*batch_size",
+                    "max_len",
+                    768
+                ],
+                "dtype": "f32"
+            },
+            "guidance_scale": {
+                "shape": 2,
+                "dtype": "f32"
+            }
+        },
+        "vae_encode": {
+            "image" : {
+                "shape" : [
+                    "1*batch_size",3,"8*height","8*width"
+                ],
+                "dtype":"f32"
+            }
+        },
+        "vae": {
+            "latents" : {
+                "shape" : [
+                    "1*batch_size",4,"height","width"
+                ],
+                "dtype":"f32"
+            }
+        },
+        "clip": {
+            "token" : {
+                "shape" : [
+                    "2*batch_size",
+                    "max_len"
+                ],
+                "dtype":"i64"
+            }
+        }
+    },
+    "stabilityai/stable-diffusion-2-inpainting": {
+        "unet": {
+            "latents": {
+                "shape": [
+                    "1*batch_size",
+                    9,
+                    "height",
+                    "width"
+                ],
+                "dtype": "f32"
+            },
+            "timesteps": {
+                "shape": [
+                    1
+                ],
+                "dtype": "f32"
+            },
+            "embedding": {
+                "shape": [
+                    "2*batch_size",
+                    "max_len",
+                    1024
+                ],
+                "dtype": "f32"
+            },
+            "guidance_scale": {
+                "shape": 2,
+                "dtype": "f32"
+            }
+        },
+        "vae_encode": {
+            "image" : {
+                "shape" : [
+                    "1*batch_size",3,"8*height","8*width"
+                ],
+                "dtype":"f32"
+            }
+        },
+        "vae": {
+            "latents" : {
+                "shape" : [
+                    "1*batch_size",4,"height","width"
+                ],
+                "dtype":"f32"
+            }
+        },
+        "clip": {
+            "token" : {
+                "shape" : [
+                    "2*batch_size",
+                    "max_len"
+                ],
+                "dtype":"i64"
+            }
+        }
+    }
+}

apps/stable_diffusion/src/utils/resources/model_config.json

@@ -0,0 +1,23 @@
+[
+  {
+    "stablediffusion/v1_4":"CompVis/stable-diffusion-v1-4",
+    "stablediffusion/v2_1base":"stabilityai/stable-diffusion-2-1-base",
+    "stablediffusion/v2_1":"stabilityai/stable-diffusion-2-1",
+    "stablediffusion/inpaint_v1":"runwayml/stable-diffusion-inpainting",
+    "stablediffusion/inpaint_v2":"stabilityai/stable-diffusion-2-inpainting",
+    "anythingv3/v1_4":"Linaqruf/anything-v3.0",
+    "analogdiffusion/v1_4":"wavymulder/Analog-Diffusion",
+    "openjourney/v1_4":"prompthero/openjourney",
+    "dreamlike/v1_4":"dreamlike-art/dreamlike-diffusion-1.0"
+  },
+  {
+    "stablediffusion/fp16":"fp16",
+    "stablediffusion/fp32":"main",
+    "anythingv3/fp16":"diffusers",
+    "anythingv3/fp32":"diffusers",
+    "analogdiffusion/fp16":"main",
+    "analogdiffusion/fp32":"main",
+    "openjourney/fp16":"main",
+    "openjourney/fp32":"main"
+  }
+]

apps/stable_diffusion/src/utils/resources/model_db.json

@@ -0,0 +1,91 @@
+[
+  {
+    "stablediffusion/untuned":"gs://shark_tank/sd_untuned",
+    "stablediffusion/tuned":"gs://shark_tank/sd_tuned",
+    "stablediffusion/tuned/cuda":"gs://shark_tank/sd_tuned/cuda",
+    "anythingv3/untuned":"gs://shark_tank/sd_anythingv3",
+    "anythingv3/tuned":"gs://shark_tank/sd_tuned",
+    "anythingv3/tuned/cuda":"gs://shark_tank/sd_tuned/cuda",
+    "analogdiffusion/untuned":"gs://shark_tank/sd_analog_diffusion",
+    "analogdiffusion/tuned":"gs://shark_tank/sd_tuned",
+    "analogdiffusion/tuned/cuda":"gs://shark_tank/sd_tuned/cuda",
+    "openjourney/untuned":"gs://shark_tank/sd_openjourney",
+    "openjourney/tuned":"gs://shark_tank/sd_tuned",
+    "dreamlike/untuned":"gs://shark_tank/sd_dreamlike_diffusion"
+  },
+  {
+    "stablediffusion/v1_4/unet/fp16/length_77/untuned":"unet_8dec_fp16",
+    "stablediffusion/v1_4/unet/fp16/length_77/tuned":"unet_8dec_fp16_tuned",
+    "stablediffusion/v1_4/unet/fp16/length_77/tuned/cuda":"unet_8dec_fp16_cuda_tuned",
+    "stablediffusion/v1_4/unet/fp32/length_77/untuned":"unet_1dec_fp32",
+    "stablediffusion/v1_4/vae/fp16/length_77/untuned":"vae_19dec_fp16",
+    "stablediffusion/v1_4/vae/fp16/length_77/tuned":"vae_19dec_fp16_tuned",
+    "stablediffusion/v1_4/vae/fp16/length_77/tuned/cuda":"vae_19dec_fp16_cuda_tuned",
+    "stablediffusion/v1_4/vae/fp16/length_77/untuned/base":"vae_8dec_fp16",
+    "stablediffusion/v2_1base/unet/fp16/length_77/untuned":"unet77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1base/unet/fp16/length_77/tuned":"unet2base_8dec_fp16_tuned_v2",
+    "stablediffusion/v2_1base/unet/fp16/length_77/tuned/cuda":"unet2base_8dec_fp16_cuda_tuned",
+    "stablediffusion/v2_1base/unet/fp16/length_64/untuned":"unet64_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1base/unet/fp16/length_64/tuned":"unet_19dec_v2p1base_fp16_64_tuned",
+    "stablediffusion/v2_1base/unet/fp16/length_64/tuned/cuda":"unet_19dec_v2p1base_fp16_64_cuda_tuned",
+    "stablediffusion/v2_1base/vae/fp16/length_77/untuned":"vae77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1base/vae/fp16/length_77/tuned":"vae2base_19dec_fp16_tuned",
+    "stablediffusion/v2_1base/vae/fp16/length_77/tuned/cuda":"vae2base_19dec_fp16_cuda_tuned",
+    "stablediffusion/v2_1base/vae/fp16/length_77/untuned/base":"vae2base_8dec_fp16",
+    "stablediffusion/v2_1base/vae/fp16/length_77/tuned/base":"vae2base_8dec_fp16_tuned",
+    "stablediffusion/v2_1base/vae/fp16/length_77/tuned/base/cuda":"vae2base_8dec_fp16_cuda_tuned",
+    "stablediffusion/v2_1base/clip/fp32/length_77/untuned":"clip77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1base/clip/fp32/length_64/untuned":"clip64_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1/unet/fp16/length_77/untuned":"unet77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1/vae/fp16/length_77/untuned":"vae77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/v2_1/vae/fp16/length_77/untuned/base":"vae2_8dec_fp16",
+    "stablediffusion/v2_1/clip/fp32/length_77/untuned":"clip77_512_512_fp16_stabilityai_stable_diffusion_2_1_base",
+    "stablediffusion/inpaint_v1/unet/fp16/length_77/untuned":"unet_inpaint_fp16",
+    "stablediffusion/inpaint_v1/unet/fp32/length_77/untuned":"unet_inpaint_fp32",
+    "stablediffusion/inpaint_v1/vae_encode/fp16/length_77/untuned":"vae_encode_inpaint_fp16",
+    "stablediffusion/inpaint_v1/vae_encode/fp32/length_77/untuned":"vae_encode_inpaint_fp32",
+    "stablediffusion/inpaint_v1/vae/fp16/length_77/untuned":"vae_inpaint_fp16",
+    "stablediffusion/inpaint_v1/vae/fp32/length_77/untuned":"vae_inpaint_fp32",
+    "stablediffusion/inpaint_v1/clip/fp32/length_77/untuned":"clip_inpaint_fp32",
+    "stablediffusion/inpaint_v2/unet/fp16/length_77/untuned":"unet_inpaint_fp16",
+    "stablediffusion/inpaint_v2/vae_encode/fp16/length_77/untuned":"vae_encode_inpaint_fp16",
+    "stablediffusion/inpaint_v2/vae/fp16/length_77/untuned":"vae_inpaint_fp16",
+    "stablediffusion/inpaint_v2/clip/fp32/length_77/untuned":"clip_inpaint_fp32",
+    "anythingv3/v1_4/unet/fp16/length_77/untuned":"av3_unet_19dec_fp16",
+    "anythingv3/v1_4/unet/fp16/length_77/tuned":"av3_unet_19dec_fp16_tuned",
+    "anythingv3/v1_4/unet/fp16/length_77/tuned/cuda":"av3_unet_19dec_fp16_cuda_tuned",
+    "anythingv3/v1_4/unet/fp32/length_77/untuned":"av3_unet_19dec_fp32",
+    "anythingv3/v1_4/vae/fp16/length_77/untuned":"av3_vae_19dec_fp16",
+    "anythingv3/v1_4/vae/fp16/length_77/tuned":"av3_vae_19dec_fp16_tuned",
+    "anythingv3/v1_4/vae/fp16/length_77/tuned/cuda":"av3_vae_19dec_fp16_cuda_tuned",
+    "anythingv3/v1_4/vae/fp16/length_77/untuned/base":"av3_vaebase_22dec_fp16",
+    "anythingv3/v1_4/vae/fp32/length_77/untuned":"av3_vae_19dec_fp32",
+    "anythingv3/v1_4/vae/fp32/length_77/untuned/base":"av3_vaebase_22dec_fp32",
+    "anythingv3/v1_4/clip/fp32/length_77/untuned":"av3_clip_19dec_fp32",
+    "analogdiffusion/v1_4/unet/fp16/length_77/untuned":"ad_unet_19dec_fp16",
+    "analogdiffusion/v1_4/unet/fp16/length_77/tuned":"ad_unet_19dec_fp16_tuned",
+    "analogdiffusion/v1_4/unet/fp16/length_77/tuned/cuda":"ad_unet_19dec_fp16_cuda_tuned",
+    "analogdiffusion/v1_4/unet/fp32/length_77/untuned":"ad_unet_19dec_fp32",
+    "analogdiffusion/v1_4/vae/fp16/length_77/untuned":"ad_vae_19dec_fp16",
+    "analogdiffusion/v1_4/vae/fp16/length_77/tuned":"ad_vae_19dec_fp16_tuned",
+    "analogdiffusion/v1_4/vae/fp16/length_77/tuned/cuda":"ad_vae_19dec_fp16_cuda_tuned",
+    "analogdiffusion/v1_4/vae/fp16/length_77/untuned/base":"ad_vaebase_22dec_fp16",
+    "analogdiffusion/v1_4/vae/fp32/length_77/untuned":"ad_vae_19dec_fp32",
+    "analogdiffusion/v1_4/vae/fp32/length_77/untuned/base":"ad_vaebase_22dec_fp32",
+    "analogdiffusion/v1_4/clip/fp32/length_77/untuned":"ad_clip_19dec_fp32",
+    "openjourney/v1_4/unet/fp16/length_64/untuned":"oj_unet_22dec_fp16_64",
+    "openjourney/v1_4/unet/fp32/length_64/untuned":"oj_unet_22dec_fp32_64",
+    "openjourney/v1_4/vae/fp16/length_77/untuned":"oj_vae_22dec_fp16",
+    "openjourney/v1_4/vae/fp16/length_77/untuned/base":"oj_vaebase_22dec_fp16",
+    "openjourney/v1_4/vae/fp32/length_77/untuned":"oj_vae_22dec_fp32",
+    "openjourney/v1_4/vae/fp32/length_77/untuned/base":"oj_vaebase_22dec_fp32",
+    "openjourney/v1_4/clip/fp32/length_64/untuned":"oj_clip_22dec_fp32_64",
+    "dreamlike/v1_4/unet/fp16/length_77/untuned":"dl_unet_23dec_fp16_77",
+    "dreamlike/v1_4/unet/fp32/length_77/untuned":"dl_unet_23dec_fp32_77",
+    "dreamlike/v1_4/vae/fp16/length_77/untuned":"dl_vae_23dec_fp16",
+    "dreamlike/v1_4/vae/fp16/length_77/untuned/base":"dl_vaebase_23dec_fp16",
+    "dreamlike/v1_4/vae/fp32/length_77/untuned":"dl_vae_23dec_fp32",
+    "dreamlike/v1_4/vae/fp32/length_77/untuned/base":"dl_vaebase_23dec_fp32",
+    "dreamlike/v1_4/clip/fp32/length_77/untuned":"dl_clip_23dec_fp32_77"
+  }
+]

apps/stable_diffusion/src/utils/resources/opt_flags.json

@@ -0,0 +1,84 @@
+{
+  "unet": {
+    "tuned": {
+      "fp16": {
+        "default_compilation_flags": []
+      },
+      "fp32": {
+        "default_compilation_flags": []
+      }
+    },
+    "untuned": {
+      "fp16": {
+        "default_compilation_flags": [
+          "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-detach-elementwise-from-named-ops,iree-flow-convert-1x1-filter-conv2d-to-matmul,iree-preprocessing-convert-conv2d-to-img2col,iree-preprocessing-pad-linalg-ops{pad-size=32}))"
+        ]
+      },
+      "fp32": {
+        "default_compilation_flags": [
+          "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-detach-elementwise-from-named-ops,iree-flow-convert-1x1-filter-conv2d-to-matmul,iree-preprocessing-convert-conv2d-to-img2col,iree-preprocessing-pad-linalg-ops{pad-size=16}))"
+        ]
+      }
+    }
+  },
+  "vae": {
+    "tuned": {
+      "fp16": {
+        "default_compilation_flags": [],
+        "specified_compilation_flags": {
+          "cuda": [],
+          "default_device": [
+            "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-detach-elementwise-from-named-ops,iree-flow-convert-1x1-filter-conv2d-to-matmul,iree-preprocessing-convert-conv2d-to-img2col,iree-preprocessing-pad-linalg-ops{pad-size=32},iree-linalg-ext-convert-conv2d-to-winograd))"
+          ]
+        }
+      },
+      "fp32": {
+        "default_compilation_flags": [],
+        "specified_compilation_flags": {
+          "cuda": [],
+          "default_device": [
+            "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-detach-elementwise-from-named-ops,iree-flow-convert-1x1-filter-conv2d-to-matmul,iree-preprocessing-convert-conv2d-to-img2col,iree-preprocessing-pad-linalg-ops{pad-size=16},iree-linalg-ext-convert-conv2d-to-winograd))"
+          ]
+        }
+      }
+    },
+    "untuned": {
+      "fp16": {
+        "default_compilation_flags": [
+          "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-detach-elementwise-from-named-ops,iree-preprocessing-pad-linalg-ops{pad-size=32}))"
+        ]
+      },
+      "fp32": {
+        "default_compilation_flags": [
+          "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-detach-elementwise-from-named-ops,iree-preprocessing-pad-linalg-ops{pad-size=16}))"
+        ]
+      }
+    }
+  },
+  "clip": {
+    "tuned": {
+      "fp16": {
+        "default_compilation_flags": [
+          "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-preprocessing-pad-linalg-ops{pad-size=16}))"
+        ]
+      },
+      "fp32": {
+        "default_compilation_flags": [
+          "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-preprocessing-pad-linalg-ops{pad-size=16}))"
+        ]
+      }
+    },
+    "untuned": {
+      "fp16": {
+        "default_compilation_flags": [
+          "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-preprocessing-pad-linalg-ops{pad-size=16}))"
+        ]
+      },
+      "fp32": {
+        "default_compilation_flags": [
+          "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-preprocessing-pad-linalg-ops{pad-size=16}))"
+        ]
+      }
+    }
+  }
+}

apps/stable_diffusion/src/utils/resources/prompts.json

@@ -0,0 +1,8 @@
+[["A high tech solarpunk utopia in the Amazon rainforest"],
+["A pikachu fine dining with a view to the Eiffel Tower"],
+["A mecha robot in a favela in expressionist style"],
+["an insect robot preparing a delicious meal"],
+["A digital Illustration of the Babel tower, 4k, detailed, trending in artstation, fantasy vivid colors"],
+["Cluttered house in the woods, anime, oil painting, high resolution, cottagecore, ghibli inspired, 4k"],
+["A beautiful mansion beside a waterfall in the woods, by josef thoma, matte painting, trending on artstation HQ"],
+["portrait photo of a asia old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes"]]

apps/stable_diffusion/src/utils/sd_annotation.py

@@ -0,0 +1,227 @@
+import os
+import io
+from shark.model_annotation import model_annotation, create_context
+from shark.iree_utils._common import iree_target_map, run_cmd
+from shark.shark_downloader import (
+    download_model,
+    download_public_file,
+    WORKDIR,
+)
+from shark.parser import shark_args
+from apps.stable_diffusion.src.utils.stable_args import args
+
+
+def get_device():
+    device = (
+        args.device
+        if "://" not in args.device
+        else args.device.split("://")[0]
+    )
+    return device
+
+
+def get_device_args():
+    device = get_device()
+    device_spec_args = []
+    if device == "cuda":
+        from shark.iree_utils.gpu_utils import get_iree_gpu_args
+
+        gpu_flags = get_iree_gpu_args()
+        for flag in gpu_flags:
+            device_spec_args.append(flag)
+    elif device == "vulkan":
+        device_spec_args.append(
+            f"--iree-vulkan-target-triple={args.iree_vulkan_target_triple} "
+        )
+    return device, device_spec_args
+
+
+# Download the model (Unet or VAE fp16) from shark_tank
+def load_model_from_tank():
+    from apps.stable_diffusion.src.models import (
+        get_params,
+        get_variant_version,
+    )
+
+    variant, version = get_variant_version(args.hf_model_id)
+
+    shark_args.local_tank_cache = args.local_tank_cache
+    bucket_key = f"{variant}/untuned"
+    if args.annotation_model == "unet":
+        model_key = f"{variant}/{version}/unet/{args.precision}/length_{args.max_length}/untuned"
+    elif args.annotation_model == "vae":
+        is_base = "/base" if args.use_base_vae else ""
+        model_key = f"{variant}/{version}/vae/{args.precision}/length_77/untuned{is_base}"
+
+    bucket, model_name, iree_flags = get_params(
+        bucket_key, model_key, args.annotation_model, "untuned", args.precision
+    )
+    mlir_model, func_name, inputs, golden_out = download_model(
+        model_name,
+        tank_url=bucket,
+        frontend="torch",
+    )
+    return mlir_model, model_name
+
+
+# Download the tuned config files from shark_tank
+def load_winograd_configs():
+    device = get_device()
+    config_bucket = "gs://shark_tank/sd_tuned/configs/"
+    config_name = f"{args.annotation_model}_winograd_{device}.json"
+    full_gs_url = config_bucket + config_name
+    winograd_config_dir = f"{WORKDIR}configs/" + config_name
+    print("Loading Winograd config file from ", winograd_config_dir)
+    download_public_file(full_gs_url, winograd_config_dir, True)
+    return winograd_config_dir
+
+
+def load_lower_configs():
+    from apps.stable_diffusion.src.models import get_variant_version
+
+    variant, version = get_variant_version(args.hf_model_id)
+
+    config_bucket = "gs://shark_tank/sd_tuned_configs/"
+
+    device, device_spec_args = get_device_args()
+    spec = ""
+    if device_spec_args:
+        spec = device_spec_args[-1].split("=")[-1].strip()
+        if device == "vulkan":
+            spec = spec.split("-")[0]
+
+    if args.annotation_model == "vae":
+        if not spec or spec in ["rdna3", "sm_80"]:
+            config_name = (
+                f"{args.annotation_model}_{args.precision}_{device}.json"
+            )
+        else:
+            config_name = f"{args.annotation_model}_{args.precision}_{device}_{spec}.json"
+    else:
+        if not spec or spec in ["rdna3", "sm_80"]:
+            config_name = f"{args.annotation_model}_{version}_{args.precision}_{device}.json"
+        else:
+            config_name = f"{args.annotation_model}_{version}_{args.precision}_{device}_{spec}.json"
+
+    full_gs_url = config_bucket + config_name
+    lowering_config_dir = f"{WORKDIR}configs/" + config_name
+    print("Loading lowering config file from ", lowering_config_dir)
+    download_public_file(full_gs_url, lowering_config_dir, True)
+    return lowering_config_dir
+
+
+# Annotate the model with Winograd attribute on selected conv ops
+def annotate_with_winograd(input_mlir, winograd_config_dir, model_name):
+    with create_context() as ctx:
+        winograd_model = model_annotation(
+            ctx,
+            input_contents=input_mlir,
+            config_path=winograd_config_dir,
+            search_op="conv",
+            winograd=True,
+        )
+
+    bytecode_stream = io.BytesIO()
+    winograd_model.operation.write_bytecode(bytecode_stream)
+    bytecode = bytecode_stream.getvalue()
+
+    if args.save_annotation:
+        if model_name.split("_")[-1] != "tuned":
+            out_file_path = (
+                f"{args.annotation_output}/{model_name}_tuned_torch.mlir"
+            )
+        else:
+            out_file_path = f"{args.annotation_output}/{model_name}_torch.mlir"
+        with open(out_file_path, "w") as f:
+            f.write(str(winograd_model))
+            f.close()
+
+    return bytecode
+
+
+def dump_after_mlir(input_mlir, use_winograd):
+    import iree.compiler as ireec
+
+    device, device_spec_args = get_device_args()
+    if use_winograd:
+        preprocess_flag = "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-detach-elementwise-from-named-ops,iree-flow-convert-1x1-filter-conv2d-to-matmul,iree-preprocessing-convert-conv2d-to-img2col,iree-preprocessing-pad-linalg-ops{pad-size=32},iree-linalg-ext-convert-conv2d-to-winograd))"
+    else:
+        preprocess_flag = "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-detach-elementwise-from-named-ops,iree-flow-convert-1x1-filter-conv2d-to-matmul,iree-preprocessing-convert-conv2d-to-img2col,iree-preprocessing-pad-linalg-ops{pad-size=32}))"
+
+    dump_module = ireec.compile_str(
+        input_mlir,
+        target_backends=[iree_target_map(device)],
+        extra_args=device_spec_args
+        + [
+            preprocess_flag,
+            "--compile-to=preprocessing",
+        ],
+    )
+    return dump_module
+
+
+# For Unet annotate the model with tuned lowering configs
+def annotate_with_lower_configs(
+    input_mlir, lowering_config_dir, model_name, use_winograd
+):
+    # Dump IR after padding/img2col/winograd passes
+    dump_module = dump_after_mlir(input_mlir, use_winograd)
+    print("Applying tuned configs on", model_name)
+
+    # Annotate the model with lowering configs in the config file
+    with create_context() as ctx:
+        tuned_model = model_annotation(
+            ctx,
+            input_contents=dump_module,
+            config_path=lowering_config_dir,
+            search_op="all",
+        )
+
+    bytecode_stream = io.BytesIO()
+    tuned_model.operation.write_bytecode(bytecode_stream)
+    bytecode = bytecode_stream.getvalue()
+
+    if args.save_annotation:
+        if model_name.split("_")[-1] != "tuned":
+            out_file_path = (
+                f"{args.annotation_output}/{model_name}_tuned_torch.mlir"
+            )
+        else:
+            out_file_path = f"{args.annotation_output}/{model_name}_torch.mlir"
+        with open(out_file_path, "w") as f:
+            f.write(str(tuned_model))
+            f.close()
+
+    return bytecode
+
+
+def sd_model_annotation(mlir_model, model_name):
+    device = get_device()
+    if args.annotation_model == "unet" and device == "vulkan":
+        use_winograd = True
+        winograd_config_dir = load_winograd_configs()
+        winograd_model = annotate_with_winograd(
+            mlir_model, winograd_config_dir, model_name
+        )
+        lowering_config_dir = load_lower_configs()
+        tuned_model = annotate_with_lower_configs(
+            winograd_model, lowering_config_dir, model_name, use_winograd
+        )
+    elif args.annotation_model == "vae" and device == "vulkan":
+        use_winograd = True
+        winograd_config_dir = load_winograd_configs()
+        tuned_model = annotate_with_winograd(
+            mlir_model, winograd_config_dir, model_name
+        )
+    else:
+        use_winograd = False
+        lowering_config_dir = load_lower_configs()
+        tuned_model = annotate_with_lower_configs(
+            mlir_model, lowering_config_dir, model_name, use_winograd
+        )
+    return tuned_model
+
+
+if __name__ == "__main__":
+    mlir_model, model_name = load_model_from_tank()
+    sd_model_annotation(mlir_model, model_name)

apps/stable_diffusion/src/utils/stable_args.py

@@ -0,0 +1,363 @@
+import argparse
+from pathlib import Path
+
+
+def path_expand(s):
+    return Path(s).expanduser().resolve()
+
+
+p = argparse.ArgumentParser(
+    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+)
+
+##############################################################################
+### Stable Diffusion Params
+##############################################################################
+
+p.add_argument(
+    "-p",
+    "--prompts",
+    nargs="+",
+    default=["cyberpunk forest by Salvador Dali"],
+    help="text of which images to be generated.",
+)
+
+p.add_argument(
+    "--negative_prompts",
+    nargs="+",
+    default=["trees, green"],
+    help="text you don't want to see in the generated image.",
+)
+
+p.add_argument(
+    "--img_path",
+    type=str,
+    help="Path to the image input for img2img/inpainting",
+)
+
+p.add_argument(
+    "--mask_path",
+    type=str,
+    help="Path to the mask image input for inpainting",
+)
+
+p.add_argument(
+    "--steps",
+    type=int,
+    default=50,
+    help="the no. of steps to do the sampling.",
+)
+
+p.add_argument(
+    "--seed",
+    type=int,
+    default=-1,
+    help="the seed to use. -1 for a random one.",
+)
+
+p.add_argument(
+    "--batch_size",
+    type=int,
+    default=1,
+    choices=range(1, 4),
+    help="the number of inferences to be made in a single `batch_count`.",
+)
+
+p.add_argument(
+    "--height",
+    type=int,
+    default=512,
+    help="the height of the output image.",
+)
+
+p.add_argument(
+    "--width",
+    type=int,
+    default=512,
+    help="the width of the output image.",
+)
+
+p.add_argument(
+    "--guidance_scale",
+    type=float,
+    default=7.5,
+    help="the value to be used for guidance scaling.",
+)
+
+p.add_argument(
+    "--max_length",
+    type=int,
+    default=64,
+    help="max length of the tokenizer output, options are 64 and 77.",
+)
+
+p.add_argument(
+    "--strength",
+    type=float,
+    default=0.8,
+    help="the strength of change applied on the given input image for img2img",
+)
+##############################################################################
+### Model Config and Usage Params
+##############################################################################
+
+p.add_argument(
+    "--device", type=str, default="vulkan", help="device to run the model."
+)
+
+p.add_argument(
+    "--precision", type=str, default="fp16", help="precision to run the model."
+)
+
+p.add_argument(
+    "--import_mlir",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="imports the model from torch module to shark_module otherwise downloads the model from shark_tank.",
+)
+
+p.add_argument(
+    "--load_vmfb",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="attempts to load the model from a precompiled flatbuffer and compiles + saves it if not found.",
+)
+
+p.add_argument(
+    "--save_vmfb",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="saves the compiled flatbuffer to the local directory",
+)
+
+p.add_argument(
+    "--use_tuned",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="Download and use the tuned version of the model if available",
+)
+
+p.add_argument(
+    "--use_base_vae",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Do conversion from the VAE output to pixel space on cpu.",
+)
+
+p.add_argument(
+    "--scheduler",
+    type=str,
+    default="SharkEulerDiscrete",
+    help="other supported schedulers are [PNDM, DDIM, LMSDiscrete, EulerDiscrete, DPMSolverMultistep]",
+)
+
+p.add_argument(
+    "--output_img_format",
+    type=str,
+    default="png",
+    help="specify the format in which output image is save. Supported options: jpg / png",
+)
+
+p.add_argument(
+    "--output_dir",
+    type=str,
+    default=None,
+    help="Directory path to save the output images and json",
+)
+
+p.add_argument(
+    "--batch_count",
+    type=int,
+    default=1,
+    help="number of batch to be generated with random seeds in single execution",
+)
+
+p.add_argument(
+    "--ckpt_loc",
+    type=str,
+    default="",
+    help="Path to SD's .ckpt file.",
+)
+
+p.add_argument(
+    "--custom_vae",
+    type=str,
+    default="",
+    help="HuggingFace repo-id or path to SD model's checkpoint whose Vae needs to be plugged in.",
+)
+
+p.add_argument(
+    "--hf_model_id",
+    type=str,
+    default="stabilityai/stable-diffusion-2-1-base",
+    help="The repo-id of hugging face.",
+)
+
+##############################################################################
+### IREE - Vulkan supported flags
+##############################################################################
+
+p.add_argument(
+    "--iree_vulkan_target_triple",
+    type=str,
+    default="",
+    help="Specify target triple for vulkan",
+)
+
+p.add_argument(
+    "--vulkan_debug_utils",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Profiles vulkan device and collects the .rdc info",
+)
+
+p.add_argument(
+    "--vulkan_large_heap_block_size",
+    default="4147483648",
+    help="flag for setting VMA preferredLargeHeapBlockSize for vulkan device, default is 4G",
+)
+
+p.add_argument(
+    "--vulkan_validation_layers",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for disabling vulkan validation layers when benchmarking",
+)
+
+##############################################################################
+### Misc. Debug and Optimization flags
+##############################################################################
+
+p.add_argument(
+    "--use_compiled_scheduler",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="use the default scheduler precompiled into the model if available",
+)
+
+p.add_argument(
+    "--local_tank_cache",
+    default="",
+    help="Specify where to save downloaded shark_tank artifacts. If this is not set, the default is ~/.local/shark_tank/.",
+)
+
+p.add_argument(
+    "--dump_isa",
+    default=False,
+    action="store_true",
+    help="When enabled call amdllpc to get ISA dumps. use with dispatch benchmarks.",
+)
+
+p.add_argument(
+    "--dispatch_benchmarks",
+    default=None,
+    help='dispatches to return benchamrk data on.  use "All" for all, and None for none.',
+)
+
+p.add_argument(
+    "--dispatch_benchmarks_dir",
+    default="temp_dispatch_benchmarks",
+    help='directory where you want to store dispatch data generated with "--dispatch_benchmarks"',
+)
+
+p.add_argument(
+    "--enable_rgp",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for inserting debug frames between iterations for use with rgp.",
+)
+
+p.add_argument(
+    "--hide_steps",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="flag for hiding the details of iteration/sec for each step.",
+)
+
+p.add_argument(
+    "--warmup_count",
+    type=int,
+    default=0,
+    help="flag setting warmup count for clip and vae [>= 0].",
+)
+
+p.add_argument(
+    "--clear_all",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag to clear all mlir and vmfb from common locations. Recompiling will take several minutes",
+)
+
+p.add_argument(
+    "--save_metadata_to_json",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for whether or not to save a generation information json file with the image.",
+)
+
+p.add_argument(
+    "--write_metadata_to_png",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="flag for whether or not to save generation information in PNG chunk text to generated images.",
+)
+
+##############################################################################
+### Web UI flags
+##############################################################################
+
+p.add_argument(
+    "--progress_bar",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="flag for removing the progress bar animation during image generation",
+)
+
+p.add_argument(
+    "--ckpt_dir",
+    type=str,
+    default="",
+    help="Path to directory where all .ckpts are stored in order to populate them in the web UI",
+)
+
+
+p.add_argument(
+    "--share",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for generating a public URL",
+)
+
+p.add_argument(
+    "--server_port",
+    type=int,
+    default=8080,
+    help="flag for setting server port",
+)
+
+##############################################################################
+### SD model auto-annotation flags
+##############################################################################
+
+p.add_argument(
+    "--annotation_output",
+    type=path_expand,
+    default="./",
+    help="Directory to save the annotated mlir file",
+)
+
+p.add_argument(
+    "--annotation_model",
+    type=str,
+    default="unet",
+    help="Options are unet and vae.",
+)
+
+p.add_argument(
+    "--save_annotation",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Save annotated mlir file",
+)
+
+args, unknown = p.parse_known_args()

apps/stable_diffusion/src/utils/utils.py

@@ -0,0 +1,601 @@
+import os
+import gc
+import json
+import re
+from PIL import PngImagePlugin
+from datetime import datetime as dt
+from csv import DictWriter
+from pathlib import Path
+import numpy as np
+from random import randint
+from shark.shark_inference import SharkInference
+from shark.shark_importer import import_with_fx
+from shark.iree_utils.vulkan_utils import (
+    set_iree_vulkan_runtime_flags,
+    get_vulkan_target_triple,
+)
+from shark.iree_utils.gpu_utils import get_cuda_sm_cc
+from apps.stable_diffusion.src.utils.stable_args import args
+from apps.stable_diffusion.src.utils.resources import opt_flags
+from apps.stable_diffusion.src.utils.sd_annotation import sd_model_annotation
+import sys
+from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
+    load_pipeline_from_original_stable_diffusion_ckpt,
+)
+
+
+def get_extended_name(model_name):
+    device = (
+        args.device
+        if "://" not in args.device
+        else "-".join(args.device.split("://"))
+    )
+    extended_name = "{}_{}".format(model_name, device)
+    return extended_name
+
+
+def get_vmfb_path_name(model_name):
+    vmfb_path = os.path.join(os.getcwd(), model_name + ".vmfb")
+    return vmfb_path
+
+
+def _compile_module(shark_module, model_name, extra_args=[]):
+    if args.load_vmfb or args.save_vmfb:
+        vmfb_path = get_vmfb_path_name(model_name)
+        if args.load_vmfb and os.path.isfile(vmfb_path) and not args.save_vmfb:
+            print(f"loading existing vmfb from: {vmfb_path}")
+            shark_module.load_module(vmfb_path, extra_args=extra_args)
+        else:
+            if args.save_vmfb:
+                print("Saving to {}".format(vmfb_path))
+            else:
+                print(
+                    "No vmfb found. Compiling and saving to {}".format(
+                        vmfb_path
+                    )
+                )
+            path = shark_module.save_module(
+                os.getcwd(), model_name, extra_args
+            )
+            shark_module.load_module(path, extra_args=extra_args)
+    else:
+        shark_module.compile(extra_args)
+    return shark_module
+
+
+# Downloads the model from shark_tank and returns the shark_module.
+def get_shark_model(tank_url, model_name, extra_args=[]):
+    from shark.parser import shark_args
+
+    # Set local shark_tank cache directory.
+    shark_args.local_tank_cache = args.local_tank_cache
+
+    from shark.shark_downloader import download_model
+
+    if "cuda" in args.device:
+        shark_args.enable_tf32 = True
+
+    mlir_model, func_name, inputs, golden_out = download_model(
+        model_name,
+        tank_url=tank_url,
+        frontend="torch",
+    )
+    shark_module = SharkInference(
+        mlir_model, device=args.device, mlir_dialect="linalg"
+    )
+    return _compile_module(shark_module, model_name, extra_args)
+
+
+# Converts the torch-module into a shark_module.
+def compile_through_fx(
+    model,
+    inputs,
+    model_name,
+    is_f16=False,
+    f16_input_mask=None,
+    use_tuned=False,
+    extra_args=[],
+):
+    from shark.parser import shark_args
+
+    if "cuda" in args.device:
+        shark_args.enable_tf32 = True
+
+    mlir_module, func_name = import_with_fx(
+        model, inputs, is_f16, f16_input_mask
+    )
+
+    if use_tuned:
+        if "vae" in model_name.split("_")[0]:
+            args.annotation_model = "vae"
+        mlir_module = sd_model_annotation(mlir_module, model_name)
+
+    shark_module = SharkInference(
+        mlir_module,
+        device=args.device,
+        mlir_dialect="linalg",
+    )
+
+    del mlir_module
+    gc.collect()
+
+    return _compile_module(shark_module, model_name, extra_args)
+
+
+def set_iree_runtime_flags():
+    vulkan_runtime_flags = [
+        f"--vulkan_large_heap_block_size={args.vulkan_large_heap_block_size}",
+        f"--vulkan_validation_layers={'true' if args.vulkan_validation_layers else 'false'}",
+    ]
+    if args.enable_rgp:
+        vulkan_runtime_flags += [
+            f"--enable_rgp=true",
+            f"--vulkan_debug_utils=true",
+        ]
+    set_iree_vulkan_runtime_flags(flags=vulkan_runtime_flags)
+
+
+def get_all_devices(driver_name):
+    """
+    Inputs: driver_name
+    Returns a list of all the available devices for a given driver sorted by
+    the iree path names of the device as in --list_devices option in iree.
+    """
+    from iree.runtime import get_driver
+
+    driver = get_driver(driver_name)
+    device_list_src = driver.query_available_devices()
+    device_list_src.sort(key=lambda d: d["path"])
+    return device_list_src
+
+
+def get_device_mapping(driver, key_combination=3):
+    """This method ensures consistent device ordering when choosing
+    specific devices for execution
+    Args:
+        driver (str): execution driver (vulkan, cuda, rocm, etc)
+        key_combination (int, optional): choice for mapping value for device name.
+        1 : path
+        2 : name
+        3 : (name, path)
+        Defaults to 3.
+    Returns:
+        dict: map to possible device names user can input mapped to desired combination of name/path.
+    """
+    from shark.iree_utils._common import iree_device_map
+
+    driver = iree_device_map(driver)
+    device_list = get_all_devices(driver)
+    device_map = dict()
+
+    def get_output_value(dev_dict):
+        if key_combination == 1:
+            return f"{driver}://{dev_dict['path']}"
+        if key_combination == 2:
+            return dev_dict["name"]
+        if key_combination == 3:
+            return (dev_dict["name"], f"{driver}://{dev_dict['path']}")
+
+    # mapping driver name to default device (driver://0)
+    device_map[f"{driver}"] = get_output_value(device_list[0])
+    for i, device in enumerate(device_list):
+        # mapping with index
+        device_map[f"{driver}://{i}"] = get_output_value(device)
+        # mapping with full path
+        device_map[f"{driver}://{device['path']}"] = get_output_value(device)
+    return device_map
+
+
+def map_device_to_name_path(device, key_combination=3):
+    """Gives the appropriate device data (supported name/path) for user selected execution device
+    Args:
+        device (str): user
+        key_combination (int, optional): choice for mapping value for device name.
+        1 : path
+        2 : name
+        3 : (name, path)
+        Defaults to 3.
+    Raises:
+        ValueError:
+    Returns:
+        str / tuple: returns the mapping str or tuple of mapping str for the device depending on key_combination value
+    """
+    driver = device.split("://")[0]
+    device_map = get_device_mapping(driver, key_combination)
+    try:
+        device_mapping = device_map[device]
+    except KeyError:
+        raise ValueError(f"Device '{device}' is not a valid device.")
+    return device_mapping
+
+
+def set_init_device_flags():
+    if "vulkan" in args.device:
+        # set runtime flags for vulkan.
+        set_iree_runtime_flags()
+
+        # set triple flag to avoid multiple calls to get_vulkan_triple_flag
+        device_name, args.device = map_device_to_name_path(args.device)
+        if not args.iree_vulkan_target_triple:
+            triple = get_vulkan_target_triple(device_name)
+            if triple is not None:
+                args.iree_vulkan_target_triple = triple
+        print(
+            f"Found device {device_name}. Using target triple {args.iree_vulkan_target_triple}."
+        )
+    elif "cuda" in args.device:
+        args.device = "cuda"
+    elif "cpu" in args.device:
+        args.device = "cpu"
+
+    # set max_length based on availability.
+    if args.hf_model_id in [
+        "Linaqruf/anything-v3.0",
+        "wavymulder/Analog-Diffusion",
+        "dreamlike-art/dreamlike-diffusion-1.0",
+    ]:
+        args.max_length = 77
+    elif args.hf_model_id == "prompthero/openjourney":
+        args.max_length = 64
+
+    # Use tuned models in the case of fp16, vulkan rdna3 or cuda sm devices.
+    if (
+        args.hf_model_id
+        in [
+            "runwayml/stable-diffusion-inpainting",
+            "stabilityai/stable-diffusion-2-inpainting",
+        ]
+        or args.ckpt_loc != ""
+        or args.precision != "fp16"
+        or args.height != 512
+        or args.width != 512
+        or args.batch_size != 1
+        or ("vulkan" not in args.device and "cuda" not in args.device)
+    ):
+        args.use_tuned = False
+
+    elif "vulkan" in args.device and not any(
+        x in args.iree_vulkan_target_triple for x in ["rdna2", "rdna3"]
+    ):
+        args.use_tuned = False
+
+    elif "cuda" in args.device and get_cuda_sm_cc() not in ["sm_80", "sm_89"]:
+        args.use_tuned = False
+
+    elif args.use_base_vae and args.hf_model_id not in [
+        "stabilityai/stable-diffusion-2-1-base",
+        "CompVis/stable-diffusion-v1-4",
+    ]:
+        args.use_tuned = False
+
+    if args.use_tuned:
+        print(f"Using tuned models for {args.hf_model_id}/fp16/{args.device}.")
+    else:
+        print("Tuned models are currently not supported for this setting.")
+
+    # set import_mlir to True for unuploaded models.
+    if args.ckpt_loc != "":
+        args.import_mlir = True
+
+    elif args.hf_model_id not in [
+        "Linaqruf/anything-v3.0",
+        "dreamlike-art/dreamlike-diffusion-1.0",
+        "prompthero/openjourney",
+        "wavymulder/Analog-Diffusion",
+        "stabilityai/stable-diffusion-2-1",
+        "stabilityai/stable-diffusion-2-1-base",
+        "CompVis/stable-diffusion-v1-4",
+        "runwayml/stable-diffusion-inpainting",
+        "stabilityai/stable-diffusion-2-inpainting",
+    ]:
+        args.import_mlir = True
+
+    elif args.height != 512 or args.width != 512 or args.batch_size != 1:
+        args.import_mlir = True
+
+    elif args.use_tuned and args.hf_model_id in [
+        "dreamlike-art/dreamlike-diffusion-1.0",
+        "prompthero/openjourney",
+        "stabilityai/stable-diffusion-2-1",
+    ]:
+        args.import_mlir = True
+
+    elif (
+        args.use_tuned
+        and "vulkan" in args.device
+        and "rdna2" in args.iree_vulkan_target_triple
+    ):
+        args.import_mlir = True
+
+    elif (
+        args.use_tuned
+        and "cuda" in args.device
+        and get_cuda_sm_cc() == "sm_89"
+    ):
+        args.import_mlir = True
+
+
+# Utility to get list of devices available.
+def get_available_devices():
+    def get_devices_by_name(driver_name):
+        from shark.iree_utils._common import iree_device_map
+
+        device_list = []
+        try:
+            driver_name = iree_device_map(driver_name)
+            device_list_dict = get_all_devices(driver_name)
+            print(f"{driver_name} devices are available.")
+        except:
+            print(f"{driver_name} devices are not available.")
+        else:
+            for i, device in enumerate(device_list_dict):
+                device_list.append(f"{device['name']} => {driver_name}://{i}")
+        return device_list
+
+    set_iree_runtime_flags()
+
+    available_devices = []
+    vulkan_devices = get_devices_by_name("vulkan")
+    available_devices.extend(vulkan_devices)
+    cuda_devices = get_devices_by_name("cuda")
+    available_devices.extend(cuda_devices)
+    available_devices.append("cpu")
+    return available_devices
+
+
+def disk_space_check(path, lim=20):
+    from shutil import disk_usage
+
+    du = disk_usage(path)
+    free = du.free / (1024 * 1024 * 1024)
+    if free <= lim:
+        print(f"[WARNING] Only {free:.2f}GB space available in {path}.")
+
+
+def get_opt_flags(model, precision="fp16"):
+    iree_flags = []
+    is_tuned = "tuned" if args.use_tuned else "untuned"
+    if len(args.iree_vulkan_target_triple) > 0:
+        iree_flags.append(
+            f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
+        )
+
+    # Disable bindings fusion to work with moltenVK.
+    if sys.platform == "darwin":
+        iree_flags.append("-iree-stream-fuse-binding=false")
+
+    if "default_compilation_flags" in opt_flags[model][is_tuned][precision]:
+        iree_flags += opt_flags[model][is_tuned][precision][
+            "default_compilation_flags"
+        ]
+
+    if "specified_compilation_flags" in opt_flags[model][is_tuned][precision]:
+        device = (
+            args.device
+            if "://" not in args.device
+            else args.device.split("://")[0]
+        )
+        if (
+            device
+            not in opt_flags[model][is_tuned][precision][
+                "specified_compilation_flags"
+            ]
+        ):
+            device = "default_device"
+        iree_flags += opt_flags[model][is_tuned][precision][
+            "specified_compilation_flags"
+        ][device]
+    return iree_flags
+
+
+def get_path_stem(path):
+    path = Path(path)
+    return path.stem
+
+
+def get_path_to_diffusers_checkpoint(custom_weights):
+    path = Path(custom_weights)
+    diffusers_path = path.parent.absolute()
+    diffusers_directory_name = path.stem
+    complete_path_to_diffusers = diffusers_path / diffusers_directory_name
+    complete_path_to_diffusers.mkdir(parents=True, exist_ok=True)
+    path_to_diffusers = complete_path_to_diffusers.as_posix()
+    return path_to_diffusers
+
+
+def preprocessCKPT(custom_weights):
+    path_to_diffusers = get_path_to_diffusers_checkpoint(custom_weights)
+    if next(Path(path_to_diffusers).iterdir(), None):
+        print("Checkpoint already loaded at : ", path_to_diffusers)
+        return
+    else:
+        print(
+            "Diffusers' checkpoint will be identified here : ",
+            path_to_diffusers,
+        )
+    from_safetensors = (
+        True if custom_weights.lower().endswith(".safetensors") else False
+    )
+    # EMA weights usually yield higher quality images for inference but non-EMA weights have
+    # been yielding better results in our case.
+    # TODO: Add an option `--ema` (`--no-ema`) for users to specify if they want to go for EMA
+    #       weight extraction or not.
+    extract_ema = False
+    print(
+        "Loading diffusers' pipeline from original stable diffusion checkpoint"
+    )
+    pipe = load_pipeline_from_original_stable_diffusion_ckpt(
+        checkpoint_path=custom_weights,
+        extract_ema=extract_ema,
+        from_safetensors=from_safetensors,
+    )
+    pipe.save_pretrained(path_to_diffusers)
+    print("Loading complete")
+
+
+def load_vmfb(vmfb_path, model, precision):
+    model = "vae" if "base_vae" in model or "vae_encode" in model else model
+    precision = "fp32" if "clip" in model else precision
+    extra_args = get_opt_flags(model, precision)
+    shark_module = SharkInference(mlir_module=None, device=args.device)
+    shark_module.load_module(vmfb_path, extra_args=extra_args)
+    return shark_module
+
+
+# This utility returns vmfbs of Clip, Unet, Vae and Vae_encode, in case all of them
+# are present; deletes them otherwise.
+def fetch_or_delete_vmfbs(
+    extended_model_name, need_vae_encode, precision="fp32"
+):
+    vmfb_path = [
+        get_vmfb_path_name(extended_model_name[model])
+        for model in extended_model_name
+    ]
+    vmfb_present = [os.path.isfile(vmfb) for vmfb in vmfb_path]
+    all_vmfb_present = True
+    compiled_models = []
+    for i in range(3):
+        all_vmfb_present = all_vmfb_present and vmfb_present[i]
+        compiled_models.append(None)
+    if need_vae_encode:
+        all_vmfb_present = all_vmfb_present and vmfb_present[3]
+        compiled_models.append(None)
+
+    # We need to delete vmfbs only if some of the models were compiled.
+    if not all_vmfb_present:
+        for i in range(len(compiled_models)):
+            if vmfb_present[i]:
+                os.remove(vmfb_path[i])
+                print("Deleted: ", vmfb_path[i])
+    else:
+        model_name = [model for model in extended_model_name.keys()]
+        for i in range(len(compiled_models)):
+            compiled_models[i] = load_vmfb(
+                vmfb_path[i], model_name[i], precision
+            )
+    return compiled_models
+
+
+# `fetch_and_update_base_model_id` is a resource utility function which
+# helps maintaining mapping of the model to run with its base model.
+# If `base_model` is "", then this function tries to fetch the base model
+# info for the `model_to_run`.
+def fetch_and_update_base_model_id(model_to_run, base_model=""):
+    variants_path = os.path.join(os.getcwd(), "variants.json")
+    data = {model_to_run: base_model}
+    json_data = {}
+    if os.path.exists(variants_path):
+        with open(variants_path, "r", encoding="utf-8") as jsonFile:
+            json_data = json.load(jsonFile)
+            # Return with base_model's info if base_model is "".
+            if base_model == "":
+                if model_to_run in json_data:
+                    base_model = json_data[model_to_run]
+                return base_model
+    elif base_model == "":
+        return base_model
+    # Update JSON data to contain an entry mapping model_to_run with base_model.
+    json_data.update(data)
+    with open(variants_path, "w", encoding="utf-8") as jsonFile:
+        json.dump(json_data, jsonFile)
+
+
+# Generate and return a new seed if the provided one is not in the supported range (including -1)
+def sanitize_seed(seed):
+    uint32_info = np.iinfo(np.uint32)
+    uint32_min, uint32_max = uint32_info.min, uint32_info.max
+    if seed < uint32_min or seed >= uint32_max:
+        seed = randint(uint32_min, uint32_max)
+    return seed
+
+
+# clear all the cached objects to recompile cleanly.
+def clear_all():
+    print("CLEARING ALL, EXPECT SEVERAL MINUTES TO RECOMPILE")
+    from glob import glob
+    import shutil
+
+    vmfbs = glob(os.path.join(os.getcwd(), "*.vmfb"))
+    for vmfb in vmfbs:
+        if os.path.exists(vmfb):
+            os.remove(vmfb)
+    # Temporary workaround of deleting yaml files to incorporate diffusers' pipeline.
+    # TODO: Remove this once we have better weight updation logic.
+    inference_yaml = ["v2-inference-v.yaml", "v1-inference.yaml"]
+    for yaml in inference_yaml:
+        if os.path.exists(yaml):
+            os.remove(yaml)
+    home = os.path.expanduser("~")
+    if os.name == "nt":  # Windows
+        appdata = os.getenv("LOCALAPPDATA")
+        shutil.rmtree(os.path.join(appdata, "AMD/VkCache"), ignore_errors=True)
+        shutil.rmtree(os.path.join(home, "shark_tank"), ignore_errors=True)
+    elif os.name == "unix":
+        shutil.rmtree(os.path.join(home, ".cache/AMD/VkCache"))
+        shutil.rmtree(os.path.join(home, ".local/shark_tank"))
+
+
+# save output images and the inputs corresponding to it.
+def save_output_img(output_img, img_seed):
+    output_path = args.output_dir if args.output_dir else Path.cwd()
+    generated_imgs_path = Path(
+        output_path, "generated_imgs", dt.now().strftime("%Y%m%d")
+    )
+    generated_imgs_path.mkdir(parents=True, exist_ok=True)
+    csv_path = Path(generated_imgs_path, "imgs_details.csv")
+
+    prompt_slice = re.sub("[^a-zA-Z0-9]", "_", args.prompts[0][:15])
+    out_img_name = (
+        f"{prompt_slice}_{img_seed}_{dt.now().strftime('%y%m%d_%H%M%S')}"
+    )
+
+    img_model = args.hf_model_id
+    if args.ckpt_loc:
+        img_model = os.path.basename(args.ckpt_loc)
+
+    if args.output_img_format == "jpg":
+        out_img_path = Path(generated_imgs_path, f"{out_img_name}.jpg")
+        output_img.save(out_img_path, quality=95, subsampling=0)
+    else:
+        out_img_path = Path(generated_imgs_path, f"{out_img_name}.png")
+        pngInfo = PngImagePlugin.PngInfo()
+
+        if args.write_metadata_to_png:
+            pngInfo.add_text(
+                "parameters",
+                f"{args.prompts[0]}\nNegative prompt: {args.negative_prompts[0]}\nSteps:{args.steps}, Sampler: {args.scheduler}, CFG scale: {args.guidance_scale}, Seed: {img_seed}, Size: {args.width}x{args.height}, Model: {img_model}",
+            )
+
+        output_img.save(out_img_path, "PNG", pnginfo=pngInfo)
+
+        if args.output_img_format not in ["png", "jpg"]:
+            print(
+                f"[ERROR] Format {args.output_img_format} is not supported yet."
+                "Image saved as png instead. Supported formats: png / jpg"
+            )
+
+    new_entry = {
+        "VARIANT": img_model,
+        "SCHEDULER": args.scheduler,
+        "PROMPT": args.prompts[0],
+        "NEG_PROMPT": args.negative_prompts[0],
+        "SEED": img_seed,
+        "CFG_SCALE": args.guidance_scale,
+        "PRECISION": args.precision,
+        "STEPS": args.steps,
+        "HEIGHT": args.height,
+        "WIDTH": args.width,
+        "MAX_LENGTH": args.max_length,
+        "OUTPUT": out_img_path,
+    }
+
+    with open(csv_path, "a") as csv_obj:
+        dictwriter_obj = DictWriter(csv_obj, fieldnames=list(new_entry.keys()))
+        dictwriter_obj.writerow(new_entry)
+        csv_obj.close()
+
+    if args.save_metadata_to_json:
+        del new_entry["OUTPUT"]
+        json_path = Path(generated_imgs_path, f"{out_img_name}.json")
+        with open(json_path, "w") as f:
+            json.dump(new_entry, f, indent=4)

apps/stable_diffusion/stable_diffusion_amd.md

@@ -0,0 +1,58 @@
+# Stable Diffusion optimized for AMD RDNA2/RDNA3 GPUs
+
+Before you start, please be aware that this is beta software that relies on a special AMD driver. Like all StableDiffusion GUIs published so far, you need some technical expertise to set it up. We apologize in advance if you bump into issues. If that happens, please don't hesitate to ask our Discord community for help! Please be assured that we (Nod and AMD) are working hard to improve the user experience in coming months.
+If it works well for you, please "star" the following GitHub projects... this is one of the best ways to help and spread the word!
+
+* https://github.com/nod-ai/SHARK
+* https://github.com/iree-org/iree
+
+## Install this latest AMD Drivers
+
+### AMD KB Drivers for RDNA2 and RDNA3:
+
+*AMD Software: (Adrenalin Edition 23.2.1) [https://www.amd.com/en/support/kb/release-notes/rn-rad-win-23-2-1] 
+
+## Installation
+
+Download the latest Windows SHARK SD binary [530 here](https://github.com/nod-ai/SHARK/releases/download/20230214.530/shark_sd_20230214_530.exe) in a folder of your choice. If you want nighly builds, you can look for them on the GitHub releases page.
+
+Notes:
+* We recommend that you download this EXE in a new folder, whenever you download a new EXE version. If you download it in the same folder as a previous install, you must delete the old `*.vmfb` files. Those contain Vulkan dispatches compiled from MLIR which can be outdated if you run a new EXE from the same folder. You can use `--clear_all` flag once to clean all the old files. 
+* If you recently updated the driver or this binary (EXE file), we recommend you clear all the local artifacts with `--clear_all` OR 
+
+## Running
+
+* Open a Command Prompt or Powershell terminal, change folder (`cd`) to the .exe folder. Then run the EXE from the command prompt. That way, if an error occurs, you'll be able to cut-and-paste it to ask for help. (if it always works for you without error, you may simply double-click the EXE to start the web browser)
+* The first run may take few minutes when the models are downloaded and compiled. Your patience is appreciated. The download could be about 5GB.
+* If successful, you will likely see a Windows Defender message asking you to give permission to open a web server port. Accept it.
+* Open a browser to access the Stable Diffusion web server. By default, the port is 8080, so you can go to http://localhost:8080/.
+
+## Stopping
+
+* Select the command prompt that's running the EXE. Press CTRL-C and wait a moment. The application should stop. 
+* Please make sure to do the above step before you attempt to update the EXE to a new version.
+
+# Results
+
+<img width="1607" alt="webui" src="https://user-images.githubusercontent.com/74956/204939260-b8308bc2-8dc4-47f6-9ac0-f60b66edab99.png">
+
+
+Here are some samples generated:
+
+![tajmahal, snow, sunflowers, oil on canvas_0](https://user-images.githubusercontent.com/74956/204934186-141f7e43-6eb2-4e89-a99c-4704d20444b3.jpg)
+
+![a photo of a crab playing a trumpet](https://user-images.githubusercontent.com/74956/204933258-252e7240-8548-45f7-8253-97647d38313d.jpg)
+
+
+The output on a 7900XTX would like:
+
+```shell 
+Stats for run 0:
+Average step time: 47.19188690185547ms/it
+Clip Inference time (ms) = 109.531
+VAE Inference time (ms): 78.590
+
+Total image generation time: 2.5788655281066895sec
+```
+
+Find us on [SHARK Discord server](https://discord.gg/RUqY2h2s9u) if you have any trouble with running it on your hardware. 

apps/stable_diffusion/stable_diffusion_telegram_bot.md

@@ -0,0 +1,15 @@
+You need to pre-create your bot (https://core.telegram.org/bots#how-do-i-create-a-bot)
+Then create in the directory web file .env
+In it the record:
+TG_TOKEN="your_token"
+specifying your bot's token from previous step.
+Then run telegram_bot.py with the same parameters that you use when running index.py, for example:
+python telegram_bot.py --max_length=77 --vulkan_large_heap_block_size=0 --use_base_vae --local_tank_cache h:\shark\TEMP
+
+Bot commands:
+/select_model
+/select_scheduler
+/set_steps "integer number of steps"
+/set_guidance_scale "integer number"
+/set_negative_prompt "negative text"
+Any other text triggers the creation of an image based on it.

apps/stable_diffusion/web/index.py

@@ -0,0 +1,44 @@
+import os
+import sys
+
+if sys.platform == "darwin":
+    os.environ["DYLD_LIBRARY_PATH"] = "/usr/local/lib"
+
+import gradio as gr
+from apps.stable_diffusion.src import args, clear_all
+from apps.stable_diffusion.web.utils.gradio_configs import (
+    clear_gradio_tmp_imgs_folder,
+)
+
+# clear all gradio tmp images from the last session
+clear_gradio_tmp_imgs_folder()
+
+if args.clear_all:
+    clear_all()
+
+
+def resource_path(relative_path):
+    """Get absolute path to resource, works for dev and for PyInstaller"""
+    base_path = getattr(
+        sys, "_MEIPASS", os.path.dirname(os.path.abspath(__file__))
+    )
+    return os.path.join(base_path, relative_path)
+
+
+dark_theme = resource_path("ui/css/sd_dark_theme.css")
+
+from apps.stable_diffusion.web.ui import txt2img_web, img2img_web
+
+sd_web = gr.TabbedInterface(
+    [txt2img_web, img2img_web],
+    ["Text-to-Image", "Image-to-Image"],
+    css=dark_theme,
+)
+
+sd_web.queue()
+sd_web.launch(
+    share=args.share,
+    inbrowser=True,
+    server_name="0.0.0.0",
+    server_port=args.server_port,
+)

apps/stable_diffusion/web/ui/__init__.py

@@ -0,0 +1,2 @@
+from apps.stable_diffusion.web.ui.txt2img_ui import txt2img_web
+from apps.stable_diffusion.web.ui.img2img_ui import img2img_web

apps/stable_diffusion/web/ui/css/sd_dark_theme.css

@@ -0,0 +1,209 @@
+
+/* Overwrite the Gradio default theme with their .dark theme declarations */
+
+:root {
+    --color-focus-primary: var(--color-grey-700);
+    --color-focus-secondary: var(--color-grey-600);
+    --color-focus-ring: rgb(55 65 81);
+    --color-background-primary: var(--color-grey-950);
+    --color-background-secondary: var(--color-grey-900);
+    --color-background-tertiary: var(--color-grey-800);
+    --color-text-body: var(--color-grey-100);
+    --color-text-label: var(--color-grey-200);
+    --color-text-placeholder: var(--color-grey);
+    --color-text-subdued: var(--color-grey-400);
+    --color-text-link-base: var(--color-blue-500);
+    --color-text-link-hover: var(--color-blue-400);
+    --color-text-link-visited: var(--color-blue-600);
+    --color-text-link-active: var(--color-blue-500);
+    --color-text-code-background: var(--color-grey-800);
+    --color-text-code-border: color.border-primary;
+    --color-border-primary: var(--color-grey-700);
+    --color-border-secondary: var(--color-grey-600);
+    --color-border-highlight: var(--color-accent-base);
+    --color-accent-base: var(--color-orange-500);
+    --color-accent-light: var(--color-orange-300);
+    --color-accent-dark: var(--color-orange-700);
+    --color-functional-error-base: var(--color-red-400);
+    --color-functional-error-subdued: var(--color-red-300);
+    --color-functional-error-background: var(--color-background-primary);
+    --color-functional-info-base: var(--color-yellow);
+    --color-functional-info-subdued: var(--color-yellow-300);
+    --color-functional-success-base: var(--color-green);
+    --color-functional-success-subdued: var(--color-green-300);
+    --shadow-spread: 2px;
+    --api-background: linear-gradient(to bottom, rgba(255, 216, 180, .05), transparent);
+    --api-pill-background: var(--color-orange-400);
+    --api-pill-border: var(--color-orange-600);
+    --api-pill-text: var(--color-orange-900);
+    --block-border-color: var(--color-border-primary);
+    --block-background: var(--color-background-tertiary);
+    --uploadable-border-color-hover: var(--color-border-primary);
+    --uploadable-border-color-loaded: var(--color-functional-success);
+    --uploadable-text-color: var(--color-text-subdued);
+    --block_label-border-color: var(--color-border-primary);
+    --block_label-icon-color: var(--color-text-label);
+    --block_label-shadow: var(--shadow-drop);
+    --block_label-background: var(--color-background-secondary);
+    --icon_button-icon-color-base: var(--color-text-label);
+    --icon_button-icon-color-hover: var(--color-text-label);
+    --icon_button-background-base: var(--color-background-primary);
+    --icon_button-background-hover: var(--color-background-primary);
+    --icon_button-border-color-base: var(--color-background-primary);
+    --icon_button-border-color-hover: var(--color-border-secondary);
+    --input-text-color: var(--color-text-body);
+    --input-border-color-base: var(--color-border-primary);
+    --input-border-color-hover: var(--color-border-primary);
+    --input-border-color-focus: var(--color-border-primary);
+    --input-background-base: var(--color-background-tertiary);
+    --input-background-hover: var(--color-background-tertiary);
+    --input-background-focus: var(--color-background-tertiary);
+    --input-shadow: var(--shadow-inset);
+    --checkbox-border-color-base: var(--color-border-primary);
+    --checkbox-border-color-hover: var(--color-focus-primary);
+    --checkbox-border-color-focus: var(--color-blue-500);
+    --checkbox-background-base: var(--color-background-primary);
+    --checkbox-background-hover: var(--color-background-primary);
+    --checkbox-background-focus: var(--color-background-primary);
+    --checkbox-background-selected: var(--color-blue-600);
+    --checkbox-label-border-color-base: var(--color-border-primary);
+    --checkbox-label-border-color-hover: var(--color-border-primary);
+    --checkbox-label-border-color-focus: var(--color-border-secondary);
+    --checkbox-label-background-base: linear-gradient(to top, var(--color-grey-900), var(--color-grey-800));
+    --checkbox-label-background-hover: linear-gradient(to top, var(--color-grey-900), var(--color-grey-800));
+    --checkbox-label-background-focus: linear-gradient(to top, var(--color-grey-900), var(--color-grey-800));
+    --form-seperator-color: var(--color-border-primary);
+    --button-primary-border-color-base: var(--color-orange-600);
+    --button-primary-border-color-hover: var(--color-orange-600);
+    --button-primary-border-color-focus: var(--color-orange-600);
+    --button-primary-text-color-base: white;
+    --button-primary-text-color-hover: white;
+    --button-primary-text-color-focus: white;
+    --button-primary-background-base: linear-gradient(to bottom right, var(--color-orange-700), var(--color-orange-700));
+    --button-primary-background-hover: linear-gradient(to bottom right, var(--color-orange-700), var(--color-orange-500));
+    --button-primary-background-focus: linear-gradient(to bottom right, var(--color-orange-700), var(--color-orange-500));
+    --button-secondary-border-color-base: var(--color-grey-600);
+    --button-secondary-border-color-hover: var(--color-grey-600);
+    --button-secondary-border-color-focus: var(--color-grey-600);
+    --button-secondary-text-color-base: white;
+    --button-secondary-text-color-hover: white;
+    --button-secondary-text-color-focus: white;
+    --button-secondary-background-base: linear-gradient(to bottom right, var(--color-grey-600), var(--color-grey-700));
+    --button-secondary-background-hover: linear-gradient(to bottom right, var(--color-grey-600), var(--color-grey-600));
+    --button-secondary-background-focus: linear-gradient(to bottom right, var(--color-grey-600), var(--color-grey-600));
+    --button-cancel-border-color-base: var(--color-red-600);
+    --button-cancel-border-color-hover: var(--color-red-600);
+    --button-cancel-border-color-focus: var(--color-red-600);
+    --button-cancel-text-color-base: white;
+    --button-cancel-text-color-hover: white;
+    --button-cancel-text-color-focus: white;
+    --button-cancel-background-base: linear-gradient(to bottom right, var(--color-red-700), var(--color-red-700));
+    --button-cancel-background-focus: linear-gradient(to bottom right, var(--color-red-700), var(--color-red-500));
+    --button-cancel-background-hover: linear-gradient(to bottom right, var(--color-red-700), var(--color-red-500));
+    --button-plain-border-color-base: var(--color-grey-600);
+    --button-plain-border-color-hover: var(--color-grey-500);
+    --button-plain-border-color-focus: var(--color-grey-500);
+    --button-plain-text-color-base: var(--color-text-body);
+    --button-plain-text-color-hover: var(--color-text-body);
+    --button-plain-text-color-focus: var(--color-text-body);
+    --button-plain-background-base: var(--color-grey-700);
+    --button-plain-background-hover: var(--color-grey-700);
+    --button-plain-background-focus: var(--color-grey-700);
+    --gallery-label-background-base: var(--color-grey-50);
+    --gallery-label-background-hover: var(--color-grey-50);
+    --gallery-label-border-color-base: var(--color-border-primary);
+    --gallery-label-border-color-hover: var(--color-border-primary);
+    --gallery-thumb-background-base: var(--color-grey-900);
+    --gallery-thumb-background-hover: var(--color-grey-900);
+    --gallery-thumb-border-color-base: var(--color-border-primary);
+    --gallery-thumb-border-color-hover: var(--color-accent-base);
+    --gallery-thumb-border-color-focus: var(--color-blue-500);
+    --gallery-thumb-border-color-selected: var(--color-accent-base);
+    --chatbot-border-border-color-base: transparent;
+    --chatbot-border-border-color-latest: transparent;
+    --chatbot-user-background-base: ;
+    --chatbot-user-background-latest: ;
+    --chatbot-user-text-color-base: white;
+    --chatbot-user-text-color-latest: white;
+    --chatbot-bot-background-base: ;
+    --chatbot-bot-background-latest: ;
+    --chatbot-bot-text-color-base: white;
+    --chatbot-bot-text-color-latest: white;
+    --label-gradient-from: var(--color-orange-400);
+    --label-gradient-to: var(--color-orange-600);
+    --table-odd-background: var(--color-grey-900);
+    --table-even-background: var(--color-grey-950);
+    --table-background-edit: transparent;
+    --dataset-gallery-background-base: var(--color-background-primary);
+    --dataset-gallery-background-hover: var(--color-grey-800);
+    --dataset-dataframe-border-base: var(--color-border-primary);
+    --dataset-dataframe-border-hover: var(--color-border-secondary);
+    --dataset-table-background-base: transparent;
+    --dataset-table-background-hover: var(--color-grey-700);
+    --dataset-table-border-base: var(--color-grey-800);
+    --dataset-table-border-hover: var(--color-grey-800);
+}
+
+/* SHARK theme customization */
+
+.gradio-container {
+    background-color: var(--color-background-primary);
+}
+
+.container {
+    background-color: black !important;
+    padding-top: 20px !important;
+}
+
+#ui_title {
+    padding: 10px !important;
+}
+
+#top_logo {
+    background-color: transparent;
+    border-radius: 0 !important;
+    border: 0;
+}
+
+#demo_title {
+    background-color: var(--color-background-primary);
+    border-radius: 0 !important;
+    border: 0;
+    padding-top: 15px;
+    padding-bottom: 0px;
+    width: 350px !important;
+}
+
+#demo_title_outer {
+    border-radius: 0;
+}
+
+#prompt_box_outer div:first-child {
+    border-radius: 0 !important
+}
+
+#prompt_box textarea {
+    background-color: var(--color-background-primary) !important;
+}
+
+#prompt_examples {
+    margin: 0 !important;
+}
+
+#prompt_examples svg {
+    display: none !important;
+}
+
+#ui_body {
+    background-color: var(--color-background-secondary) !important;
+    padding: 10px !important;
+    border-radius: 0.5em !important;
+}
+
+#img_result+div {
+    display: none !important;
+}
+
+footer {
+    display: none !important;
+}

apps/stable_diffusion/web/ui/img2img_ui.py

@@ -0,0 +1,239 @@
+import os
+import sys
+import glob
+from pathlib import Path
+import gradio as gr
+from PIL import Image
+from apps.stable_diffusion.scripts import img2img_inf
+from apps.stable_diffusion.src import args
+from apps.stable_diffusion.web.ui.utils import (
+    available_devices,
+    nodlogo_loc,
+    sdlogo_loc,
+)
+
+
+with gr.Blocks(title="Image-to-Image") as img2img_web:
+    with gr.Row(elem_id="ui_title"):
+        nod_logo = Image.open(nodlogo_loc)
+        logo2 = Image.open(sdlogo_loc)
+        with gr.Row():
+            with gr.Column(scale=1, elem_id="demo_title_outer"):
+                gr.Image(
+                    value=nod_logo,
+                    show_label=False,
+                    interactive=False,
+                    elem_id="top_logo",
+                ).style(width=150, height=100)
+            with gr.Column(scale=5, elem_id="demo_title_outer"):
+                gr.Image(
+                    value=logo2,
+                    show_label=False,
+                    interactive=False,
+                    elem_id="demo_title",
+                ).style(width=150, height=100)
+    with gr.Row(elem_id="ui_body"):
+        with gr.Row():
+            with gr.Column(scale=1, min_width=600):
+                with gr.Row():
+                    ckpt_path = (
+                        Path(args.ckpt_dir)
+                        if args.ckpt_dir
+                        else Path(Path.cwd(), "models")
+                    )
+                    ckpt_path.mkdir(parents=True, exist_ok=True)
+                    types = (
+                        "*.ckpt",
+                        "*.safetensors",
+                    )  # the tuple of file types
+                    ckpt_files = ["None"]
+                    for extn in types:
+                        files = glob.glob(os.path.join(ckpt_path, extn))
+                        ckpt_files.extend(files)
+                    custom_model = gr.Dropdown(
+                        label=f"Models (Custom Model path: {ckpt_path})",
+                        value=args.ckpt_loc if args.ckpt_loc else "None",
+                        choices=ckpt_files
+                        + [
+                            "Linaqruf/anything-v3.0",
+                            "prompthero/openjourney",
+                            "wavymulder/Analog-Diffusion",
+                            "stabilityai/stable-diffusion-2-1",
+                            "stabilityai/stable-diffusion-2-1-base",
+                            "CompVis/stable-diffusion-v1-4",
+                        ],
+                    )
+                    hf_model_id = gr.Textbox(
+                        placeholder="Select 'None' in the Models dropdown on the left and enter model ID here e.g: SG161222/Realistic_Vision_V1.3",
+                        value="",
+                        label="HuggingFace Model ID",
+                        lines=3,
+                    )
+
+                with gr.Group(elem_id="prompt_box_outer"):
+                    prompt = gr.Textbox(
+                        label="Prompt",
+                        value=args.prompts[0],
+                        lines=1,
+                        elem_id="prompt_box",
+                    )
+                    negative_prompt = gr.Textbox(
+                        label="Negative Prompt",
+                        value=args.negative_prompts[0],
+                        lines=1,
+                        elem_id="negative_prompt_box",
+                    )
+
+                init_image = gr.Image(label="Input Image", type="filepath")
+
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            label="Scheduler",
+                            value="PNDM",
+                            choices=[
+                                "DDIM",
+                                "PNDM",
+                                "DPMSolverMultistep",
+                                "EulerAncestralDiscrete",
+                            ],
+                        )
+                        with gr.Group():
+                            save_metadata_to_png = gr.Checkbox(
+                                label="Save prompt information to PNG",
+                                value=args.write_metadata_to_png,
+                                interactive=True,
+                            )
+                            save_metadata_to_json = gr.Checkbox(
+                                label="Save prompt information to JSON file",
+                                value=args.save_metadata_to_json,
+                                interactive=True,
+                            )
+                    with gr.Row():
+                        height = gr.Slider(
+                            384, 786, value=args.height, step=8, label="Height"
+                        )
+                        width = gr.Slider(
+                            384, 786, value=args.width, step=8, label="Width"
+                        )
+                        precision = gr.Radio(
+                            label="Precision",
+                            value=args.precision,
+                            choices=[
+                                "fp16",
+                                "fp32",
+                            ],
+                            visible=False,
+                        )
+                        max_length = gr.Radio(
+                            label="Max Length",
+                            value=args.max_length,
+                            choices=[
+                                64,
+                                77,
+                            ],
+                            visible=False,
+                        )
+                    with gr.Row():
+                        steps = gr.Slider(
+                            1, 100, value=args.steps, step=1, label="Steps"
+                        )
+                        strength = gr.Slider(
+                            0,
+                            1,
+                            value=args.strength,
+                            step=0.1,
+                            label="Strength",
+                        )
+                    with gr.Row():
+                        guidance_scale = gr.Slider(
+                            0,
+                            50,
+                            value=args.guidance_scale,
+                            step=0.1,
+                            label="CFG Scale",
+                        )
+                        batch_count = gr.Slider(
+                            1,
+                            100,
+                            value=args.batch_count,
+                            step=1,
+                            label="Batch Count",
+                            interactive=True,
+                        )
+                        batch_size = gr.Slider(
+                            1,
+                            4,
+                            value=args.batch_size,
+                            step=1,
+                            label="Batch Size",
+                            interactive=False,
+                            visible=False,
+                        )
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    random_seed = gr.Button("Randomize Seed")
+                    random_seed.click(
+                        None,
+                        inputs=[],
+                        outputs=[seed],
+                        _js="() => Math.floor(Math.random() * 4294967295)",
+                    )
+                    stable_diffusion = gr.Button("Generate Image")
+
+            with gr.Column(scale=1, min_width=600):
+                with gr.Group():
+                    gallery = gr.Gallery(
+                        label="Generated images",
+                        show_label=False,
+                        elem_id="gallery",
+                    ).style(grid=[2], height="auto")
+                    std_output = gr.Textbox(
+                        value="Nothing to show.",
+                        lines=4,
+                        show_label=False,
+                    )
+                output_dir = args.output_dir if args.output_dir else Path.cwd()
+                output_dir = Path(output_dir, "generated_imgs")
+                output_loc = gr.Textbox(
+                    label="Saving Images at",
+                    value=output_dir,
+                    interactive=False,
+                )
+        kwargs = dict(
+            fn=img2img_inf,
+            inputs=[
+                prompt,
+                negative_prompt,
+                init_image,
+                height,
+                width,
+                steps,
+                strength,
+                guidance_scale,
+                seed,
+                batch_count,
+                batch_size,
+                scheduler,
+                custom_model,
+                hf_model_id,
+                precision,
+                device,
+                max_length,
+                save_metadata_to_json,
+                save_metadata_to_png,
+            ],
+            outputs=[gallery, std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt.submit(**kwargs)
+        stable_diffusion.click(**kwargs)

apps/stable_diffusion/web/ui/txt2img_ui.py

@@ -0,0 +1,237 @@
+import os
+import sys
+import glob
+from pathlib import Path
+import gradio as gr
+from PIL import Image
+from apps.stable_diffusion.scripts import txt2img_inf
+from apps.stable_diffusion.src import prompt_examples, args
+from apps.stable_diffusion.web.ui.utils import (
+    available_devices,
+    nodlogo_loc,
+    sdlogo_loc,
+)
+
+
+with gr.Blocks(title="Text-to-Image") as txt2img_web:
+    with gr.Row(elem_id="ui_title"):
+        nod_logo = Image.open(nodlogo_loc)
+        logo2 = Image.open(sdlogo_loc)
+        with gr.Row():
+            with gr.Column(scale=1, elem_id="demo_title_outer"):
+                gr.Image(
+                    value=nod_logo,
+                    show_label=False,
+                    interactive=False,
+                    elem_id="top_logo",
+                ).style(width=150, height=100)
+            with gr.Column(scale=5, elem_id="demo_title_outer"):
+                gr.Image(
+                    value=logo2,
+                    show_label=False,
+                    interactive=False,
+                    elem_id="demo_title",
+                ).style(width=150, height=100)
+
+    with gr.Row(elem_id="ui_body"):
+        with gr.Row():
+            with gr.Column(scale=1, min_width=600):
+                with gr.Row():
+                    ckpt_path = (
+                        Path(args.ckpt_dir)
+                        if args.ckpt_dir
+                        else Path(Path.cwd(), "models")
+                    )
+                    ckpt_path.mkdir(parents=True, exist_ok=True)
+                    types = (
+                        "*.ckpt",
+                        "*.safetensors",
+                    )  # the tuple of file types
+                    ckpt_files = ["None"]
+                    for extn in types:
+                        files = glob.glob(os.path.join(ckpt_path, extn))
+                        ckpt_files.extend(files)
+                    custom_model = gr.Dropdown(
+                        label=f"Models (Custom Model path: {ckpt_path})",
+                        value=args.ckpt_loc if args.ckpt_loc else "None",
+                        choices=ckpt_files
+                        + [
+                            "Linaqruf/anything-v3.0",
+                            "prompthero/openjourney",
+                            "wavymulder/Analog-Diffusion",
+                            "stabilityai/stable-diffusion-2-1",
+                            "stabilityai/stable-diffusion-2-1-base",
+                            "CompVis/stable-diffusion-v1-4",
+                        ],
+                    )
+                    hf_model_id = gr.Textbox(
+                        placeholder="Select 'None' in the Models dropdown on the left and enter model ID here e.g: SG161222/Realistic_Vision_V1.3",
+                        value="",
+                        label="HuggingFace Model ID",
+                        lines=3,
+                    )
+
+                with gr.Group(elem_id="prompt_box_outer"):
+                    prompt = gr.Textbox(
+                        label="Prompt",
+                        value=args.prompts[0],
+                        lines=1,
+                        elem_id="prompt_box",
+                    )
+                    negative_prompt = gr.Textbox(
+                        label="Negative Prompt",
+                        value=args.negative_prompts[0],
+                        lines=1,
+                        elem_id="negative_prompt_box",
+                    )
+                with gr.Accordion(label="Advanced Options", open=False):
+                    with gr.Row():
+                        scheduler = gr.Dropdown(
+                            label="Scheduler",
+                            value=args.scheduler,
+                            choices=[
+                                "DDIM",
+                                "PNDM",
+                                "LMSDiscrete",
+                                "DPMSolverMultistep",
+                                "EulerDiscrete",
+                                "EulerAncestralDiscrete",
+                                "SharkEulerDiscrete",
+                            ],
+                        )
+                        with gr.Group():
+                            save_metadata_to_png = gr.Checkbox(
+                                label="Save prompt information to PNG",
+                                value=args.write_metadata_to_png,
+                                interactive=True,
+                            )
+                            save_metadata_to_json = gr.Checkbox(
+                                label="Save prompt information to JSON file",
+                                value=args.save_metadata_to_json,
+                                interactive=True,
+                            )
+                    with gr.Row():
+                        height = gr.Slider(
+                            384, 786, value=args.height, step=8, label="Height"
+                        )
+                        width = gr.Slider(
+                            384, 786, value=args.width, step=8, label="Width"
+                        )
+                        precision = gr.Radio(
+                            label="Precision",
+                            value=args.precision,
+                            choices=[
+                                "fp16",
+                                "fp32",
+                            ],
+                            visible=False,
+                        )
+                        max_length = gr.Radio(
+                            label="Max Length",
+                            value=args.max_length,
+                            choices=[
+                                64,
+                                77,
+                            ],
+                            visible=False,
+                        )
+                    with gr.Row():
+                        steps = gr.Slider(
+                            1, 100, value=args.steps, step=1, label="Steps"
+                        )
+                        guidance_scale = gr.Slider(
+                            0,
+                            50,
+                            value=args.guidance_scale,
+                            step=0.1,
+                            label="CFG Scale",
+                        )
+                    with gr.Row():
+                        batch_count = gr.Slider(
+                            1,
+                            100,
+                            value=args.batch_count,
+                            step=1,
+                            label="Batch Count",
+                            interactive=True,
+                        )
+                        batch_size = gr.Slider(
+                            1,
+                            4,
+                            value=args.batch_size,
+                            step=1,
+                            label="Batch Size",
+                            interactive=True,
+                        )
+                with gr.Row():
+                    seed = gr.Number(
+                        value=args.seed, precision=0, label="Seed"
+                    )
+                    device = gr.Dropdown(
+                        label="Device",
+                        value=available_devices[0],
+                        choices=available_devices,
+                    )
+                with gr.Row():
+                    random_seed = gr.Button("Randomize Seed")
+                    random_seed.click(
+                        None,
+                        inputs=[],
+                        outputs=[seed],
+                        _js="() => Math.floor(Math.random() * 4294967295)",
+                    )
+                    stable_diffusion = gr.Button("Generate Image")
+                with gr.Accordion(label="Prompt Examples!", open=False):
+                    ex = gr.Examples(
+                        examples=prompt_examples,
+                        inputs=prompt,
+                        cache_examples=False,
+                        elem_id="prompt_examples",
+                    )
+
+            with gr.Column(scale=1, min_width=600):
+                with gr.Group():
+                    gallery = gr.Gallery(
+                        label="Generated images",
+                        show_label=False,
+                        elem_id="gallery",
+                    ).style(grid=[2], height="auto")
+                    std_output = gr.Textbox(
+                        value="Nothing to show.",
+                        lines=4,
+                        show_label=False,
+                    )
+                output_dir = args.output_dir if args.output_dir else Path.cwd()
+                output_dir = Path(output_dir, "generated_imgs")
+                output_loc = gr.Textbox(
+                    label="Saving Images at",
+                    value=output_dir,
+                    interactive=False,
+                )
+        kwargs = dict(
+            fn=txt2img_inf,
+            inputs=[
+                prompt,
+                negative_prompt,
+                height,
+                width,
+                steps,
+                guidance_scale,
+                seed,
+                batch_count,
+                batch_size,
+                scheduler,
+                custom_model,
+                hf_model_id,
+                precision,
+                device,
+                max_length,
+                save_metadata_to_json,
+                save_metadata_to_png,
+            ],
+            outputs=[gallery, std_output],
+            show_progress=args.progress_bar,
+        )
+
+        prompt.submit(**kwargs)
+        stable_diffusion.click(**kwargs)

apps/stable_diffusion/web/ui/utils.py

@@ -0,0 +1,16 @@
+import os
+import sys
+from apps.stable_diffusion.src import get_available_devices
+
+
+def resource_path(relative_path):
+    """Get absolute path to resource, works for dev and for PyInstaller"""
+    base_path = getattr(
+        sys, "_MEIPASS", os.path.dirname(os.path.abspath(__file__))
+    )
+    return os.path.join(base_path, relative_path)
+
+
+nodlogo_loc = resource_path("logos/nod-logo.png")
+sdlogo_loc = resource_path("logos/sd-demo-logo.png")
+available_devices = get_available_devices()

apps/stable_diffusion/web/utils/gradio_configs.py

@@ -0,0 +1,31 @@
+import os
+import tempfile
+import gradio
+from os import listdir
+
+gradio_tmp_imgs_folder = os.path.join(os.getcwd(), "shark_tmp/")
+
+
+# Clear all gradio tmp images
+def clear_gradio_tmp_imgs_folder():
+    if not os.path.exists(gradio_tmp_imgs_folder):
+        return
+    for fileName in listdir(gradio_tmp_imgs_folder):
+        # Delete tmp png files
+        if fileName.startswith("tmp") and fileName.endswith(".png"):
+            os.remove(gradio_tmp_imgs_folder + fileName)
+
+
+# Overwrite save_pil_to_file from gradio to save tmp images generated by gradio into our own tmp folder
+def save_pil_to_file(pil_image, dir=None):
+    if not os.path.exists(gradio_tmp_imgs_folder):
+        os.mkdir(gradio_tmp_imgs_folder)
+    file_obj = tempfile.NamedTemporaryFile(
+        delete=False, suffix=".png", dir=gradio_tmp_imgs_folder
+    )
+    pil_image.save(file_obj)
+    return file_obj
+
+
+# Register save_pil_to_file override
+gradio.processing_utils.save_pil_to_file = save_pil_to_file

benchmarks/tests/test_benchmark.py

@@ -42,7 +42,7 @@ class TFHuggingFaceLanguage(tf.Module):
             input_ids=x, attention_mask=y, token_type_ids=z, training=False
         )
 
-    @tf.function(input_signature=tf_bert_input)
+    @tf.function(input_signature=tf_bert_input, jit_compile=True)
     def forward(self, input_ids, attention_mask, token_type_ids):
         return self.m.predict(input_ids, attention_mask, token_type_ids)
 

build_tools/image_comparison.py

@@ -0,0 +1,45 @@
+import argparse
+from PIL import Image
+import numpy as np
+
+import requests
+import shutil
+import os
+import subprocess
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("-n", "--newfile")
+parser.add_argument(
+    "-g",
+    "--golden_url",
+    default="https://storage.googleapis.com/shark_tank/testdata/cyberpunk_fores_42_0_230119_021148.png",
+)
+
+
+def get_image(url, local_filename):
+    res = requests.get(url, stream=True)
+    if res.status_code == 200:
+        with open(local_filename, "wb") as f:
+            shutil.copyfileobj(res.raw, f)
+
+
+def compare_images(new_filename, golden_filename):
+    new = np.array(Image.open(new_filename)) / 255.0
+    golden = np.array(Image.open(golden_filename)) / 255.0
+    diff = np.abs(new - golden)
+    mean = np.mean(diff)
+    if mean > 0.1:
+        subprocess.run(
+            ["gsutil", "cp", new_filename, "gs://shark_tank/testdata/builder/"]
+        )
+        raise SystemExit("new and golden not close")
+    else:
+        print("SUCCESS")
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    tempfile_name = os.path.join(os.getcwd(), "golden.png")
+    get_image(args.golden_url, tempfile_name)
+    compare_images(args.newfile, tempfile_name)

build_tools/populate_sharktank_ci.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
 
-IMPORTER=1 ./setup_venv.sh
+IMPORTER=1 BENCHMARK=1 ./setup_venv.sh
 source $GITHUB_WORKSPACE/shark.venv/bin/activate
-python generate_sharktank.py --upload=False --ci_tank_dir=True
+python generate_sharktank.py

build_tools/stable_diffusion_testing.py

@@ -0,0 +1,78 @@
+import os
+import subprocess
+from apps.stable_diffusion.src.utils.resources import (
+    get_json_file,
+)
+from shark.shark_downloader import download_public_file
+from image_comparison import compare_images
+import argparse
+from glob import glob
+import shutil
+
+model_config_dicts = get_json_file(
+    os.path.join(
+        os.getcwd(),
+        "apps/stable_diffusion/src/utils/resources/model_config.json",
+    )
+)
+
+
+def test_loop(device="vulkan", beta=False, extra_flags=[]):
+    # Get golden values from tank
+    shutil.rmtree("./test_images", ignore_errors=True)
+    os.mkdir("./test_images")
+    os.mkdir("./test_images/golden")
+    hf_model_names = model_config_dicts[0].values()
+    tuned_options = ["--no-use_tuned", "use_tuned"]
+    if beta:
+        extra_flags.append("--beta_models=True")
+    for model_name in hf_model_names:
+        for use_tune in tuned_options:
+            command = [
+                "python",
+                "apps/stable_diffusion/scripts/txt2img.py",
+                "--device=" + device,
+                "--prompt=cyberpunk forest by Salvador Dali",
+                "--output_dir="
+                + os.path.join(os.getcwd(), "test_images", model_name),
+                "--hf_model_id=" + model_name,
+                use_tune,
+            ]
+            command += extra_flags
+            generated_image = not subprocess.call(
+                command, stdout=subprocess.DEVNULL
+            )
+            if generated_image:
+                print(" ".join(command))
+                print("Successfully generated image")
+                os.makedirs(
+                    "./test_images/golden/" + model_name, exist_ok=True
+                )
+                download_public_file(
+                    "gs://shark_tank/testdata/golden/" + model_name,
+                    "./test_images/golden/" + model_name,
+                )
+                test_file_path = os.path.join(
+                    os.getcwd(), "test_images", model_name, "generated_imgs"
+                )
+                test_file = glob(test_file_path + "/*.png")[0]
+                golden_path = "./test_images/golden/" + model_name + "/*.png"
+                golden_file = glob(golden_path)[0]
+                compare_images(test_file, golden_file)
+            else:
+                print(" ".join(command))
+                print("failed to generate image for this configuration")
+
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("-d", "--device", default="vulkan")
+parser.add_argument(
+    "-b", "--beta", action=argparse.BooleanOptionalAction, default=False
+)
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    print(args)
+    test_loop(args.device, args.beta, [])

conftest.py

@@ -36,6 +36,12 @@ def pytest_addoption(parser):
         default="False",
         help="Enables uploading of reproduction artifacts upon test case failure during iree-compile or validation. Must be passed with --ci_sha option ",
     )
+    parser.addoption(
+        "--update_tank",
+        action="store_true",
+        default="False",
+        help="Update local shark tank with latest artifacts.",
+    )
     parser.addoption(
         "--ci_sha",
         action="store",

cpp/.gitignore

@@ -0,0 +1,3 @@
+*.mlir
+*.vmfb
+*.ini

cpp/README.md

@@ -54,5 +54,29 @@ python -m pip install tensorflow
 
 *Run the vulkan_gui*
 ```bash
-./build/vulkan_gui/iree-samples-vulkan-gui
+./build/vulkan_gui/iree-samples-resnet-vulkan-gui
+```
+
+## Other models
+A tool for benchmarking other models is built and can be invoked with a command like the following
+```bash
+./build/vulkan_gui/iree-vulkan-gui --module-file=path/to/.vmfb --function_input=...
+```
+see `./build/vulkan_gui/iree-vulkan-gui --help` for an explanation on the function input. For example, stable diffusion unet can be tested with the following commands:
+```bash
+wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/stable_diff_tf.mlir
+iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvm-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 stable_diff_tf.mlir -o stable_diff_tf.vmfb
+./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=2x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32
+```
+VAE and Autoencoder are also available
+```bash
+# VAE
+wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/vae_tf/vae.mlir
+iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvm-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 vae.mlir -o vae.vmfb
+./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=1x4x64x64xf32
+
+# CLIP Autoencoder
+wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/clip_tf/clip_autoencoder.mlir
+iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvm-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 clip_autoencoder.mlir -o clip_autoencoder.vmfb
+./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=1x77xi32 --function_input=1x77xi32
 ```

cpp/save_img.py

@@ -1,7 +1,6 @@
 import numpy as np
 import tensorflow as tf
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_tf_model
 
 
 def load_and_preprocess_image(fname: str):

cpp/vulkan_gui/CMakeLists.txt

@@ -40,45 +40,77 @@ set(IMGUI_DIR ${CMAKE_BINARY_DIR}/_deps/imgui-src)
 message("Looking for Imgui in ${IMGUI_DIR}")
 include_directories(${IMGUI_DIR} ${IMGUI_DIR}/backends ..)
 
-# Define the sample executable.
-set(_NAME "iree-samples-vulkan-gui")
-add_executable(${_NAME} "")
-target_sources(${_NAME}
-  PRIVATE
-    vulkan_inference_gui.cc
-    "${IMGUI_DIR}/backends/imgui_impl_sdl.cpp"
-    "${IMGUI_DIR}/backends/imgui_impl_vulkan.cpp"
-    "${IMGUI_DIR}/imgui.cpp"
-    "${IMGUI_DIR}/imgui_draw.cpp"
-    "${IMGUI_DIR}/imgui_demo.cpp"
-    "${IMGUI_DIR}/imgui_tables.cpp"
-    "${IMGUI_DIR}/imgui_widgets.cpp"
-)
-set_target_properties(${_NAME} PROPERTIES OUTPUT_NAME "iree-samples-vulkan-gui")
-target_include_directories(${_NAME} PUBLIC
-    $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}>
-)
-target_link_libraries(${_NAME}
-  SDL2::SDL2
-  Vulkan::Vulkan
-  iree_runtime_runtime
-  iree_base_internal_main
-  iree_hal_drivers_vulkan_registration_registration
-  iree_modules_hal_hal
-  iree_vm_vm
-  iree_vm_bytecode_module
-  iree_vm_cc
+
+function(iree_vulkan_sample)
+
+  cmake_parse_arguments(
+    _RULE
+    ""
+    "NAME"
+    "SRCS"
+    ${ARGN}
+  )
+
+
+  # Define the sample executable.
+  set(_NAME "${_RULE_NAME}")
+  set(SRCS "${_RULE_SRCS}")
+  add_executable(${_NAME} "")
+  target_sources(${_NAME}
+    PRIVATE
+      ${SRCS}
+      "${IMGUI_DIR}/backends/imgui_impl_sdl.cpp"
+      "${IMGUI_DIR}/backends/imgui_impl_vulkan.cpp"
+      "${IMGUI_DIR}/imgui.cpp"
+      "${IMGUI_DIR}/imgui_draw.cpp"
+      "${IMGUI_DIR}/imgui_demo.cpp"
+      "${IMGUI_DIR}/imgui_tables.cpp"
+      "${IMGUI_DIR}/imgui_widgets.cpp"
+  )
+  set_target_properties(${_NAME} PROPERTIES OUTPUT_NAME "${_NAME}")
+  target_include_directories(${_NAME} PUBLIC
+      $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}>
+  )
+  target_link_libraries(${_NAME}
+    SDL2::SDL2
+    Vulkan::Vulkan
+    iree_runtime_runtime
+    iree_base_internal_main
+    iree_hal_drivers_vulkan_registration_registration
+    iree_modules_hal_hal
+    iree_vm_vm
+    iree_vm_bytecode_module
+    iree_vm_cc
+    iree_tooling_vm_util_cc
+    iree_tooling_context_util
+  )
+
+  if(${CMAKE_SYSTEM_NAME} STREQUAL "Windows")
+    set(_GUI_LINKOPTS "-SUBSYSTEM:CONSOLE")
+  else()
+    set(_GUI_LINKOPTS "")
+  endif()
+
+  target_link_options(${_NAME}
+    PRIVATE
+      ${_GUI_LINKOPTS}
+  )
+endfunction()
+
+iree_vulkan_sample(
+    NAME
+      iree-samples-resnet-vulkan-gui
+
+    SRCS
+      vulkan_resnet_inference_gui.cc
 )
 
-if(${CMAKE_SYSTEM_NAME} STREQUAL "Windows")
-  set(_GUI_LINKOPTS "-SUBSYSTEM:CONSOLE")
-else()
-  set(_GUI_LINKOPTS "")
-endif()
+iree_vulkan_sample(
+    NAME
+      iree-vulkan-gui
 
-target_link_options(${_NAME}
-  PRIVATE
-    ${_GUI_LINKOPTS}
+    SRCS
+      vulkan_inference_gui.cc
 )
 
 message(STATUS "Configured vulkan_gui sample successfully")

cpp/vulkan_gui/vulkan_inference_gui.cc

@@ -18,6 +18,12 @@
 #include <set>
 #include <vector>
 #include <fstream>
+#include <array>
+#include <cstdio>
+#include <cstdlib>
+#include <iterator>
+#include <string>
+#include <utility>
 
 #include "iree/hal/drivers/vulkan/api.h"
 
@@ -30,6 +36,15 @@
 #include "iree/vm/bytecode_module.h"
 #include "iree/vm/ref_cc.h"
 
+// iree-run-module
+#include "iree/base/internal/flags.h"
+#include "iree/base/status_cc.h"
+#include "iree/base/tracing.h"
+#include "iree/modules/hal/types.h"
+#include "iree/tooling/comparison.h"
+#include "iree/tooling/context_util.h"
+#include "iree/tooling/vm_util_cc.h"
+
 // Other dependencies (helpers, etc.)
 #include "iree/base/internal/main.h"
 
@@ -38,6 +53,49 @@
 #define STB_IMAGE_IMPLEMENTATION
 #include "stb_image.h"
 
+IREE_FLAG(string, entry_function, "",
+          "Name of a function contained in the module specified by module_file "
+          "to run.");
+
+// TODO(benvanik): move --function_input= flag into a util.
+static iree_status_t parse_function_io(iree_string_view_t flag_name,
+                                       void* storage,
+                                       iree_string_view_t value) {
+  auto* list = (std::vector<std::string>*)storage;
+  list->push_back(std::string(value.data, value.size));
+  return iree_ok_status();
+}
+static void print_function_io(iree_string_view_t flag_name, void* storage,
+                              FILE* file) {
+  auto* list = (std::vector<std::string>*)storage;
+  if (list->empty()) {
+    fprintf(file, "# --%.*s=\n", (int)flag_name.size, flag_name.data);
+  } else {
+    for (size_t i = 0; i < list->size(); ++i) {
+      fprintf(file, "--%.*s=\"%s\"\n", (int)flag_name.size, flag_name.data,
+              list->at(i).c_str());
+    }
+  }
+}
+static std::vector<std::string> FLAG_function_inputs;
+IREE_FLAG_CALLBACK(
+    parse_function_io, print_function_io, &FLAG_function_inputs, function_input,
+    "An input (a) value or (b) buffer of the format:\n"
+    "  (a) scalar value\n"
+    "     value\n"
+    "     e.g.: --function_input=\"3.14\"\n"
+    "  (b) buffer:\n"
+    "     [shape]xtype=[value]\n"
+    "     e.g.: --function_input=\"2x2xi32=1 2 3 4\"\n"
+    "Optionally, brackets may be used to separate the element values:\n"
+    "  2x2xi32=[[1 2][3 4]]\n"
+    "Raw binary files can be read to provide buffer contents:\n"
+    "  2x2xi32=@some/file.bin\n"
+    "numpy npy files (from numpy.save) can be read to provide 1+ values:\n"
+    "  @some.npy\n"
+    "Each occurrence of the flag indicates an input in the order they were\n"
+    "specified on the command line.");
+
 typedef struct iree_file_toc_t {
   const char* name;             // the file's original name
   char* data;             // beginning of the file
@@ -87,225 +145,6 @@ static void check_vk_result(VkResult err) {
   abort();
 }
 
-// Helper function to find Vulkan memory type bits. See ImGui_ImplVulkan_MemoryType() in imgui_impl_vulkan.cpp
-uint32_t findMemoryType(uint32_t type_filter, VkMemoryPropertyFlags properties)
-{
-  VkPhysicalDeviceMemoryProperties mem_properties;
-  vkGetPhysicalDeviceMemoryProperties(g_PhysicalDevice, &mem_properties);
-
-  for (uint32_t i = 0; i < mem_properties.memoryTypeCount; i++)
-  {
-    if ((type_filter & (1 << i)) && (mem_properties.memoryTypes[i].propertyFlags & properties) == properties)
-    {
-      return i;
-    }
-  }
-
-  return 0xFFFFFFFF; // Unable to find memoryType
-}
-
-// Helper function to load an image with common settings and return a VkDescriptorSet as a sort of Vulkan pointer
-bool LoadTextureFromFile(const char* filename, VkDescriptorSet* img_ds, int* image_width, int* image_height)
-{
-  // Specifying 4 channels forces stb to load the image in RGBA which is an easy format for Vulkan
-  int image_channels = 4;
-  unsigned char* image_data = stbi_load(filename, image_width, image_height, 0, image_channels);
-
-  if (image_data == NULL)
-  {
-    return false;
-  }
-
-  // Calculate allocation size (in number of bytes)
-  size_t image_size = (*image_width)*(*image_height)*image_channels;
-
-  VkResult err;
-
-  // Create the Vulkan image.
-  VkImage texture_image;
-  VkDeviceMemory texture_image_memory;
-  {
-    VkImageCreateInfo info = {};
-    info.sType = VK_STRUCTURE_TYPE_IMAGE_CREATE_INFO;
-    info.imageType = VK_IMAGE_TYPE_2D;
-    info.format = VK_FORMAT_R8G8B8A8_UNORM;
-    info.extent.width = *image_width;
-    info.extent.height = *image_height;
-    info.extent.depth = 1;
-    info.mipLevels = 1;
-    info.arrayLayers = 1;
-    info.samples = VK_SAMPLE_COUNT_1_BIT;
-    info.tiling = VK_IMAGE_TILING_OPTIMAL;
-    info.usage = VK_IMAGE_USAGE_SAMPLED_BIT | VK_IMAGE_USAGE_TRANSFER_DST_BIT;
-    info.sharingMode = VK_SHARING_MODE_EXCLUSIVE;
-    info.initialLayout = VK_IMAGE_LAYOUT_UNDEFINED;
-    err = vkCreateImage(g_Device, &info, g_Allocator, &texture_image);
-    check_vk_result(err);
-    VkMemoryRequirements req;
-    vkGetImageMemoryRequirements(g_Device, texture_image, &req);
-    VkMemoryAllocateInfo alloc_info = {};
-    alloc_info.sType = VK_STRUCTURE_TYPE_MEMORY_ALLOCATE_INFO;
-    alloc_info.allocationSize = req.size;
-    alloc_info.memoryTypeIndex = findMemoryType(req.memoryTypeBits, VK_MEMORY_PROPERTY_DEVICE_LOCAL_BIT);
-    err = vkAllocateMemory(g_Device, &alloc_info, g_Allocator, &texture_image_memory);
-    check_vk_result(err);
-    err = vkBindImageMemory(g_Device, texture_image, texture_image_memory, 0);
-    check_vk_result(err);
-  }
-
-  // Create the Image View
-  VkImageView image_view;
-  {
-    VkImageViewCreateInfo info = {};
-    info.sType = VK_STRUCTURE_TYPE_IMAGE_VIEW_CREATE_INFO;
-    info.image = texture_image;
-    info.viewType = VK_IMAGE_VIEW_TYPE_2D;
-    info.format = VK_FORMAT_R8G8B8A8_UNORM;
-    info.subresourceRange.aspectMask = VK_IMAGE_ASPECT_COLOR_BIT;
-    info.subresourceRange.levelCount = 1;
-    info.subresourceRange.layerCount = 1;
-    err = vkCreateImageView(g_Device, &info, g_Allocator, &image_view);
-    check_vk_result(err);
-  }
-
-  // Create Sampler
-  VkSampler sampler;
-  {
-    VkSamplerCreateInfo sampler_info{};
-    sampler_info.sType = VK_STRUCTURE_TYPE_SAMPLER_CREATE_INFO;
-    sampler_info.magFilter = VK_FILTER_LINEAR;
-    sampler_info.minFilter = VK_FILTER_LINEAR;
-    sampler_info.mipmapMode  = VK_SAMPLER_MIPMAP_MODE_LINEAR;
-    sampler_info.addressModeU = VK_SAMPLER_ADDRESS_MODE_REPEAT; // outside image bounds just use border color
-    sampler_info.addressModeV = VK_SAMPLER_ADDRESS_MODE_REPEAT;
-    sampler_info.addressModeW = VK_SAMPLER_ADDRESS_MODE_REPEAT;
-    sampler_info.minLod = -1000;
-    sampler_info.maxLod = 1000;
-    sampler_info.maxAnisotropy = 1.0f;
-    err = vkCreateSampler(g_Device, &sampler_info, g_Allocator, &sampler);
-    check_vk_result(err);
-  }
-
-  // Create Descriptor Set using ImGUI's implementation
-  *img_ds = ImGui_ImplVulkan_AddTexture(sampler, image_view, VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL);
-
-  // Create Upload Buffer
-  VkBuffer upload_buffer;
-  VkDeviceMemory upload_buffer_memory;
-  {
-    VkBufferCreateInfo buffer_info = {};
-    buffer_info.sType = VK_STRUCTURE_TYPE_BUFFER_CREATE_INFO;
-    buffer_info.size = image_size;
-    buffer_info.usage = VK_BUFFER_USAGE_TRANSFER_SRC_BIT;
-    buffer_info.sharingMode = VK_SHARING_MODE_EXCLUSIVE;
-    err = vkCreateBuffer(g_Device, &buffer_info, g_Allocator, &upload_buffer);
-    check_vk_result(err);
-    VkMemoryRequirements req;
-    vkGetBufferMemoryRequirements(g_Device, upload_buffer, &req);
-    VkMemoryAllocateInfo alloc_info = {};
-    alloc_info.sType = VK_STRUCTURE_TYPE_MEMORY_ALLOCATE_INFO;
-    alloc_info.allocationSize = req.size;
-    alloc_info.memoryTypeIndex = findMemoryType(req.memoryTypeBits, VK_MEMORY_PROPERTY_HOST_VISIBLE_BIT);
-    err = vkAllocateMemory(g_Device, &alloc_info, g_Allocator, &upload_buffer_memory);
-    check_vk_result(err);
-    err = vkBindBufferMemory(g_Device, upload_buffer, upload_buffer_memory, 0);
-    check_vk_result(err);
-  }
-
-  // Upload to Buffer:
-  {
-    void* map = NULL;
-    err = vkMapMemory(g_Device, upload_buffer_memory, 0, image_size, 0, &map);
-    check_vk_result(err);
-    memcpy(map, image_data, image_size);
-    VkMappedMemoryRange range[1] = {};
-    range[0].sType = VK_STRUCTURE_TYPE_MAPPED_MEMORY_RANGE;
-    range[0].memory = upload_buffer_memory;
-    range[0].size = image_size;
-    err = vkFlushMappedMemoryRanges(g_Device, 1, range);
-    check_vk_result(err);
-    vkUnmapMemory(g_Device, upload_buffer_memory);
-  }
-
-  // Release image memory using stb
-  stbi_image_free(image_data);
-
-  // Create a command buffer that will perform following steps when hit in the command queue.
-  // TODO: this works in the example, but may need input if this is an acceptable way to access the pool/create the command buffer.
-  VkCommandPool command_pool = g_MainWindowData.Frames[g_MainWindowData.FrameIndex].CommandPool;
-  VkCommandBuffer command_buffer;
-  {
-    VkCommandBufferAllocateInfo alloc_info{};
-    alloc_info.sType = VK_STRUCTURE_TYPE_COMMAND_BUFFER_ALLOCATE_INFO;
-    alloc_info.level = VK_COMMAND_BUFFER_LEVEL_PRIMARY;
-    alloc_info.commandPool = command_pool;
-    alloc_info.commandBufferCount = 1;
-
-    err = vkAllocateCommandBuffers(g_Device, &alloc_info, &command_buffer);
-    check_vk_result(err);
-
-    VkCommandBufferBeginInfo begin_info = {};
-    begin_info.sType = VK_STRUCTURE_TYPE_COMMAND_BUFFER_BEGIN_INFO;
-    begin_info.flags |= VK_COMMAND_BUFFER_USAGE_ONE_TIME_SUBMIT_BIT;
-    err = vkBeginCommandBuffer(command_buffer, &begin_info);
-    check_vk_result(err);
-  }
-
-  // Copy to Image
-  {
-    VkImageMemoryBarrier copy_barrier[1] = {};
-    copy_barrier[0].sType = VK_STRUCTURE_TYPE_IMAGE_MEMORY_BARRIER;
-    copy_barrier[0].dstAccessMask = VK_ACCESS_TRANSFER_WRITE_BIT;
-    copy_barrier[0].oldLayout = VK_IMAGE_LAYOUT_UNDEFINED;
-    copy_barrier[0].newLayout = VK_IMAGE_LAYOUT_TRANSFER_DST_OPTIMAL;
-    copy_barrier[0].srcQueueFamilyIndex = VK_QUEUE_FAMILY_IGNORED;
-    copy_barrier[0].dstQueueFamilyIndex = VK_QUEUE_FAMILY_IGNORED;
-    copy_barrier[0].image = texture_image;
-    copy_barrier[0].subresourceRange.aspectMask = VK_IMAGE_ASPECT_COLOR_BIT;
-    copy_barrier[0].subresourceRange.levelCount = 1;
-    copy_barrier[0].subresourceRange.layerCount = 1;
-    vkCmdPipelineBarrier(command_buffer, VK_PIPELINE_STAGE_HOST_BIT, VK_PIPELINE_STAGE_TRANSFER_BIT, 0, 0, NULL, 0, NULL, 1, copy_barrier);
-
-    VkBufferImageCopy region = {};
-    region.imageSubresource.aspectMask = VK_IMAGE_ASPECT_COLOR_BIT;
-    region.imageSubresource.layerCount = 1;
-    region.imageExtent.width = *image_width;
-    region.imageExtent.height = *image_height;
-    region.imageExtent.depth = 1;
-    vkCmdCopyBufferToImage(command_buffer, upload_buffer, texture_image, VK_IMAGE_LAYOUT_TRANSFER_DST_OPTIMAL, 1, &region);
-
-    VkImageMemoryBarrier use_barrier[1] = {};
-    use_barrier[0].sType = VK_STRUCTURE_TYPE_IMAGE_MEMORY_BARRIER;
-    use_barrier[0].srcAccessMask = VK_ACCESS_TRANSFER_WRITE_BIT;
-    use_barrier[0].dstAccessMask = VK_ACCESS_SHADER_READ_BIT;
-    use_barrier[0].oldLayout = VK_IMAGE_LAYOUT_TRANSFER_DST_OPTIMAL;
-    use_barrier[0].newLayout = VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL;
-    use_barrier[0].srcQueueFamilyIndex = VK_QUEUE_FAMILY_IGNORED;
-    use_barrier[0].dstQueueFamilyIndex = VK_QUEUE_FAMILY_IGNORED;
-    use_barrier[0].image = texture_image;
-    use_barrier[0].subresourceRange.aspectMask = VK_IMAGE_ASPECT_COLOR_BIT;
-    use_barrier[0].subresourceRange.levelCount = 1;
-    use_barrier[0].subresourceRange.layerCount = 1;
-    vkCmdPipelineBarrier(command_buffer, VK_PIPELINE_STAGE_TRANSFER_BIT, VK_PIPELINE_STAGE_FRAGMENT_SHADER_BIT, 0, 0, NULL, 0, NULL, 1, use_barrier);
-  }
-
-  // End command buffer
-  {
-    VkSubmitInfo end_info = {};
-    end_info.sType = VK_STRUCTURE_TYPE_SUBMIT_INFO;
-    end_info.commandBufferCount = 1;
-    end_info.pCommandBuffers = &command_buffer;
-    err = vkEndCommandBuffer(command_buffer);
-    check_vk_result(err);
-    err = vkQueueSubmit(g_Queue, 1, &end_info, VK_NULL_HANDLE);
-    check_vk_result(err);
-    err = vkDeviceWaitIdle(g_Device);
-    check_vk_result(err);
-  }
-
-  return true;
-}
-
 // Returns the names of the Vulkan layers used for the given IREE
 // |extensibility_set| and |features|.
 std::vector<const char*> GetIreeLayers(
@@ -723,7 +562,16 @@ namespace iree {
 
 extern "C" int iree_main(int argc, char** argv) {
 
-  fprintf(stdout, "starting yo\n");
+  iree_flags_parse_checked(IREE_FLAGS_PARSE_MODE_DEFAULT, &argc, &argv);
+  if (argc > 1) {
+    // Avoid iree-run-module spinning endlessly on stdin if the user uses single
+    // dashes for flags.
+    printf(
+        "[ERROR] unexpected positional argument (expected none)."
+        " Did you use pass a flag with a single dash ('-')?"
+        " Use '--' instead.\n");
+    return 1;
+  }
 
   // --------------------------------------------------------------------------
   // Create a window.
@@ -835,8 +683,6 @@ extern "C" int iree_main(int argc, char** argv) {
 
   // Demo state.
   bool show_iree_window = true;
-  // --------------------------------------------------------------------------
-
   // --------------------------------------------------------------------------
   // Setup IREE.
 
@@ -900,69 +746,44 @@ extern "C" int iree_main(int argc, char** argv) {
 
 
   // Load bytecode module
-  iree_file_toc_t module_file_toc;
-  const char network_model[] = "resnet50_tf.vmfb";
-  fprintf(stdout, "Loading: %s\n", network_model);
-  if (load_file(network_model, &module_file_toc.data, &module_file_toc.size) == false)
-  {
-      abort();
-      return 1;
-  }
-  fprintf(stdout, "module size: %zu\n", module_file_toc.size);
-
-  static float input_res50[224*224*3];
-  static float output_res50[1000];
-
-  char filename[] = "dog_imagenet.jpg";
-  fprintf(stdout, "loading: %s\n", filename);
-  int x,y,n;
-  //unsigned char *image_raw = stbi_load(filename, &x, &y, &n, 3);
-  stbi_load(filename, &x, &y, &n, 3);
-  fprintf(stdout, "res: %i x %i x %i\n", x, y, n);
-
-  /* Preprocessing needs to go here. For now use a buffer preprocessed in python.
-
-  //convert image into floating point format
-  for(int i=0;i<224*224*3;i++)
-  {
-    input_res50[i]= ((float)image_raw[i])/255.0f;
-  }*/
-
-  std::ifstream fin("dog.bin", std::ifstream::in | std::ifstream::binary);
-  fin.read((char*)input_res50, 224*224*3*sizeof(float));
-
-  // load image again so imgui can display it
-  int my_image_width = 0;
-  int my_image_height = 0;
-  VkDescriptorSet my_image_texture = 0;
-  bool ret = LoadTextureFromFile(filename, &my_image_texture, &my_image_width, &my_image_height);
-  fprintf(stdout, "creating vulkan image: %s\n", ret ?"OK":"FAIL");
-  IM_ASSERT(ret);
+  //iree_file_toc_t module_file_toc;
+  //const char network_model[] = "resnet50_tf.vmfb";
+  //fprintf(stdout, "Loading: %s\n", network_model);
+  //if (load_file(network_model, &module_file_toc.data, &module_file_toc.size) == false)
+  //{
+  //    abort();
+  //    return 1;
+  //}
+  //fprintf(stdout, "module size: %zu\n", module_file_toc.size);
 
   iree_vm_module_t* bytecode_module = nullptr;
-  IREE_CHECK_OK(iree_vm_bytecode_module_create(
-      iree_instance,
-      iree_const_byte_span_t{
-          reinterpret_cast<const uint8_t*>(module_file_toc.data),
-          module_file_toc.size},
-      iree_allocator_null(), iree_allocator_system(), &bytecode_module));
-  // Query for details about what is in the loaded module.
-  iree_vm_module_signature_t bytecode_module_signature =
-      iree_vm_module_signature(bytecode_module);
-  fprintf(stdout, "Module loaded, have <%" PRIhsz "> exported functions:\n",
-          bytecode_module_signature.export_function_count);
-  for (int i = 0; i < bytecode_module_signature.export_function_count; ++i) {
-    iree_vm_function_t function;
-    IREE_CHECK_OK(iree_vm_module_lookup_function_by_ordinal(
-        bytecode_module, IREE_VM_FUNCTION_LINKAGE_EXPORT, i, &function));
-    auto function_name = iree_vm_function_name(&function);
-    auto function_signature = iree_vm_function_signature(&function);
+  iree_status_t module_status = iree_tooling_load_module_from_flags(
+      iree_instance, iree_allocator_system(), &bytecode_module);
+  if (!iree_status_is_ok(module_status))
+    return -1;
+  //IREE_CHECK_OK(iree_vm_bytecode_module_create(
+  //    iree_instance,
+  //    iree_const_byte_span_t{
+  //        reinterpret_cast<const uint8_t*>(module_file_toc.data),
+  //        module_file_toc.size},
+  //    iree_allocator_null(), iree_allocator_system(), &bytecode_module));
+  //// Query for details about what is in the loaded module.
+  //iree_vm_module_signature_t bytecode_module_signature =
+  //    iree_vm_module_signature(bytecode_module);
+  //fprintf(stdout, "Module loaded, have <%" PRIhsz "> exported functions:\n",
+  //        bytecode_module_signature.export_function_count);
+  //for (int i = 0; i < bytecode_module_signature.export_function_count; ++i) {
+  //  iree_vm_function_t function;
+  //  IREE_CHECK_OK(iree_vm_module_lookup_function_by_ordinal(
+  //      bytecode_module, IREE_VM_FUNCTION_LINKAGE_EXPORT, i, &function));
+  //  auto function_name = iree_vm_function_name(&function);
+  //  auto function_signature = iree_vm_function_signature(&function);
 
-    fprintf(stdout, "  %d: '%.*s' with calling convention '%.*s'\n", i,
-            (int)function_name.size, function_name.data,
-            (int)function_signature.calling_convention.size,
-            function_signature.calling_convention.data);
-  }
+  //  fprintf(stdout, "  %d: '%.*s' with calling convention '%.*s'\n", i,
+  //          (int)function_name.size, function_name.data,
+  //          (int)function_signature.calling_convention.size,
+  //          function_signature.calling_convention.data);
+  //}
 
   // Allocate a context that will hold the module state across invocations.
   iree_vm_context_t* iree_context = nullptr;
@@ -988,33 +809,42 @@ extern "C" int iree_main(int argc, char** argv) {
         // Write inputs into mappable buffers.
         iree_hal_allocator_t* allocator =
             iree_hal_device_allocator(iree_vk_device);
-        iree_hal_memory_type_t input_memory_type =
-            static_cast<iree_hal_memory_type_t>(
-                IREE_HAL_MEMORY_TYPE_HOST_LOCAL |
-                IREE_HAL_MEMORY_TYPE_DEVICE_VISIBLE);
-        iree_hal_buffer_usage_t input_buffer_usage =
-            static_cast<iree_hal_buffer_usage_t>(IREE_HAL_BUFFER_USAGE_DEFAULT);
-        iree_hal_buffer_params_t buffer_params;
-        buffer_params.type = input_memory_type;
-        buffer_params.usage = input_buffer_usage;
-        buffer_params.access = IREE_HAL_MEMORY_ACCESS_READ | IREE_HAL_MEMORY_ACCESS_WRITE;
+        //iree_hal_memory_type_t input_memory_type =
+        //    static_cast<iree_hal_memory_type_t>(
+        //        IREE_HAL_MEMORY_TYPE_HOST_LOCAL |
+        //        IREE_HAL_MEMORY_TYPE_DEVICE_VISIBLE);
+        //iree_hal_buffer_usage_t input_buffer_usage =
+        //    static_cast<iree_hal_buffer_usage_t>(IREE_HAL_BUFFER_USAGE_DEFAULT);
+        //iree_hal_buffer_params_t buffer_params;
+        //buffer_params.type = input_memory_type;
+        //buffer_params.usage = input_buffer_usage;
+        //buffer_params.access = IREE_HAL_MEMORY_ACCESS_READ | IREE_HAL_MEMORY_ACCESS_WRITE;
 
        // Wrap input buffers in buffer views.
 
-        iree_hal_buffer_view_t* input0_buffer_view = nullptr;
-        constexpr iree_hal_dim_t input_buffer_shape[] = {1, 224, 224, 3};
-        IREE_CHECK_OK(iree_hal_buffer_view_allocate_buffer(
-            allocator,
-            /*shape_rank=*/4, /*shape=*/input_buffer_shape,
-            IREE_HAL_ELEMENT_TYPE_FLOAT_32,
-            IREE_HAL_ENCODING_TYPE_DENSE_ROW_MAJOR, buffer_params,
-            iree_make_const_byte_span(&input_res50, sizeof(input_res50)),
-            &input0_buffer_view));
-
         vm::ref<iree_vm_list_t> inputs;
-        IREE_CHECK_OK(iree_vm_list_create(/*element_type=*/nullptr, 6, iree_allocator_system(), &inputs));
-        auto input0_buffer_view_ref = iree_hal_buffer_view_move_ref(input0_buffer_view);
-        IREE_CHECK_OK(iree_vm_list_push_ref_move(inputs.get(), &input0_buffer_view_ref));
+        iree_status_t input_status = ParseToVariantList(
+            allocator,
+            iree::span<const std::string>{FLAG_function_inputs.data(),
+                                          FLAG_function_inputs.size()},
+            iree_allocator_system(), &inputs);
+        if (!iree_status_is_ok(input_status))
+            return -1;
+        //vm::ref<iree_vm_list_t> inputs;
+        //IREE_CHECK_OK(iree_vm_list_create(/*element_type=*/nullptr, 6, iree_allocator_system(), &inputs));
+
+        //iree_hal_buffer_view_t* input0_buffer_view = nullptr;
+        //constexpr iree_hal_dim_t input_buffer_shape[] = {1, 224, 224, 3};
+        //IREE_CHECK_OK(iree_hal_buffer_view_allocate_buffer(
+        //    allocator,
+        //    /*shape_rank=*/4, /*shape=*/input_buffer_shape,
+        //    IREE_HAL_ELEMENT_TYPE_FLOAT_32,
+        //    IREE_HAL_ENCODING_TYPE_DENSE_ROW_MAJOR, buffer_params,
+        //    iree_make_const_byte_span(&input_res50, sizeof(input_res50)),
+        //    &input0_buffer_view));
+
+        //auto input0_buffer_view_ref = iree_hal_buffer_view_move_ref(input0_buffer_view);
+        //IREE_CHECK_OK(iree_vm_list_push_ref_move(inputs.get(), &input0_buffer_view_ref));
 
         // Prepare outputs list to accept results from the invocation.
 
@@ -1023,6 +853,7 @@ extern "C" int iree_main(int argc, char** argv) {
         IREE_CHECK_OK(iree_vm_list_create(/*element_type=*/nullptr, kOutputCount * sizeof(float), iree_allocator_system(), &outputs));
 
   // --------------------------------------------------------------------------
+
   // Main loop.
   bool done = false;
   while (!done) {
@@ -1076,46 +907,11 @@ extern "C" int iree_main(int argc, char** argv) {
                                      /*policy=*/nullptr, inputs.get(),
                                      outputs.get(), iree_allocator_system()));
 
-        // Read back the results.
-        auto* output_buffer_view = reinterpret_cast<iree_hal_buffer_view_t*>(
-            iree_vm_list_get_ref_deref(outputs.get(),
-            0,
-            iree_hal_buffer_view_get_descriptor()));
-        IREE_CHECK_OK(iree_hal_device_transfer_d2h(
-            iree_vk_device,
-            iree_hal_buffer_view_buffer(output_buffer_view),
-            0,
-            output_res50, sizeof(output_res50),
-            IREE_HAL_TRANSFER_BUFFER_FLAG_DEFAULT, iree_infinite_timeout()));
 
         // we want to run continuously so we can use tools like RenderDoc, RGP, etc...
         dirty = true;
       }
 
-      // find maxarg from results
-      float max = 0.0f;
-      int max_idx = -1;
-      for(int i=0;i<1000;i++)
-      {
-        if (output_res50[i] > max)
-        {
-          max = output_res50[i];
-          max_idx = i;
-        }
-      }
-
-      ImGui::Text("pointer = %p", my_image_texture);
-      ImGui::Text("size = %d x %d", my_image_width, my_image_height);
-      ImGui::Image((ImTextureID)my_image_texture, ImVec2(my_image_width, my_image_height));
-
-      // Display the latest computation output.
-      ImGui::Text("Max   idx = [%i]", max_idx);
-      ImGui::Text("Max value = [%f]", max);
-
-      ImGui::Text("Resnet50 categories:");
-      ImGui::PlotHistogram("Histogram", output_res50, IM_ARRAYSIZE(output_res50), 0, NULL, 0.0f, 1.0f, ImVec2(0,80));
-      ImGui::Separator();
-
       // Framerate counter.
       ImGui::Text("Application average %.3f ms/frame (%.1f FPS)",
                   1000.0f / ImGui::GetIO().Framerate, ImGui::GetIO().Framerate);
@@ -1137,6 +933,7 @@ extern "C" int iree_main(int argc, char** argv) {
   iree_vm_module_release(bytecode_module);
   iree_vm_context_release(iree_context);
   iree_hal_device_release(iree_vk_device);
+  iree_hal_allocator_release(allocator);
   iree_hal_driver_release(iree_vk_driver);
   iree_hal_vulkan_syms_release(iree_vk_syms);
   iree_vm_instance_release(iree_instance);

dataset/README.md

@@ -0,0 +1,27 @@
+# Dataset annotation tool
+
+SHARK annotator for adding or modifying prompts of dataset images
+
+## Set up
+
+Activate SHARK Python virtual environment and install additional packages
+```shell
+source ../shark.venv/bin/activate
+pip install -r requirements.txt
+```
+
+## Run annotator
+
+```shell
+python annotation_tool.py
+```
+
+<img width="1280" alt="annotator" src="https://user-images.githubusercontent.com/49575973/214521137-7ef6ae10-7cd8-46e6-b270-b6c0445157f1.png">
+
+* Select a dataset from `Dataset` dropdown list
+* Select an image from `Image` dropdown list
+* Image and the existing prompt will be loaded
+* Select a prompt from `Prompt` dropdown list to modify or "Add new" to add a prompt
+* Click `Save` to save changes, click `Delete` to delete prompt
+* Click `Back` or `Next` to switch image, you could also select other images from `Image`
+* Click `Finish` when finishing annotation or before switching dataset

dataset/annotation_tool.py

@@ -0,0 +1,247 @@
+import gradio as gr
+import json
+import jsonlines
+import os
+from args import args
+from pathlib import Path
+from PIL import Image
+from utils import get_datasets
+
+
+shark_root = Path(__file__).parent.parent
+demo_css = shark_root.joinpath("web/demo.css").resolve()
+nodlogo_loc = shark_root.joinpath(
+    "web/models/stable_diffusion/logos/nod-logo.png"
+)
+
+
+with gr.Blocks(title="Dataset Annotation Tool", css=demo_css) as shark_web:
+    with gr.Row(elem_id="ui_title"):
+        nod_logo = Image.open(nodlogo_loc)
+        with gr.Column(scale=1, elem_id="demo_title_outer"):
+            gr.Image(
+                value=nod_logo,
+                show_label=False,
+                interactive=False,
+                elem_id="top_logo",
+            ).style(width=150, height=100)
+
+    datasets, images, ds_w_prompts = get_datasets(args.gs_url)
+    prompt_data = dict()
+
+    with gr.Row(elem_id="ui_body"):
+        # TODO: add multiselect dataset, there is a gradio version conflict
+        dataset = gr.Dropdown(label="Dataset", choices=datasets)
+        image_name = gr.Dropdown(label="Image", choices=[])
+
+    with gr.Row(elem_id="ui_body"):
+        # TODO: add ability to search image by typing
+        with gr.Column(scale=1, min_width=600):
+            image = gr.Image(type="filepath").style(height=512)
+
+        with gr.Column(scale=1, min_width=600):
+            prompts = gr.Dropdown(
+                label="Prompts",
+                choices=[],
+            )
+            prompt = gr.Textbox(
+                label="Editor",
+                lines=3,
+            )
+            with gr.Row():
+                save = gr.Button("Save")
+                delete = gr.Button("Delete")
+            with gr.Row():
+                back_image = gr.Button("Back")
+                next_image = gr.Button("Next")
+            finish = gr.Button("Finish")
+
+    def filter_datasets(dataset):
+        if dataset is None:
+            return gr.Dropdown.update(value=None, choices=[])
+
+        # create the dataset dir if doesn't exist and download prompt file
+        dataset_path = str(shark_root) + "/dataset/" + dataset
+        if not os.path.exists(dataset_path):
+            os.mkdir(dataset_path)
+
+        # read prompt jsonlines file
+        prompt_data.clear()
+        if dataset in ds_w_prompts:
+            prompt_gs_path = args.gs_url + "/" + dataset + "/metadata.jsonl"
+            os.system(f'gsutil cp "{prompt_gs_path}" "{dataset_path}"/')
+            with jsonlines.open(dataset_path + "/metadata.jsonl") as reader:
+                for line in reader.iter(type=dict, skip_invalid=True):
+                    prompt_data[line["file_name"]] = (
+                        [line["text"]]
+                        if type(line["text"]) is str
+                        else line["text"]
+                    )
+
+        return gr.Dropdown.update(choices=images[dataset])
+
+    dataset.change(fn=filter_datasets, inputs=dataset, outputs=image_name)
+
+    def display_image(dataset, image_name):
+        if dataset is None or image_name is None:
+            return gr.Image.update(value=None), gr.Dropdown.update(value=None)
+
+        # download and load the image
+        img_gs_path = args.gs_url + "/" + dataset + "/" + image_name
+        img_sub_path = "/".join(image_name.split("/")[:-1])
+        img_dst_path = (
+            str(shark_root) + "/dataset/" + dataset + "/" + img_sub_path + "/"
+        )
+        if not os.path.exists(img_dst_path):
+            os.mkdir(img_dst_path)
+        os.system(f'gsutil cp "{img_gs_path}" "{img_dst_path}"')
+        img = Image.open(img_dst_path + image_name.split("/")[-1])
+
+        if image_name not in prompt_data.keys():
+            prompt_data[image_name] = []
+        prompt_choices = ["Add new"]
+        prompt_choices += prompt_data[image_name]
+        return gr.Image.update(value=img), gr.Dropdown.update(
+            choices=prompt_choices
+        )
+
+    image_name.change(
+        fn=display_image,
+        inputs=[dataset, image_name],
+        outputs=[image, prompts],
+    )
+
+    def edit_prompt(prompts):
+        if prompts == "Add new":
+            return gr.Textbox.update(value=None)
+
+        return gr.Textbox.update(value=prompts)
+
+    prompts.change(fn=edit_prompt, inputs=prompts, outputs=prompt)
+
+    def save_prompt(dataset, image_name, prompts, prompt):
+        if (
+            dataset is None
+            or image_name is None
+            or prompts is None
+            or prompt is None
+        ):
+            return
+
+        if prompts == "Add new":
+            prompt_data[image_name].append(prompt)
+        else:
+            idx = prompt_data[image_name].index(prompts)
+            prompt_data[image_name][idx] = prompt
+
+        prompt_path = (
+            str(shark_root) + "/dataset/" + dataset + "/metadata.jsonl"
+        )
+        # write prompt jsonlines file
+        with open(prompt_path, "w") as f:
+            for key, value in prompt_data.items():
+                if not value:
+                    continue
+                v = value if len(value) > 1 else value[0]
+                f.write(json.dumps({"file_name": key, "text": v}))
+                f.write("\n")
+
+        prompt_choices = ["Add new"]
+        prompt_choices += prompt_data[image_name]
+        return gr.Dropdown.update(choices=prompt_choices, value=None)
+
+    save.click(
+        fn=save_prompt,
+        inputs=[dataset, image_name, prompts, prompt],
+        outputs=prompts,
+    )
+
+    def delete_prompt(dataset, image_name, prompts):
+        if dataset is None or image_name is None or prompts is None:
+            return
+        if prompts == "Add new":
+            return
+
+        prompt_data[image_name].remove(prompts)
+        prompt_path = (
+            str(shark_root) + "/dataset/" + dataset + "/metadata.jsonl"
+        )
+        # write prompt jsonlines file
+        with open(prompt_path, "w") as f:
+            for key, value in prompt_data.items():
+                if not value:
+                    continue
+                v = value if len(value) > 1 else value[0]
+                f.write(json.dumps({"file_name": key, "text": v}))
+                f.write("\n")
+
+        prompt_choices = ["Add new"]
+        prompt_choices += prompt_data[image_name]
+        return gr.Dropdown.update(choices=prompt_choices, value=None)
+
+    delete.click(
+        fn=delete_prompt,
+        inputs=[dataset, image_name, prompts],
+        outputs=prompts,
+    )
+
+    def get_back_image(dataset, image_name):
+        if dataset is None or image_name is None:
+            return
+
+        # remove local image
+        img_path = str(shark_root) + "/dataset/" + dataset + "/" + image_name
+        os.system(f'rm "{img_path}"')
+        # get the index for the back image
+        idx = images[dataset].index(image_name)
+        if idx == 0:
+            return gr.Dropdown.update(value=None)
+
+        return gr.Dropdown.update(value=images[dataset][idx - 1])
+
+    back_image.click(
+        fn=get_back_image, inputs=[dataset, image_name], outputs=image_name
+    )
+
+    def get_next_image(dataset, image_name):
+        if dataset is None or image_name is None:
+            return
+
+        # remove local image
+        img_path = str(shark_root) + "/dataset/" + dataset + "/" + image_name
+        os.system(f'rm "{img_path}"')
+        # get the index for the next image
+        idx = images[dataset].index(image_name)
+        if idx == len(images[dataset]) - 1:
+            return gr.Dropdown.update(value=None)
+
+        return gr.Dropdown.update(value=images[dataset][idx + 1])
+
+    next_image.click(
+        fn=get_next_image, inputs=[dataset, image_name], outputs=image_name
+    )
+
+    def finish_annotation(dataset):
+        if dataset is None:
+            return
+
+        # upload prompt and remove local data
+        dataset_path = str(shark_root) + "/dataset/" + dataset
+        dataset_gs_path = args.gs_url + "/" + dataset + "/"
+        os.system(
+            f'gsutil cp "{dataset_path}/metadata.jsonl" "{dataset_gs_path}"'
+        )
+        os.system(f'rm -rf "{dataset_path}"')
+
+        return gr.Dropdown.update(value=None)
+
+    finish.click(fn=finish_annotation, inputs=dataset, outputs=dataset)
+
+
+if __name__ == "__main__":
+    shark_web.launch(
+        share=args.share,
+        inbrowser=True,
+        server_name="0.0.0.0",
+        server_port=args.server_port,
+    )

dataset/args.py

@@ -0,0 +1,34 @@
+import argparse
+
+p = argparse.ArgumentParser(
+    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+)
+
+##############################################################################
+### Dataset Annotator flags
+##############################################################################
+
+p.add_argument(
+    "--gs_url",
+    type=str,
+    required=True,
+    help="URL to datasets in GS bucket",
+)
+
+p.add_argument(
+    "--share",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for generating a public URL",
+)
+
+p.add_argument(
+    "--server_port",
+    type=int,
+    default=8080,
+    help="flag for setting server port",
+)
+
+##############################################################################
+
+args = p.parse_args()

dataset/requirements.txt

@@ -0,0 +1,3 @@
+# SHARK Annotator
+gradio==3.15.0
+jsonlines

dataset/utils.py

@@ -0,0 +1,29 @@
+from google.cloud import storage
+
+
+def get_datasets(gs_url):
+    datasets = set()
+    images = dict()
+    ds_w_prompts = []
+
+    storage_client = storage.Client()
+    bucket_name = gs_url.split("/")[2]
+    source_blob_name = "/".join(gs_url.split("/")[3:])
+    blobs = storage_client.list_blobs(bucket_name, prefix=source_blob_name)
+
+    for blob in blobs:
+        dataset_name = blob.name.split("/")[1]
+        if dataset_name == "":
+            continue
+        datasets.add(dataset_name)
+        if dataset_name not in images.keys():
+            images[dataset_name] = []
+
+        # check if image or jsonl
+        file_sub_path = "/".join(blob.name.split("/")[2:])
+        if "/" in file_sub_path:
+            images[dataset_name] += [file_sub_path]
+        elif "metadata.jsonl" in file_sub_path:
+            ds_w_prompts.append(dataset_name)
+
+    return list(datasets), images, ds_w_prompts

generate_sharktank.py

@@ -2,33 +2,26 @@
 """SHARK Tank"""
 # python generate_sharktank.py, you have to give a csv tile with [model_name, model_download_url]
 # will generate local shark tank folder like this:
-#   HOME
-#     /.local
-#       /shark_tank
-#           /albert_lite_base
-#           /...model_name...
+#   /SHARK
+#     /gen_shark_tank
+#       /albert_lite_base
+#       /...model_name...
 #
 
 import os
 import csv
 import argparse
 from shark.shark_importer import SharkImporter
-from shark.parser import shark_args
-import tensorflow as tf
 import subprocess as sp
 import hashlib
 import numpy as np
 from pathlib import Path
-
-visible_default = tf.config.list_physical_devices("GPU")
-try:
-    tf.config.set_visible_devices([], "GPU")
-    visible_devices = tf.config.get_visible_devices()
-    for device in visible_devices:
-        assert device.device_type != "GPU"
-except:
-    # Invalid device or cannot modify virtual devices once initialized.
-    pass
+from apps.stable_diffusion.src.models import (
+    model_wrappers as mw,
+)
+from apps.stable_diffusion.src.utils.stable_args import (
+    args,
+)
 
 
 def create_hash(file_name):
@@ -41,9 +34,12 @@ def create_hash(file_name):
 
 
 def save_torch_model(torch_model_list):
-    from tank.model_utils import get_hf_model
-    from tank.model_utils import get_vision_model
-    from tank.model_utils import get_hf_img_cls_model
+    from tank.model_utils import (
+        get_hf_model,
+        get_vision_model,
+        get_hf_img_cls_model,
+        get_fp16_model,
+    )
 
     with open(torch_model_list) as csvfile:
         torch_reader = csv.reader(csvfile, delimiter=",")
@@ -59,13 +55,39 @@ def save_torch_model(torch_model_list):
 
             model = None
             input = None
+            if model_type == "stable_diffusion":
+                args.use_tuned = False
+                args.import_mlir = True
+                args.use_tuned = False
+                args.local_tank_cache = WORKDIR
+
+                precision_values = ["fp16"]
+                seq_lengths = [64, 77]
+                for precision_value in precision_values:
+                    args.precision = precision_value
+                    for length in seq_lengths:
+                        model = mw.SharkifyStableDiffusionModel(
+                            model_id=torch_model_name,
+                            custom_weights="",
+                            precision=precision_value,
+                            max_len=length,
+                            width=512,
+                            height=512,
+                            use_base_vae=False,
+                            debug=True,
+                            sharktank_dir=WORKDIR,
+                            generate_vmfb=False,
+                        )
+                        model()
+                continue
             if model_type == "vision":
                 model, input, _ = get_vision_model(torch_model_name)
             elif model_type == "hf":
                 model, input, _ = get_hf_model(torch_model_name)
             elif model_type == "hf_img_cls":
                 model, input, _ = get_hf_img_cls_model(torch_model_name)
-
+            elif model_type == "fp16":
+                model, input, _ = get_fp16_model(torch_model_name)
             torch_model_name = torch_model_name.replace("/", "_")
             torch_model_dir = os.path.join(
                 WORKDIR, str(torch_model_name) + "_torch"
@@ -106,6 +128,17 @@ def save_tf_model(tf_model_list):
         get_keras_model,
         get_TFhf_model,
     )
+    import tensorflow as tf
+
+    visible_default = tf.config.list_physical_devices("GPU")
+    try:
+        tf.config.set_visible_devices([], "GPU")
+        visible_devices = tf.config.get_visible_devices()
+        for device in visible_devices:
+            assert device.device_type != "GPU"
+    except:
+        # Invalid device or cannot modify virtual devices once initialized.
+        pass
 
     with open(tf_model_list) as csvfile:
         tf_reader = csv.reader(csvfile, delimiter=",")
@@ -201,51 +234,48 @@ def is_valid_file(arg):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--torch_model_csv",
-        type=lambda x: is_valid_file(x),
-        default="./tank/pytorch/torch_model_list.csv",
-        help="""Contains the file with torch_model name and args.
-             Please see: https://github.com/nod-ai/SHARK/blob/main/tank/pytorch/torch_model_list.csv""",
-    )
-    parser.add_argument(
-        "--tf_model_csv",
-        type=lambda x: is_valid_file(x),
-        default="./tank/tf/tf_model_list.csv",
-        help="Contains the file with tf model name and args.",
-    )
-    parser.add_argument(
-        "--tflite_model_csv",
-        type=lambda x: is_valid_file(x),
-        default="./tank/tflite/tflite_model_list.csv",
-        help="Contains the file with tf model name and args.",
-    )
-    parser.add_argument(
-        "--ci_tank_dir",
-        type=bool,
-        default=False,
-    )
-    parser.add_argument("--upload", type=bool, default=False)
+    # Note, all of these flags are overridden by the import of args from stable_args.py, flags are duplicated temporarily to preserve functionality
+    # parser = argparse.ArgumentParser()
+    # parser.add_argument(
+    #    "--torch_model_csv",
+    #    type=lambda x: is_valid_file(x),
+    #    default="./tank/torch_model_list.csv",
+    #    help="""Contains the file with torch_model name and args.
+    #         Please see: https://github.com/nod-ai/SHARK/blob/main/tank/torch_model_list.csv""",
+    # )
+    # parser.add_argument(
+    #    "--tf_model_csv",
+    #    type=lambda x: is_valid_file(x),
+    #    default="./tank/tf_model_list.csv",
+    #    help="Contains the file with tf model name and args.",
+    # )
+    # parser.add_argument(
+    #    "--tflite_model_csv",
+    #    type=lambda x: is_valid_file(x),
+    #    default="./tank/tflite/tflite_model_list.csv",
+    #    help="Contains the file with tf model name and args.",
+    # )
+    # parser.add_argument(
+    #    "--ci_tank_dir",
+    #    type=bool,
+    #    default=False,
+    # )
+    # parser.add_argument("--upload", type=bool, default=False)
 
-    args = parser.parse_args()
+    # old_args = parser.parse_args()
 
     home = str(Path.home())
-    if args.ci_tank_dir == True:
-        WORKDIR = os.path.join(os.path.dirname(__file__), "gen_shark_tank")
-    else:
-        WORKDIR = os.path.join(home, ".local/shark_tank/")
+    WORKDIR = os.path.join(os.path.dirname(__file__), "gen_shark_tank")
+    torch_model_csv = os.path.join(
+        os.path.dirname(__file__), "tank", "torch_model_list.csv"
+    )
+    tf_model_csv = os.path.join(
+        os.path.dirname(__file__), "tank", "tf_model_list.csv"
+    )
+    tflite_model_csv = os.path.join(
+        os.path.dirname(__file__), "tank", "tflite", "tflite_model_list.csv"
+    )
 
-    if args.torch_model_csv:
-        save_torch_model(args.torch_model_csv)
-
-    if args.tf_model_csv:
-        save_tf_model(args.tf_model_csv)
-
-    if args.tflite_model_csv:
-        save_tflite_model(args.tflite_model_csv)
-
-    if args.upload:
-        git_hash = sp.getoutput("git log -1 --format='%h'") + "/"
-        print("uploading files to gs://shark_tank/" + git_hash)
-        os.system(f"gsutil cp -r {WORKDIR}* gs://shark_tank/" + git_hash)
+    save_torch_model(torch_model_csv)
+    save_tf_model(tf_model_csv)
+    save_tflite_model(tflite_model_csv)

process_skipfiles.py

@@ -0,0 +1,34 @@
+# This script will toggle the comment/uncommenting aspect for dealing
+# with __file__ AttributeError arising in case of a few modules in
+# `torch/_dynamo/skipfiles.py` (within shark.venv)
+
+from distutils.sysconfig import get_python_lib
+import fileinput
+from pathlib import Path
+
+path_to_skipfiles = Path(get_python_lib() + "/torch/_dynamo/skipfiles.py")
+
+modules_to_comment = ["abc,", "os,", "posixpath,", "_collections_abc,"]
+startMonitoring = 0
+for line in fileinput.input(path_to_skipfiles, inplace=True):
+    if "SKIP_DIRS = " in line:
+        startMonitoring = 1
+        print(line, end="")
+    elif startMonitoring in [1, 2]:
+        if "]" in line:
+            startMonitoring += 1
+            print(line, end="")
+        else:
+            flag = True
+            for module in modules_to_comment:
+                if module in line:
+                    if not line.startswith("#"):
+                        print(f"#{line}", end="")
+                    else:
+                        print(f"{line[1:]}", end="")
+                    flag = False
+                    break
+            if flag:
+                print(line, end="")
+    else:
+        print(line, end="")

pyproject.toml

@@ -4,9 +4,9 @@ requires = [
     "wheel",
     "packaging",
 
-    "numpy==1.22.4",
-    "torch-mlir>=20220428.420",
-    "iree-compiler>=20220427.13",
-    "iree-runtime>=20220427.13",
+    "numpy>=1.22.4",
+    "torch-mlir>=20221021.633",
+    "iree-compiler>=20221022.190",
+    "iree-runtime>=20221022.190",
 ]
 build-backend = "setuptools.build_meta"

pytest.ini

@@ -1,3 +1,3 @@
 [pytest]
 addopts = --verbose -p no:warnings
-norecursedirs = inference tank/tflite 
+norecursedirs = inference tank/tflite examples benchmarks shark 

requirements-importer-macos.txt

@@ -1,4 +1,4 @@
--f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
+-f https://download.pytorch.org/whl/nightly/cpu/
 --pre
 
 numpy
@@ -28,6 +28,7 @@ Pillow
 
 # web dependecies.
 gradio
+altair
 
 # Testing and support.
 #lit

requirements-importer.txt

@@ -2,8 +2,9 @@
 --pre
 
 numpy==1.22.4
-torch
 torchvision
+pytorch-triton
+tabulate
 
 tqdm
 
@@ -14,7 +15,8 @@ iree-tools-tf
 
 # TensorFlow and JAX.
 gin-config
-tensorflow
+tensorflow==2.10.1
+keras==2.10
 #tf-models-nightly
 #tensorflow-text-nightly
 transformers
@@ -34,6 +36,7 @@ sacremoses
 
 # web dependecies.
 gradio
+altair
 scipy
 
 #ONNX and ORT for benchmarking

requirements.txt

@@ -5,10 +5,25 @@ wheel
 tqdm
 
 # SHARK Downloader
-gsutil
+google-cloud-storage
 
 # Testing
 pytest
 pytest-xdist
+pytest-forked
 Pillow
 parameterized
+
+# Add transformers, diffusers and scipy since it most commonly used
+transformers
+diffusers @ git+https://github.com/huggingface/diffusers@4c52982a0be7dd850fb9eac55b11509846e4bbe6
+scipy
+ftfy
+gradio
+altair
+omegaconf
+safetensors
+
+# Keep PyInstaller at the end. Sometimes Windows Defender flags it but most folks can continue even if it errors
+pefile
+pyinstaller

setup.py

@@ -2,16 +2,17 @@ from setuptools import find_packages
 from setuptools import setup
 
 import os
+import glob
 
 with open("README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()
 
-PACKAGE_VERSION = os.environ.get("SHARK_PACKAGE_VERSION") or "0.0.4"
+PACKAGE_VERSION = os.environ.get("SHARK_PACKAGE_VERSION") or "0.0.5"
 backend_deps = []
 if "NO_BACKEND" in os.environ.keys():
     backend_deps = [
-        "iree-compiler>=20220427.13",
-        "iree-runtime>=20220427.13",
+        "iree-compiler>=20221022.190",
+        "iree-runtime>=20221022.190",
     ]
 
 setup(
@@ -33,11 +34,12 @@ setup(
         "Operating System :: OS Independent",
     ],
     packages=find_packages(exclude=("examples")),
-    python_requires=">=3.7",
+    python_requires=">=3.9",
+    data_files=glob.glob("apps/stable_diffusion/resources/**"),
     install_requires=[
         "numpy",
         "PyYAML",
-        "torch-mlir>=20220428.420",
+        "torch-mlir>=20221021.633",
     ]
     + backend_deps,
 )

setup_venv.ps1

@@ -0,0 +1,45 @@
+param([string]$arguments)
+
+if ($arguments -eq "--update-src"){
+	git pull
+}
+
+#Write-Host "Installing python"
+
+#Start-Process winget install Python.Python.3.10 '/quiet InstallAllUsers=1 PrependPath=1' -wait -NoNewWindow
+
+#Write-Host "python installation completed successfully"
+
+#Write-Host "Reload environment variables"
+#$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
+#Write-Host "Reloaded environment variables"
+
+
+# redirect stderr into stdout
+$p = &{python -V} 2>&1
+# check if an ErrorRecord was returned
+$version = if($p -is [System.Management.Automation.ErrorRecord])
+{
+    # grab the version string from the error message
+    $p.Exception.Message
+}
+else
+{
+    # otherwise return as is
+    $p
+}
+
+Write-Host "Python version found is"
+Write-Host $p
+
+
+Write-Host "Installing Build Dependencies"
+python -m venv .\shark.venv\
+.\shark.venv\Scripts\activate
+pip install -r requirements.txt
+pip install --pre torch-mlir torch torchvision --extra-index-url https://download.pytorch.org/whl/nightly/cpu -f https://llvm.github.io/torch-mlir/package-index/
+pip install --upgrade -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html iree-compiler iree-runtime
+Write-Host "Building SHARK..."
+pip install -e . -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
+Write-Host "Build and installation completed successfully"
+Write-Host "Source your venv with ./shark.venv/Scripts/activate"

setup_venv.sh

@@ -42,7 +42,7 @@ Green=`tput setaf 2`
 Yellow=`tput setaf 3`
 
 # Assume no binary torch-mlir.
-# Currently available for macOS m1&intel (3.10) and Linux(3.7,3.8,3.9,3.10)
+# Currently available for macOS m1&intel (3.11) and Linux(3.8,3.10,3.11)
 torch_mlir_bin=false
 if [[ $(uname -s) = 'Darwin' ]]; then
   echo "${Yellow}Apple macOS detected"
@@ -60,12 +60,12 @@ if [[ $(uname -s) = 'Darwin' ]]; then
   fi
   echo "${Yellow}Run the following commands to setup your SSL certs for your Python version if you see SSL errors with tests"
   echo "${Yellow}/Applications/Python\ 3.XX/Install\ Certificates.command"
-  if [ "$PYTHON_VERSION_X_Y" == "3.10" ]; then
+  if [ "$PYTHON_VERSION_X_Y" == "3.11" ]; then
     torch_mlir_bin=true
   fi
 elif [[ $(uname -s) = 'Linux' ]]; then
   echo "${Yellow}Linux detected"
-  if [ "$PYTHON_VERSION_X_Y" == "3.7" ] || [ "$PYTHON_VERSION_X_Y" == "3.8" ]  || [ "$PYTHON_VERSION_X_Y" == "3.9" ] || [ "$PYTHON_VERSION_X_Y" == "3.10" ] ; then
+  if [ "$PYTHON_VERSION_X_Y" == "3.8" ]  || [ "$PYTHON_VERSION_X_Y" == "3.10" ] || [ "$PYTHON_VERSION_X_Y" == "3.11" ] ; then
     torch_mlir_bin=true
   fi
 else
@@ -76,50 +76,64 @@ fi
 $PYTHON -m pip install --upgrade pip || die "Could not upgrade pip"
 $PYTHON -m pip install --upgrade -r "$TD/requirements.txt"
 if [ "$torch_mlir_bin" = true ]; then
-  $PYTHON -m pip install --pre torch-mlir -f https://llvm.github.io/torch-mlir/package-index/
-  if [ $? -eq 0 ];then
-    echo "Successfully Installed torch-mlir"
+  if [[ $(uname -s) = 'Darwin' ]]; then
+    echo "MacOS detected. Installing torch-mlir from .whl, to avoid dependency problems with torch."
+    $PYTHON -m pip install --pre --no-cache-dir  torch-mlir -f https://llvm.github.io/torch-mlir/package-index/ -f https://download.pytorch.org/whl/nightly/torch/
   else
-    echo "Could not install torch-mlir" >&2
+    $PYTHON -m pip install --pre torch-mlir -f https://llvm.github.io/torch-mlir/package-index/
+    if [ $? -eq 0 ];then
+      echo "Successfully Installed torch-mlir"
+    else
+      echo "Could not install torch-mlir" >&2
+    fi
   fi
 else
   echo "${Red}No binaries found for Python $PYTHON_VERSION_X_Y on $(uname -s)"
-  echo "${Yello}Python 3.10 supported on macOS and 3.7,3.8,3.9 and 3.10 on Linux"
+  echo "${Yello}Python 3.11 supported on macOS and 3.8,3.10 and 3.11 on Linux"
   echo "${Red}Please build torch-mlir from source in your environment"
   exit 1
 fi
 if [[ -z "${USE_IREE}" ]]; then
-  RUNTIME="nod-ai/SHARK-Runtime"
+  rm .use-iree
+  RUNTIME="https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html"
 else
-  RUNTIME="google/iree"
+  touch ./.use-iree
+  RUNTIME="https://iree-org.github.io/iree/pip-release-links.html"
 fi
 if [[ -z "${NO_BACKEND}" ]]; then
   echo "Installing ${RUNTIME}..."
-  $PYTHON -m pip install --find-links https://github.com/${RUNTIME}/releases iree-compiler iree-runtime
+  $PYTHON -m pip install --upgrade --find-links ${RUNTIME} iree-compiler iree-runtime
 else
   echo "Not installing a backend, please make sure to add your backend to PYTHONPATH"
 fi
+
 if [[ ! -z "${IMPORTER}" ]]; then
   echo "${Yellow}Installing importer tools.."
   if [[ $(uname -s) = 'Linux' ]]; then
     echo "${Yellow}Linux detected.. installing Linux importer tools"
-    $PYTHON -m pip install --upgrade -r "$TD/requirements-importer.txt" -f https://github.com/${RUNTIME}/releases --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+    #Always get the importer tools from upstream IREE
+    $PYTHON -m pip install --no-warn-conflicts --upgrade -r "$TD/requirements-importer.txt" -f https://iree-org.github.io/iree/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
   elif [[ $(uname -s) = 'Darwin' ]]; then
     echo "${Yellow}macOS detected.. installing macOS importer tools"
     #Conda seems to have some problems installing these packages and hope they get resolved upstream.
-    $PYTHON -m pip install --upgrade -r "$TD/requirements-importer-macos.txt" -f https://github.com/${RUNTIME}/releases --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+    $PYTHON -m pip install --no-warn-conflicts --upgrade -r "$TD/requirements-importer-macos.txt" -f ${RUNTIME} --extra-index-url https://download.pytorch.org/whl/nightly/cpu
   fi
 fi
 
-$PYTHON -m pip install -e . -f https://llvm.github.io/torch-mlir/package-index/ -f https://github.com/${RUNTIME}/releases
+$PYTHON -m pip install --no-warn-conflicts -e . -f https://llvm.github.io/torch-mlir/package-index/ -f ${RUNTIME} -f https://download.pytorch.org/whl/nightly/torch/
 
 if [[ $(uname -s) = 'Linux' && ! -z "${BENCHMARK}" ]]; then
+  T_VER=$($PYTHON -m pip show torch | grep Version)
+  TORCH_VERSION=${T_VER:9:17}
+  TV_VER=$($PYTHON -m pip show torchvision | grep Version)
+  TV_VERSION=${TV_VER:9:18}
   $PYTHON -m pip uninstall -y torch torchvision
-  $PYTHON -m pip install --pre torch torchvision --extra-index-url https://download.pytorch.org/whl/nightly/cu116
+  $PYTHON -m pip install -U --pre --no-warn-conflicts triton
+  $PYTHON -m pip install --no-deps https://download.pytorch.org/whl/nightly/cu117/torch-${TORCH_VERSION}%2Bcu117-cp310-cp310-linux_x86_64.whl https://download.pytorch.org/whl/nightly/cu117/torchvision-${TV_VERSION}%2Bcu117-cp310-cp310-linux_x86_64.whl
   if [ $? -eq 0 ];then
-    echo "Successfully Installed torch + cu116."
+    echo "Successfully Installed torch + cu117."
   else
-    echo "Could not install torch + cu116." >&2
+    echo "Could not install torch + cu117." >&2
   fi
 fi
 

shark/examples/shark_dynamo/basic_examples.py

@@ -1,6 +1,6 @@
-import torchdynamo
 import torch
 import torch_mlir
+import torch._dynamo as torchdynamo
 from shark.sharkdynamo.utils import make_shark_compiler
 
 

shark/examples/shark_eager/dynamo_demo.ipynb

@@ -36,7 +36,9 @@
     "    from torchdynamo.optimizations.backends import create_backend\n",
     "    from torchdynamo.optimizations.subgraph import SubGraph\n",
     "except ModuleNotFoundError:\n",
-    "    print(\"Please install TorchDynamo using pip install git+https://github.com/pytorch/torchdynamo\")\n",
+    "    print(\n",
+    "        \"Please install TorchDynamo using pip install git+https://github.com/pytorch/torchdynamo\"\n",
+    "    )\n",
     "    exit()\n",
     "\n",
     "# torch-mlir imports for compiling\n",
@@ -97,7 +99,9 @@
     "\n",
     "        for node in fx_g.graph.nodes:\n",
     "            if node.op == \"output\":\n",
-    "                assert len(node.args) == 1, \"Output node must have a single argument\"\n",
+    "                assert (\n",
+    "                    len(node.args) == 1\n",
+    "                ), \"Output node must have a single argument\"\n",
     "                node_arg = node.args[0]\n",
     "                if isinstance(node_arg, tuple) and len(node_arg) == 1:\n",
     "                    node.args = (node_arg[0],)\n",
@@ -116,8 +120,12 @@
     "    if len(args) == 1 and isinstance(args[0], list):\n",
     "        args = args[0]\n",
     "\n",
-    "    linalg_module = compile(ts_graph, args, output_type=OutputType.LINALG_ON_TENSORS)\n",
-    "    callable, _ = get_iree_compiled_module(linalg_module, \"cuda\", func_name=\"forward\")\n",
+    "    linalg_module = compile(\n",
+    "        ts_graph, args, output_type=OutputType.LINALG_ON_TENSORS\n",
+    "    )\n",
+    "    callable, _ = get_iree_compiled_module(\n",
+    "        linalg_module, \"cuda\", func_name=\"forward\"\n",
+    "    )\n",
     "\n",
     "    def forward(*inputs):\n",
     "        return callable(*inputs)\n",
@@ -212,6 +220,7 @@
     "    assert isinstance(subgraph, SubGraph), \"Model must be a dynamo SubGraph.\"\n",
     "    return __torch_mlir(subgraph.model, *list(subgraph.example_inputs))\n",
     "\n",
+    "\n",
     "@torchdynamo.optimize(\"torch_mlir\")\n",
     "def toy_example2(*args):\n",
     "    a, b = args\n",

shark/examples/shark_inference/CLIPModel_tf.py

@@ -22,7 +22,7 @@ class CLIPModule(tf.Module):
             input_ids=x, attention_mask=y, pixel_values=z
         )
 
-    @tf.function(input_signature=clip_vit_inputs)
+    @tf.function(input_signature=clip_vit_inputs, jit_compile=True)
     def forward(self, input_ids, attention_mask, pixel_values):
         return self.m.predict(
             input_ids, attention_mask, pixel_values

shark/examples/shark_inference/ESRGAN/README.md

@@ -0,0 +1,15 @@
+## Running ESRGAN
+
+```
+1. pip install numpy opencv-python
+2. mkdir InputImages
+   (this is where all the input images will reside in)
+3. mkdir OutputImages
+   (this is where the model will generate all the images)
+4. mkdir models
+   (save the .pth checkpoint file here)
+5. python esrgan.py
+```
+
+- Download [RRDB_ESRGAN_x4.pth](https://drive.google.com/drive/u/0/folders/17VYV_SoZZesU6mbxz2dMAIccSSlqLecY) and place it in the `models` directory as mentioned above in step 4.
+- Credits : [ESRGAN](https://github.com/xinntao/ESRGAN)

shark/examples/shark_inference/ESRGAN/esrgan.py

@@ -0,0 +1,239 @@
+from ast import arg
+import os.path as osp
+import glob
+import cv2
+import numpy as np
+import torch
+
+from torch.fx.experimental.proxy_tensor import make_fx
+from torch._decomp import get_decompositions
+from shark.shark_inference import SharkInference
+import torch_mlir
+import tempfile
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def make_layer(block, n_layers):
+    layers = []
+    for _ in range(n_layers):
+        layers.append(block())
+    return nn.Sequential(*layers)
+
+
+class ResidualDenseBlock_5C(nn.Module):
+    def __init__(self, nf=64, gc=32, bias=True):
+        super(ResidualDenseBlock_5C, self).__init__()
+        # gc: growth channel, i.e. intermediate channels
+        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
+        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
+        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+        # initialization
+        # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    def forward(self, x):
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        return x5 * 0.2 + x
+
+
+class RRDB(nn.Module):
+    """Residual in Residual Dense Block"""
+
+    def __init__(self, nf, gc=32):
+        super(RRDB, self).__init__()
+        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
+
+    def forward(self, x):
+        out = self.RDB1(x)
+        out = self.RDB2(out)
+        out = self.RDB3(out)
+        return out * 0.2 + x
+
+
+class RRDBNet(nn.Module):
+    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
+        super(RRDBNet, self).__init__()
+        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
+
+        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
+        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
+        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        #### upsampling
+        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        fea = self.conv_first(x)
+        trunk = self.trunk_conv(self.RRDB_trunk(fea))
+        fea = fea + trunk
+
+        fea = self.lrelu(
+            self.upconv1(F.interpolate(fea, scale_factor=2, mode="nearest"))
+        )
+        fea = self.lrelu(
+            self.upconv2(F.interpolate(fea, scale_factor=2, mode="nearest"))
+        )
+        out = self.conv_last(self.lrelu(self.HRconv(fea)))
+
+        return out
+
+
+############### Parsing args #####################
+import argparse
+
+p = argparse.ArgumentParser(
+    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+)
+
+p.add_argument("--device", type=str, default="cpu", help="the device to use")
+p.add_argument(
+    "--mlir_loc",
+    type=str,
+    default=None,
+    help="location of the model's mlir file",
+)
+args = p.parse_args()
+###################################################
+
+
+def inference(input_m):
+    return model(input_m)
+
+
+def load_mlir(mlir_loc):
+    import os
+
+    if mlir_loc == None:
+        return None
+    print(f"Trying to load the model from {mlir_loc}.")
+    with open(os.path.join(mlir_loc)) as f:
+        mlir_module = f.read()
+    return mlir_module
+
+
+def compile_through_fx(model, inputs, mlir_loc=None):
+    module = load_mlir(mlir_loc)
+    if module == None:
+        fx_g = make_fx(
+            model,
+            decomposition_table=get_decompositions(
+                [
+                    torch.ops.aten.embedding_dense_backward,
+                    torch.ops.aten.native_layer_norm_backward,
+                    torch.ops.aten.slice_backward,
+                    torch.ops.aten.select_backward,
+                    torch.ops.aten.norm.ScalarOpt_dim,
+                    torch.ops.aten.native_group_norm,
+                    torch.ops.aten.upsample_bilinear2d.vec,
+                    torch.ops.aten.split.Tensor,
+                    torch.ops.aten.split_with_sizes,
+                ]
+            ),
+        )(inputs)
+
+        fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
+        fx_g.recompile()
+
+        def strip_overloads(gm):
+            """
+            Modifies the target of graph nodes in :attr:`gm` to strip overloads.
+            Args:
+                gm(fx.GraphModule): The input Fx graph module to be modified
+            """
+            for node in gm.graph.nodes:
+                if isinstance(node.target, torch._ops.OpOverload):
+                    node.target = node.target.overloadpacket
+            gm.recompile()
+
+        strip_overloads(fx_g)
+
+        ts_g = torch.jit.script(fx_g)
+
+        print("Torchscript graph generated successfully")
+        module = torch_mlir.compile(
+            ts_g,
+            inputs,
+            torch_mlir.OutputType.LINALG_ON_TENSORS,
+            use_tracing=False,
+            verbose=False,
+        )
+
+    mlir_model = str(module)
+    func_name = "forward"
+    shark_module = SharkInference(
+        mlir_model, func_name, device=args.device, mlir_dialect="linalg"
+    )
+    shark_module.compile()
+
+    return shark_module
+
+
+model_path = "models/RRDB_ESRGAN_x4.pth"  # models/RRDB_ESRGAN_x4.pth OR models/RRDB_PSNR_x4.pth
+# device = torch.device('cuda')  # if you want to run on CPU, change 'cuda' -> cpu
+device = torch.device("cpu")
+
+test_img_folder = "InputImages/*"
+
+model = RRDBNet(3, 3, 64, 23, gc=32)
+model.load_state_dict(torch.load(model_path), strict=True)
+model.eval()
+model = model.to(device)
+
+print("Model path {:s}. \nTesting...".format(model_path))
+
+if __name__ == "__main__":
+    idx = 0
+    for path in glob.glob(test_img_folder):
+        idx += 1
+        base = osp.splitext(osp.basename(path))[0]
+        print(idx, base)
+        # read images
+        img = cv2.imread(path, cv2.IMREAD_COLOR)
+        img = img * 1.0 / 255
+        img = torch.from_numpy(
+            np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))
+        ).float()
+        img_LR = img.unsqueeze(0)
+        img_LR = img_LR.to(device)
+
+        with torch.no_grad():
+            shark_module = compile_through_fx(inference, img_LR)
+            shark_output = shark_module.forward((img_LR,))
+            shark_output = torch.from_numpy(shark_output)
+            shark_output = (
+                shark_output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+            )
+            esrgan_output = (
+                model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy()
+            )
+        # SHARK OUTPUT
+        shark_output = np.transpose(shark_output[[2, 1, 0], :, :], (1, 2, 0))
+        shark_output = (shark_output * 255.0).round()
+        cv2.imwrite(
+            "OutputImages/{:s}_rlt_shark_output.png".format(base), shark_output
+        )
+        print("Generated SHARK's output")
+        # ESRGAN OUTPUT
+        esrgan_output = np.transpose(esrgan_output[[2, 1, 0], :, :], (1, 2, 0))
+        esrgan_output = (esrgan_output * 255.0).round()
+        cv2.imwrite(
+            "OutputImages/{:s}_rlt_esrgan_output.png".format(base),
+            esrgan_output,
+        )
+        print("Generated ESRGAN's output")

shark/examples/shark_inference/albert_maskfill_tf.py

@@ -28,7 +28,7 @@ class AlbertModule(tf.Module):
         self.m = TFAutoModelForMaskedLM.from_pretrained("albert-base-v2")
         self.m.predict = lambda x, y: self.m(input_ids=x, attention_mask=y)
 
-    @tf.function(input_signature=t5_inputs)
+    @tf.function(input_signature=t5_inputs, jit_compile=True)
     def forward(self, input_ids, attention_mask):
         return self.m.predict(input_ids, attention_mask)
 

shark/examples/shark_inference/bloom_tank.py

@@ -1,7 +1,9 @@
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
+from shark.shark_downloader import download_model
 
-mlir_model, func_name, inputs, golden_out = download_torch_model("bloom")
+mlir_model, func_name, inputs, golden_out = download_model(
+    "bloom", frontend="torch"
+)
 
 shark_module = SharkInference(
     mlir_model, func_name, device="cpu", mlir_dialect="tm_tensor"

shark/examples/shark_inference/gpt2_tf.py

@@ -19,7 +19,7 @@ class GPT2Module(tf.Module):
 
         self.m.predict = lambda x, y: self.m(input_ids=x, attention_mask=y)
 
-    @tf.function(input_signature=gpt2_inputs)
+    @tf.function(input_signature=gpt2_inputs, jit_compile=True)
     def forward(self, input_ids, attention_mask):
         return self.m.predict(input_ids, attention_mask)
 

shark/examples/shark_inference/minilm_benchmark_tf.py

@@ -26,7 +26,7 @@ class BertModule(tf.Module):
             input_ids=x, attention_mask=y, token_type_ids=z, training=False
         )
 
-    @tf.function(input_signature=bert_input)
+    @tf.function(input_signature=bert_input, jit_compile=True)
     def forward(self, input_ids, attention_mask, token_type_ids):
         return self.m.predict(input_ids, attention_mask, token_type_ids)
 

shark/examples/shark_inference/minilm_jit.py

@@ -1,9 +1,10 @@
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
+from shark.shark_downloader import download_model
 
 
-mlir_model, func_name, inputs, golden_out = download_torch_model(
-    "microsoft/MiniLM-L12-H384-uncased"
+mlir_model, func_name, inputs, golden_out = download_model(
+    "microsoft/MiniLM-L12-H384-uncased",
+    frontend="torch",
 )
 
 

shark/examples/shark_inference/minilm_tf.py

@@ -26,7 +26,7 @@ class BertModule(tf.Module):
             input_ids=x, attention_mask=y, token_type_ids=z, training=False
         )
 
-    @tf.function(input_signature=bert_input)
+    @tf.function(input_signature=bert_input, jit_compile=True)
     def forward(self, input_ids, attention_mask, token_type_ids):
         return self.m.predict(input_ids, attention_mask, token_type_ids)
 

shark/examples/shark_inference/resnet50_script.py

@@ -5,7 +5,7 @@ import torchvision.models as models
 from torchvision import transforms
 import sys
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
+from shark.shark_downloader import download_model
 
 
 ################################## Preprocessing inputs and model ############
@@ -66,10 +66,12 @@ labels = load_labels()
 
 
 ## Can pass any img or input to the forward module.
-mlir_model, func_name, inputs, golden_out = download_torch_model("resnet50")
+mlir_model, func_name, inputs, golden_out = download_model(
+    "resnet50", frontend="torch"
+)
 
 shark_module = SharkInference(mlir_model, func_name, mlir_dialect="linalg")
-# shark_module.compile()
+shark_module.compile()
 path = shark_module.save_module()
 shark_module.load_module(path)
 result = shark_module.forward((img.detach().numpy(),))

shark/examples/shark_inference/sharded_bloom.py

@@ -0,0 +1,421 @@
+####################################################################################
+# Please make sure you have transformers 4.21.2 installed before running this demo
+#
+# -p --model_path: the directory in which you want to store the bloom files.
+# -dl --device_list: the list of device indices you want to use.  if you want to only use the first device, or you are running on cpu leave this blank.
+#                     Otherwise, please give this argument in this format: "[0, 1, 2]"
+# -de --device: the device you want to run bloom on.  E.G. cpu, cuda
+# -c, --recompile: set to true if you want to recompile to vmfb.
+# -d, --download: set to true if you want to redownload the mlir files
+# -t --token_count: the number of tokens you want to generate
+# -pr --prompt: the prompt you want to feed to the model
+#####################################################################################
+
+import os
+import torch
+import torch.nn as nn
+from collections import OrderedDict
+import torch_mlir
+from torch_mlir import TensorPlaceholder
+import re
+from transformers.models.bloom.configuration_bloom import BloomConfig
+import json
+import sys
+import argparse
+from cuda.cudart import cudaSetDevice
+import json
+
+from torch.fx.experimental.proxy_tensor import make_fx
+from torch._decomp import get_decompositions
+from shark.shark_inference import SharkInference
+from shark.shark_downloader import download_public_file
+
+from transformers.models.bloom.modeling_bloom import (
+    BloomBlock,
+    build_alibi_tensor,
+)
+
+IS_CUDA = False
+
+
+class ShardedBloom:
+    def __init__(self, src_folder):
+        f = open(f"{src_folder}/config.json")
+        config = json.load(f)
+        f.close()
+
+        self.layers_initialized = False
+
+        self.src_folder = src_folder
+        self.n_embed = config["n_embed"]
+        self.vocab_size = config["vocab_size"]
+        self.n_layer = config["n_layer"]
+        self.n_head = config["num_attention_heads"]
+
+    def _init_layer(self, layer_name, device, replace, device_idx):
+        if replace or not os.path.exists(
+            f"{self.src_folder}/{layer_name}.vmfb"
+        ):
+            f_ = open(f"{self.src_folder}/{layer_name}.mlir")
+            module = f_.read()
+            f_.close()
+            module = bytes(module, "utf-8")
+            shark_module = SharkInference(
+                module,
+                device=device,
+                mlir_dialect="tm_tensor",
+                device_idx=device_idx,
+            )
+            shark_module.save_module(
+                module_name=f"{self.src_folder}/{layer_name}",
+                extra_args=[
+                    "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
+                    "--iree-stream-resource-max-allocation-size=1000000000",
+                    "--iree-codegen-check-ir-before-llvm-conversion=false",
+                ],
+            )
+        else:
+            shark_module = SharkInference(
+                "",
+                device=device,
+                mlir_dialect="tm_tensor",
+                device_idx=device_idx,
+            )
+
+        return shark_module
+
+    def init_layers(self, device, replace=False, device_idx=[0]):
+        if device_idx is not None:
+            n_devices = len(device_idx)
+
+        self.word_embeddings_module = self._init_layer(
+            "word_embeddings",
+            device,
+            replace,
+            device_idx if device_idx is None else device_idx[0 % n_devices],
+        )
+        self.word_embeddings_layernorm_module = self._init_layer(
+            "word_embeddings_layernorm",
+            device,
+            replace,
+            device_idx if device_idx is None else device_idx[1 % n_devices],
+        )
+        self.ln_f_module = self._init_layer(
+            "ln_f",
+            device,
+            replace,
+            device_idx if device_idx is None else device_idx[2 % n_devices],
+        )
+        self.lm_head_module = self._init_layer(
+            "lm_head",
+            device,
+            replace,
+            device_idx if device_idx is None else device_idx[3 % n_devices],
+        )
+        self.block_modules = [
+            self._init_layer(
+                f"bloom_block_{i}",
+                device,
+                replace,
+                device_idx
+                if device_idx is None
+                else device_idx[(i + 4) % n_devices],
+            )
+            for i in range(self.n_layer)
+        ]
+
+        self.layers_initialized = True
+
+    def load_layers(self):
+        assert self.layers_initialized
+
+        self.word_embeddings_module.load_module(
+            f"{self.src_folder}/word_embeddings.vmfb"
+        )
+        self.word_embeddings_layernorm_module.load_module(
+            f"{self.src_folder}/word_embeddings_layernorm.vmfb"
+        )
+        for block_module, i in zip(self.block_modules, range(self.n_layer)):
+            block_module.load_module(f"{self.src_folder}/bloom_block_{i}.vmfb")
+        self.ln_f_module.load_module(f"{self.src_folder}/ln_f.vmfb")
+        self.lm_head_module.load_module(f"{self.src_folder}/lm_head.vmfb")
+
+    def forward_pass(self, input_ids, device):
+        if IS_CUDA:
+            cudaSetDevice(self.word_embeddings_module.device_idx)
+
+        input_embeds = self.word_embeddings_module(
+            inputs=(input_ids,), function_name="forward"
+        )
+
+        input_embeds = torch.tensor(input_embeds).float()
+        if IS_CUDA:
+            cudaSetDevice(self.word_embeddings_layernorm_module.device_idx)
+        hidden_states = self.word_embeddings_layernorm_module(
+            inputs=(input_embeds,), function_name="forward"
+        )
+
+        hidden_states = torch.tensor(hidden_states).float()
+
+        attention_mask = torch.ones(
+            [hidden_states.shape[0], len(input_ids[0])]
+        )
+        alibi = build_alibi_tensor(
+            attention_mask,
+            self.n_head,
+            hidden_states.dtype,
+            hidden_states.device,
+        )
+
+        causal_mask = _prepare_attn_mask(
+            attention_mask, input_ids.size(), input_embeds, 0
+        )
+        causal_mask = torch.tensor(causal_mask).float()
+
+        presents = ()
+        all_hidden_states = tuple(hidden_states)
+
+        for block_module, i in zip(self.block_modules, range(self.n_layer)):
+            if IS_CUDA:
+                cudaSetDevice(block_module.device_idx)
+
+            output = block_module(
+                inputs=(
+                    hidden_states.detach().numpy(),
+                    alibi.detach().numpy(),
+                    causal_mask.detach().numpy(),
+                ),
+                function_name="forward",
+            )
+            hidden_states = torch.tensor(output[0]).float()
+            all_hidden_states = all_hidden_states + (hidden_states,)
+            presents = presents + (
+                tuple(
+                    (
+                        output[1],
+                        output[2],
+                    )
+                ),
+            )
+        if IS_CUDA:
+            cudaSetDevice(self.ln_f_module.device_idx)
+
+        hidden_states = self.ln_f_module(
+            inputs=(hidden_states,), function_name="forward"
+        )
+        if IS_CUDA:
+            cudaSetDevice(self.lm_head_module.device_idx)
+
+        logits = self.lm_head_module(
+            inputs=(hidden_states,), function_name="forward"
+        )
+        logits = torch.tensor(logits).float()
+
+        return torch.argmax(logits[:, -1, :], dim=-1)
+
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size,
+    dtype: torch.dtype,
+    past_key_values_length: int = 0,
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    batch_size, target_length = input_ids_shape
+    mask = torch.full((target_length, target_length), torch.finfo(dtype).min)
+    mask_cond = torch.arange(mask.size(-1))
+    intermediate_mask = mask_cond < (mask_cond + 1).view(mask.size(-1), 1)
+    mask.masked_fill_(intermediate_mask, 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat(
+            [
+                torch.zeros(
+                    target_length, past_key_values_length, dtype=dtype
+                ),
+                mask,
+            ],
+            dim=-1,
+        )
+    expanded_mask = mask[None, None, :, :].expand(
+        batch_size, 1, target_length, target_length + past_key_values_length
+    )
+    return expanded_mask
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: int = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    batch_size, source_length = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else source_length
+
+    expanded_mask = (
+        mask[:, None, None, :]
+        .expand(batch_size, 1, tgt_len, source_length)
+        .to(dtype)
+    )
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
+
+def _prepare_attn_mask(
+    attention_mask, input_shape, inputs_embeds, past_key_values_length
+):
+    # create causal mask
+    # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+    combined_attention_mask = None
+    if input_shape[-1] > 1:
+        combined_attention_mask = _make_causal_mask(
+            input_shape,
+            inputs_embeds.dtype,
+            past_key_values_length=past_key_values_length,
+        ).to(attention_mask.device)
+
+    if attention_mask is not None:
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        expanded_attn_mask = _expand_mask(
+            attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+        )
+        combined_attention_mask = (
+            expanded_attn_mask
+            if combined_attention_mask is None
+            else expanded_attn_mask + combined_attention_mask
+        )
+
+    return combined_attention_mask
+
+
+def download_560m(destination_folder):
+    download_public_file(
+        "https://bloom-560m/bloom_block_0.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_1.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_2.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_3.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_4.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_5.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_6.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_7.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_8.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_9.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_10.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_11.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_12.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_13.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_14.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_15.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_16.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_17.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_18.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_19.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_20.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_21.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_22.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/bloom_block_23.mlir", destination_folder
+    )
+    download_public_file("https://bloom-560m/config.json", destination_folder)
+    download_public_file("https://bloom-560m/lm_head.mlir", destination_folder)
+    download_public_file("https://bloom-560m/ln_f.mlir", destination_folder)
+    download_public_file(
+        "https://bloom-560m/word_embeddings.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/word_embeddings_layernorm.mlir", destination_folder
+    )
+    download_public_file(
+        "https://bloom-560m/tokenizer.json", destination_folder
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(prog="Bloom-560m")
+    parser.add_argument("-p", "--model_path")
+    parser.add_argument("-dl", "--device_list", default=None)
+    parser.add_argument("-de", "--device", default="cpu")
+    parser.add_argument("-c", "--recompile", default=False, type=bool)
+    parser.add_argument("-d", "--download", default=False, type=bool)
+    parser.add_argument("-t", "--token_count", default=10, type=int)
+    parser.add_argument(
+        "-pr",
+        "--prompt",
+        default="The SQL command to extract all the users whose name starts with A is: ",
+    )
+    args = parser.parse_args()
+
+    if args.device_list is not None:
+        args.device_list = json.loads(args.device_list)
+
+    if args.device == "cuda" and args.device_list is not None:
+        IS_CUDA = True
+    if args.download:
+        download_560m(args.model_path)
+    from transformers import AutoTokenizer, AutoModelForCausalLM, BloomConfig
+
+    tokenizer = AutoTokenizer.from_pretrained(args.model_path)
+    input_ids = tokenizer.encode(args.prompt, return_tensors="pt")
+
+    shardedbloom = ShardedBloom(args.model_path)
+    shardedbloom.init_layers(
+        device=args.device, replace=args.recompile, device_idx=args.device_list
+    )
+    shardedbloom.load_layers()
+
+    for _ in range(args.token_count):
+        next_token = shardedbloom.forward_pass(
+            torch.tensor(input_ids), device=args.device
+        )
+        input_ids = torch.cat([input_ids, next_token.unsqueeze(-1)], dim=-1)
+
+    print(tokenizer.decode(input_ids.squeeze()))

shark/examples/shark_inference/simple_dlrm.py

@@ -151,7 +151,6 @@ class DLRM_Net(nn.Module):
             and (ln_top is not None)
             and (arch_interaction_op is not None)
         ):
-
             # save arguments
             self.output_d = 0
             self.arch_interaction_op = arch_interaction_op
@@ -216,7 +215,6 @@ class DLRM_Net(nn.Module):
         return ly
 
     def interact_features(self, x, ly):
-
         if self.arch_interaction_op == "dot":
             # concatenate dense and sparse features
             (batch_size, d) = x.shape

shark/examples/shark_inference/sparse_arch.py

@@ -99,7 +99,6 @@ class SparseArchShark(nn.Module):
         )
 
     def forward(self, *batched_inputs):
-
         concatenated_list = []
         input_enum, embedding_enum = 0, 0
 
@@ -121,7 +120,6 @@ class SparseArchShark(nn.Module):
 
 
 def test_sparse_arch() -> None:
-
     D = 3
     eb1_config = EmbeddingBagConfig(
         name="t1",
@@ -211,7 +209,6 @@ class DLRMShark(nn.Module):
     def forward(
         self, dense_features: torch.Tensor, *sparse_features
     ) -> torch.Tensor:
-
         embedded_dense = self.dense_arch(dense_features)
         embedded_sparse = self.sparse_arch(*sparse_features)
         concatenated_dense = self.inter_arch(

shark/examples/shark_inference/stable_diff.py

@@ -1,268 +0,0 @@
-from transformers import CLIPTextModel, CLIPTokenizer
-from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler
-import torch
-from PIL import Image
-from diffusers import LMSDiscreteScheduler
-from tqdm.auto import tqdm
-from shark.shark_inference import SharkInference
-from torch.fx.experimental.proxy_tensor import make_fx
-from torch._decomp import get_decompositions
-import torch_mlir
-import tempfile
-import numpy as np
-
-# pip install diffusers
-# pip install scipy
-
-############### Parsing args #####################
-import argparse
-
-p = argparse.ArgumentParser(
-    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
-)
-
-p.add_argument(
-    "--prompt",
-    type=str,
-    default="a photograph of an astronaut riding a horse",
-    help="the text prompt to use",
-)
-p.add_argument("--device", type=str, default="cpu", help="the device to use")
-p.add_argument("--steps", type=int, default=10, help="the device to use")
-p.add_argument("--mlir_loc", type=str, default=None, help="the device to use")
-p.add_argument("--vae_loc", type=str, default=None, help="the device to use")
-args = p.parse_args()
-
-#####################################################
-
-
-def load_mlir(mlir_loc):
-    import os
-
-    if mlir_loc == None:
-        return None
-    print(f"Trying to load the model from {mlir_loc}.")
-    with open(os.path.join(mlir_loc)) as f:
-        mlir_module = f.read()
-    return mlir_module
-
-
-def compile_through_fx(model, inputs, mlir_loc=None):
-
-    module = load_mlir(mlir_loc)
-    if mlir_loc == None:
-        fx_g = make_fx(
-            model,
-            decomposition_table=get_decompositions(
-                [
-                    torch.ops.aten.embedding_dense_backward,
-                    torch.ops.aten.native_layer_norm_backward,
-                    torch.ops.aten.slice_backward,
-                    torch.ops.aten.select_backward,
-                    torch.ops.aten.norm.ScalarOpt_dim,
-                    torch.ops.aten.native_group_norm,
-                    torch.ops.aten.upsample_bilinear2d.vec,
-                    torch.ops.aten.split.Tensor,
-                    torch.ops.aten.split_with_sizes,
-                ]
-            ),
-        )(*inputs)
-
-        fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
-        fx_g.recompile()
-
-        def strip_overloads(gm):
-            """
-            Modifies the target of graph nodes in :attr:`gm` to strip overloads.
-            Args:
-                gm(fx.GraphModule): The input Fx graph module to be modified
-            """
-            for node in gm.graph.nodes:
-                if isinstance(node.target, torch._ops.OpOverload):
-                    node.target = node.target.overloadpacket
-            gm.recompile()
-
-        strip_overloads(fx_g)
-
-        ts_g = torch.jit.script(fx_g)
-
-        module = torch_mlir.compile(
-            ts_g,
-            inputs,
-            torch_mlir.OutputType.LINALG_ON_TENSORS,
-            use_tracing=False,
-            verbose=False,
-        )
-
-    mlir_model = module
-    func_name = "forward"
-
-    shark_module = SharkInference(
-        mlir_model, func_name, device=args.device, mlir_dialect="tm_tensor"
-    )
-    shark_module.compile()
-
-    return shark_module
-
-
-if __name__ == "__main__":
-
-    YOUR_TOKEN = "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"
-
-    # 1. Load the autoencoder model which will be used to decode the latents into image space.
-    vae = AutoencoderKL.from_pretrained(
-        "CompVis/stable-diffusion-v1-4",
-        subfolder="vae",
-        use_auth_token=YOUR_TOKEN,
-    )
-
-    # 2. Load the tokenizer and text encoder to tokenize and encode the text.
-    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-    text_encoder = CLIPTextModel.from_pretrained(
-        "openai/clip-vit-large-patch14"
-    )
-
-    class VaeModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.vae = AutoencoderKL.from_pretrained(
-                "CompVis/stable-diffusion-v1-4",
-                subfolder="vae",
-                use_auth_token=YOUR_TOKEN,
-            )
-
-        def forward(self, input):
-            return self.vae.decode(input, return_dict=False)[0]
-
-    vae = VaeModel()
-    vae_input = torch.rand(1, 4, 64, 64)
-    shark_vae = compile_through_fx(vae, (vae_input,), args.vae_loc)
-
-    # Wrap the unet model to return tuples.
-    class UnetModel(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.unet = UNet2DConditionModel.from_pretrained(
-                "CompVis/stable-diffusion-v1-4",
-                subfolder="unet",
-                use_auth_token=YOUR_TOKEN,
-            )
-            self.in_channels = self.unet.in_channels
-            self.train(False)
-
-        def forward(self, x, y, z):
-            return self.unet.forward(x, y, z, return_dict=False)[0]
-
-    # 3. The UNet model for generating the latents.
-    unet = UnetModel()
-    latent_model_input = torch.rand([2, 4, 64, 64])
-    text_embeddings = torch.rand([2, 77, 768])
-    shark_unet = compile_through_fx(
-        unet,
-        (latent_model_input, torch.tensor([1.0]), text_embeddings),
-        args.mlir_loc,
-    )
-
-    # torch.jit.script(unet)
-
-    scheduler = LMSDiscreteScheduler(
-        beta_start=0.00085,
-        beta_end=0.012,
-        beta_schedule="scaled_linear",
-        num_train_timesteps=1000,
-    )
-
-    prompt = [args.prompt]
-
-    height = 512  # default height of Stable Diffusion
-    width = 512  # default width of Stable Diffusion
-
-    num_inference_steps = args.steps  # Number of denoising steps
-
-    guidance_scale = 7.5  # Scale for classifier-free guidance
-
-    generator = torch.manual_seed(
-        42
-    )  # Seed generator to create the inital latent noise
-
-    batch_size = len(prompt)
-
-    text_input = tokenizer(
-        prompt,
-        padding="max_length",
-        max_length=tokenizer.model_max_length,
-        truncation=True,
-        return_tensors="pt",
-    )
-
-    text_embeddings = text_encoder(text_input.input_ids)[0]
-
-    max_length = text_input.input_ids.shape[-1]
-    uncond_input = tokenizer(
-        [""] * batch_size,
-        padding="max_length",
-        max_length=max_length,
-        return_tensors="pt",
-    )
-    uncond_embeddings = text_encoder(uncond_input.input_ids)[0]
-
-    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-    latents = torch.randn(
-        (batch_size, unet.in_channels, height // 8, width // 8),
-        generator=generator,
-    )
-    # latents = latents.to(torch_device)
-
-    scheduler.set_timesteps(num_inference_steps)
-
-    latents = latents * scheduler.sigmas[0]
-    # print(latents, latents.shape)
-
-    for i, t in tqdm(enumerate(scheduler.timesteps)):
-
-        print(f"i = {i} t = {t}")
-        # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
-        latent_model_input = torch.cat([latents] * 2)
-        sigma = scheduler.sigmas[i]
-        latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
-
-        # predict the noise residual
-
-        # with torch.no_grad():
-        # noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings)
-
-        latent_model_input_numpy = latent_model_input.detach().numpy()
-        text_embeddings_numpy = text_embeddings.detach().numpy()
-
-        noise_pred = shark_unet.forward(
-            (
-                latent_model_input_numpy,
-                np.array([t]).astype(np.float32),
-                text_embeddings_numpy,
-            )
-        )
-        noise_pred = torch.from_numpy(noise_pred)
-
-        # perform guidance
-        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-        noise_pred = noise_pred_uncond + guidance_scale * (
-            noise_pred_text - noise_pred_uncond
-        )
-
-        # compute the previous noisy sample x_t -> x_t-1
-        latents = scheduler.step(noise_pred, i, latents)["prev_sample"]
-
-    # print("Latents shape : ", latents.shape)
-
-    # scale and decode the image latents with vae
-    latents = 1 / 0.18215 * latents
-    latents_numpy = latents.detach().numpy()
-    image = shark_vae.forward((latents_numpy,))
-    image = torch.from_numpy(image)
-
-    image = (image / 2 + 0.5).clamp(0, 1)
-    image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
-    images = (image * 255).round().astype("uint8")
-    pil_images = [Image.fromarray(image) for image in images]
-    pil_images[0].save("astro.jpg")

shark/examples/shark_inference/stable_diff_tf.py

@@ -1,313 +0,0 @@
-import math
-import numpy as np
-import tensorflow as tf
-from tensorflow import keras
-from keras_cv.models.generative.stable_diffusion.clip_tokenizer import (
-    SimpleTokenizer,
-)
-from keras_cv.models.generative.stable_diffusion.constants import (
-    _ALPHAS_CUMPROD,
-)
-from keras_cv.models.generative.stable_diffusion.constants import (
-    _UNCONDITIONAL_TOKENS,
-)
-from keras_cv.models.generative.stable_diffusion.decoder import Decoder
-from keras_cv.models.generative.stable_diffusion.text_encoder import (
-    TextEncoder,
-)
-
-from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_tf_model
-from PIL import Image
-
-# pip install "git+https://github.com/keras-team/keras-cv.git"
-# pip install tensorflow_dataset
-
-############### Parsing args #####################
-import argparse
-
-p = argparse.ArgumentParser(
-    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
-)
-
-p.add_argument(
-    "--prompt",
-    type=str,
-    default="a photograph of an astronaut riding a horse",
-    help="the text prompt to use",
-)
-p.add_argument("--device", type=str, default="cpu", help="the device to use")
-p.add_argument(
-    "--steps", type=int, default=10, help="the number of steps to use"
-)
-p.add_argument(
-    "--save_path",
-    type=str,
-    default=None,
-    help="the file to save the resulting image to. (default to <input prompt>.jpg)",
-)
-args = p.parse_args()
-
-#####################################################
-
-MAX_PROMPT_LENGTH = 77
-
-
-class SharkStableDiffusion:
-    """Shark implementation of Stable Diffusion based on model from keras_cv.
-    Stable Diffusion is a powerful image generation model that can be used,
-    among other things, to generate pictures according to a short text description
-    (called a "prompt").
-    Arguments:
-        device: Device to use with SHARK. Default: cpu
-        jit_compile: Whether to compile the underlying models to XLA.
-            This can lead to a significant speedup on some systems. Default: False.
-    References:
-    - [About Stable Diffusion](https://stability.ai/blog/stable-diffusion-announcement)
-    - [Original implementation](https://github.com/CompVis/stable-diffusion)
-    """
-
-    def __init__(self, device="cpu", jit_compile=True):
-        self.img_height = 512
-        self.img_width = 512
-        self.tokenizer = SimpleTokenizer()
-
-        # Create models
-        self.text_encoder = TextEncoder(MAX_PROMPT_LENGTH)
-
-        mlir_model, func_name, inputs, golden_out = download_tf_model(
-            "stable_diff", tank_url="gs://shark_tank/quinn"
-        )
-        shark_module = SharkInference(
-            mlir_model, func_name, device=device, mlir_dialect="mhlo"
-        )
-        shark_module.compile()
-        self.diffusion_model = shark_module
-        self.decoder = Decoder(self.img_height, self.img_width)
-        if jit_compile:
-            self.text_encoder.compile(jit_compile=True)
-            self.decoder.compile(jit_compile=True)
-
-        print(
-            "By using this model checkpoint, you acknowledge that its usage is "
-            "subject to the terms of the CreativeML Open RAIL-M license at "
-            "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/LICENSE"
-        )
-        # Load weights
-        text_encoder_weights_fpath = keras.utils.get_file(
-            origin="https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv_encoder.h5",
-            file_hash="4789e63e07c0e54d6a34a29b45ce81ece27060c499a709d556c7755b42bb0dc4",
-        )
-        decoder_weights_fpath = keras.utils.get_file(
-            origin="https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv_decoder.h5",
-            file_hash="ad350a65cc8bc4a80c8103367e039a3329b4231c2469a1093869a345f55b1962",
-        )
-        self.text_encoder.load_weights(text_encoder_weights_fpath)
-        self.decoder.load_weights(decoder_weights_fpath)
-
-    def text_to_image(
-        self,
-        prompt,
-        batch_size=1,
-        num_steps=25,
-        unconditional_guidance_scale=7.5,
-        seed=None,
-    ):
-        encoded_text = self.encode_text(prompt)
-
-        return self.generate_image(
-            encoded_text,
-            batch_size=batch_size,
-            num_steps=num_steps,
-            unconditional_guidance_scale=unconditional_guidance_scale,
-            seed=seed,
-        )
-
-    def encode_text(self, prompt):
-        """Encodes a prompt into a latent text encoding.
-        The encoding produced by this method should be used as the
-        `encoded_text` parameter of `StableDiffusion.generate_image`. Encoding
-        text separately from generating an image can be used to arbitrarily
-        modify the text encoding priot to image generation, e.g. for walking
-        between two prompts.
-        Args:
-            prompt: a string to encode, must be 77 tokens or shorter.
-        Example:
-        ```python
-        from keras_cv.models import StableDiffusion
-        model = StableDiffusion(img_height=512, img_width=512, jit_compile=True)
-        encoded_text  = model.encode_text("Tacos at dawn")
-        img = model.generate_image(encoded_text)
-        ```
-        """
-        # Tokenize prompt (i.e. starting context)
-        inputs = self.tokenizer.encode(prompt)
-        if len(inputs) > MAX_PROMPT_LENGTH:
-            raise ValueError(
-                f"Prompt is too long (should be <= {MAX_PROMPT_LENGTH} tokens)"
-            )
-        phrase = inputs + [49407] * (MAX_PROMPT_LENGTH - len(inputs))
-        phrase = tf.convert_to_tensor([phrase], dtype=tf.int32)
-
-        context = self.text_encoder.predict_on_batch(
-            [phrase, self._get_pos_ids()]
-        )
-
-        return context
-
-    def generate_image(
-        self,
-        encoded_text,
-        batch_size=1,
-        num_steps=25,
-        unconditional_guidance_scale=7.5,
-        diffusion_noise=None,
-        seed=None,
-    ):
-        """Generates an image based on encoded text.
-        The encoding passed to this method should be derived from
-        `StableDiffusion.encode_text`.
-        Args:
-            encoded_text: Tensor of shape (`batch_size`, 77, 768), or a Tensor
-            of shape (77, 768). When the batch axis is omitted, the same encoded
-            text will be used to produce every generated image.
-            batch_size: number of images to generate. Default: 1.
-            num_steps: number of diffusion steps (controls image quality).
-                Default: 25.
-            unconditional_guidance_scale: float controling how closely the image
-                should adhere to the prompt. Larger values result in more
-                closely adhering to the prompt, but will make the image noisier.
-                Default: 7.5.
-            diffusion_noise: Tensor of shape (`batch_size`, img_height // 8,
-                img_width // 8, 4), or a Tensor of shape (img_height // 8,
-                img_width // 8, 4). Optional custom noise to seed the diffusion
-                process. When the batch axis is omitted, the same noise will be
-                used to seed diffusion for every generated image.
-            seed: integer which is used to seed the random generation of
-                diffusion noise, only to be specified if `diffusion_noise` is
-                None.
-        Example:
-        ```python
-        from keras_cv.models import StableDiffusion
-        batch_size = 8
-        model = StableDiffusion(img_height=512, img_width=512, jit_compile=True)
-        e_tacos = model.encode_text("Tacos at dawn")
-        e_watermelons = model.encode_text("Watermelons at dusk")
-        e_interpolated = tf.linspace(e_tacos, e_watermelons, batch_size)
-        images = model.generate_image(e_interpolated, batch_size=batch_size)
-        ```
-        """
-        if diffusion_noise is not None and seed is not None:
-            raise ValueError(
-                "`diffusion_noise` and `seed` should not both be passed to "
-                "`generate_image`. `seed` is only used to generate diffusion "
-                "noise when it's not already user-specified."
-            )
-
-        encoded_text = tf.squeeze(encoded_text)
-        if encoded_text.shape.rank == 2:
-            encoded_text = tf.repeat(
-                tf.expand_dims(encoded_text, axis=0), batch_size, axis=0
-            )
-
-        context = encoded_text
-        unconditional_context = tf.repeat(
-            self._get_unconditional_context(), batch_size, axis=0
-        )
-        context = tf.concat([context, unconditional_context], 0)
-
-        if diffusion_noise is not None:
-            diffusion_noise = tf.squeeze(diffusion_noise)
-            if diffusion_noise.shape.rank == 3:
-                diffusion_noise = tf.repeat(
-                    tf.expand_dims(diffusion_noise, axis=0), batch_size, axis=0
-                )
-            latent = diffusion_noise
-        else:
-            latent = self._get_initial_diffusion_noise(batch_size, seed)
-
-        # Iterative reverse diffusion stage
-        timesteps = tf.range(1, 1000, 1000 // num_steps)
-        alphas, alphas_prev = self._get_initial_alphas(timesteps)
-        progbar = keras.utils.Progbar(len(timesteps))
-        iteration = 0
-        for index, timestep in list(enumerate(timesteps))[::-1]:
-            latent_prev = latent  # Set aside the previous latent vector
-            t_emb = self._get_timestep_embedding(timestep, batch_size)
-
-            # Prepare the latent and unconditional latent to be run with a single forward call
-            latent = tf.concat([latent, latent], 0)
-            t_emb = tf.concat([t_emb, t_emb], 0)
-            latent_numpy = self.diffusion_model.forward(
-                [latent.numpy(), t_emb.numpy(), context.numpy()]
-            )
-            latent = tf.convert_to_tensor(latent_numpy, dtype=tf.float32)
-            latent, unconditional_latent = tf.split(latent, 2)
-
-            latent = unconditional_latent + unconditional_guidance_scale * (
-                latent - unconditional_latent
-            )
-            a_t, a_prev = alphas[index], alphas_prev[index]
-            pred_x0 = (latent_prev - math.sqrt(1 - a_t) * latent) / math.sqrt(
-                a_t
-            )
-            latent = (
-                latent * math.sqrt(1.0 - a_prev) + math.sqrt(a_prev) * pred_x0
-            )
-            iteration += 1
-            progbar.update(iteration)
-
-        # Decoding stage
-        decoded = self.decoder.predict_on_batch(latent)
-        decoded = ((decoded + 1) / 2) * 255
-        return np.clip(decoded, 0, 255).astype("uint8")
-
-    def _get_unconditional_context(self):
-        unconditional_tokens = tf.convert_to_tensor(
-            [_UNCONDITIONAL_TOKENS], dtype=tf.int32
-        )
-        unconditional_context = self.text_encoder.predict_on_batch(
-            [unconditional_tokens, self._get_pos_ids()]
-        )
-
-        return unconditional_context
-
-    def _get_timestep_embedding(
-        self, timestep, batch_size, dim=320, max_period=10000
-    ):
-        half = dim // 2
-        freqs = tf.math.exp(
-            -math.log(max_period) * tf.range(0, half, dtype=tf.float32) / half
-        )
-        args = tf.convert_to_tensor([timestep], dtype=tf.float32) * freqs
-        embedding = tf.concat([tf.math.cos(args), tf.math.sin(args)], 0)
-        embedding = tf.reshape(embedding, [1, -1])
-        return tf.repeat(embedding, batch_size, axis=0)
-
-    def _get_initial_alphas(self, timesteps):
-        alphas = [_ALPHAS_CUMPROD[t] for t in timesteps]
-        alphas_prev = [1.0] + alphas[:-1]
-
-        return alphas, alphas_prev
-
-    def _get_initial_diffusion_noise(self, batch_size, seed):
-        return tf.random.normal(
-            (batch_size, self.img_height // 8, self.img_width // 8, 4),
-            seed=seed,
-        )
-
-    @staticmethod
-    def _get_pos_ids():
-        return tf.convert_to_tensor(
-            [list(range(MAX_PROMPT_LENGTH))], dtype=tf.int32
-        )
-
-
-if __name__ == "__main__":
-    SD = SharkStableDiffusion(device=args.device)
-    images = SD.text_to_image(args.prompt, num_steps=args.steps)
-    pil_images = [Image.fromarray(image) for image in images]
-    save_fname = args.prompt + ".jpg"
-    if args.save_path is not None:
-        save_fname = args.save_path
-    pil_images[0].save(save_fname)

shark/examples/shark_inference/t5_tf.py

@@ -18,7 +18,7 @@ class T5Module(tf.Module):
         self.m = TFT5Model.from_pretrained("t5-small")
         self.m.predict = lambda x, y: self.m(input_ids=x, decoder_input_ids=y)
 
-    @tf.function(input_signature=t5_inputs)
+    @tf.function(input_signature=t5_inputs, jit_compile=True)
     def forward(self, input_ids, decoder_input_ids):
         return self.m.predict(input_ids, decoder_input_ids)
 

shark/examples/shark_inference/upscaler/main.py

@@ -0,0 +1,21 @@
+import requests
+from PIL import Image
+from io import BytesIO
+from pipeline_shark_stable_diffusion_upscale import (
+    SharkStableDiffusionUpscalePipeline,
+)
+import torch
+
+model_id = "stabilityai/stable-diffusion-x4-upscaler"
+pipeline = SharkStableDiffusionUpscalePipeline(model_id)
+
+# let's download an  image
+url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
+response = requests.get(url)
+low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
+low_res_img = low_res_img.resize((128, 128))
+
+prompt = "a white cat"
+
+upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
+upscaled_image.save("upsampled_cat.png")

shark/examples/shark_inference/upscaler/model_wrappers.py

@@ -0,0 +1,98 @@
+from diffusers import AutoencoderKL, UNet2DConditionModel
+from transformers import CLIPTextModel
+from utils import compile_through_fx
+import torch
+
+model_id = "stabilityai/stable-diffusion-x4-upscaler"
+
+model_input = {
+    "clip": (torch.randint(1, 2, (1, 77)),),
+    "vae": (torch.randn(1, 4, 128, 128),),
+    "unet": (
+        torch.randn(2, 7, 128, 128),  # latents
+        torch.tensor([1]).to(torch.float32),  # timestep
+        torch.randn(2, 77, 1024),  # embedding
+        torch.randn(2).to(torch.int64),  # noise_level
+    ),
+}
+
+
+def get_clip_mlir(model_name="clip_text", extra_args=[]):
+    text_encoder = CLIPTextModel.from_pretrained(
+        model_id,
+        subfolder="text_encoder",
+    )
+
+    class CLIPText(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.text_encoder = text_encoder
+
+        def forward(self, input):
+            return self.text_encoder(input)[0]
+
+    clip_model = CLIPText()
+    shark_clip = compile_through_fx(
+        clip_model,
+        model_input["clip"],
+        model_name=model_name,
+        extra_args=extra_args,
+    )
+    return shark_clip
+
+
+def get_vae_mlir(model_name="vae", extra_args=[]):
+    class VaeModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.vae = AutoencoderKL.from_pretrained(
+                model_id,
+                subfolder="vae",
+            )
+
+        def forward(self, input):
+            x = self.vae.decode(input, return_dict=False)[0]
+            return x
+
+    vae = VaeModel()
+    shark_vae = compile_through_fx(
+        vae,
+        model_input["vae"],
+        model_name=model_name,
+        extra_args=extra_args,
+    )
+    return shark_vae
+
+
+def get_unet_mlir(model_name="unet", extra_args=[]):
+    class UnetModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.unet = UNet2DConditionModel.from_pretrained(
+                model_id,
+                subfolder="unet",
+            )
+            self.in_channels = self.unet.in_channels
+            self.train(False)
+
+        def forward(self, latent, timestep, text_embedding, noise_level):
+            unet_out = self.unet.forward(
+                latent,
+                timestep,
+                text_embedding,
+                noise_level,
+                return_dict=False,
+            )[0]
+            return unet_out
+
+    unet = UnetModel()
+    f16_input_mask = (True, True, True, False)
+    shark_unet = compile_through_fx(
+        unet,
+        model_input["unet"],
+        model_name=model_name,
+        is_f16=True,
+        f16_input_mask=f16_input_mask,
+        extra_args=extra_args,
+    )
+    return shark_unet

shark/examples/shark_inference/upscaler/opt_params.py

@@ -0,0 +1,48 @@
+import sys
+from model_wrappers import (
+    get_vae_mlir,
+    get_unet_mlir,
+    get_clip_mlir,
+)
+from upscaler_args import args
+from utils import get_shark_model
+
+BATCH_SIZE = len(args.prompts)
+if BATCH_SIZE != 1:
+    sys.exit("Only batch size 1 is supported.")
+
+
+unet_flag = [
+    "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-preprocessing-convert-conv2d-to-img2col,iree-preprocessing-pad-linalg-ops{pad-size=32}))"
+]
+
+vae_flag = [
+    "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-flow-convert-conv-nchw-to-nhwc,iree-preprocessing-pad-linalg-ops{pad-size=16}))"
+]
+
+clip_flag = [
+    "--iree-preprocessing-pass-pipeline=builtin.module(func.func(iree-preprocessing-pad-linalg-ops{pad-size=16}))"
+]
+
+bucket = "gs://shark_tank/stable_diffusion/"
+
+
+def get_unet():
+    model_name = "upscaler_unet"
+    if args.import_mlir:
+        return get_unet_mlir(model_name, unet_flag)
+    return get_shark_model(bucket, model_name, unet_flag)
+
+
+def get_vae():
+    model_name = "upscaler_vae"
+    if args.import_mlir:
+        return get_vae_mlir(model_name, vae_flag)
+    return get_shark_model(bucket, model_name, vae_flag)
+
+
+def get_clip():
+    model_name = "upscaler_clip"
+    if args.import_mlir:
+        return get_clip_mlir(model_name, clip_flag)
+    return get_shark_model(bucket, model_name, clip_flag)

shark/examples/shark_inference/upscaler/pipeline_shark_stable_diffusion_upscale.py

@@ -0,0 +1,489 @@
+import inspect
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+
+import PIL
+from PIL import Image
+from diffusers.utils import is_accelerate_available
+from transformers import CLIPTextModel, CLIPTokenizer
+from diffusers import AutoencoderKL, UNet2DConditionModel
+from diffusers import (
+    DDIMScheduler,
+    DDPMScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from diffusers import logging
+from diffusers.pipeline_utils import ImagePipelineOutput
+from opt_params import get_unet, get_vae, get_clip
+from tqdm.auto import tqdm
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def preprocess(image):
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [image]
+
+    if isinstance(image[0], PIL.Image.Image):
+        w, h = image[0].size
+        w, h = map(
+            lambda x: x - x % 64, (w, h)
+        )  # resize to integer multiple of 64
+
+        image = [np.array(i.resize((w, h)))[None, :] for i in image]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = 2.0 * image - 1.0
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        image = torch.cat(image, dim=0)
+    return image
+
+
+def shark_run_wrapper(model, *args):
+    np_inputs = tuple([x.detach().numpy() for x in args])
+    outputs = model("forward", np_inputs)
+    return torch.from_numpy(outputs)
+
+
+class SharkStableDiffusionUpscalePipeline:
+    def __init__(
+        self,
+        model_id,
+    ):
+        self.tokenizer = CLIPTokenizer.from_pretrained(
+            model_id, subfolder="tokenizer"
+        )
+        self.low_res_scheduler = DDPMScheduler.from_pretrained(
+            model_id,
+            subfolder="scheduler",
+        )
+        self.scheduler = DDIMScheduler.from_pretrained(
+            model_id,
+            subfolder="scheduler",
+        )
+        self.vae = get_vae()
+        self.unet = get_unet()
+        self.text_encoder = get_clip()
+        self.max_noise_level = (350,)
+        self._execution_device = "cpu"
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+        Args:
+            prompt (`str` or `list(int)`):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(
+            prompt, padding="longest", return_tensors="pt"
+        ).input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[
+            -1
+        ] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(
+                untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+            )
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        # if (
+        # hasattr(self.text_encoder.config, "use_attention_mask")
+        # and self.text_encoder.config.use_attention_mask
+        # ):
+        # attention_mask = text_inputs.attention_mask.to(device)
+        # else:
+        # attention_mask = None
+
+        text_embeddings = shark_run_wrapper(
+            self.text_encoder, text_input_ids.to(device)
+        )
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(
+            bs_embed * num_images_per_prompt, seq_len, -1
+        )
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            # if (
+            # hasattr(self.text_encoder.config, "use_attention_mask")
+            # and self.text_encoder.config.use_attention_mask
+            # ):
+            # attention_mask = uncond_input.attention_mask.to(device)
+            # else:
+            # attention_mask = None
+
+            uncond_embeddings = shark_run_wrapper(
+                self.text_encoder,
+                uncond_input.input_ids.to(device),
+            )
+            uncond_embeddings = uncond_embeddings
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(
+                1, num_images_per_prompt, 1
+            )
+            uncond_embeddings = uncond_embeddings.view(
+                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 = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # 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
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(
+            inspect.signature(self.scheduler.step).parameters.keys()
+        )
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents with 0.18215->0.08333
+    def decode_latents(self, latents):
+        latents = 1 / 0.08333 * latents
+        image = shark_run_wrapper(self.vae, latents)
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def check_inputs(self, prompt, image, noise_level, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(
+                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
+            )
+
+        if (
+            not isinstance(image, torch.Tensor)
+            and not isinstance(image, PIL.Image.Image)
+            and not isinstance(image, list)
+        ):
+            raise ValueError(
+                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or `list` but is {type(image)}"
+            )
+
+        # verify batch size of prompt and image are same if image is a list or tensor
+        if isinstance(image, list) or isinstance(image, torch.Tensor):
+            if isinstance(prompt, str):
+                batch_size = 1
+            else:
+                batch_size = len(prompt)
+            if isinstance(image, list):
+                image_batch_size = len(image)
+            else:
+                image_batch_size = image.shape[0]
+            if batch_size != image_batch_size:
+                raise ValueError(
+                    f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}."
+                    " Please make sure that passed `prompt` matches the batch size of `image`."
+                )
+
+    @staticmethod
+    def numpy_to_pil(images):
+        """
+        Convert a numpy image or a batch of images to a PIL image.
+        """
+        if images.ndim == 3:
+            images = images[None, ...]
+        images = (images * 255).round().astype("uint8")
+        if images.shape[-1] == 1:
+            # special case for grayscale (single channel) images
+            pil_images = [
+                Image.fromarray(image.squeeze(), mode="L") for image in images
+            ]
+        else:
+            pil_images = [Image.fromarray(image) for image in images]
+
+        return pil_images
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        shape = (batch_size, num_channels_latents, height, width)
+        if latents is None:
+            if device == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(
+                    shape, generator=generator, device="cpu", dtype=dtype
+                ).to(device)
+            else:
+                latents = torch.randn(
+                    shape, generator=generator, device=device, dtype=dtype
+                )
+        else:
+            if latents.shape != shape:
+                raise ValueError(
+                    f"Unexpected latents shape, got {latents.shape}, expected {shape}"
+                )
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[
+            torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]
+        ],
+        num_inference_steps: int = 75,
+        guidance_scale: float = 9.0,
+        noise_level: int = 20,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[
+            Union[torch.Generator, List[torch.Generator]]
+        ] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[
+            Callable[[int, int, torch.FloatTensor], None]
+        ] = None,
+        callback_steps: Optional[int] = 1,
+    ):
+        # 1. Check inputs
+        self.check_inputs(prompt, image, noise_level, callback_steps)
+
+        # 2. Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        device = self._execution_device
+        # 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
+
+        # 3. Encode input prompt
+        text_embeddings = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+        )
+
+        # 4. Preprocess image
+        image = preprocess(image)
+        image = image.to(dtype=text_embeddings.dtype, device=device)
+
+        # 5. set timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Add noise to image
+        noise_level = torch.tensor(
+            [noise_level], dtype=torch.long, device=device
+        )
+        if device == "mps":
+            # randn does not work reproducibly on mps
+            noise = torch.randn(
+                image.shape,
+                generator=generator,
+                device="cpu",
+                dtype=text_embeddings.dtype,
+            ).to(device)
+        else:
+            noise = torch.randn(
+                image.shape,
+                generator=generator,
+                device=device,
+                dtype=text_embeddings.dtype,
+            )
+        image = self.low_res_scheduler.add_noise(image, noise, noise_level)
+
+        batch_multiplier = 2 if do_classifier_free_guidance else 1
+        image = torch.cat([image] * batch_multiplier * num_images_per_prompt)
+        noise_level = torch.cat([noise_level] * image.shape[0])
+
+        # 6. Prepare latent variables
+        height, width = image.shape[2:]
+        # num_channels_latents = self.vae.config.latent_channels
+        num_channels_latents = 4
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            text_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 7. Check that sizes of image and latents match
+        num_channels_image = image.shape[1]
+        # if (
+        # num_channels_latents + num_channels_image
+        # != self.unet.config.in_channels
+        # ):
+        # raise ValueError(
+        # f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
+        # f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
+        # f" `num_channels_image`: {num_channels_image} "
+        # f" = {num_channels_latents+num_channels_image}. Please verify the config of"
+        # " `pipeline.unet` or your `image` input."
+        # )
+
+        # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 9. Denoising loop
+        num_warmup_steps = (
+            len(timesteps) - num_inference_steps * self.scheduler.order
+        )
+        for i, t in tqdm(enumerate(timesteps)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = (
+                torch.cat([latents] * 2)
+                if do_classifier_free_guidance
+                else latents
+            )
+
+            # concat latents, mask, masked_image_latents in the channel dimension
+            latent_model_input = self.scheduler.scale_model_input(
+                latent_model_input, t
+            )
+            latent_model_input = torch.cat([latent_model_input, image], dim=1)
+
+            timestep = torch.tensor([t]).to(torch.float32)
+
+            # predict the noise residual
+            noise_pred = shark_run_wrapper(
+                self.unet,
+                latent_model_input.half(),
+                timestep,
+                text_embeddings.half(),
+                noise_level,
+            )
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            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(
+                noise_pred, t, latents, **extra_step_kwargs
+            ).prev_sample
+
+            # # call the callback, if provided
+            # if i == len(timesteps) - 1 or (
+            # (i + 1) > num_warmup_steps
+            # and (i + 1) % self.scheduler.order == 0
+            # ):
+            # progress_bar.update()
+            # if callback is not None and i % callback_steps == 0:
+            # callback(i, t, latents)
+
+        # 10. Post-processing
+        # make sure the VAE is in float32 mode, as it overflows in float16
+        # self.vae.to(dtype=torch.float32)
+        image = self.decode_latents(latents.float())
+
+        # 11. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)

shark/examples/shark_inference/upscaler/upscaler_args.py

@@ -0,0 +1,111 @@
+import argparse
+
+p = argparse.ArgumentParser(
+    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+)
+
+##############################################################################
+### Stable Diffusion Params
+##############################################################################
+
+p.add_argument(
+    "--prompts",
+    nargs="+",
+    default=["cyberpunk forest by Salvador Dali"],
+    help="text of which images to be generated.",
+)
+
+p.add_argument(
+    "--negative-prompts",
+    nargs="+",
+    default=[""],
+    help="text you don't want to see in the generated image.",
+)
+
+p.add_argument(
+    "--steps",
+    type=int,
+    default=50,
+    help="the no. of steps to do the sampling.",
+)
+
+p.add_argument(
+    "--seed",
+    type=int,
+    default=42,
+    help="the seed to use.",
+)
+
+p.add_argument(
+    "--guidance_scale",
+    type=float,
+    default=7.5,
+    help="the value to be used for guidance scaling.",
+)
+
+##############################################################################
+### Model Config and Usage Params
+##############################################################################
+
+p.add_argument(
+    "--device", type=str, default="vulkan", help="device to run the model."
+)
+
+p.add_argument(
+    "--precision", type=str, default="fp16", help="precision to run the model."
+)
+
+p.add_argument(
+    "--import_mlir",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="imports the model from torch module to shark_module otherwise downloads the model from shark_tank.",
+)
+
+p.add_argument(
+    "--load_vmfb",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="attempts to load the model from a precompiled flatbuffer and compiles + saves it if not found.",
+)
+
+p.add_argument(
+    "--save_vmfb",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="saves the compiled flatbuffer to the local directory",
+)
+
+##############################################################################
+### IREE - Vulkan supported flags
+##############################################################################
+
+p.add_argument(
+    "--iree-vulkan-target-triple",
+    type=str,
+    default="",
+    help="Specify target triple for vulkan",
+)
+
+p.add_argument(
+    "--vulkan_debug_utils",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Profiles vulkan device and collects the .rdc info",
+)
+
+p.add_argument(
+    "--vulkan_large_heap_block_size",
+    default="4147483648",
+    help="flag for setting VMA preferredLargeHeapBlockSize for vulkan device, default is 4G",
+)
+
+p.add_argument(
+    "--vulkan_validation_layers",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for disabling vulkan validation layers when benchmarking",
+)
+
+
+args = p.parse_args()

shark/examples/shark_inference/upscaler/utils.py

@@ -0,0 +1,232 @@
+import os
+import torch
+from shark.shark_inference import SharkInference
+from upscaler_args import args
+from shark.shark_importer import import_with_fx
+from shark.iree_utils.vulkan_utils import (
+    set_iree_vulkan_runtime_flags,
+    get_vulkan_target_triple,
+)
+
+
+def _compile_module(shark_module, model_name, extra_args=[]):
+    if args.load_vmfb or args.save_vmfb:
+        device = (
+            args.device
+            if "://" not in args.device
+            else "-".join(args.device.split("://"))
+        )
+        extended_name = "{}_{}".format(model_name, device)
+        vmfb_path = os.path.join(os.getcwd(), extended_name + ".vmfb")
+        if args.load_vmfb and os.path.isfile(vmfb_path) and not args.save_vmfb:
+            print(f"loading existing vmfb from: {vmfb_path}")
+            shark_module.load_module(vmfb_path, extra_args=extra_args)
+        else:
+            if args.save_vmfb:
+                print("Saving to {}".format(vmfb_path))
+            else:
+                print(
+                    "No vmfb found. Compiling and saving to {}".format(
+                        vmfb_path
+                    )
+                )
+            path = shark_module.save_module(
+                os.getcwd(), extended_name, extra_args
+            )
+            shark_module.load_module(path, extra_args=extra_args)
+    else:
+        shark_module.compile(extra_args)
+    return shark_module
+
+
+# Downloads the model from shark_tank and returns the shark_module.
+def get_shark_model(tank_url, model_name, extra_args=[]):
+    from shark.shark_downloader import download_model
+    from shark.parser import shark_args
+
+    # Set local shark_tank cache directory.
+    # shark_args.local_tank_cache = args.local_tank_cache
+
+    mlir_model, func_name, inputs, golden_out = download_model(
+        model_name,
+        tank_url=tank_url,
+        frontend="torch",
+    )
+    shark_module = SharkInference(
+        mlir_model, device=args.device, mlir_dialect="linalg"
+    )
+    return _compile_module(shark_module, model_name, extra_args)
+
+
+# Converts the torch-module into a shark_module.
+def compile_through_fx(
+    model, inputs, model_name, is_f16=False, f16_input_mask=None, extra_args=[]
+):
+    mlir_module, func_name = import_with_fx(
+        model, inputs, is_f16, f16_input_mask
+    )
+    shark_module = SharkInference(
+        mlir_module,
+        device=args.device,
+        mlir_dialect="linalg",
+    )
+
+    return _compile_module(shark_module, model_name, extra_args)
+
+
+def set_iree_runtime_flags():
+    vulkan_runtime_flags = [
+        f"--vulkan_large_heap_block_size={args.vulkan_large_heap_block_size}",
+        f"--vulkan_validation_layers={'true' if args.vulkan_validation_layers else 'false'}",
+    ]
+    if args.enable_rgp:
+        vulkan_runtime_flags += [
+            f"--enable_rgp=true",
+            f"--vulkan_debug_utils=true",
+        ]
+    set_iree_vulkan_runtime_flags(flags=vulkan_runtime_flags)
+
+
+def get_all_devices(driver_name):
+    """
+    Inputs: driver_name
+    Returns a list of all the available devices for a given driver sorted by
+    the iree path names of the device as in --list_devices option in iree.
+    """
+    from iree.runtime import get_driver
+
+    driver = get_driver(driver_name)
+    device_list_src = driver.query_available_devices()
+    device_list_src.sort(key=lambda d: d["path"])
+    return device_list_src
+
+
+def get_device_mapping(driver, key_combination=3):
+    """This method ensures consistent device ordering when choosing
+    specific devices for execution
+    Args:
+        driver (str): execution driver (vulkan, cuda, rocm, etc)
+        key_combination (int, optional): choice for mapping value for device name.
+        1 : path
+        2 : name
+        3 : (name, path)
+        Defaults to 3.
+    Returns:
+        dict: map to possible device names user can input mapped to desired combination of name/path.
+    """
+    from shark.iree_utils._common import iree_device_map
+
+    driver = iree_device_map(driver)
+    device_list = get_all_devices(driver)
+    device_map = dict()
+
+    def get_output_value(dev_dict):
+        if key_combination == 1:
+            return f"{driver}://{dev_dict['path']}"
+        if key_combination == 2:
+            return dev_dict["name"]
+        if key_combination == 3:
+            return (dev_dict["name"], f"{driver}://{dev_dict['path']}")
+
+    # mapping driver name to default device (driver://0)
+    device_map[f"{driver}"] = get_output_value(device_list[0])
+    for i, device in enumerate(device_list):
+        # mapping with index
+        device_map[f"{driver}://{i}"] = get_output_value(device)
+        # mapping with full path
+        device_map[f"{driver}://{device['path']}"] = get_output_value(device)
+    return device_map
+
+
+def map_device_to_name_path(device, key_combination=3):
+    """Gives the appropriate device data (supported name/path) for user selected execution device
+    Args:
+        device (str): user
+        key_combination (int, optional): choice for mapping value for device name.
+        1 : path
+        2 : name
+        3 : (name, path)
+        Defaults to 3.
+    Raises:
+        ValueError:
+    Returns:
+        str / tuple: returns the mapping str or tuple of mapping str for the device depending on key_combination value
+    """
+    driver = device.split("://")[0]
+    device_map = get_device_mapping(driver, key_combination)
+    try:
+        device_mapping = device_map[device]
+    except KeyError:
+        raise ValueError(f"Device '{device}' is not a valid device.")
+    return device_mapping
+
+
+def set_init_device_flags():
+    if "vulkan" in args.device:
+        # set runtime flags for vulkan.
+        set_iree_runtime_flags()
+
+        # set triple flag to avoid multiple calls to get_vulkan_triple_flag
+        device_name, args.device = map_device_to_name_path(args.device)
+        if not args.iree_vulkan_target_triple:
+            triple = get_vulkan_target_triple(device_name)
+            if triple is not None:
+                args.iree_vulkan_target_triple = triple
+        print(
+            f"Found device {device_name}. Using target triple {args.iree_vulkan_target_triple}."
+        )
+    elif "cuda" in args.device:
+        args.device = "cuda"
+    elif "cpu" in args.device:
+        args.device = "cpu"
+
+    # set max_length based on availability.
+    if args.variant in ["anythingv3", "analogdiffusion", "dreamlike"]:
+        args.max_length = 77
+    elif args.variant == "openjourney":
+        args.max_length = 64
+
+    # use tuned models only in the case of stablediffusion/fp16 and rdna3 cards.
+    if (
+        args.variant in ["openjourney", "dreamlike"]
+        or args.precision != "fp16"
+        or "vulkan" not in args.device
+        or "rdna3" not in args.iree_vulkan_target_triple
+    ):
+        args.use_tuned = False
+        print("Tuned models are currently not supported for this setting.")
+
+    elif args.use_base_vae and args.variant != "stablediffusion":
+        args.use_tuned = False
+        print("Tuned models are currently not supported for this setting.")
+
+    if args.use_tuned:
+        print("Using tuned models for stablediffusion/fp16 and rdna3 card.")
+
+
+# Utility to get list of devices available.
+def get_available_devices():
+    def get_devices_by_name(driver_name):
+        from shark.iree_utils._common import iree_device_map
+
+        device_list = []
+        try:
+            driver_name = iree_device_map(driver_name)
+            device_list_dict = get_all_devices(driver_name)
+            print(f"{driver_name} devices are available.")
+        except:
+            print(f"{driver_name} devices are not available.")
+        else:
+            for i, device in enumerate(device_list_dict):
+                device_list.append(f"{driver_name}://{i} => {device['name']}")
+        return device_list
+
+    set_iree_runtime_flags()
+
+    available_devices = []
+    vulkan_devices = get_devices_by_name("vulkan")
+    available_devices.extend(vulkan_devices)
+    cuda_devices = get_devices_by_name("cuda")
+    available_devices.extend(cuda_devices)
+    available_devices.append("cpu")
+    return available_devices

shark/examples/shark_inference/v_diffusion.py

@@ -1,8 +1,10 @@
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
+from shark.shark_downloader import download_model
 
 
-mlir_model, func_name, inputs, golden_out = download_torch_model("v_diffusion")
+mlir_model, func_name, inputs, golden_out = download_model(
+    "v_diffusion", frontend="torch"
+)
 
 shark_module = SharkInference(
     mlir_model, func_name, device="vulkan", mlir_dialect="linalg"